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Al-Nefaie AH, Aldhyani THH, Farhah N, Koundal D. Intelligent diagnosis system based on artificial intelligence models for predicting freezing of gait in Parkinson's disease. Front Med (Lausanne) 2024; 11:1418684. [PMID: 38966531 PMCID: PMC11222646 DOI: 10.3389/fmed.2024.1418684] [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: 04/16/2024] [Accepted: 05/29/2024] [Indexed: 07/06/2024] Open
Abstract
Introduction Freezing of gait (FoG) is a significant issue for those with Parkinson's disease (PD) since it is a primary contributor to falls and is linked to a poor superiority of life. The underlying apparatus is still not understood; however, it is postulated that it is associated with cognitive disorders, namely impairments in executive and visuospatial functions. During episodes of FoG, patients may experience the risk of falling, which significantly effects their quality of life. Methods This research aims to systematically evaluate the effectiveness of machine learning approaches in accurately predicting a FoG event before it occurs. The system was tested using a dataset collected from the Kaggle repository and comprises 3D accelerometer data collected from the lower backs of people who suffer from episodes of FoG, a severe indication frequently realized in persons with Parkinson's disease. Data were acquired by measuring acceleration from 65 patients and 20 healthy senior adults while they engaged in simulated daily life tasks. Of the total participants, 45 exhibited indications of FoG. This research utilizes seven machine learning methods, namely the decision tree, random forest, Knearest neighbors algorithm, LightGBM, and CatBoost models. The Gated Recurrent Unit (GRU)-Transformers and Longterm Recurrent Convolutional Networks (LRCN) models were applied to predict FoG. The construction and model parameters were planned to enhance performance by mitigating computational difficulty and evaluation duration. Results The decision tree exhibited exceptional performance, achieving sensitivity rates of 91% in terms of accuracy, precision, recall, and F1- score metrics for the FoG, transition, and normal activity classes, respectively. It has been noted that the system has the capacity to anticipate FoG objectively and precisely. This system will be instrumental in advancing consideration in furthering the comprehension and handling of FoG.
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Affiliation(s)
- Abdullah H. Al-Nefaie
- King Salman Center for Disability Research, Riyadh, Saudi Arabia
- Department of Quantitative Methods, School of Business, King Faisal University, Al-Ahsa, Saudi Arabia
| | - Theyazn H. H. Aldhyani
- King Salman Center for Disability Research, Riyadh, Saudi Arabia
- Applied College in Abqaiq, King Faisal University, Al-Ahsa, Saudi Arabia
| | - Nesren Farhah
- Department of Health Informatics, College of Health Sciences, Saudi Electronic University, Riyadh, Saudi Arabia
| | - Deepika Koundal
- King Salman Center for Disability Research, Riyadh, Saudi Arabia
- School of Computer Science, University of Petroleum and Energy Studies, Dehradun, India
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Elbatanouny H, Kleanthous N, Dahrouj H, Alusi S, Almajali E, Mahmoud S, Hussain A. Insights into Parkinson's Disease-Related Freezing of Gait Detection and Prediction Approaches: A Meta Analysis. SENSORS (BASEL, SWITZERLAND) 2024; 24:3959. [PMID: 38931743 PMCID: PMC11207947 DOI: 10.3390/s24123959] [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: 04/30/2024] [Revised: 05/29/2024] [Accepted: 05/29/2024] [Indexed: 06/28/2024]
Abstract
Parkinson's Disease (PD) is a complex neurodegenerative disorder characterized by a spectrum of motor and non-motor symptoms, prominently featuring the freezing of gait (FOG), which significantly impairs patients' quality of life. Despite extensive research, the precise mechanisms underlying FOG remain elusive, posing challenges for effective management and treatment. This paper presents a comprehensive meta-analysis of FOG prediction and detection methodologies, with a focus on the integration of wearable sensor technology and machine learning (ML) approaches. Through an exhaustive review of the literature, this study identifies key trends, datasets, preprocessing techniques, feature extraction methods, evaluation metrics, and comparative analyses between ML and non-ML approaches. The analysis also explores the utilization of cueing devices. The limited adoption of explainable AI (XAI) approaches in FOG prediction research represents a significant gap. Improving user acceptance and comprehension requires an understanding of the logic underlying algorithm predictions. Current FOG detection and prediