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Chan LLY, Yang S, Aswani M, Kark L, Henderson E, Lord SR, Brodie MA. Development, Validation, and Limits of Freezing of Gait Detection Using a Single Waist-Worn Device. IEEE Trans Biomed Eng 2024; 71:3024-3031. [PMID: 38814761 DOI: 10.1109/tbme.2024.3407059] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/01/2024]
Abstract
OBJECTIVE Freezing of Gait (FOG) is prevalent in people with Parkinson's disease (PD) and severely disrupts mobility. Detecting the exact boundaries of FOG episodes may facilitate new technologies in "breaking" FOG in real-time. This study investigates the performance of automatic device-based FOG detection. METHODS Eight machine-learning classifiers (including Neural Networks, Ensemble methods, and Support Vector Machines) were developed using (i) accelerometer and (ii) combined accelerometer and gyroscope data from a waist-worn device. While wearing the device, 107 people with PD completed mobility tasks designed to elicit FOG. Two clinicians independently annotated exact FOG episodes using synchronized video and a flowchart algorithm based on international guidelines. Device-detected FOG episodes were compared to annotated episodes using 10-fold cross-validation and Interclass Correlation Coefficients (ICC) for agreement. RESULTS Development used 50,962 windows of data and annotated activities (>10 hours). Strong agreement between clinicians for precise FOG episodes was observed (90% sensitivity, 92% specificity, and ICC1,1 = 0.97 for total FOG duration). Device performance varied by method, complexity, and cost matrix. The Neural Network using 67 accelerometer features achieved high sensitivity to FOG (89% sensitivity, 81% specificity, and ICC1,1 = 0.83) and stability (validation loss 5%). CONCLUSION The waist-worn device consistently reported accurate detection of precise FOG episodes and compared well to more complex systems. The strong clinician agreement indicates room for improvement in future device-based FOG detection. SIGNIFICANCE This study may enhance PD care by reducing reliance on visual FOG inspection, demonstrating that high sensitivity in automatic FOG detection is achievable.
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Jiao Y, Liu Z, Li J, Su Y, Chen X. Knowledge mapping of freezing of gait in Parkinson's disease: a bibliometric analysis. Front Neurosci 2024; 18:1388326. [PMID: 39315077 PMCID: PMC11417103 DOI: 10.3389/fnins.2024.1388326] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2024] [Accepted: 08/27/2024] [Indexed: 09/25/2024] Open
Abstract
Objective Among the disturbing motor symptoms in Parkinson's disease (PD), freezing of gait (FOG) stands out as one of the most severe challenges. It typically arises during the initiation of gait or when turning. This phenomenon not only impose a heavy burden on patients, but also on their families. We conduct a bibliometric analysis to summarize current research hotspots and trends concerning freezing of gait in Parkinson's disease (PD-FOG) over past two decades. Methods We retrieved articles and reviews published in English about PD-FOG in the Web of science Core Collection database from 2000 to 2023 on November 30,2023. The tools VOSviewer and CiteSpace facilitated a visual analysis covering various aspects such as publications, countries/regions, organizations, authors, journals, cited references, and keywords. Result This study includes 1,340 articles from 64 countries/regions. There is a growth in publications related to PD-FOG over the past two decades, maintaining a stable high output since 2018, indicating a promising research landscape in the field of PD-FOG. The United States holds a leading position in this field, with Nieuwboer A and Giladi N being two of the most influential researchers. Over the past two decades, the research hotspots for PD-FOG have primarily encompassed the kinematic characteristics, diagnosis and detection, cognitive deficits and neural connectivity, as well as therapy and rehabilitation of PD-FOG. Topics including functional connectivity, virtual reality, deep learning and machine learning will be focal points of future research. Conclusion This is the first bibliometric analysis of PD-FOG. We construct this study to summarize the research in this field over past two decades, visually show the current hotspots and trends, and offer scholars in this field concepts and strategies for subsequent studies.
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Affiliation(s)
- Yue Jiao
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Zaichao Liu
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Juan Li
- Department of Neurology, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Yan Su
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Xianwen Chen
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
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Shushtari M, Foellmer J, Arami A. Human-exoskeleton interaction portrait. J Neuroeng Rehabil 2024; 21:152. [PMID: 39232812 PMCID: PMC11373187 DOI: 10.1186/s12984-024-01447-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2024] [Accepted: 08/14/2024] [Indexed: 09/06/2024] Open
Abstract
Human-robot physical interaction contains crucial information for optimizing user experience, enhancing robot performance, and objectively assessing user adaptation. This study introduces a new method to evaluate human-robot interaction and co-adaptation in lower limb exoskeletons by analyzing muscle activity and interaction torque as a two-dimensional random variable. We introduce the interaction portrait (IP), which visualizes this variable's distribution in polar coordinates. We applied IP to compare a recently developed hybrid torque controller (HTC) based on kinematic state feedback and a novel adaptive model-based torque controller (AMTC) with online learning, proposed herein, against a time-based controller (TBC) during treadmill walking at varying speeds. Compared to TBC, both HTC and AMTC significantly lower users' normalized oxygen uptake, suggesting enhanced user-exoskeleton coordination. IP analysis reveals that this improvement stems from two distinct co-adaptation strategies, unidentifiable by traditional muscle activity or interaction torque analyses alone. HTC encourages users to yield control to the exoskeleton, decreasing overall muscular effort but increasing interaction torque, as the exoskeleton compensates for user dynamics. Conversely, AMTC promotes user engagement through increased muscular effort and reduces interaction torques, aligning it more closely with rehabilitation and gait training applications. IP phase evolution provides insight into each user's interaction strategy formation, showcasing IP analysis's potential in comparing and designing novel controllers to optimize human-robot interaction in wearable robots.
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Affiliation(s)
- Mohammad Shushtari
- Department of Mechanical and Mechatronics Engineering, University of Waterloo, Waterloo, ON, N2L 3G1, Canada
| | - Julia Foellmer
- Mechanics and Ocean Engineering Department, Hamburg University of Technology, 21071, Hamburg, Germany
| | - Arash Arami
- Department of Mechanical and Mechatronics Engineering, University of Waterloo, Waterloo, ON, N2L 3G1, Canada.
- Toronto Rehabilitation Institute (KITE), University Health Network, Toronto, ON, M5G 2A2, Canada.
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Hao J, He Z, Li Y, Huang B, Remis A, Yao Z, Zhu D. Virtual reality-based supportive care interventions for patients with cancer: an umbrella review of systematic reviews and meta-analyses. Support Care Cancer 2024; 32:603. [PMID: 39167153 DOI: 10.1007/s00520-024-08813-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2024] [Accepted: 08/14/2024] [Indexed: 08/23/2024]
Abstract
PURPOSE This umbrella review aimed to identify, critically appraise, and synthesize current evidence from systematic reviews and meta-analyses on the applications of virtual reality-based supportive care interventions in cancer. METHODS Three bibliographic databases were searched from inception to February 1, 2024. Two independent reviewers screened the titles and abstracts of 421 records and retrieved 26 full-text systematic reviews. Assessing the Methodological Quality of Systematic Reviews 2 (AMSTAR-2) was used for quality assessment. Qualitative syntheses were performed to investigate the effects of virtual reality-based supportive care interventions on quality of life and physical, psychological, cognitive, and functional outcomes. Meta-analysis was performed based on data from the distinct primary studies that were extracted from the included reviews. RESULTS This umbrella review included 20 systematic reviews that were published between 2018 and 2023; nine of them conducted meta-analyses. A total of 86 distinct primary studies were identified. According to the AMSTAR-2 assessment, two reviews were evaluated as moderate quality, nine as low, and nine as critically low. Meta-analyses of primary studies revealed significant effects of virtual reality on anxiety (p < 0.001), depression (p < 0.001), and pain (p < 0.001), but not fatigue (p = 0.263). Qualitative syntheses revealed positive effects of virtual reality on physical function, cognitive function, and quality of life. Limited evidence was reported regarding outcomes of balance, gait, mobility, and activities of daily living. CONCLUSION Virtual reality has proven to be a safe and feasible approach to deliver supportive care in cancer. Virtual reality can be implemented in various stages and settings of the cancer care continuum to support patients undergoing painful procedures, during or after chemotherapy, and after surgical interventions. Virtual reality can serve as an effective supportive care intervention to manage anxiety, pain, and depression for patients with cancer.
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Affiliation(s)
- Jie Hao
- Department of Physical Therapy and Rehabilitation, Southeast Colorado Hospital, Springfield, CO, USA.
- Department of Health & Rehabilitation Sciences, College of Allied Health Professions, University of Nebraska Medical Center, Omaha, NE, USA.
| | - Zhengting He
- Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
| | - Yanfei Li
- Department of Health & Rehabilitation Sciences, College of Allied Health Professions, University of Nebraska Medical Center, Omaha, NE, USA
| | - Biying Huang
- Department of Health & Rehabilitation Sciences, College of Allied Health Professions, University of Nebraska Medical Center, Omaha, NE, USA
| | - Andréas Remis
- Health Research Association of Keck Medicine, University of Southern California, Los Angeles, CA, USA
| | - Zixuan Yao
- Department of Rehabilitation Medicine, Beijing Hospital, National Center of Gerontology, Institution of Geriatric Medicine, Chinese Academy of Medical Science, Beijing, P.R. China
| | - Dongqi Zhu
- Department of Rehabilitation Medicine, Shanghai Sixth People's Hospital, Shanghai, P.R. China
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Sapienza S, Tsurkalenko O, Giraitis M, Mejia AC, Zelimkhanov G, Schwaninger I, Klucken J. Assessing the clinical utility of inertial sensors for home monitoring in Parkinson's disease: a comprehensive review. NPJ Parkinsons Dis 2024; 10:161. [PMID: 39164257 PMCID: PMC11335938 DOI: 10.1038/s41531-024-00755-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Accepted: 07/24/2024] [Indexed: 08/22/2024] Open
Abstract
This review screened 296 articles on wearable sensors for home monitoring of people with Parkinson's Disease within the PubMed Database, from January 2017 to May 2023. A three-level maturity framework was applied for classifying the aims of 59 studies included: demonstrating technical efficacy, diagnostic sensitivity, or clinical utility. As secondary analysis, user experience (usability and patient adherence) was evaluated. The evidences provided by the studies were categorized and stratified according to the level of maturity. Our results indicate that approximately 75% of articles investigated diagnostic sensitivity, i.e. correlation of sensor-data with clinical parameters. Evidence of clinical utility, defined as improvement on health outcomes or clinical decisions after the use of the wearables, was found only in nine papers. A third of the articles included reported evidence of user experience. Future research should focus more on clinical utility, to facilitate the translation of research results within the management of Parkinson's Disease.
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Affiliation(s)
- Stefano Sapienza
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg
- Luxembourg Institute of Health (LIH), Strassen, Luxembourg
| | - Olena Tsurkalenko
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg
- Luxembourg Institute of Health (LIH), Strassen, Luxembourg
- Centre Hospitalier de Luxembourg (CHL), Rollengergronn-belair-nord, Luxembourg
| | - Marijus Giraitis
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg
- Luxembourg Institute of Health (LIH), Strassen, Luxembourg
- Centre Hospitalier de Luxembourg (CHL), Rollengergronn-belair-nord, Luxembourg
| | - Alan Castro Mejia
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg
- Luxembourg Institute of Health (LIH), Strassen, Luxembourg
| | - Gelani Zelimkhanov
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg
- Luxembourg Institute of Health (LIH), Strassen, Luxembourg
- Centre Hospitalier de Luxembourg (CHL), Rollengergronn-belair-nord, Luxembourg
| | - Isabel Schwaninger
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg
- Luxembourg Institute of Health (LIH), Strassen, Luxembourg
| | - Jochen Klucken
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg.
- Luxembourg Institute of Health (LIH), Strassen, Luxembourg.
- Centre Hospitalier de Luxembourg (CHL), Rollengergronn-belair-nord, Luxembourg.
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Khalil RM, Shulman LM, Gruber-Baldini AL, Shakya S, Fenderson R, Van Hoven M, Hausdorff JM, von Coelln R, Cummings MP. Simplification of Mobility Tests and Data Processing to Increase Applicability of Wearable Sensors as Diagnostic Tools for Parkinson's Disease. SENSORS (BASEL, SWITZERLAND) 2024; 24:4983. [PMID: 39124030 PMCID: PMC11314738 DOI: 10.3390/s24154983] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/19/2024] [Revised: 07/21/2024] [Accepted: 07/29/2024] [Indexed: 08/12/2024]
Abstract
Quantitative mobility analysis using wearable sensors, while promising as a diagnostic tool for Parkinson's disease (PD), is not commonly applied in clinical settings. Major obstacles include uncertainty regarding the best protocol for instrumented mobility testing and subsequent data processing, as well as the added workload and complexity of this multi-step process. To simplify sensor-based mobility testing in diagnosing PD, we analyzed data from 262 PD participants and 50 controls performing several motor tasks wearing a sensor on their lower back containing a triaxial accelerometer and a triaxial gyroscope. Using ensembles of heterogeneous machine learning models incorporating a range of classifiers trained on a set of sensor features, we show that our models effectively differentiate between participants with PD and controls, both for mixed-stage PD (92.6% accuracy) and a group selected for mild PD only (89.4% accuracy). Omitting algorithmic segmentation of complex mobility tasks decreased the diagnostic accuracy of our models, as did the inclusion of kinesiological features. Feature importance analysis revealed that Timed Up and Go (TUG) tasks to contribute the highest-yield predictive features, with only minor decreases in accuracy for models based on cognitive TUG as a single mobility task. Our machine learning approach facilitates major simplification of instrumented mobility testing without compromising predictive performance.
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Affiliation(s)
- Rana M. Khalil
- Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD 20742, USA;
| | - Lisa M. Shulman
- Department of Neurology, University of Maryland School of Medicine, Baltimore, MD 21201, USA; (L.M.S.); (R.F.); (M.V.H.)
| | - Ann L. Gruber-Baldini
- Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, MD 21201, USA; (A.L.G.-B.); (S.S.)
| | - Sunita Shakya
- Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, MD 21201, USA; (A.L.G.-B.); (S.S.)
| | - Rebecca Fenderson
- Department of Neurology, University of Maryland School of Medicine, Baltimore, MD 21201, USA; (L.M.S.); (R.F.); (M.V.H.)
| | - Maxwell Van Hoven
- Department of Neurology, University of Maryland School of Medicine, Baltimore, MD 21201, USA; (L.M.S.); (R.F.); (M.V.H.)
| | - Jeffrey M. Hausdorff
- Center for the Study of Movement, Cognition, and Mobility, Neurological Institute, Tel Aviv Medical Center, Tel Aviv 6492416, Israel;
- Department of Physical Therapy, Faculty of Medicine & Health Sciences, Tel Aviv University, Tel Aviv 6997801, Israel
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv 6997801, Israel
- Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, IL 60612, USA
- Department of Orthopedic Surgery, Rush University Medical Center, Chicago, IL 60612, USA
| | - Rainer von Coelln
- Department of Neurology, University of Maryland School of Medicine, Baltimore, MD 21201, USA; (L.M.S.); (R.F.); (M.V.H.)
| | - Michael P. Cummings
- Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD 20742, USA;
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Saad M, Hefner S, Donovan S, Bernhard D, Tripathi R, Factor SA, Powell JM, Kwon H, Sameni R, Esper CD, McKay JL. Development of a Tremor Detection Algorithm for Use in an Academic Movement Disorders Center. SENSORS (BASEL, SWITZERLAND) 2024; 24:4960. [PMID: 39124007 PMCID: PMC11314995 DOI: 10.3390/s24154960] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/25/2024] [Revised: 07/24/2024] [Accepted: 07/28/2024] [Indexed: 08/12/2024]
Abstract
Tremor, defined as an "involuntary, rhythmic, oscillatory movement of a body part", is a key feature of many neurological conditions including Parkinson's disease and essential tremor. Clinical assessment continues to be performed by visual observation with quantification on clinical scales. Methodologies for objectively quantifying tremor are promising but remain non-standardized across centers. Our center performs full-body behavioral testing with 3D motion capture for clinical and research purposes in patients with Parkinson's disease, essential tremor, and other conditions. The objective of this study was to assess the ability of several candidate processing pipelines to identify the presence or absence of tremor in kinematic data from patients with confirmed movement disorders and compare them to expert ratings from movement disorders specialists. We curated a database of 2272 separate kinematic data recordings from our center, each of which was contemporaneously annotated as tremor present or absent by a movement physician. We compared the ability of six separate processing pipelines to recreate clinician ratings based on F1 score, in addition to accuracy, precision, and recall. The performance across algorithms was generally comparable. The average F1 score was 0.84±0.02 (mean ± SD; range 0.81-0.87). The second highest performing algorithm (cross-validated F1=0.87) was a hybrid that used engineered features adapted from an algorithm in longstanding clinical use with a modern Support Vector Machine classifier. Taken together, our results suggest the potential to update legacy clinical decision support systems to incorporate modern machine learning classifiers to create better-performing tools.
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Affiliation(s)
- Mark Saad
- Jean and Paul Amos Parkinson’s Disease and Movement Disorders Program, Department of Neurology, School of Medicine, Emory University, Atlanta, GA 30322, USA; (M.S.)
| | - Sofia Hefner
- Department of Neuroscience, Georgia Institute of Technology, Atlanta, GA 30322, USA
| | - Suzann Donovan
- Department of Neuroscience and Behavioral Biology, College of Arts and Sciences, Emory University, Atlanta, GA 30322, USA
| | - Doug Bernhard
- Jean and Paul Amos Parkinson’s Disease and Movement Disorders Program, Department of Neurology, School of Medicine, Emory University, Atlanta, GA 30322, USA; (M.S.)
| | - Richa Tripathi
- Jean and Paul Amos Parkinson’s Disease and Movement Disorders Program, Department of Neurology, School of Medicine, Emory University, Atlanta, GA 30322, USA; (M.S.)
| | - Stewart A. Factor
- Jean and Paul Amos Parkinson’s Disease and Movement Disorders Program, Department of Neurology, School of Medicine, Emory University, Atlanta, GA 30322, USA; (M.S.)
| | - Jeanne M. Powell
- Department of Psychology, Laney Graduate School, Emory University, Atlanta, GA 30322, USA
| | - Hyeokhyen Kwon
- Department of Biomedical Informatics, School of Medicine, Emory University, Atlanta, GA 30322, USA (R.S.)
| | - Reza Sameni
- Department of Biomedical Informatics, School of Medicine, Emory University, Atlanta, GA 30322, USA (R.S.)
- Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA 30322, USA
| | - Christine D. Esper
- Jean and Paul Amos Parkinson’s Disease and Movement Disorders Program, Department of Neurology, School of Medicine, Emory University, Atlanta, GA 30322, USA; (M.S.)
| | - J. Lucas McKay
- Jean and Paul Amos Parkinson’s Disease and Movement Disorders Program, Department of Neurology, School of Medicine, Emory University, Atlanta, GA 30322, USA; (M.S.)
- Department of Biomedical Informatics, School of Medicine, Emory University, Atlanta, GA 30322, USA (R.S.)
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D'Ascanio I, Giannini G, Baldelli L, Cani I, Giannoni A, Leogrande G, Lopane G, Calandra-Buonaura G, Cortelli P, Chiari L, Palmerini L. A method for the synchronization of inertial sensor signals and local field potentials from deep brain stimulation systems. Biomed Phys Eng Express 2024; 10:057001. [PMID: 38959873 DOI: 10.1088/2057-1976/ad5e83] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2024] [Accepted: 07/03/2024] [Indexed: 07/05/2024]
Abstract
Objective. Recent innovative neurostimulators allow recording local field potentials (LFPs) while performing motor tasks monitored by wearable sensors. Inertial sensors can provide quantitative measures of motor impairment in people with subthalamic nucleus deep brain stimulation. To the best of our knowledge, there is no validated method to synchronize inertial sensors and neurostimulators without an additional device. This study aims to define a new synchronization method to analyze disease-related brain activity patterns during specific motor tasks and evaluate how LFPs are affected by stimulation and medication.Approach. Fourteen male subjects treated with subthalamic nucleus deep brain stimulation were recruited to perform motor tasks in four different medication and stimulation conditions. In each condition, a synchronization protocol was performed consisting of taps on the implanted neurostimulator, which produces artifacts in the LFPs that a nearby inertial sensor can simultaneously record.Main results. In 64% of the recruited subjects, induced artifacts were detected at least in one condition. Among those subjects, 83% of the recordings could be synchronized offline analyzing LFPs and wearables data. The remaining recordings were synchronized by video analysis.Significance. The proposed synchronization method does not require an external system (e.g., TENS electrodes) and can be easily integrated into clinical practice. The procedure is simple and can be carried out in a short time. A proper and simple synchronization will also be useful to analyze subthalamic neural activity in the presence of specific events (e.g., freezing of gait events) to identify predictive biomarkers.
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Affiliation(s)
- Ilaria D'Ascanio
- Department of Electrical, Electronic, and Information Engineering "Guglielmo Marconi" (DEI), University of Bologna, Bologna, Italy
| | - Giulia Giannini
- Department of Biomedical and NeuroMotor Sciences (DIBINEM), University of Bologna, Bologna, Italy
- IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy
| | - Luca Baldelli
- Department of Biomedical and NeuroMotor Sciences (DIBINEM), University of Bologna, Bologna, Italy
- IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy
| | - Ilaria Cani
- Department of Biomedical and NeuroMotor Sciences (DIBINEM), University of Bologna, Bologna, Italy
- IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy
| | - Alice Giannoni
- Department of Electrical, Electronic, and Information Engineering "Guglielmo Marconi" (DEI), University of Bologna, Bologna, Italy
| | | | - Giovanna Lopane
- IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy
| | - Giovanna Calandra-Buonaura
- Department of Biomedical and NeuroMotor Sciences (DIBINEM), University of Bologna, Bologna, Italy
- IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy
| | - Pietro Cortelli
- Department of Biomedical and NeuroMotor Sciences (DIBINEM), University of Bologna, Bologna, Italy
- IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy
| | - Lorenzo Chiari
- Department of Electrical, Electronic, and Information Engineering "Guglielmo Marconi" (DEI), University of Bologna, Bologna, Italy
- Health Sciences and Technologies - Interdepartmental Center for Industrial Research (CIRI-SDV), University of Bologna, Bologna, Italy
| | - Luca Palmerini
- Department of Electrical, Electronic, and Information Engineering "Guglielmo Marconi" (DEI), University of Bologna, Bologna, Italy
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Guo R, Xie Z, Zhang C, Qian X. Causality-Enhanced Multiple Instance Learning With Graph Convolutional Networks for Parkinsonian Freezing-of-Gait Assessment. IEEE TRANSACTIONS ON IMAGE PROCESSING : A PUBLICATION OF THE IEEE SIGNAL PROCESSING SOCIETY 2024; 33:3991-4001. [PMID: 38913508 DOI: 10.1109/tip.2024.3416052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/26/2024]
Abstract
Freezing of gait (FoG) is a common disabling symptom of Parkinson's disease (PD). It is clinically characterized by sudden and transient walking interruptions for specific human body parts, and it presents the localization in time and space. Due to the difficulty in extracting global fine-grained features from lengthy videos, developing an automated five-point FoG scoring system is quite challenging. Therefore, we propose a novel video-based automated five-classification FoG assessment method with a causality-enhanced multiple-instance-learning graph convolutional network (GCN). This method involves developing a temporal segmentation GCN to segment each video into three motion stages for stage-level feature modeling, followed by a multiple-instance-learning framework to divide each stage into short clips for instance-level feature extraction. Subsequently, an uncertainty-driven multiple-instance-learning GCN is developed to capture spatial and temporal fine-grained features through GCN scheme and uncertainty learning, respectively, for acquiring global representations. Finally, a causality-enhanced graph generation strategy is proposed to exploit causal inference for mining and enhancing human structures causally related to clinical assessment, thereby extracting spatial causal features. Extensive experimental results demonstrate the excellent performance of the proposed method on five-classification FoG assessment with an accuracy of 62.72% and an acceptable accuracy of 91.32%, which is confirmed by independent testing. Additionally, it enables temporal and spatial localization of FoG events to a certain extent, facilitating reasonable clinical interpretations. In conclusion, our method provides a valuable tool for automated FoG assessment in PD, and the proposed causality-related component exhibits promising potential for extension to other general and medical fine-grained action recognition tasks.
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Salomon A, Gazit E, Ginis P, Urazalinov B, Takoi H, Yamaguchi T, Goda S, Lander D, Lacombe J, Sinha AK, Nieuwboer A, Kirsch LC, Holbrook R, Manor B, Hausdorff JM. A machine learning contest enhances automated freezing of gait detection and reveals time-of-day effects. Nat Commun 2024; 15:4853. [PMID: 38844449 PMCID: PMC11156937 DOI: 10.1038/s41467-024-49027-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Accepted: 05/22/2024] [Indexed: 06/09/2024] Open
Abstract
Freezing of gait (FOG) is a debilitating problem that markedly impairs the mobility and independence of 38-65% of people with Parkinson's disease. During a FOG episode, patients report that their feet are suddenly and inexplicably "glued" to the floor. The lack of a widely applicable, objective FOG detection method obstructs research and treatment. To address this problem, we organized a 3-month machine-learning contest, inviting experts from around the world to develop wearable sensor-based FOG detection algorithms. 1,379 teams from 83 countries submitted 24,862 solutions. The winning solutions demonstrated high accuracy, high specificity, and good precision in FOG detection, with strong correlations to gold-standard references. When applied to continuous 24/7 data, the solutions revealed previously unobserved patterns in daily living FOG occurrences. This successful endeavor underscores the potential of machine learning contests to rapidly engage AI experts in addressing critical medical challenges and provides a promising means for objective FOG quantification.
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Affiliation(s)
- Amit Salomon
- Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Medical Center, Tel Aviv, Israel
| | - Eran Gazit
- Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Medical Center, Tel Aviv, Israel
| | - Pieter Ginis
- KU Leuven, Department of Rehabilitation Science, Neuromotor Rehabilitation Research Group (eNRGy), Leuven, Belgium
| | | | | | | | | | | | | | | | - Alice Nieuwboer
- KU Leuven, Department of Rehabilitation Science, Neuromotor Rehabilitation Research Group (eNRGy), Leuven, Belgium
| | - Leslie C Kirsch
- Michael J. Fox Foundation for Parkinson's Research, New York, NY, USA
| | | | - Brad Manor
- Hinda and Arthur Marcus Institute for Aging Research at Hebrew SeniorLife, Boston, MA, USA
- Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
- Harvard Medical School, MA, Boston, USA
| | - Jeffrey M Hausdorff
- Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Medical Center, Tel Aviv, Israel.
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel.
- Department of Physical Therapy, Faculty of Medical & Health Sciences, Tel Aviv University, Tel Aviv, Israel.
- Department of Orthopedic Surgery and Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA.
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11
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Zoetewei D, Herman T, Ginis P, Palmerini L, Brozgol M, Thumm PC, Ferrari A, Ceulemans E, Decaluwé E, Hausdorff JM, Nieuwboer A. On-Demand Cueing for Freezing of Gait in Parkinson's Disease: A Randomized Controlled Trial. Mov Disord 2024; 39:876-886. [PMID: 38486430 DOI: 10.1002/mds.29762] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Revised: 01/24/2024] [Accepted: 02/09/2024] [Indexed: 05/18/2024] Open
Abstract
BACKGROUND Cueing can alleviate freezing of gait (FOG) in people with Parkinson's disease (PD), but using the same cues continuously in daily life may compromise effectiveness. Therefore, we developed the DeFOG-system to deliver personalized auditory cues on detection of a FOG episode. OBJECTIVES We aimed to evaluate the effects of DeFOG during a FOG-provoking protocol: (1) after 4 weeks of DeFOG-use in daily life against an active control group; (2) after immediate DeFOG-use (within-group) in different medication states. METHOD In this randomized controlled trial, 63 people with PD and daily FOG were allocated to the DeFOG or active control group. Both groups received feedback on their daily living step counts using the device, but the DeFOG group also received on-demand cueing. Video-rated FOG severity was compared pre- and post-intervention through a FOG-provoking protocol administered at home off and on-medication, but without using DeFOG. Within-group effects were tested by comparing FOG during the protocol with and without DeFOG. RESULTS DeFOG-use during the 4 weeks was similar between groups, but we found no between-group differences in FOG-severity. However, the within-group analysis showed that FOG was alleviated by DeFOG (effect size d = 0.57), regardless of medication state. Combining DeFOG and medication yielded an effect size of d = 0.67. CONCLUSIONS DeFOG reduced FOG considerably in a population of severe freezers both off and on medication. Nonetheless, 4 weeks of DeFOG-use in daily life did not ameliorate FOG during the protocol unless DeFOG was worn. These findings suggest that on-demand cueing is only effective when used, similar to other walking aids. © 2024 International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Demi Zoetewei
- KU Leuven, Department of Rehabilitation Sciences, Neurorehabilitation Research Group (eNRGy), Leuven, Belgium
| | - Talia Herman
- Center for the Study of Movement, Cognition, and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
| | - Pieter Ginis
- KU Leuven, Department of Rehabilitation Sciences, Neurorehabilitation Research Group (eNRGy), Leuven, Belgium
| | - Luca Palmerini
- Department of Electrical, Electronic, and Information Engineering "Guglielmo Marconi", University of Bologna, Bologna, Italy
- Health Sciences and Technologies-Interdepartmental Center for Industrial Research (CIRI-SDV), University of Bologna, Bologna, Italy
| | - Marina Brozgol
- Center for the Study of Movement, Cognition, and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
| | - Pablo Cornejo Thumm
- Center for the Study of Movement, Cognition, and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
| | - Alberto Ferrari
- Department of Engineering "Enzo Ferrari", University of Modena and Reggio Emilia, Modena, Italy
- Science and Technology Park for Medicine, TPM, Democenter Foundation Mirandola, Modena, Italy
| | - Eva Ceulemans
- KU Leuven, Department of Rehabilitation Sciences, Neurorehabilitation Research Group (eNRGy), Leuven, Belgium
| | - Eva Decaluwé
- KU Leuven, Department of Rehabilitation Sciences, Neurorehabilitation Research Group (eNRGy), Leuven, Belgium
| | - Jeffrey M Hausdorff
- Center for the Study of Movement, Cognition, and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
- Sagol School of Neuroscience, Tel Aviv University, Israel
- Department of Physical Therapy, Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
- Rush Alzheimer's Disease Center and Department of Orthopedic Surgery, Rush University, Chicago, Illinois, USA
| | - Alice Nieuwboer
- KU Leuven, Department of Rehabilitation Sciences, Neurorehabilitation Research Group (eNRGy), Leuven, Belgium
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12
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Zampogna A, Borzì L, Rinaldi D, Artusi CA, Imbalzano G, Patera M, Lopiano L, Pontieri F, Olmo G, Suppa A. Unveiling the Unpredictable in Parkinson's Disease: Sensor-Based Monitoring of Dyskinesias and Freezing of Gait in Daily Life. Bioengineering (Basel) 2024; 11:440. [PMID: 38790307 PMCID: PMC11117481 DOI: 10.3390/bioengineering11050440] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2024] [Revised: 04/23/2024] [Accepted: 04/28/2024] [Indexed: 05/26/2024] Open
Abstract
BACKGROUND Dyskinesias and freezing of gait are episodic disorders in Parkinson's disease, characterized by a fluctuating and unpredictable nature. This cross-sectional study aims to objectively monitor Parkinsonian patients experiencing dyskinesias and/or freezing of gait during activities of daily living and assess possible changes in spatiotemporal gait parameters. METHODS Seventy-one patients with Parkinson's disease (40 with dyskinesias and 33 with freezing of gait) were continuously monitored at home for a minimum of 5 days using a single wearable sensor. Dedicated machine-learning algorithms were used to categorize patients based on the occurrence of dyskinesias and freezing of gait. Additionally, specific spatiotemporal gait parameters were compared among patients with and without dyskinesias and/or freezing of gait. RESULTS The wearable sensor algorithms accurately classified patients with and without dyskinesias as well as those with and without freezing of gait based on the recorded dyskinesias and freezing of gait episodes. Standard spatiotemporal gait parameters did not differ significantly between patients with and without dyskinesias or freezing of gait. Both the time spent with dyskinesias and the number of freezing of gait episodes positively correlated with the disease severity and medication dosage. CONCLUSIONS A single inertial wearable sensor shows promise in monitoring complex, episodic movement patterns, such as dyskinesias and freezing of gait, during daily activities. This approach may help implement targeted therapeutic and preventive strategies for Parkinson's disease.
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Affiliation(s)
- Alessandro Zampogna
- Department of Human Neurosciences, Sapienza University of Rome, 00185 Rome, Italy; (A.Z.); (M.P.)
- IRCCS Neuromed Institute, 86077 Pozzilli, IS, Italy
| | - Luigi Borzì
- Data Analytics and Technologies for Health Lab (ANTHEA), Department of Control and Computer Engineering, Politecnico di Torino, 10129 Turin, Italy; (L.B.); (G.O.)
| | - Domiziana Rinaldi
- Department of Neuroscience, Mental Health and Sense Organs (NESMOS), Sapienza University of Rome, 00189 Rome, Italy; (D.R.); (F.P.)
| | - Carlo Alberto Artusi
- Department of Neuroscience “Rita Levi Montalcini”, University of Turin, 10126 Torino, Italy; (C.A.A.); (G.I.); (L.L.)
- Neurology 2 Unit, A.O.U, Città della Salute e della Scienza di Torino, 10126 Torino, Italy
| | - Gabriele Imbalzano
- Department of Neuroscience “Rita Levi Montalcini”, University of Turin, 10126 Torino, Italy; (C.A.A.); (G.I.); (L.L.)
| | - Martina Patera
- Department of Human Neurosciences, Sapienza University of Rome, 00185 Rome, Italy; (A.Z.); (M.P.)
| | - Leonardo Lopiano
- Department of Neuroscience “Rita Levi Montalcini”, University of Turin, 10126 Torino, Italy; (C.A.A.); (G.I.); (L.L.)
- Neurology 2 Unit, A.O.U, Città della Salute e della Scienza di Torino, 10126 Torino, Italy
| | - Francesco Pontieri
- Department of Neuroscience, Mental Health and Sense Organs (NESMOS), Sapienza University of Rome, 00189 Rome, Italy; (D.R.); (F.P.)
| | - Gabriella Olmo
- Data Analytics and Technologies for Health Lab (ANTHEA), Department of Control and Computer Engineering, Politecnico di Torino, 10129 Turin, Italy; (L.B.); (G.O.)
| | - Antonio Suppa
- Department of Human Neurosciences, Sapienza University of Rome, 00185 Rome, Italy; (A.Z.); (M.P.)
- IRCCS Neuromed Institute, 86077 Pozzilli, IS, Italy
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13
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Sabeti S, Morris N, Shoghli O. Mixed-method usability investigation of ARROWS: augmented reality for roadway work zone safety. INTERNATIONAL JOURNAL OF OCCUPATIONAL SAFETY AND ERGONOMICS 2024; 30:292-303. [PMID: 38097505 DOI: 10.1080/10803548.2023.2295660] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2024]
Abstract
This article explores the usability and user experience challenges of ARROWS, a novel augmented reality (AR) and wearable technology (WT) safety system for roadway work zones, an area with limited existing usability research. We utilized a mixed-method approach with two complementary experiments in indoor and outdoor settings, using the Wizard of Oz methodology and a high-fidelity prototype. We focused on identifying usability challenges, factors contributing to user experience and the distinct needs of highway workers, documenting results using the system usability scale (SUS), the rating scale mental effort (RSME) and a trust score. Participants rated the usability of ARROWS above average in both settings, while making a reasonable level of mental effort. The findings also indicate a significant correlation between perceived trust and usability, highlighting the importance of trust in user experience.
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Affiliation(s)
- Sepehr Sabeti
- William States Lee College of Engineering, UNC Charlotte, USA
| | - Nichole Morris
- Department of Mechanical Engineering, University of Minnesota, USA
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14
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Yang PK, Filtjens B, Ginis P, Goris M, Nieuwboer A, Gilat M, Slaets P, Vanrumste B. Freezing of gait assessment with inertial measurement units and deep learning: effect of tasks, medication states, and stops. J Neuroeng Rehabil 2024; 21:24. [PMID: 38350964 PMCID: PMC10865632 DOI: 10.1186/s12984-024-01320-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Accepted: 01/30/2024] [Indexed: 02/15/2024] Open
Abstract
BACKGROUND Freezing of gait (FOG) is an episodic and highly disabling symptom of Parkinson's Disease (PD). Traditionally, FOG assessment relies on time-consuming visual inspection of camera footage. Therefore, previous studies have proposed portable and automated solutions to annotate FOG. However, automated FOG assessment is challenging due to gait variability caused by medication effects and varying FOG-provoking tasks. Moreover, whether automated approaches can differentiate FOG from typical everyday movements, such as volitional stops, remains to be determined. To address these questions, we evaluated an automated FOG assessment model with deep learning (DL) based on inertial measurement units (IMUs). We assessed its performance trained on all standardized FOG-provoking tasks and medication states, as well as on specific tasks and medication states. Furthermore, we examined the effect of adding stopping periods on FOG detection performance. METHODS Twelve PD patients with self-reported FOG (mean age 69.33 ± 6.02 years) completed a FOG-provoking protocol, including timed-up-and-go and 360-degree turning-in-place tasks in On/Off dopaminergic medication states with/without volitional stopping. IMUs were attached to the pelvis and both sides of the tibia and talus. A temporal convolutional network (TCN) was used to detect FOG episodes. FOG severity was quantified by the percentage of time frozen (%TF) and the number of freezing episodes (#FOG). The agreement between the model-generated outcomes and the gold standard experts' video annotation was assessed by the intra-class correlation coefficient (ICC). RESULTS For FOG assessment in trials without stopping, the agreement of our model was strong (ICC (%TF) = 0.92 [0.68, 0.98]; ICC(#FOG) = 0.95 [0.72, 0.99]). Models trained on a specific FOG-provoking task could not generalize to unseen tasks, while models trained on a specific medication state could generalize to unseen states. For assessment in trials with stopping, the agreement of our model was moderately strong (ICC (%TF) = 0.95 [0.73, 0.99]; ICC (#FOG) = 0.79 [0.46, 0.94]), but only when stopping was included in the training data. CONCLUSION A TCN trained on IMU signals allows valid FOG assessment in trials with/without stops containing different medication states and FOG-provoking tasks. These results are encouraging and enable future work investigating automated FOG assessment during everyday life.
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Affiliation(s)
- Po-Kai Yang
- eMedia Research Lab/STADIUS, Department of Electrical Engineering (ESAT), KU Leuven, Andreas Vesaliusstraat 13, 3000, Leuven, Belgium.
- Intelligent Mobile Platforms Research Group, Department of Mechanical Engineering, KU Leuven, Andreas Vesaliusstraat 13, 3000, Leuven, Belgium.
| | - Benjamin Filtjens
- eMedia Research Lab/STADIUS, Department of Electrical Engineering (ESAT), KU Leuven, Andreas Vesaliusstraat 13, 3000, Leuven, Belgium
- Intelligent Mobile Platforms Research Group, Department of Mechanical Engineering, KU Leuven, Andreas Vesaliusstraat 13, 3000, Leuven, Belgium
| | - Pieter Ginis
- Research Group for Neurorehabilitation (eNRGy), Department of Rehabilitation Sciences, KU Leuven, Tervuursevest 101, 3001, Heverlee, Belgium
| | - Maaike Goris
- Research Group for Neurorehabilitation (eNRGy), Department of Rehabilitation Sciences, KU Leuven, Tervuursevest 101, 3001, Heverlee, Belgium
| | - Alice Nieuwboer
- Research Group for Neurorehabilitation (eNRGy), Department of Rehabilitation Sciences, KU Leuven, Tervuursevest 101, 3001, Heverlee, Belgium
| | - Moran Gilat
- Research Group for Neurorehabilitation (eNRGy), Department of Rehabilitation Sciences, KU Leuven, Tervuursevest 101, 3001, Heverlee, Belgium
| | - Peter Slaets
- Intelligent Mobile Platforms Research Group, Department of Mechanical Engineering, KU Leuven, Andreas Vesaliusstraat 13, 3000, Leuven, Belgium
| | - Bart Vanrumste
- eMedia Research Lab/STADIUS, Department of Electrical Engineering (ESAT), KU Leuven, Andreas Vesaliusstraat 13, 3000, Leuven, Belgium
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15
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Herrin KR, Kwak ST, Rock CG, Chang YH. Gait quality in prosthesis users is reflected by force-based metrics when learning to walk on a new research-grade powered prosthesis. FRONTIERS IN REHABILITATION SCIENCES 2024; 5:1339856. [PMID: 38370855 PMCID: PMC10869520 DOI: 10.3389/fresc.2024.1339856] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Accepted: 01/12/2024] [Indexed: 02/20/2024]
Abstract
Introduction Powered prosthetic feet require customized tuning to ensure comfort and long-term success for the user, but tuning in both clinical and research settings is subjective, time intensive, and the standard for tuning can vary depending on the patient's and the prosthetist's experience levels. Methods Therefore, we studied eight different metrics of gait quality associated with use of a research-grade powered prosthetic foot in seven individuals with transtibial amputation during treadmill walking. We compared clinically tuned and untuned conditions with the goal of identifying performance-based metrics capable of distinguishing between good (as determined by a clinician) from poor gait quality. Results Differences between the tuned and untuned conditions were reflected in ankle power, both the vertical and anterior-posterior impulse symmetry indices, limb-force alignment, and positive ankle work, with improvements seen in all metrics during use of the tuned prosthesis. Discussion Notably, all of these metrics relate to the timing of force generation during walking which is information not directly accessible to a prosthetist during a typical tuning process. This work indicates that relevant, real-time biomechanical data provided to the prosthetist through the future provision of wearable sensors may enhance and improve future clinical tuning procedures associated with powered prostheses as well as their long-term outcomes.
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Affiliation(s)
- Kinsey R. Herrin
- Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA, United States
- Institute for Robotics and Intelligent Machines, Georgia Institute of Technology, Atlanta, GA, United States
| | - Samuel T. Kwak
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, United States
| | - Chase G. Rock
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, United States
| | - Young-Hui Chang
- Institute for Robotics and Intelligent Machines, Georgia Institute of Technology, Atlanta, GA, United States
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, United States
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16
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Zhang W, Sun H, Huang D, Zhang Z, Li J, Wu C, Sun Y, Gong M, Wang Z, Sun C, Cui G, Guo Y, Chan P. Detection and prediction of freezing of gait with wearable sensors in Parkinson's disease. Neurol Sci 2024; 45:431-453. [PMID: 37843692 DOI: 10.1007/s10072-023-07017-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2023] [Accepted: 08/06/2023] [Indexed: 10/17/2023]
Abstract
Freezing of gait (FoG) is one of the most distressing symptoms of Parkinson's Disease (PD), commonly occurring in patients at middle and late stages of the disease. Automatic and accurate FoG detection and prediction have emerged as a promising tool for long-term monitoring of PD and implementation of gait assistance systems. This paper reviews the recent development of FoG detection and prediction using wearable sensors, with attention on identifying knowledge gaps that need to be filled in future research. This review searched the PubMed and Web of Science databases to collect studies that detect or predict FoG with wearable sensors. After screening, 89 of 270 articles were included. The data description, extracted features, detection/prediction methods, and classification performance were extracted from the articles. As the number of papers of this area is increasing, the performance has been steadily improved. However, small datasets and inconsistent evaluation processes still hinder the application of FoG detection and prediction with wearable sensors in clinical practice.
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Affiliation(s)
- Wei Zhang
- Department of Neurology, Suining County People's Hospital, Xuzhou, 221200, Jiangsu, China
- Department of Neurology, Neurobiology and Geriatrics, Beijing Institute of Geriatrics, Xuanwu Hospital of Capital Medical University, Beijing, 100053, China
- Department of Neurology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, 221006, Jiangsu, China
- Jiangsu Key Laboratory of Brain Disease Bioinformation, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China
| | - Hong Sun
- Department of Neurology, Neurobiology and Geriatrics, Beijing Institute of Geriatrics, Xuanwu Hospital of Capital Medical University, Beijing, 100053, China
- Clinical Center for Parkinson's Disease, Capital Medical University, Beijing, 100053, China
- National Clinical Research Center of Geriatric Disorders, Key Laboratory for Neurodegenerative Disease of the Ministry of Education, Beijing Key Laboratory for Parkinson's Disease, Parkinson Disease Center of Beijing Institute for Brain Disorders, Beijing, 100053, China
| | - Debin Huang
- Department of Automation Science and Electrical Engineering, Beihang University, Beijing, 100191, China
| | - Zixuan Zhang
- Department of Neurology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, 221006, Jiangsu, China
| | - Jinyu Li
- Department of Neurology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, 221006, Jiangsu, China
| | - Chan Wu
- Dongzhimen Hospital, Beijing University of Traditional Chinese Medicine, Beijing, 100029, China
| | - Yingying Sun
- Department of Neurology, Suining County People's Hospital, Xuzhou, 221200, Jiangsu, China
- Department of Neurology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, 221006, Jiangsu, China
| | - Mengyi Gong
- Department of Neurology, Suining County People's Hospital, Xuzhou, 221200, Jiangsu, China
- Department of Neurology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, 221006, Jiangsu, China
| | - Zhi Wang
- Department of Neurology, Suining County People's Hospital, Xuzhou, 221200, Jiangsu, China
- Department of Neurology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, 221006, Jiangsu, China
| | - Chao Sun
- Department of Neurology, Suining County People's Hospital, Xuzhou, 221200, Jiangsu, China
- Department of Neurology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, 221006, Jiangsu, China
| | - Guiyun Cui
- Department of Neurology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, 221006, Jiangsu, China.
| | - Yuzhu Guo
- Department of Automation Science and Electrical Engineering, Beihang University, Beijing, 100191, China.
| | - Piu Chan
- Department of Neurology, Neurobiology and Geriatrics, Beijing Institute of Geriatrics, Xuanwu Hospital of Capital Medical University, Beijing, 100053, China.
- Clinical Center for Parkinson's Disease, Capital Medical University, Beijing, 100053, China.
- National Clinical Research Center of Geriatric Disorders, Key Laboratory for Neurodegenerative Disease of the Ministry of Education, Beijing Key Laboratory for Parkinson's Disease, Parkinson Disease Center of Beijing Institute for Brain Disorders, Beijing, 100053, China.
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17
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Salaorni F, Bonardi G, Schena F, Tinazzi M, Gandolfi M. Wearable devices for gait and posture monitoring via telemedicine in people with movement disorders and multiple sclerosis: a systematic review. Expert Rev Med Devices 2024; 21:121-140. [PMID: 38124300 DOI: 10.1080/17434440.2023.2298342] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Accepted: 12/19/2023] [Indexed: 12/23/2023]
Abstract
INTRODUCTION Wearable devices and telemedicine are increasingly used to track health-related parameters across patient populations. Since gait and postural control deficits contribute to mobility deficits in persons with movement disorders and multiple sclerosis, we thought it interesting to evaluate devices in telemedicine for gait and posture monitoring in such patients. METHODS For this systematic review, we searched the electronic databases MEDLINE (PubMed), SCOPUS, Cochrane Library, and SPORTDiscus. Of the 452 records retrieved, 12 met the inclusion/exclusion criteria. Data about (1) study characteristics and clinical aspects, (2) technical, and (3) telemonitoring and teleconsulting were retrieved, The studies were quality assessed. RESULTS All studies involved patients with Parkinson's disease; most used triaxial accelerometers for general assessment (n = 4), assessment of motor fluctuation (n = 3), falls (n = 2), and turning (n = 3). Sensor placement and count varied widely across studies. Nine used lab-validated algorithms for data analysis. Only one discussed synchronous patient feedback and asynchronous teleconsultation. CONCLUSIONS Wearable devices enable real-world patient monitoring and suggest biomarkers for symptoms and behaviors related to underlying gait disorders. thus enriching clinical assessment and personalized treatment plans. As digital healthcare evolves, further research is needed to enhance device accuracy, assess user acceptability, and integrate these tools into telemedicine infrastructure. PROSPERO REGISTRATION CRD42022355460.
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Affiliation(s)
- Francesca Salaorni
- Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, Verona, Italy
| | - Giulia Bonardi
- Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, Verona, Italy
| | - Federico Schena
- Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, Verona, Italy
| | - Michele Tinazzi
- Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, Verona, Italy
| | - Marialuisa Gandolfi
- Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, Verona, Italy
- Neuromotor and Cognitive Rehabilitation Research Centre (CRRNC), University of Verona, Verona, Italy
- Neurorehabilitation Unit - Azienda Ospedaliera Universitaria Integrata, Verona
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18
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Kim J, Porciuncula F, Yang HD, Wendel N, Baker T, Chin A, Ellis TD, Walsh CJ. Soft robotic apparel to avert freezing of gait in Parkinson's disease. Nat Med 2024; 30:177-185. [PMID: 38182783 DOI: 10.1038/s41591-023-02731-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Accepted: 11/21/2023] [Indexed: 01/07/2024]
Abstract
Freezing of gait (FoG) is a profoundly disruptive gait disturbance in Parkinson's disease, causing unintended stops while walking. Therapies for FoG reveal modest and transient effects, resulting in a lack of effective treatments. Here we show proof of concept that FoG can be averted using soft robotic apparel that augments hip flexion. The wearable garment uses cable-driven actuators and sensors, generating assistive moments in concert with biological muscles. In this n-of-1 trial with five repeated measurements spanning 6 months, a 73-year-old male with Parkinson's disease and substantial FoG demonstrated a robust response to robotic apparel. With assistance, FoG was instantaneously eliminated during indoor walking (0% versus 39 ± 16% time spent freezing when unassisted), accompanied by 49 ± 11 m (+55%) farther walking compared to unassisted walking, faster speeds (+0.18 m s-1) and improved gait quality (-25% in gait variability). FoG-targeting effects were repeatable across multiple days, provoking conditions and environment contexts, demonstrating potential for community use. This study demonstrated that FoG was averted using soft robotic apparel in an individual with Parkinson's disease, serving as an impetus for technological advancements in response to this serious yet unmet need.
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Affiliation(s)
- Jinsoo Kim
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, MA, USA
| | - Franchino Porciuncula
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, MA, USA
- Department of Physical Therapy, Boston University, Boston, MA, USA
| | - Hee Doo Yang
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, MA, USA
| | - Nicholas Wendel
- Department of Physical Therapy, Boston University, Boston, MA, USA
| | - Teresa Baker
- Department of Physical Therapy, Boston University, Boston, MA, USA
| | - Andrew Chin
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, MA, USA
| | - Terry D Ellis
- Department of Physical Therapy, Boston University, Boston, MA, USA.
| | - Conor J Walsh
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, MA, USA.
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, USA.
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19
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Ben Chaabane N, Conze PH, Lempereur M, Quellec G, Rémy-Néris O, Brochard S, Cochener B, Lamard M. Quantitative gait analysis and prediction using artificial intelligence for patients with gait disorders. Sci Rep 2023; 13:23099. [PMID: 38155189 PMCID: PMC10754876 DOI: 10.1038/s41598-023-49883-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Accepted: 12/13/2023] [Indexed: 12/30/2023] Open
Abstract
Quantitative Gait Analysis (QGA) is considered as an objective measure of gait performance. In this study, we aim at designing an artificial intelligence that can efficiently predict the progression of gait quality using kinematic data obtained from QGA. For this purpose, a gait database collected from 734 patients with gait disorders is used. As the patient walks, kinematic data is collected during the gait session. This data is processed to generate the Gait Profile Score (GPS) for each gait cycle. Tracking potential GPS variations enables detecting changes in gait quality. In this regard, our work is driven by predicting such future variations. Two approaches were considered: signal-based and image-based. The signal-based one uses raw gait cycles, while the image-based one employs a two-dimensional Fast Fourier Transform (2D FFT) representation of gait cycles. Several architectures were developed, and the obtained Area Under the Curve (AUC) was above 0.72 for both approaches. To the best of our knowledge, our study is the first to apply neural networks for gait prediction tasks.
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Affiliation(s)
- Nawel Ben Chaabane
- LaTIM UMR 1101 Laboratory, Inserm, Brest, France.
- Western Brittany University, Brest, France.
| | - Pierre-Henri Conze
- LaTIM UMR 1101 Laboratory, Inserm, Brest, France
- IMT Atlantique, Brest, France
| | - Mathieu Lempereur
- LaTIM UMR 1101 Laboratory, Inserm, Brest, France
- Western Brittany University, Brest, France
- University Hospital of Brest, Brest, France
| | | | - Olivier Rémy-Néris
- LaTIM UMR 1101 Laboratory, Inserm, Brest, France
- Western Brittany University, Brest, France
- University Hospital of Brest, Brest, France
| | - Sylvain Brochard
- LaTIM UMR 1101 Laboratory, Inserm, Brest, France
- Western Brittany University, Brest, France
- University Hospital of Brest, Brest, France
| | - Béatrice Cochener
- LaTIM UMR 1101 Laboratory, Inserm, Brest, France
- Western Brittany University, Brest, France
- University Hospital of Brest, Brest, France
| | - Mathieu Lamard
- LaTIM UMR 1101 Laboratory, Inserm, Brest, France
- Western Brittany University, Brest, France
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20
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Memeo M, Sandini G, Cocchi E, Brayda L. Blind people can actively manipulate virtual objects with a novel tactile device. Sci Rep 2023; 13:22845. [PMID: 38129483 PMCID: PMC10739710 DOI: 10.1038/s41598-023-49507-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Accepted: 12/08/2023] [Indexed: 12/23/2023] Open
Abstract
Frequently in rehabilitation, visually impaired persons are passive agents of exercises with fixed environmental constraints. In fact, a printed tactile map, i.e. a particular picture with a specific spatial arrangement, can usually not be edited. Interaction with map content, instead, facilitates the learning of spatial skills because it exploits mental imagery, manipulation and strategic planning simultaneously. However, it has rarely been applied to maps, mainly because of technological limitations. This study aims to understand if visually impaired people can autonomously build objects that are completely virtual. Specifically, we investigated if a group of twelve blind persons, with a wide age range, could exploit mental imagery to interact with virtual content and actively manipulate it by means of a haptic device. The device is mouse-shaped and designed to jointly perceive, with one finger only, local tactile height and inclination cues of arbitrary scalar fields. Spatial information can be mentally constructed by integrating local tactile cues, given by the device, with global proprioceptive cues, given by hand and arm motion. The experiment consisted of a bi-manual task, in which one hand explored some basic virtual objects and the other hand acted on a keyboard to change the position of one object in real-time. The goal was to merge basic objects into more complex objects, like a puzzle. The experiment spanned different resolutions of the tactile information. We measured task accuracy, efficiency, usability and execution time. The average accuracy in solving the puzzle was 90.5%. Importantly, accuracy was linearly predicted by efficiency, measured as the number of moves needed to solve the task. Subjective parameters linked to usability and spatial resolutions did not predict accuracy; gender modulated the execution time, with men being faster than women. Overall, we show that building purely virtual tactile objects is possible in absence of vision and that the process is measurable and achievable in partial autonomy. Introducing virtual tactile graphics in rehabilitation protocols could facilitate the stimulation of mental imagery, a basic element for the ability to orient in space. The behavioural variable introduced in the current study can be calculated after each trial and therefore could be used to automatically measure and tailor protocols to specific user needs. In perspective, our experimental setup can inspire remote rehabilitation scenarios for visually impaired people.
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Affiliation(s)
- Mariacarla Memeo
- Robotics, Brain and Cognitive Sciences Department Now With Center for Human Technologies, Fondazione Istituto Italiano di Tecnologia, Via Enrico Melen 83, Genoa, Italy
| | - Giulio Sandini
- Robotics, Brain and Cognitive Sciences Department, Fondazione Istituto Italiano di Tecnologia, Via Enrico Melen 83, Genoa, Italy
| | - Elena Cocchi
- Istituto David Chiossone per Ciechi e Ipovedenti Onlus, Geona, Italy
| | - Luca Brayda
- Robotics, Brain and Cognitive Sciences Department, Fondazione Istituto Italiano di Tecnologia, Via Enrico Melen 83, Genoa, Italy.
- Acoesis srl, Via Enrico Melen 83, Genoa, Italy.
- Nextage srl, Piazza della Vittoria 12, Genova, Italia.
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21
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Moreau C, Rouaud T, Grabli D, Benatru I, Remy P, Marques AR, Drapier S, Mariani LL, Roze E, Devos D, Dupont G, Bereau M, Fabbri M. Overview on wearable sensors for the management of Parkinson's disease. NPJ Parkinsons Dis 2023; 9:153. [PMID: 37919332 PMCID: PMC10622581 DOI: 10.1038/s41531-023-00585-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Accepted: 10/02/2023] [Indexed: 11/04/2023] Open
Abstract
Parkinson's disease (PD) is affecting about 1.2 million patients in Europe with a prevalence that is expected to have an exponential increment, in the next decades. This epidemiological evolution will be challenged by the low number of neurologists able to deliver expert care for PD. As PD is better recognized, there is an increasing demand from patients for rigorous control of their symptoms and for therapeutic education. In addition, the highly variable nature of symtoms between patients and the fluctuations within the same patient requires innovative tools to help doctors and patients monitor the disease in their usual living environment and adapt treatment in a more relevant way. Nowadays, there are various body-worn sensors (BWS) proposed to monitor parkinsonian clinical features, such as motor fluctuations, dyskinesia, tremor, bradykinesia, freezing of gait (FoG) or gait disturbances. BWS have been used as add-on tool for patients' management or research purpose. Here, we propose a practical anthology, summarizing the characteristics of the most used BWS for PD patients in Europe, focusing on their role as tools to improve treatment management. Consideration regarding the use of technology to monitor non-motor features is also included. BWS obviously offer new opportunities for improving management strategy in PD but their precise scope of use in daily routine care should be clarified.
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Affiliation(s)
- Caroline Moreau
- Department of Neurology, Parkinson's disease expert Center, Lille University, INSERM UMRS_1172, University Hospital Center, Lille, France
- The French Ns-Park Network, Paris, France
| | - Tiphaine Rouaud
- The French Ns-Park Network, Paris, France
- CHU Nantes, Centre Expert Parkinson, Department of Neurology, Nantes, F-44093, France
| | - David Grabli
- The French Ns-Park Network, Paris, France
- Assistance Publique Hôpitaux de Paris, Department of Neurology, CIC Neurosciences, Pitié-Salpêtrière Hospital, Sorbonne University, Paris, France
- Sorbonne University, Paris Brain Institute - ICM, Inserm, CNRS, Paris, France
| | - Isabelle Benatru
- The French Ns-Park Network, Paris, France
- Department of Neurology, University Hospital of Poitiers, Poitiers, France
- INSERM, CHU de Poitiers, University of Poitiers, Centre d'Investigation Clinique CIC1402, Poitiers, France
| | - Philippe Remy
- The French Ns-Park Network, Paris, France
- Centre Expert Parkinson, NS-Park/FCRIN Network, CHU Henri Mondor, AP-HP, Equipe NPI, IMRB, INSERM et Faculté de Santé UPE-C, Créteil, FranceService de neurologie, hôpital Henri-Mondor, AP-HP, Créteil, France
| | - Ana-Raquel Marques
- The French Ns-Park Network, Paris, France
- Université Clermont Auvergne, CNRS, Clermont Auvergne INP, Institut Pascal, Clermont-Ferrand University Hospital, Neurology department, Clermont-Ferrand, France
| | - Sophie Drapier
- The French Ns-Park Network, Paris, France
- Pontchaillou University Hospital, Department of Neurology, CIC INSERM 1414, Rennes, France
| | - Louise-Laure Mariani
- The French Ns-Park Network, Paris, France
- Assistance Publique Hôpitaux de Paris, Department of Neurology, CIC Neurosciences, Pitié-Salpêtrière Hospital, Sorbonne University, Paris, France
- Sorbonne University, Paris Brain Institute - ICM, Inserm, CNRS, Paris, France
| | - Emmanuel Roze
- The French Ns-Park Network, Paris, France
- Assistance Publique Hôpitaux de Paris, Department of Neurology, CIC Neurosciences, Pitié-Salpêtrière Hospital, Sorbonne University, Paris, France
- Sorbonne University, Paris Brain Institute - ICM, Inserm, CNRS, Paris, France
| | - David Devos
- The French Ns-Park Network, Paris, France
- Parkinson's Disease Centre of Excellence, Department of Medical Pharmacology, Univ. Lille, INSERM; CHU Lille, U1172 - Degenerative & Vascular Cognitive Disorders, LICEND, NS-Park Network, F-59000, Lille, France
| | - Gwendoline Dupont
- The French Ns-Park Network, Paris, France
- Centre hospitalier universitaire François Mitterrand, Département de Neurologie, Université de Bourgogne, Dijon, France
| | - Matthieu Bereau
- The French Ns-Park Network, Paris, France
- Service de neurologie, université de Franche-Comté, CHRU de Besançon, 25030, Besançon, France
| | - Margherita Fabbri
- The French Ns-Park Network, Paris, France.
- Department of Neurosciences, Clinical Investigation Center CIC 1436, Parkinson Toulouse Expert Centre, NS-Park/FCRIN Network and NeuroToul COEN Center, Toulouse University Hospital, INSERM, University of Toulouse 3, Toulouse, France.
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22
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Chen M, Sun Z, Xin T, Chen Y, Su F. An Interpretable Deep Learning Optimized Wearable Daily Detection System for Parkinson's Disease. IEEE Trans Neural Syst Rehabil Eng 2023; 31:3937-3946. [PMID: 37695969 DOI: 10.1109/tnsre.2023.3314100] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/13/2023]
Abstract
Walking detection in the daily life of patients with Parkinson's disease (PD) is of great significance for tracking the progress of the disease. This study aims to implement an accurate, objective, and passive detection algorithm optimized based on an interpretable deep learning architecture for the daily walking of patients with PD and to explore the most representative spatiotemporal motor features. Five inertial measurement units attached to the wrist, ankle, and waist are used to collect motion data from 100 subjects during a 10-meter walking test. The raw data of each sensor are subjected to the continuous wavelet transform to train the classification model of the constructed 6-channel convolutional neural network (CNN). The results show that the sensor located at the waist has the best classification performance with an accuracy of 98.01%±0.85% and the area under the receiver operating characteristic curve (AUC) of 0.9981±0.0017 under ten-fold cross-validation. The gradient-weighted class activation mapping shows that the feature points with greater contribution to PD were concentrated in the lower frequency band (0.5~3Hz) compared with healthy controls. The visual maps of the 3D CNN show that only three out of the six time series have a greater contribution, which is used as a basis to further optimize the model input, greatly reducing the raw data processing costs (50%) while ensuring its performance (AUC=0.9929±0.0019). To the best of our knowledge, this is the first study to consider the visual interpretation-based optimization of an intelligent classification model in the intelligent diagnosis of PD.
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23
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Ratanasutiranont C, Srisilpa K, Termsarasab P, Ruthiraphong P. Smart ankle bracelet-laser device to improve gait and detect freezing of gait in Parkinsonism patients: a case series. Assist Technol 2023; 35:417-424. [PMID: 36136608 DOI: 10.1080/10400435.2022.2113179] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/02/2022] [Indexed: 10/14/2022] Open
Abstract
Freezing of gait (FOG) is one of the most disabling symptoms of Parkinsonism. Moreover, it does not respond well to medication. Visual cues have been shown to alleviate FOG in Parkinsonism; however, their efficacy is inconsistent. Currently, most mobile cueing devices are used as an open-loop cueing system, which requires manual control to enable constant visual cues. Thus, such devices may not be suitable for some people, especially those who have attention deficits. In addition, objective measurements of FOG in real-life situations remain challenging. Therefore, we developed a smart-ankle laser as a closed-loop cueing system that can detect the patient's walking pattern and automatically project a laser line that follows each walking step, thus requires less attention. Real-time motion was also recorded within the device for FOG measurement. We studied the efficacy of the device in three Parkinsonism patients with FOG (one man and two women, aged 58-76 years) immediately after use and two patients at 1-week follow-up. Gait speed, Timed Up and Go test performance, stride length, and % FOG improved with the use of the laser, without adverse effects.
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Affiliation(s)
- Chompoonuch Ratanasutiranont
- Department of Rehabilitation Medicine, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand
| | - Kwan Srisilpa
- Department of Rehabilitation Medicine, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand
| | - Pichet Termsarasab
- Department of Medicine, Division of Neurology, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand
| | - Peeraya Ruthiraphong
- Department of Rehabilitation Medicine, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand
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Gefen N, Archambault PS, Rigbi A, Weiss PL. Pediatric powered mobility training: powered wheelchair versus simulator-based practice. Assist Technol 2023; 35:389-398. [PMID: 35737961 DOI: 10.1080/10400435.2022.2084183] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/23/2022] [Indexed: 10/17/2022] Open
Abstract
METHOD Participants included 30 children and adolescents (23 males, 13 females) with cerebral palsy and other neuromuscular diseases, aged 6-18. Data were collected and compared at baseline and after 12 weeks of home-based practice via a powered wheelchair or a simulator. Powered mobility ability was determined by the Powered Mobility Program (PMP), the Israel Ministry of Health's Powered Mobility Proficiency Test (PM-PT) and the Assessment of Learning Powered Mobility (ALP). RESULTS All participants practiced for the required amount of time and both groups reported a similar user experience. Both groups achieved significant improvement following the practice period as assessed by the PMP and PM-PT assessments, with no significant differences between them. A significant improvement was found in the ALP assessment outcomes for the powered wheelchair group only. CONCLUSIONS This is the first study, to our knowledge, that compares two different wheelchair training methods. Simulator-based practice is an effective training option for powered mobility for children with physical disabilities aged 6-18 years old, demonstrating that it is possible to provide driving skill practice opportunities safe, controlled environments.
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Affiliation(s)
- Naomi Gefen
- Deputy Director General, ALYN Hospital, Jerusalem, Israel
| | - Philippe S Archambault
- School of Physical and Occupational Therapy, McGill University, Montreal, Canada
- McGill, University of Montreal, University of Quebec in Montreal
| | - Amihai Rigbi
- Faculty of Education, Beit Berl College, Kfar-Saba, Israel
| | - Patrice L Weiss
- Department of Occupational Therapy, University of Haifa, Haifa, Israel
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25
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Richings L, Nelson D, Goosey-Tolfrey V, Donnellan C, Booth V. Effectiveness of the "Evidence-Based Scientific Exercise Guidelines" in Increasing Cardiorespiratory Fitness, Cardiometabolic Health, and Muscle Strength in Acute Spinal Cord Injury Rehabilitation: A Systematic Review. Arch Rehabil Res Clin Transl 2023; 5:100278. [PMID: 37744200 PMCID: PMC10517363 DOI: 10.1016/j.arrct.2023.100278] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/26/2023] Open
Abstract
Objective To determine the effect of exercise and physical activity interventions that meet current guideline recommendations on cardiorespiratory fitness, cardiometabolic health, and muscle strength in adults in the acute stage (<1 year post onset) of spinal cord injury (SCI) rehabilitation. Data Sources Six electronic databases (PubMed, CINAHL, SPORTDiscus, Google Scholar, National Institute Clinical Excellence, World Health Organization) were searched (January 2016-March 2022) to extend a previously published review. Study Selection Included studies implemented exercise interventions in the acute stage of SCI rehabilitation participants which met the exercise guidelines and measured cardiorespiratory fitness, cardiometabolic health, and strength outcomes. Data Extraction Titles and abstracts were screened against eligibility criteria and duplicates removed using EndNote X8. Full texts were independently assessed and results presented in a Preferred Reporting Items for Systematic Reviews and Meta-Analyses flowchart. Data extraction was completed on included studies by 2 reviewers (L.R. and V.B.) using a modified Cochrane Group form. Data Synthesis Data were synthesized, appraised using the Modified Downs & Black checklist and presented in narrative and tabular format. This review was registered on PROSPERO (Register ID:CRD42021249441). Of the 1255 studies, 4 were included, featuring 108 total participants <1-year post-SCI. Functional electrical stimulation cycle ergometry reduced muscle atrophy after 3 months training and increased lean body mass after 6 months. Resistance training increased muscle peak torque, perceived muscle strength and function. Aerobic exercise interventions did not increase cardiorespiratory fitness. Conclusions Interventions meeting the exercise guidelines did not increase cardiorespiratory fitness but were shown to improve cardiometabolic health and perceived muscle strength and function in adults in the acute stage of SCI rehabilitation. Further empirical research using standardized outcome measures are required to explore the effectiveness of aerobic exercise and strengthening interventions in acute stage of SCI rehabilitation to support the development of exercise guidelines.
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Affiliation(s)
| | - David Nelson
- Lincoln International Institute for Rural Health, University of Lincoln, Lincoln, UK
| | - Victoria Goosey-Tolfrey
- Peter Harrison Centre for Disability Sport, School of Sport, Exercise & Health Sciences, Loughborough University, Loughborough, UK
| | | | - Vicky Booth
- Nottingham University Hospitals NHS Trust, Nottingham, UK
- School of Medicine, University of Nottingham, Nottingham, UK
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26
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Hafer JF, Vitali R, Gurchiek R, Curtze C, Shull P, Cain SM. Challenges and advances in the use of wearable sensors for lower extremity biomechanics. J Biomech 2023; 157:111714. [PMID: 37423120 PMCID: PMC10529245 DOI: 10.1016/j.jbiomech.2023.111714] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 06/23/2023] [Accepted: 07/03/2023] [Indexed: 07/11/2023]
Abstract
The use of wearable sensors for the collection of lower extremity biomechanical data is increasing in popularity, in part due to the ease of collecting data and the ability to capture movement outside of traditional biomechanics laboratories. Consequently, an increasing number of researchers are facing the challenges that come with utilizing the data captured by wearable sensors. These challenges include identifying/calculating meaningful measures from unfamiliar data types (measures of acceleration and angular velocity instead of positions and joint angles), defining sensor-to-segment alignments for calculating traditional biomechanics metrics, using reduced sensor sets and machine learning to predict unmeasured signals, making decisions about when and how to make algorithms freely available, and developing or replicating methods to perform basic processing tasks such as recognizing activities of interest or identifying gait events. In this perspective article, we present our own approaches to common challenges in lower extremity biomechanics research using wearable sensors and share our perspectives on approaching several of these challenges. We present these perspectives with examples that come mostly from gait research, but many of the concepts also apply to other contexts where researchers may use wearable sensors. Our goal is to introduce common challenges to new users of wearable sensors, and to promote dialogue amongst experienced users towards best practices.
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Affiliation(s)
- Jocelyn F Hafer
- Department of Kinesiology and Applied Physiology, University of Delaware, Newark, DE, United States.
| | - Rachel Vitali
- Department of Mechanical Engineering, University of Iowa, Iowa City, IA, United States
| | - Reed Gurchiek
- Department of Bioengineering, Stanford University, Stanford, CA, United States
| | - Carolin Curtze
- Department of Biomechanics, University of Nebraska at Omaha, Omaha, NE, United States
| | - Peter Shull
- State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, China
| | - Stephen M Cain
- Department of Chemical and Biomedical Engineering, West Virginia University, Morgantown, WV, United States
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27
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Deshmukh S, Madhavan S. Can post stroke walking improve via telerehabilitation? A systematic review in adults with stroke. FRONTIERS IN REHABILITATION SCIENCES 2023; 4:1154686. [PMID: 37266514 PMCID: PMC10229804 DOI: 10.3389/fresc.2023.1154686] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Accepted: 04/25/2023] [Indexed: 06/03/2023]
Abstract
Objectives The purpose of this systematic review is to analyze primary studies investigating the effects of telerehabilitation on walking outcomes for the treatment of adult stroke survivors. Methods Data sources included PubMed, Embase and CINAHL searched until August 2022, using combinations of several keywords such as "telerehabilitation", "stroke", and "gait". Studies were required to have bidirectional form of videoconferencing with assessor presence, and include assessment of walking function (speed, endurance and/or balance). Data extraction was performed from each full text by one author, and quality and bias were assessed using the Physiotherapy Evidence Database (PEDro). Results Eight studies involving 248 participants met the inclusion criteria. Seven reported significant improvements in outcomes of balance and two showed improvements in endurance after telerehabilitation. Two studies observed greater balance improvements in the telerehabilitation group compared to control and/or in-person therapy. Differences in frequency, training duration, intervention type, and absence of an in-person therapy control group were identified as causes of variation between studies. Conclusions The effectiveness of telerehabilitation as a mode of therapy for walking could not be definitively determined due to the limited number of studies that directly measured walking speed or endurance. However, strong evidence was found for the use of telerehabilitation for balance improvements, which has implications for walking recovery. Impact statement Telerehabilitation appears to be safe, feasible and demonstrated high adherence. Our results highlighted limited studies using real-time supervision to administer telerehabilitation and lack of studies focusing on outcomes of walking speed and endurance, needed to fully determine the role of telerehabilitation for gait recovery. Systematic review registration number PROSPERO number CRD42021238197.
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Affiliation(s)
- Shravni Deshmukh
- Department of Physical Therapy, Brain Plasticity Laboratory, College of Applied Health Sciences, University of Illinois at Chicago, Chicago, IL, United States
- Graduate Program in Rehabilitation Science, College of Applied Health Sciences, University of Illinois at Chicago, Chicago, IL, United States
| | - Sangeetha Madhavan
- Department of Physical Therapy, Brain Plasticity Laboratory, College of Applied Health Sciences, University of Illinois at Chicago, Chicago, IL, United States
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Wang S, Zhu G, Shi L, Zhang C, Wu B, Yang A, Meng F, Jiang Y, Zhang J. Closed-Loop Adaptive Deep Brain Stimulation in Parkinson's Disease: Procedures to Achieve It and Future Perspectives. JOURNAL OF PARKINSON'S DISEASE 2023:JPD225053. [PMID: 37182899 DOI: 10.3233/jpd-225053] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2023]
Abstract
Parkinson's disease (PD) is a neurodegenerative disease with a heavy burden on patients, families, and society. Deep brain stimulation (DBS) can improve the symptoms of PD patients for whom medication is insufficient. However, current open-loop uninterrupted conventional DBS (cDBS) has inherent limitations, such as adverse effects, rapid battery consumption, and a need for frequent parameter adjustment. To overcome these shortcomings, adaptive DBS (aDBS) was proposed to provide responsive optimized stimulation for PD. This topic has attracted scientific interest, and a growing body of preclinical and clinical evidence has shown its benefits. However, both achievements and challenges have emerged in this novel field. To date, only limited reviews comprehensively analyzed the full framework and procedures for aDBS implementation. Herein, we review current preclinical and clinical data on aDBS for PD to discuss the full procedures for its achievement and to provide future perspectives on this treatment.
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Affiliation(s)
- Shu Wang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Guanyu Zhu
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Lin Shi
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Chunkui Zhang
- Center of Cognition and Brain Science, Beijing Institute of Basic Medical Sciences, Beijing, China
| | - Bing Wu
- Center of Cognition and Brain Science, Beijing Institute of Basic Medical Sciences, Beijing, China
| | - Anchao Yang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Fangang Meng
- Department of Functional Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
- Beijing Key Laboratory of Neurostimulation, Beijing, China
| | - Yin Jiang
- Department of Functional Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
- Beijing Key Laboratory of Neurostimulation, Beijing, China
| | - Jianguo Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- Department of Functional Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
- Beijing Key Laboratory of Neurostimulation, Beijing, China
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Cockx H, Nonnekes J, Bloem B, van Wezel R, Cameron I, Wang Y. Dealing with the heterogeneous presentations of freezing of gait: how reliable are the freezing index and heart rate for freezing detection? J Neuroeng Rehabil 2023; 20:53. [PMID: 37106388 PMCID: PMC10134593 DOI: 10.1186/s12984-023-01175-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Accepted: 04/12/2023] [Indexed: 04/29/2023] Open
Abstract
BACKGROUND Freezing of gait (FOG) is an unpredictable gait arrest that hampers the lives of 40% of people with Parkinson's disease. Because the symptom is heterogeneous in phenotypical presentation (it can present as trembling/shuffling, or akinesia) and manifests during various circumstances (it can be triggered by e.g. turning, passing doors, and dual-tasking), it is particularly difficult to detect with motion sensors. The freezing index (FI) is one of the most frequently used accelerometer-based methods for FOG detection. However, it might not adequately distinguish FOG from voluntary stops, certainly for the akinetic type of FOG. Interestingly, a previous study showed that heart rate signals could distinguish FOG from stopping and turning movements. This study aimed to investigate for which phenotypes and evoking circumstances the FI and heart rate might provide reliable signals for FOG detection. METHODS Sixteen people with Parkinson's disease and daily freezing completed a gait trajectory designed to provoke FOG including turns, narrow passages, starting, and stopping, with and without a cognitive or motor dual-task. We compared the FI and heart rate of 378 FOG events to baseline levels, and to stopping and normal gait events (i.e. turns and narrow passages without FOG) using mixed-effects models. We specifically evaluated the influence of different types of FOG (trembling vs akinesia) and triggering situations (turning vs narrow passages; no dual-task vs cognitive dual-task vs motor dual-task) on both outcome measures. RESULTS The FI increased significantly during trembling and akinetic FOG, but increased similarly during stopping and was therefore not significantly different from FOG. In contrast, heart rate change during FOG was for all types and during all triggering situations statistically different from stopping, but not from normal gait events. CONCLUSION When the power in the locomotion band (0.5-3 Hz) decreases, the FI increases and is unable to specify whether a stop is voluntary or involuntary (i.e. trembling or akinetic FOG). In contrast, the heart rate can reveal whether there is the intention to move, thus distinguishing FOG from stopping. We suggest that the combination of a motion sensor and a heart rate monitor may be promising for future FOG detection.
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Affiliation(s)
- Helena Cockx
- Department of Biophysics, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Heyendaalseweg 135, P.O. Box 9102, 6525AJ, Nijmegen, The Netherlands.
| | - Jorik Nonnekes
- Department of Rehabilitation, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
- Department of Rehabilitation, Sint Maartenskliniek, Nijmegen, The Netherlands
| | - Bastiaan Bloem
- Department of Neurology, Center of Expertise for Parkinson and Movement Disorders, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Richard van Wezel
- Department of Biophysics, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Heyendaalseweg 135, P.O. Box 9102, 6525AJ, Nijmegen, The Netherlands
- Biomedical Signals and Systems Group, Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS), University of Twente, Enschede, The Netherlands
| | - Ian Cameron
- Biomedical Signals and Systems Group, Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS), University of Twente, Enschede, The Netherlands
- OnePlanet Research Center, Nijmegen, The Netherlands
| | - Ying Wang
- Department of Biophysics, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Heyendaalseweg 135, P.O. Box 9102, 6525AJ, Nijmegen, The Netherlands
- Biomedical Signals and Systems Group, Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS), University of Twente, Enschede, The Netherlands
- ZGT Academy, Ziekenhuisgroep Twente, Almelo, The Netherlands
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Luo S, Androwis G, Adamovich S, Nunez E, Su H, Zhou X. Robust walking control of a lower limb rehabilitation exoskeleton coupled with a musculoskeletal model via deep reinforcement learning. J Neuroeng Rehabil 2023; 20:34. [PMID: 36935514 PMCID: PMC10024861 DOI: 10.1186/s12984-023-01147-2] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2021] [Accepted: 02/14/2023] [Indexed: 03/21/2023] Open
Abstract
BACKGROUND Few studies have systematically investigated robust controllers for lower limb rehabilitation exoskeletons (LLREs) that can safely and effectively assist users with a variety of neuromuscular disorders to walk with full autonomy. One of the key challenges for developing such a robust controller is to handle different degrees of uncertain human-exoskeleton interaction forces from the patients. Consequently, conventional walking controllers either are patient-condition specific or involve tuning of many control parameters, which could behave unreliably and even fail to maintain balance. METHODS We present a novel, deep neural network, reinforcement learning-based robust controller for a LLRE based on a decoupled offline human-exoskeleton simulation training with three independent networks, which aims to provide reliable walking assistance against various and uncertain human-exoskeleton interaction forces. The exoskeleton controller is driven by a neural network control policy that acts on a stream of the LLRE's proprioceptive signals, including joint kinematic states, and subsequently predicts real-time position control targets for the actuated joints. To handle uncertain human interaction forces, the control policy is trained intentionally with an integrated human musculoskeletal model and realistic human-exoskeleton interaction forces. Two other neural networks are connected with the control policy network to predict the interaction forces and muscle coordination. To further increase the robustness of the control policy to different human conditions, we employ domain randomization during training that includes not only randomization of exoskeleton dynamics properties but, more importantly, randomization of human muscle strength to simulate the variability of the patient's disability. Through this decoupled deep reinforcement learning framework, the trained controller of LLREs is able to provide reliable walking assistance to patients with different degrees of neuromuscular disorders without any control parameter tuning. RESULTS AND CONCLUSION A universal, RL-based walking controller is trained and virtually tested on a LLRE system to verify its effectiveness and robustness in assisting users with different disabilities such as passive muscles (quadriplegic), muscle weakness, or hemiplegic conditions without any control parameter tuning. Analysis of the RMSE for joint tracking, CoP-based stability, and gait symmetry shows the effectiveness of the controller. An ablation study also demonstrates the strong robustness of the control policy under large exoskeleton dynamic property ranges and various human-exoskeleton interaction forces. The decoupled network structure allows us to isolate the LLRE control policy network for testing and sim-to-real transfer since it uses only proprioception information of the LLRE (joint sensory state) as the input. Furthermore, the controller is shown to be able to handle different patient conditions without the need for patient-specific control parameter tuning.
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Affiliation(s)
- Shuzhen Luo
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, 07102, NJ, USA
- Lab of Biomechatronics and Intelligent Robotics, Department of Mechanical and Aerospace Engineering, North Carolina State University, Raleigh, 27695, NC, USA
| | - Ghaith Androwis
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, 07102, NJ, USA
- Kessler Foundation, West Orange, 07052, NJ, USA
| | - Sergei Adamovich
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, 07102, NJ, USA
| | - Erick Nunez
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, 07102, NJ, USA
| | - Hao Su
- Lab of Biomechatronics and Intelligent Robotics, Department of Mechanical and Aerospace Engineering, North Carolina State University, Raleigh, 27695, NC, USA
- Joint NCSU/UNC Department of Biomedical Engineering, University of North Carolina at Chapel Hill, Chapel Hill, 27599, NC, USA
| | - Xianlian Zhou
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, 07102, NJ, USA.
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Zanatta F, Farhane-Medina NZ, Adorni R, Steca P, Giardini A, D'Addario M, Pierobon A. Combining robot-assisted therapy with virtual reality or using it alone? A systematic review on health-related quality of life in neurological patients. Health Qual Life Outcomes 2023; 21:18. [PMID: 36810124 PMCID: PMC9942343 DOI: 10.1186/s12955-023-02097-y] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Accepted: 02/07/2023] [Indexed: 02/24/2023] Open
Abstract
BACKGROUND In the field of neurorehabilitation, robot-assisted therapy (RAT) and virtual reality (VR) have so far shown promising evidence on multiple motor and functional outcomes. The related effectiveness on patients' health-related quality of life (HRQoL) has been investigated across neurological populations but still remains unclear. The present study aimed to systematically review the studies investigating the effects of RAT alone and with VR on HRQoL in patients with different neurological diseases. METHODS A systematic review of the studies evaluating the impact of RAT alone and combined with VR on HRQoL in patients affected by neurological diseases (i.e., stroke, multiple sclerosis, spinal cord injury, Parkinson's Disease) was conducted according to PRISMA guidelines. Electronic searches of PubMed, Web of Science, Cochrane Library, CINAHL, Embase, and PsychINFO (2000-2022) were performed. Risk of bias was evaluated through the National Institute of Health Quality Assessment Tool. Descriptive data regarding the study design, participants, intervention, rehabilitation outcomes, robotic device typology, HRQoL measures, non-motor factors concurrently investigated, and main results were extracted and meta-synthetized. RESULTS The searches identified 3025 studies, of which 70 met the inclusion criteria. An overall heterogeneous configuration was found regarding the study design adopted, intervention procedures and technological devices implemented, rehabilitation outcomes (i.e., related to both upper and lower limb impairment), HRQoL measures administered, and main evidence. Most of the studies reported significant effects of both RAT and RAT plus VR on patients HRQoL, whether they adopted generic or disease-specific HRQoL measures. Significant post-intervention within-group changes were mainly found across neurological populations, while fewer studies reported significant between-group comparisons, and then, mostly in patients with stroke. Longitudinal investigations were also observed (up to 36 months), but significant longitudinal effects were exclusively found in patients with stroke or multiple sclerosis. Finally, concurrent evaluations on non-motor outcomes beside HRQoL included cognitive (i.e., memory, attention, executive functions) and psychological (i.e., mood, satisfaction with the treatment, device usability, fear of falling, motivation, self-efficacy, coping, and well-being) variables. CONCLUSIONS Despite the heterogeneity observed among the studies included, promising evidence was found on the effectiveness of RAT and RAT plus VR on HRQoL. However, further targeted short- and long-term investigations, are strongly recommended for specific HRQoL subcomponents and neurological populations, through the adoption of defined intervention procedures and disease-specific assessment methodology.
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Affiliation(s)
- Francesco Zanatta
- Department of Psychology, University of Milano-Bicocca, Milan, Italy
| | - Naima Z Farhane-Medina
- Maimonides Biomedical Research Institute of Córdoba (IMIBIC), Córdoba, Spain
- Department of Psychology, University of Córdoba, Córdoba, Spain
| | - Roberta Adorni
- Department of Psychology, University of Milano-Bicocca, Milan, Italy.
| | - Patrizia Steca
- Department of Psychology, University of Milano-Bicocca, Milan, Italy
| | - Anna Giardini
- Information Technology Department, Istituti Clinici Scientifici Maugeri IRCCS, Pavia, Italy
| | - Marco D'Addario
- Department of Psychology, University of Milano-Bicocca, Milan, Italy
| | - Antonia Pierobon
- Psychology Unit of Montescano Institute, Istituti Clinici Scientifici Maugeri IRCCS, Montescano, Italy
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Kwon H, Clifford GD, Genias I, Bernhard D, Esper CD, Factor SA, McKay JL. An Explainable Spatial-Temporal Graphical Convolutional Network to Score Freezing of Gait in Parkinsonian Patients. SENSORS (BASEL, SWITZERLAND) 2023; 23:1766. [PMID: 36850363 PMCID: PMC9968199 DOI: 10.3390/s23041766] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Revised: 01/27/2023] [Accepted: 02/01/2023] [Indexed: 06/18/2023]
Abstract
Freezing of gait (FOG) is a poorly understood heterogeneous gait disorder seen in patients with parkinsonism which contributes to significant morbidity and social isolation. FOG is currently measured with scales that are typically performed by movement disorders specialists (i.e., MDS-UPDRS), or through patient completed questionnaires (N-FOG-Q) both of which are inadequate in addressing the heterogeneous nature of the disorder and are unsuitable for use in clinical trials The purpose of this study was to devise a method to measure FOG objectively, hence improving our ability to identify it and accurately evaluate new therapies. A major innovation of our study is that it is the first study of its kind that uses the largest sample size (>30 h, N = 57) in order to apply explainable, multi-task deep learning models for quantifying FOG over the course of the medication cycle and at varying levels of parkinsonism severity. We trained interpretable deep learning models with multi-task learning to simultaneously score FOG (cross-validated F1 score 97.6%), identify medication state (OFF vs. ON levodopa; cross-validated F1 score 96.8%), and measure total PD severity (MDS-UPDRS-III score prediction error ≤ 2.7 points) using kinematic data of a well-characterized sample of N = 57 patients during levodopa challenge tests. The proposed model was able to explain how kinematic movements are associated with each FOG severity level that were highly consistent with the features, in which movement disorders specialists are trained to identify as characteristics of freezing. Overall, we demonstrate that deep learning models' capability to capture complex movement patterns in kinematic data can automatically and objectively score FOG with high accuracy. These models have the potential to discover novel kinematic biomarkers for FOG that can be used for hypothesis generation and potentially as clinical trial outcome measures.
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Affiliation(s)
- Hyeokhyen Kwon
- Department of Biomedical Informatics, School of Medicine, Emory University, Atlanta, GA 30322, USA
| | - Gari D. Clifford
- Department of Biomedical Informatics, School of Medicine, Emory University, Atlanta, GA 30322, USA
| | - Imari Genias
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA
| | - Doug Bernhard
- Jean and Paul Amos Parkinson’s Disease and Movement Disorders Program, Department of Neurology, School of Medicine, Emory University, Atlanta, GA 30322, USA
| | - Christine D. Esper
- Jean and Paul Amos Parkinson’s Disease and Movement Disorders Program, Department of Neurology, School of Medicine, Emory University, Atlanta, GA 30322, USA
| | - Stewart A. Factor
- Jean and Paul Amos Parkinson’s Disease and Movement Disorders Program, Department of Neurology, School of Medicine, Emory University, Atlanta, GA 30322, USA
| | - J. Lucas McKay
- Department of Biomedical Informatics, School of Medicine, Emory University, Atlanta, GA 30322, USA
- Jean and Paul Amos Parkinson’s Disease and Movement Disorders Program, Department of Neurology, School of Medicine, Emory University, Atlanta, GA 30322, USA
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Palmerini L, Reggi L, Bonci T, Del Din S, Micó-Amigo ME, Salis F, Bertuletti S, Caruso M, Cereatti A, Gazit E, Paraschiv-Ionescu A, Soltani A, Kluge F, Küderle A, Ullrich M, Kirk C, Hiden H, D’Ascanio I, Hansen C, Rochester L, Mazzà C, Chiari L. Mobility recorded by wearable devices and gold standards: the Mobilise-D procedure for data standardization. Sci Data 2023; 10:38. [PMID: 36658136 PMCID: PMC9852581 DOI: 10.1038/s41597-023-01930-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Accepted: 01/03/2023] [Indexed: 01/21/2023] Open
Abstract
Wearable devices are used in movement analysis and physical activity research to extract clinically relevant information about an individual's mobility. Still, heterogeneity in protocols, sensor characteristics, data formats, and gold standards represent a barrier for data sharing, reproducibility, and external validation. In this study, we aim at providing an example of how movement data (from the real-world and the laboratory) recorded from different wearables and gold standard technologies can be organized, integrated, and stored. We leveraged on our experience from a large multi-centric study (Mobilise-D) to provide guidelines that can prove useful to access, understand, and re-use the data that will be made available from the study. These guidelines highlight the encountered challenges and the adopted solutions with the final aim of supporting standardization and integration of data in other studies and, in turn, to increase and facilitate comparison of data recorded in the scientific community. We also provide samples of standardized data, so that both the structure of the data and the procedure can be easily understood and reproduced.
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Affiliation(s)
- Luca Palmerini
- grid.6292.f0000 0004 1757 1758University of Bologna, Department of Electrical, Electronic and Information Engineering ‘Guglielmo Marconi’, Bologna, Italy ,grid.6292.f0000 0004 1757 1758University of Bologna, Health Sciences and Technologies—Interdepartmental Center for Industrial Research (CIRI-SDV), Bologna, Italy
| | - Luca Reggi
- grid.6292.f0000 0004 1757 1758University of Bologna, Health Sciences and Technologies—Interdepartmental Center for Industrial Research (CIRI-SDV), Bologna, Italy
| | - Tecla Bonci
- grid.11835.3e0000 0004 1936 9262The University of Sheffield, INSIGNEO Institute for in silico Medicine, Sheffield, UK ,grid.11835.3e0000 0004 1936 9262The University of Sheffield, Department of Mechanical Engineering, Sheffield, UK
| | - Silvia Del Din
- grid.1006.70000 0001 0462 7212Newcastle University, Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle, UK
| | - M. Encarna Micó-Amigo
- grid.1006.70000 0001 0462 7212Newcastle University, Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle, UK
| | - Francesca Salis
- grid.11450.310000 0001 2097 9138University of Sassari, Department of Biomedical Sciences, Sassari, Italy
| | - Stefano Bertuletti
- grid.11450.310000 0001 2097 9138University of Sassari, Department of Biomedical Sciences, Sassari, Italy
| | - Marco Caruso
- grid.4800.c0000 0004 1937 0343Politecnico di Torino, Department of Electronics and Telecommunications, Torino, Italy ,grid.4800.c0000 0004 1937 0343Politecnico di Torino, PolitoBIOMed Lab – Biomedical Engineering Lab, Torino, Italy
| | - Andrea Cereatti
- grid.4800.c0000 0004 1937 0343Politecnico di Torino, Department of Electronics and Telecommunications, Torino, Italy
| | - Eran Gazit
- grid.413449.f0000 0001 0518 6922Tel Aviv Sourasky Medical Center, Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv-Yafo, Israel
| | - Anisoara Paraschiv-Ionescu
- grid.5333.60000000121839049Laboratory of Movement Analysis and Measurement, Ecole Polytechnique Federale de Lausanne, Lausanne, Switzerland
| | - Abolfazl Soltani
- grid.5333.60000000121839049Laboratory of Movement Analysis and Measurement, Ecole Polytechnique Federale de Lausanne, Lausanne, Switzerland
| | - Felix Kluge
- grid.5330.50000 0001 2107 3311Machine Learning and Data Analytics Lab, Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-University Erlangen-Nürnberg, Erlangen, Germany
| | - Arne Küderle
- grid.5330.50000 0001 2107 3311Machine Learning and Data Analytics Lab, Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-University Erlangen-Nürnberg, Erlangen, Germany
| | - Martin Ullrich
- grid.5330.50000 0001 2107 3311Machine Learning and Data Analytics Lab, Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-University Erlangen-Nürnberg, Erlangen, Germany
| | - Cameron Kirk
- grid.1006.70000 0001 0462 7212Newcastle University, Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle, UK
| | - Hugo Hiden
- grid.1006.70000 0001 0462 7212Newcastle University, School of Computing, Newcastle, UK
| | - Ilaria D’Ascanio
- grid.6292.f0000 0004 1757 1758University of Bologna, Department of Electrical, Electronic and Information Engineering ‘Guglielmo Marconi’, Bologna, Italy
| | - Clint Hansen
- grid.412468.d0000 0004 0646 2097Neurogeriatrics Kiel, Department of Neurology, University Hospital Schleswig-Holstein, Kiel, Germany
| | - Lynn Rochester
- grid.1006.70000 0001 0462 7212Newcastle University, Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle, UK ,The Newcastle upon Tyne NHS Foundation Trust, Newcastle, UK
| | - Claudia Mazzà
- grid.11835.3e0000 0004 1936 9262The University of Sheffield, INSIGNEO Institute for in silico Medicine, Sheffield, UK ,grid.11835.3e0000 0004 1936 9262The University of Sheffield, Department of Mechanical Engineering, Sheffield, UK
| | - Lorenzo Chiari
- grid.6292.f0000 0004 1757 1758University of Bologna, Department of Electrical, Electronic and Information Engineering ‘Guglielmo Marconi’, Bologna, Italy ,grid.6292.f0000 0004 1757 1758University of Bologna, Health Sciences and Technologies—Interdepartmental Center for Industrial Research (CIRI-SDV), Bologna, Italy
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Yogev-Seligmann G, Josman N, Bitterman N, Rosenblum S, Naaman S, Gilboa Y. The development of a home-based technology to improve gait in people with Parkinson's disease: a feasibility study. Biomed Eng Online 2023; 22:2. [PMID: 36658571 PMCID: PMC9851591 DOI: 10.1186/s12938-023-01066-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Accepted: 01/09/2023] [Indexed: 01/20/2023] Open
Abstract
BACKGROUND People with Parkinson's disease (PwP) may experience gait impairment and freezing of gait (FOG), a major cause of falls. External cueing, including visual (e.g., spaced lines on the floor) and auditory (e.g., rhythmic metronome beats) stimuli, are considered effective in alleviating mobility deficits and FOG. Currently, there is a need for a technology that delivers automatic, individually adjusted cues in the homes of PwP. The aims of this feasibility study were to describe the first step toward the development of a home-based technology that delivers external cues, test its effect on gait, and assess user experience. METHODS Iterative system development was performed by our multidisciplinary team. The system was designed to deliver visual and auditory cues: light stripes projected on the floor and metronome beats, separately. Initial testing was performed using the feedback of five healthy elderly individuals on the cues' clarity (clear visibility of the light stripes and the sound of metronome beats) and discomfort experienced. A pilot study was subsequently conducted in the homes of 15 PwP with daily FOG. We measured participants' walking under three conditions: baseline (with no cues), walking with light stripes, and walking to metronome beats. Outcome measures included step length and step time. User experience was also captured in semi-structured interviews. RESULTS Repeated-measures ANOVA of gait assessment in PwP revealed that light stripes significantly improved step length (p = 0.009) and step time (p = 0.019) of PwP. No significant changes were measured in the metronome condition. PwP reported that both cueing modalities improved their gait, confidence, and stability. Most PwP did not report any discomfort in either modality and expressed a desire to have such a technology in their homes. The metronome was preferred by the majority of participants. CONCLUSIONS This feasibility study demonstrated the usability and potential effect of a novel cueing technology on gait, and represents an important first step toward the development of a technology aimed to prevent FOG by delivering individually adjusted cues automatically. A further full-scale study is needed. Trial registration This study was registered in ClinicalTrials.gov at 1/2/2022 NCT05211687.
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Affiliation(s)
- Galit Yogev-Seligmann
- grid.18098.380000 0004 1937 0562Department of Occupational Therapy, Faculty of Social Welfare & Health Sciences, University of Haifa, 3498838 Haifa, Israel
| | - Naomi Josman
- grid.18098.380000 0004 1937 0562Department of Occupational Therapy, Faculty of Social Welfare & Health Sciences, University of Haifa, 3498838 Haifa, Israel
| | - Noemi Bitterman
- grid.6451.60000000121102151Technion, Israel Institute of Technology, Haifa, Israel
| | - Sara Rosenblum
- grid.18098.380000 0004 1937 0562Department of Occupational Therapy, Faculty of Social Welfare & Health Sciences, University of Haifa, 3498838 Haifa, Israel
| | - Sitar Naaman
- grid.18098.380000 0004 1937 0562Department of Physical Therapy, Faculty of Social Welfare & Health Sciences, University of Haifa, Haifa, Israel
| | - Yafit Gilboa
- grid.9619.70000 0004 1937 0538School of Occupational Therapy, Faculty of Medicine, The Hebrew University of Jerusalem, Jerusalem, Israel
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Kwon H, Clifford GD, Genias I, Bernhard D, Esper CD, Factor SA, McKay JL. An explainable spatial-temporal graphical convolutional network to score freezing of gait in parkinsonian patients. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.01.13.23284535. [PMID: 36711809 PMCID: PMC9882551 DOI: 10.1101/2023.01.13.23284535] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
Freezing of gait (FOG) is a poorly understood heterogeneous gait disorder seen in patients with parkinsonism which contributes to significant morbidity and social isolation. FOG is currently measured with scales that are typically performed by movement disorders specialists (ie. MDS-UPDRS), or through patient completed questionnaires (N-FOG-Q) both of which are inadequate in addressing the heterogeneous nature of the disorder and are unsuitable for use in clinical trials The purpose of this study was to devise a method to measure FOG objectively, hence improving our ability to identify it and accurately evaluate new therapies. We trained interpretable deep learning models with multi-task learning to simultaneously score FOG (cross-validated F1 score 97.6%), identify medication state (OFF vs. ON levodopa; cross-validated F1 score 96.8%), and measure total PD severity (MDS-UPDRS-III score prediction error ≤ 2.7 points) using kinematic data of a well-characterized sample of N=57 patients during levodopa challenge tests. The proposed model was able to identify kinematic features associated with each FOG severity level that were highly consistent with the features that movement disorders specialists are trained to identify as characteristic of freezing. In this work, we demonstrate that deep learning models' capability to capture complex movement patterns in kinematic data can automatically and objectively score FOG with high accuracy. These models have the potential to discover novel kinematic biomarkers for FOG that can be used for hypothesis generation and potentially as clinical trial outcome measures.
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Affiliation(s)
- Hyeokhyen Kwon
- Department of Biomedical Informatics, School of Medicine, Emory University, Atlanta, GA, USA
| | - Gari D. Clifford
- Department of Biomedical Informatics, School of Medicine, Emory University, Atlanta, GA, USA
| | - Imari Genias
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | - Doug Bernhard
- Jean and Paul Amos Parkinson’s disease and Movement Disorders Program, Department of Neurology, School of Medicine, Emory University, Atlanta, GA, USA
| | - Christine D. Esper
- Jean and Paul Amos Parkinson’s disease and Movement Disorders Program, Department of Neurology, School of Medicine, Emory University, Atlanta, GA, USA
| | - Stewart A. Factor
- Jean and Paul Amos Parkinson’s disease and Movement Disorders Program, Department of Neurology, School of Medicine, Emory University, Atlanta, GA, USA
| | - J. Lucas McKay
- Department of Biomedical Informatics, School of Medicine, Emory University, Atlanta, GA, USA
- Jean and Paul Amos Parkinson’s disease and Movement Disorders Program, Department of Neurology, School of Medicine, Emory University, Atlanta, GA, USA
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Huang T, Li M, Huang J. Recent trends in wearable device used to detect freezing of gait and falls in people with Parkinson's disease: A systematic review. Front Aging Neurosci 2023; 15:1119956. [PMID: 36875701 PMCID: PMC9975590 DOI: 10.3389/fnagi.2023.1119956] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Accepted: 01/23/2023] [Indexed: 02/17/2023] Open
Abstract
Background The occurrence of freezing of gait (FOG) is often observed in moderate to last-stage Parkinson's disease (PD), leading to a high risk of falls. The emergence of the wearable device has offered the possibility of FOG detection and falls of patients with PD allowing high validation in a low-cost way. Objective This systematic review seeks to provide a comprehensive overview of existing literature to establish the forefront of sensors type, placement and algorithm to detect FOG and falls among patients with PD. Methods Two electronic databases were screened by title and abstract to summarize the state of art on FOG and fall detection with any wearable technology among patients with PD. To be eligible for inclusion, papers were required to be full-text articles published in English, and the last search was completed on September 26, 2022. Studies were excluded if they; (i) only examined cueing function for FOG, (ii) only used non-wearable devices to detect or predict FOG or falls, and (iii) did not provide sufficient details about the study design and results. A total of 1,748 articles were retrieved from two databases. However, only 75 articles were deemed to meet the inclusion criteria according to the title, abstract and full-text reviewed. Variable was extracted from chosen research, including authorship, details of the experimental object, type of sensor, device location, activities, year of publication, evaluation in real-time, the algorithm and detection performance. Results A total of 72 on FOG detection and 3 on fall detection were selected for data extraction. There were wide varieties of the studied population (from 1 to 131), type of sensor, placement and algorithm. The thigh and ankle were the most popular device location, and the combination of accelerometer and gyroscope was the most frequently used inertial measurement unit (IMU). Furthermore, 41.3% of the studies used the dataset as a resource to examine the validity of their algorithm. The results also showed that increasingly complex machine-learning algorithms had become the trend in FOG and fall detection. Conclusion These data support the application of the wearable device to access FOG and falls among patients with PD and controls. Machine learning algorithms and multiple types of sensors have become the recent trend in this field. Future work should consider an adequate sample size, and the experiment should be performed in a free-living environment. Moreover, a consensus on provoking FOG/fall, methods of assessing validity and algorithm are necessary.Systematic Review Registration: PROSPERO, identifier CRD42022370911.
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Affiliation(s)
- Tinghuai Huang
- Laboratory of Laser Sports Medicine, South China Normal University, Guangzhou, Guangdong, China
| | - Meng Li
- Laboratory of Laser Sports Medicine, South China Normal University, Guangzhou, Guangdong, China
| | - Jianwei Huang
- Department of Gastroenterology, The Fifth Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University, Guangzhou, Guangdong, China
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Goh L, Paul SS, Canning CG, Ehgoetz Martens KA, Song J, Campoy SL, Allen NE. The Ziegler Test Is Reliable and Valid for Measuring Freezing of Gait in People With Parkinson Disease. Phys Ther 2022; 102:pzac122. [PMID: 36130220 PMCID: PMC10071484 DOI: 10.1093/ptj/pzac122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Revised: 05/06/2022] [Accepted: 09/07/2022] [Indexed: 11/13/2022]
Abstract
OBJECTIVE The purpose of this study was to determine interrater and test-retest reliability of the Ziegler test to measure freezing of gait (FOG) severity in people with Parkinson disease. Secondary aims were to evaluate test validity and explore Ziegler test duration as a proxy FOG severity measure. METHODS Physical therapists watched 36 videos of people with Parkinson disease and FOG perform the Ziegler test and rated FOG severity using the rating scale in real time. Two researchers rated 12 additional videos and repeated the ratings at least 1 week later. Interrater and test-retest reliability were calculated using intraclass correlation coefficients (ICCs). Bland-Altman plots were used to visualize agreement between the researchers for test-retest reliability. Correlations between the Ziegler scores, Ziegler test duration, and percentage of time frozen (based on video annotations) were determined using Pearson r. RESULTS Twenty-four physical therapists participated. Overall, the Ziegler test showed good interrater (ICC2,1 = 0.80; 95% CI = 0.65-0.92) and excellent test-retest (ICC3,1 = 0.91; 95% CI = 0.82-0.96) reliability when used to measure FOG. It was also a valid measure, with a high correlation (r = 0.72) between the scores and percentage of time frozen. Ziegler test duration was moderately correlated (r = 0.67) with percentage of time frozen and may be considered a proxy FOG severity measure. CONCLUSION The Ziegler test is a reliable and valid tool to measure FOG when used by physical therapists in real time. Ziegler test duration may be used as a proxy for measuring FOG severity. IMPACT Despite FOG being a significant contributor to falls and poor mobility in people with Parkinson disease, current tools to assess FOG are either not suitably responsive or too resource intensive for use in clinical settings. The Ziegler test is a reliable and valid measure of FOG, suitable for clinical use, and may be used by physical therapists regardless of their level of clinical experience.
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Affiliation(s)
- Lina Goh
- School of Health Sciences, Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia
| | - Serene S Paul
- School of Health Sciences, Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia
| | - Colleen G Canning
- School of Health Sciences, Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia
| | | | - Jooeun Song
- School of Health Sciences, Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia
| | - Stephanie L Campoy
- School of Health Sciences, Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia
| | - Natalie E Allen
- School of Health Sciences, Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia
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Di Libero T, Langiano E, Carissimo C, Ferrara M, Diotaiuti P, Rodio A. Technological support for people with Parkinson’s disease: a narrative review. JOURNAL OF GERONTOLOGY AND GERIATRICS 2022. [DOI: 10.36150/2499-6564-n523] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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Mason R, Byerley J, Baker A, Powell D, Pearson LT, Barry G, Godfrey A, Mancini M, Stuart S, Morris R. Suitability of a Low-Cost Wearable Sensor to Assess Turning in Healthy Adults. SENSORS (BASEL, SWITZERLAND) 2022; 22:9322. [PMID: 36502023 PMCID: PMC9737758 DOI: 10.3390/s22239322] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Revised: 11/19/2022] [Accepted: 11/24/2022] [Indexed: 06/17/2023]
Abstract
Background: Turning is a complex measure of gait that accounts for over 50% of daily steps. Traditionally, turning has been measured in a research grade laboratory setting, however, there is demand for a low-cost and portable solution to measure turning using wearable technology. This study aimed to determine the suitability of a low-cost inertial sensor-based device (AX6, Axivity) to assess turning, by simultaneously capturing and comparing to a turn algorithm output from a previously validated reference inertial sensor-based device (Opal), in healthy young adults. Methodology: Thirty participants (aged 23.9 ± 4.89 years) completed the following turning protocol wearing the AX6 and reference device: a turn course, a two-minute walk (including 180° turns) and turning in place, alternating 360° turn right and left. Both devices were attached at the lumbar spine, one Opal via a belt, and the AX6 via double sided tape attached directly to the skin. Turning measures included number of turns, average turn duration, angle, velocity, and jerk. Results: Agreement between the outcomes from the AX6 and reference device was good to excellent for all turn characteristics (all ICCs > 0.850) during the turning 360° task. There was good agreement for all turn characteristics (all ICCs > 0.800) during the two-minute walk task, except for moderate agreement for turn angle (ICC 0.683). Agreement for turn outcomes was moderate to good during the turns course (ICCs range; 0.580 to 0.870). Conclusions: A low-cost wearable sensor, AX6, can be a suitable and fit-for-purpose device when used with validated algorithms for assessment of turning outcomes, particularly during continuous turning tasks. Future work needs to determine the suitability and validity of turning in aging and clinical cohorts within low-resource settings.
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Affiliation(s)
- Rachel Mason
- Department Sport, Exercise and Rehabilitation, Northumbria University, Newcastle-upon-Tyne NE1 8ST, UK
| | - Joe Byerley
- Department Sport, Exercise and Rehabilitation, Northumbria University, Newcastle-upon-Tyne NE1 8ST, UK
| | - Andrea Baker
- Department Sport, Exercise and Rehabilitation, Northumbria University, Newcastle-upon-Tyne NE1 8ST, UK
| | - Dylan Powell
- Department Computer Science, Northumbria University, Newcastle-upon-Tyne NE1 8ST, UK
| | - Liam T. Pearson
- Department Sport, Exercise and Rehabilitation, Northumbria University, Newcastle-upon-Tyne NE1 8ST, UK
| | - Gill Barry
- Department Sport, Exercise and Rehabilitation, Northumbria University, Newcastle-upon-Tyne NE1 8ST, UK
| | - Alan Godfrey
- Department Computer Science, Northumbria University, Newcastle-upon-Tyne NE1 8ST, UK
| | - Martina Mancini
- Department of Neurology, Oregon Health and Science University, Portland, OR 97239-3098, USA
| | - Samuel Stuart
- Department Sport, Exercise and Rehabilitation, Northumbria University, Newcastle-upon-Tyne NE1 8ST, UK
- Northumbria Healthcare NHS Foundation Trust, North Shields NE29 8NH, UK
| | - Rosie Morris
- Department Sport, Exercise and Rehabilitation, Northumbria University, Newcastle-upon-Tyne NE1 8ST, UK
- Northumbria Healthcare NHS Foundation Trust, North Shields NE29 8NH, UK
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40
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Choi H, Youm C, Park H, Kim B, Cheon SM, Lee M. Association between Severity of Freezing of Gait and Turning Characteristics in People with Parkinson's Disease. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:12131. [PMID: 36231432 PMCID: PMC9564463 DOI: 10.3390/ijerph191912131] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Revised: 09/21/2022] [Accepted: 09/22/2022] [Indexed: 06/16/2023]
Abstract
For people with Parkinson's disease (PD) with freezing of gait (FOG) (freezers), symptoms mainly exhibit as unilateral motor impairments that may cause difficulty during postural transitions such as turning during daily activities. We investigated the turning characteristics that distinguished freezers among people with PD and analyzed the association between the New Freezing of Gait Questionnaire (NFOGQ) scores and the gait characteristics according to the turning direction for the affected limbs of freezers. The study recruited 57 people with PD (27 freezers, 30 non-freezers). All experiments measured the maximum 180° turning task with the "Off" medication state. Results revealed that the outer ankle range of motion in the direction of the inner step of the more affected limb (IMA) was identified to distinguish freezers and non-freezers (RN2 = 0.735). In addition, higher NFOGQ scores were associated with a more significant anteroposterior root mean square distance of the center of mass in the IMA direction and a greater inner stance phase in the outer step of the more affected limb (OMA) direction; explanatory power was 50.1%. Assessing the maximum speed and turning direction is useful for evaluating the differences in turning characteristics between freezers and non-freezers, which can help define freezers more accurately.
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Affiliation(s)
- Hyejin Choi
- Department of Health Sciences, The Graduate School, Dong-A University, Saha-gu, Busan 49315, Korea
| | - Changhong Youm
- Department of Health Sciences, The Graduate School, Dong-A University, Saha-gu, Busan 49315, Korea
- Department of Health Care and Science, Dong-A University, Saha-gu, Busan 49315, Korea
| | - Hwayoung Park
- Department of Health Sciences, The Graduate School, Dong-A University, Saha-gu, Busan 49315, Korea
| | - Bohyun Kim
- Department of Health Sciences, The Graduate School, Dong-A University, Saha-gu, Busan 49315, Korea
| | - Sang-Myung Cheon
- Department of Neurology, School of Medicine, Dong-A University, Seo-gu, Busan 49201, Korea
| | - Myeounggon Lee
- Interdisciplinary Consortium on Advanced Motion Performance (iCAMP), Michael E. DeBakey Department of Surgery, Baylor College of Medicine, Houston, TX 77030, USA
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Kuo C, Patton D, Rooks T, Tierney G, McIntosh A, Lynall R, Esquivel A, Daniel R, Kaminski T, Mihalik J, Dau N, Urban J. On-Field Deployment and Validation for Wearable Devices. Ann Biomed Eng 2022; 50:1372-1388. [PMID: 35960418 DOI: 10.1007/s10439-022-03001-3] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Accepted: 06/24/2022] [Indexed: 11/01/2022]
Abstract
Wearable sensors are an important tool in the study of head acceleration events and head impact injuries in sporting and military activities. Recent advances in sensor technology have improved our understanding of head kinematics during on-field activities; however, proper utilization and interpretation of data from wearable devices requires careful implementation of best practices. The objective of this paper is to summarize minimum requirements and best practices for on-field deployment of wearable devices for the measurement of head acceleration events in vivo to ensure data evaluated are representative of real events and limitations are accurately defined. Best practices covered in this document include the definition of a verified head acceleration event, data windowing, video verification, advanced post-processing techniques, and on-field logistics, as determined through review of the literature and expert opinion. Careful use of best practices, with accurate acknowledgement of limitations, will allow research teams to ensure data evaluated is representative of real events, will improve the robustness of head acceleration event exposure studies, and generally improve the quality and validity of research into head impact injuries.
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Affiliation(s)
- Calvin Kuo
- The University of British Columbia, Vancouver, Canada
| | - Declan Patton
- Children's Hospital of Philadelphia, Philadelphia, USA
| | - Tyler Rooks
- United States Army Aeromedical Research Laboratory, Fort Rucker, USA
| | | | - Andrew McIntosh
- McIntosh Consultancy and Research, Sydney, Australia.,Monash University Accident Research Centre Monash University, Melbourne, Australia.,School of Engineering Edith Cowan University, Perth, Australia
| | | | | | - Ray Daniel
- United States Army Aeromedical Research Laboratory, Fort Rucker, USA
| | | | - Jason Mihalik
- University of North Carolina at Chapel Hill, Chapel Hill, USA
| | - Nate Dau
- Biocore, LLC, Charlottesville, USA
| | - Jillian Urban
- Wake Forest University School of Medicine, 575 Patterson Ave, Suite 530, Winston-Salem, NC, 27101, USA.
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Gelenitis K, Foglyano K, Lombardo L, McDaniel J, Triolo R. Motorless cadence control of standard and low duty cycle-patterned neural stimulation intensity extends muscle-driven cycling output after paralysis. J Neuroeng Rehabil 2022; 19:85. [PMID: 35945575 PMCID: PMC9360711 DOI: 10.1186/s12984-022-01064-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Accepted: 07/21/2022] [Indexed: 12/01/2022] Open
Abstract
Background Stimulation-driven exercise is often limited by rapid fatigue of the activated muscles. Selective neural stimulation patterns that decrease activated fiber overlap and/or duty cycle improve cycling exercise duration and intensity. However, unequal outputs from independently activated fiber populations may cause large discrepancies in power production and crank angle velocity among pedal revolutions. Enforcing a constant cadence through feedback control of stimulus levels may address this issue and further improve endurance by targeting a submaximal but higher than steady-state exercise intensity. Methods Seven participants with paralysis cycled using standard cadence-controlled stimulation (S-Cont). Four of those participants also cycled with a low duty cycle (carousel) cadence-controlled stimulation scheme (C-Cont). S-Cont and C-Cont patterns were compared with conventional maximal stimulation (S-Max). Outcome measures include total work (W), end power (Pend), power fluctuation (PFI), charge accumulation (Q) and efficiency (η). Physiological measurements of muscle oxygenation (SmO2) and heart rate were also collected with select participants. Results At least one cadence-controlled stimulation pattern (S-Cont or C-Cont) improved Pend over S-Max in all participants and increased W in three participants. Both controlled patterns increased Q and η and reduced PFI compared with S-Max and prior open-loop studies. S-Cont stimulation also delayed declines in SmO2 and increased heart rate in one participant compared with S-Max. Conclusions Cadence-controlled selective stimulation improves cycling endurance and increases efficiency over conventional stimulation by incorporating fiber groups only as needed to maintain a desired exercise intensity. Closed-loop carousel stimulation also successfully reduces power fluctuations relative to previous open-loop efforts, which will enable neuroprosthesis recipients to better take advantage of duty cycle reducing patterns.
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Affiliation(s)
- Kristen Gelenitis
- Louis Stokes Cleveland VA Medical Center, 10701 East Blvd, Cleveland, OH, 44106, USA.
| | - Kevin Foglyano
- Louis Stokes Cleveland VA Medical Center, 10701 East Blvd, Cleveland, OH, 44106, USA
| | - Lisa Lombardo
- Louis Stokes Cleveland VA Medical Center, 10701 East Blvd, Cleveland, OH, 44106, USA
| | - John McDaniel
- Louis Stokes Cleveland VA Medical Center, 10701 East Blvd, Cleveland, OH, 44106, USA.,Kent State University, 800 E Summit St, Kent, OH, 44240, USA
| | - Ronald Triolo
- Louis Stokes Cleveland VA Medical Center, 10701 East Blvd, Cleveland, OH, 44106, USA.,Case Western Reserve University, 10900 Euclid Ave, Cleveland, OH, 44106, USA
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Khwaounjoo P, Singh G, Grenfell S, Özsoy B, MacAskill MR, Anderson TJ, Çakmak YO. Non-Contact Hand Movement Analysis for Optimal Configuration of Smart Sensors to Capture Parkinson's Disease Hand Tremor. SENSORS 2022; 22:s22124613. [PMID: 35746395 PMCID: PMC9230824 DOI: 10.3390/s22124613] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Revised: 06/09/2022] [Accepted: 06/10/2022] [Indexed: 02/01/2023]
Abstract
Parkinson’s disease affects millions worldwide with a large rise in expected burden over the coming decades. More easily accessible tools and techniques to diagnose and monitor Parkinson’s disease can improve the quality of life of patients. With the advent of new wearable technologies such as smart rings and watches, this is within reach. However, it is unclear what method for these new technologies may provide the best opportunity to capture the patient-specific severity. This study investigates which locations on the hand can be used to capture and monitor maximal movement/tremor severity. Using a Leap Motion device and custom-made software the volume, velocity, acceleration, and frequency of Parkinson’s (n = 55, all right-handed, majority right-sided onset) patients’ hand locations (25 joints inclusive of all fingers/thumb and the wrist) were captured simultaneously. Distal locations of the right hand, i.e., the ends of fingers and the wrist showed significant trends (p < 0.05) towards having the largest movement velocities and accelerations. The right hand, compared with the left hand, showed significantly greater volumes, velocities, and accelerations (p < 0.01). Supplementary analysis showed that the volumes, acceleration, and velocities had significant correlations (p < 0.001) with clinical MDS-UPDRS scores, indicating the potential suitability of using these metrics for monitoring disease progression. Maximal movements at the distal hand and wrist area indicate that these locations are best suited to capture hand tremor movements and monitor Parkinson’s disease.
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Affiliation(s)
- Prashanna Khwaounjoo
- Department of Anatomy, School of Biomedical Sciences, University of Otago, Dunedin 9016, New Zealand; (P.K.); (G.S.)
- Medical Technologies Centre of Research Excellence, Auckland 1142, New Zealand
| | - Gurleen Singh
- Department of Anatomy, School of Biomedical Sciences, University of Otago, Dunedin 9016, New Zealand; (P.K.); (G.S.)
| | - Sophie Grenfell
- New Zealand Brain Research Institute, Christchurch 8011, New Zealand; (S.G.); (M.R.M.); (T.J.A.)
| | - Burak Özsoy
- Global Dynamic Systems (GDS) ARGE, Teknopark Istanbul, Istanbul 34906, Turkey;
| | - Michael R. MacAskill
- New Zealand Brain Research Institute, Christchurch 8011, New Zealand; (S.G.); (M.R.M.); (T.J.A.)
- Department of Medicine, University of Otago, Christchurch 8140, New Zealand
| | - Tim J. Anderson
- New Zealand Brain Research Institute, Christchurch 8011, New Zealand; (S.G.); (M.R.M.); (T.J.A.)
- Department of Medicine, University of Otago, Christchurch 8140, New Zealand
| | - Yusuf O. Çakmak
- Department of Anatomy, School of Biomedical Sciences, University of Otago, Dunedin 9016, New Zealand; (P.K.); (G.S.)
- Medical Technologies Centre of Research Excellence, Auckland 1142, New Zealand
- Centre for Health Systems and Technology, Dunedin 9054, New Zealand
- Brain Health Research Centre, Dunedin 9054, New Zealand
- Centre for Bioengineering and Nanotechnology, University of Otago, Dunedin 9054, New Zealand
- Correspondence:
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Filtjens B, Ginis P, Nieuwboer A, Slaets P, Vanrumste B. Automated freezing of gait assessment with marker-based motion capture and multi-stage spatial-temporal graph convolutional neural networks. J Neuroeng Rehabil 2022; 19:48. [PMID: 35597950 PMCID: PMC9124420 DOI: 10.1186/s12984-022-01025-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Accepted: 05/10/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Freezing of gait (FOG) is a common and debilitating gait impairment in Parkinson's disease. Further insight into this phenomenon is hampered by the difficulty to objectively assess FOG. To meet this clinical need, this paper proposes an automated motion-capture-based FOG assessment method driven by a novel deep neural network. METHODS Automated FOG assessment can be formulated as an action segmentation problem, where temporal models are tasked to recognize and temporally localize the FOG segments in untrimmed motion capture trials. This paper takes a closer look at the performance of state-of-the-art action segmentation models when tasked to automatically assess FOG. Furthermore, a novel deep neural network architecture is proposed that aims to better capture the spatial and temporal dependencies than the state-of-the-art baselines. The proposed network, termed multi-stage spatial-temporal graph convolutional network (MS-GCN), combines the spatial-temporal graph convolutional network (ST-GCN) and the multi-stage temporal convolutional network (MS-TCN). The ST-GCN captures the hierarchical spatial-temporal motion among the joints inherent to motion capture, while the multi-stage component reduces over-segmentation errors by refining the predictions over multiple stages. The proposed model was validated on a dataset of fourteen freezers, fourteen non-freezers, and fourteen healthy control subjects. RESULTS The experiments indicate that the proposed model outperforms four state-of-the-art baselines. Moreover, FOG outcomes derived from MS-GCN predictions had an excellent (r = 0.93 [0.87, 0.97]) and moderately strong (r = 0.75 [0.55, 0.87]) linear relationship with FOG outcomes derived from manual annotations. CONCLUSIONS The proposed MS-GCN may provide an automated and objective alternative to labor-intensive clinician-based FOG assessment. Future work is now possible that aims to assess the generalization of MS-GCN to a larger and more varied verification cohort.
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Affiliation(s)
- Benjamin Filtjens
- Department of Electrical Engineering (ESAT), eMedia Research Lab/STADIUS, KU Leuven, Andreas Vesaliusstraat 13, 3000, Leuven, Belgium. .,Department of Mechanical Engineering, Intelligent Mobile Platforms Research Group, KU Leuven, Andreas Vesaliusstraat 13, 3000, Leuven, Belgium.
| | - Pieter Ginis
- Department of Rehabilitation Sciences, Research Group for Neurorehabilitation (eNRGy), KU Leuven, Tervuursevest 101, 3001, Heverlee, Belgium
| | - Alice Nieuwboer
- Department of Rehabilitation Sciences, Research Group for Neurorehabilitation (eNRGy), KU Leuven, Tervuursevest 101, 3001, Heverlee, Belgium
| | - Peter Slaets
- Department of Mechanical Engineering, Intelligent Mobile Platforms Research Group, KU Leuven, Andreas Vesaliusstraat 13, 3000, Leuven, Belgium
| | - Bart Vanrumste
- Department of Electrical Engineering (ESAT), eMedia Research Lab/STADIUS, KU Leuven, Andreas Vesaliusstraat 13, 3000, Leuven, Belgium
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Earley EJ, Zbinden J, Munoz-Novoa M, Mastinu E, Smiles A, Ortiz-Catalan M. Competitive motivation increased home use and improved prosthesis self-perception after Cybathlon 2020 for neuromusculoskeletal prosthesis user. J Neuroeng Rehabil 2022; 19:47. [PMID: 35578249 PMCID: PMC9112467 DOI: 10.1186/s12984-022-01024-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Accepted: 05/03/2022] [Indexed: 11/30/2022] Open
Abstract
Background Assistive technologies, such as arm prostheses, are intended to improve the quality of life of individuals with physical disabilities. However, certain training and learning is usually required from the user to make these technologies more effective. Moreover, some people can be encouraged to train more through competitive motivation. Methods In this study, we investigated if the training for and participation in a competitive event (Cybathlon 2020) could promote behavioral changes in an individual with upper limb amputation (the pilot). We defined behavioral changes as the active time while his prosthesis was actuated, ratio of opposing and simultaneous movements, and the pilot’s ability to finely modulate his movement speeds. The investigation was based on extensive home-use data from the period before, during and after the Cybathlon 2020 competition. Results Relevant behavioral changes were found from both quantitative and qualitative analyses. The pilot’s home use of his prosthesis nearly doubled in the period before the Cybathlon, and remained 66% higher than baseline after the competition. Moreover, he improved his speed modulation when controlling his prosthesis, and he learned and routinely operated new movements in the prosthesis (wrist rotation) at home. Additionally, as confirmed by semi-structured interviews, his self-perception of the prosthetic arm and its functionality also improved. Conclusions An event like the Cybathlon may indeed promote behavioral changes in how competitive individuals with amputation use their prostheses. Provided that the prosthesis is suitable in terms of form and function for both competition and at-home daily use, daily activities can become opportunities for training, which in turn can improve prosthesis function and create further opportunities for daily use. Moreover, these changes appeared to remain even well after the event, albeit relevant only for individuals who continue using the technology employed in the competition. Supplementary Information The online version contains supplementary material available at 10.1186/s12984-022-01024-4.
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Affiliation(s)
- Eric J Earley
- Center for Bionics and Pain Research, Mölndal, Sweden.,Department of Electrical Engineering, Chalmers University of Technology, Gothenburg, Sweden
| | - Jan Zbinden
- Center for Bionics and Pain Research, Mölndal, Sweden.,Department of Electrical Engineering, Chalmers University of Technology, Gothenburg, Sweden
| | - Maria Munoz-Novoa
- Center for Bionics and Pain Research, Mölndal, Sweden.,Department of Clinical Neuroscience, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Enzo Mastinu
- Center for Bionics and Pain Research, Mölndal, Sweden.,Department of Electrical Engineering, Chalmers University of Technology, Gothenburg, Sweden
| | - Andrew Smiles
- Center for Bionics and Pain Research, Mölndal, Sweden.,Department of Electrical Engineering, Chalmers University of Technology, Gothenburg, Sweden.,Waterloo Engineering Bionics Lab, University of Waterloo, Waterloo, Canada
| | - Max Ortiz-Catalan
- Center for Bionics and Pain Research, Mölndal, Sweden. .,Department of Electrical Engineering, Chalmers University of Technology, Gothenburg, Sweden. .,Operational Area 3, Sahlgrenska University Hospital, Gothenburg, Sweden. .,Department of Orthopedics, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
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Shi K, Mu F, Huang R, Huang K, Peng Z, Zou C, Yang X, Cheng H. Multimodal Human-Exoskeleton Interface for Lower Limb Movement Prediction Through a Dense Co-Attention Symmetric Mechanism. Front Neurosci 2022; 16:796290. [PMID: 35546887 PMCID: PMC9082753 DOI: 10.3389/fnins.2022.796290] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2021] [Accepted: 03/16/2022] [Indexed: 11/13/2022] Open
Abstract
A challenging task for the biological neural signal-based human-exoskeleton interface is to achieve accurate lower limb movement prediction of patients with hemiplegia in rehabilitation training scenarios. The human-exoskeleton interface based on single-modal biological signals such as electroencephalogram (EEG) is currently not mature in predicting movements, due to its unreliability. The multimodal human-exoskeleton interface is a very novel solution to this problem. This kind of interface normally combines the EEG signal with surface electromyography (sEMG) signal. However, their use for the lower limb movement prediction is still limited—the connection between sEMG and EEG signals and the deep feature fusion between them are ignored. In this article, a Dense con-attention mechanism-based Multimodal Enhance Fusion Network (DMEFNet) is proposed for predicting lower limb movement of patients with hemiplegia. The DMEFNet introduces the con-attention structure to extract the common attention between sEMG and EEG signal features. To verify the effectiveness of DMEFNet, an sEMG and EEG data acquisition experiment and an incomplete asynchronous data collection paradigm are designed. The experimental results show that DMEFNet has a good movement prediction performance in both within-subject and cross-subject situations, reaching an accuracy of 82.96 and 88.44%, respectively.
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Affiliation(s)
- Kecheng Shi
- School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Fengjun Mu
- School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Rui Huang
- School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Ke Huang
- Glasgow College, University of Electronic Science and Technology of China, Chengdu, China
| | - Zhinan Peng
- School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Chaobin Zou
- School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Xiao Yang
- Department of Orthopedics, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Hong Cheng
- School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, China
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47
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Chan YS, Teo YX, Gouwanda D, Nurzaman SG, Gopalai AA, Thannirmalai S. Musculoskeletal modelling and simulation of oil palm fresh fruit bunch harvesting. Sci Rep 2022; 12:8010. [PMID: 35568759 PMCID: PMC9107475 DOI: 10.1038/s41598-022-12088-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Accepted: 05/03/2022] [Indexed: 11/09/2022] Open
Abstract
Oil palm harvesting is a labor-intensive activity and yet it was rarely investigated. Studies showed that complementing human motion analysis with musculoskeletal modelling and simulation can provide valuable information about the dynamics of the joints and muscles. Therefore, this study aims to be the first to create and evaluate an upper extremity musculoskeletal model of the oil palm harvesting motion and to assess the associated Musculoskeletal Disorder (MSD) risk. Tests were conducted at a Malaysia oil palm plantation. Six Inertial Measurement Units (IMU) and Surface Electromyography (sEMG) were used to collect kinematics of the back, shoulder and elbow joints and to measure the muscle activations of longissimus, multifidus, biceps and triceps. The simulated joint angles and muscle activations were validated against the commercial motion capture tool and sEMG, respectively. The muscle forces, joint moments and activations of rectus abdominis, iliocostalis, external oblique, internal oblique and latissimus dorsi were investigated. Findings showed that the longissimus, iliocostalis and rectus abdominis were the primary muscles relied on during harvesting. The harvesters were exposed to a higher risk of MSD while performing back flexion and back rotation. These findings provide insights into the dynamical behavior of the upper extremity muscles and joints that can potentially be used to derive ways to improve the ergonomics of oil palm harvesting, minimize the MSD risk and to design and develop assistive engineering and technological devices or tools for this activity.
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Affiliation(s)
- Yon Sin Chan
- School of Engineering, Monash University Malaysia, Bandar Sunway, 47500, Selangor, Malaysia
| | - Yu Xuan Teo
- School of Engineering, Monash University Malaysia, Bandar Sunway, 47500, Selangor, Malaysia
| | - Darwin Gouwanda
- School of Engineering, Monash University Malaysia, Bandar Sunway, 47500, Selangor, Malaysia.
- Monash Industry Palm Oil Research Platform, Monash University Malaysia, Bandar Sunway, 47500, Selangor, Malaysia.
| | - Surya Girinatha Nurzaman
- School of Engineering, Monash University Malaysia, Bandar Sunway, 47500, Selangor, Malaysia
- Monash Industry Palm Oil Research Platform, Monash University Malaysia, Bandar Sunway, 47500, Selangor, Malaysia
| | - Alpha Agape Gopalai
- School of Engineering, Monash University Malaysia, Bandar Sunway, 47500, Selangor, Malaysia
- Monash Industry Palm Oil Research Platform, Monash University Malaysia, Bandar Sunway, 47500, Selangor, Malaysia
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48
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A low-dimensional representation of arm movements and hand grip forces in post-stroke individuals. Sci Rep 2022; 12:7601. [PMID: 35534629 PMCID: PMC9085765 DOI: 10.1038/s41598-022-11806-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Accepted: 04/28/2022] [Indexed: 11/24/2022] Open
Abstract
Characterizing post-stroke impairments in the sensorimotor control of arm and hand is essential to better understand altered mechanisms of movement generation. Herein, we used a decomposition algorithm to characterize impairments in end-effector velocity and hand grip force data collected from an instrumented functional task in 83 healthy control and 27 chronic post-stroke individuals with mild-to-moderate impairments. According to kinematic and kinetic raw data, post-stroke individuals showed reduced functional performance during all task phases. After applying the decomposition algorithm, we observed that the behavioural data from healthy controls relies on a low-dimensional representation and demonstrated that this representation is mostly preserved post-stroke. Further, it emerged that reduced functional performance post-stroke correlates to an abnormal variance distribution of the behavioural representation, except when reducing hand grip forces. This suggests that the behavioural repertoire in these post-stroke individuals is mostly preserved, thereby pointing towards therapeutic strategies that optimize movement quality and the reduction of grip forces to improve performance of daily life activities post-stroke.
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Lewis S, Factor S, Giladi N, Nieuwboer A, Nutt J, Hallett M. Stepping up to meet the challenge of freezing of gait in Parkinson's disease. Transl Neurodegener 2022; 11:23. [PMID: 35490252 PMCID: PMC9057060 DOI: 10.1186/s40035-022-00298-x] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2022] [Accepted: 03/31/2022] [Indexed: 11/20/2022] Open
Abstract
There has been a growing appreciation for freezing of gait as a disabling symptom that causes a significant burden in Parkinson’s disease. Previous research has highlighted some of the key components that underlie the phenomenon, but these reductionist approaches have yet to lead to a paradigm shift resulting in the development of novel treatment strategies. Addressing this issue will require greater integration of multi-modal data with complex computational modeling, but there are a number of critical aspects that need to be considered before embarking on such an approach. This paper highlights where the field needs to address current gaps and shortcomings including the standardization of definitions and measurement, phenomenology and pathophysiology, as well as considering what available data exist and how future studies should be constructed to achieve the greatest potential to better understand and treat this devastating symptom.
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Affiliation(s)
- Simon Lewis
- ForeFront Parkinson's Disease Research Clinic, Brain and Mind Centre, School of Medical Sciences, University of Sydney, Sydney, NSW, Australia.
| | - Stewart Factor
- Jean and Paul Amos Parkinson's Disease and Movement Disorders Program, Emory University School of Medicine, Atlanta, GA, USA
| | - Nir Giladi
- Movement Disorders Unit, Department of Neurology, Tel-Aviv Sourasky Medical Center, Sackler School of Medicine and Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - Alice Nieuwboer
- Department of Rehabilitation Sciences, KU Leuven, Leuven, Belgium
| | - John Nutt
- Movement Disorder Section, Department of Neurology, Oregon Health & Science University, Portland, OR, USA
| | - Mark Hallett
- Human Motor Control Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA
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A Multi-Modal Analysis of the Freezing of Gait Phenomenon in Parkinson’s Disease. SENSORS 2022; 22:s22072613. [PMID: 35408226 PMCID: PMC9002774 DOI: 10.3390/s22072613] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Revised: 03/11/2022] [Accepted: 03/18/2022] [Indexed: 02/05/2023]
Abstract
Background: Freezing of Gait (FOG) is one of the most disabling motor complications of Parkinson’s disease, and consists of an episodic inability to move forward, despite the intention to walk. FOG increases the risk of falls and reduces the quality of life of patients and their caregivers. The phenomenon is difficult to appreciate during outpatients visits; hence, its automatic recognition is of great clinical importance. Many types of sensors and different locations on the body have been proposed. However, the advantages of a multi-sensor configuration with respect to a single-sensor one are not clear, whereas this latter would be advisable for use in a non-supervised environment. Methods: In this study, we used a multi-modal dataset and machine learning algorithms to perform different classifications between FOG and non-FOG periods. Moreover, we explored the relevance of features in the time and frequency domains extracted from inertial sensors, electroencephalogram and skin conductance. We developed both a subject-independent and a subject-dependent algorithm, considering different sensor subsets. Results: The subject-independent and subject-dependent algorithms yielded accuracies of 85% and 88% in the leave-one-subject-out and leave-one-task-out test, respectively. Results suggest that the inertial sensors positioned on the lower limb are generally the most significant in recognizing FOG. Moreover, the performance impairment experienced when using a single tibial accelerometer instead of the optimal multi-modal configuration is limited to 2–3%. Conclusions: The achieved results disclose the possibility of getting a good FOG recognition using a minimally invasive set-up made of a single inertial sensor. This is very significant in the perspective of implementing a long-term monitoring of patients in their homes, during activities of daily living.
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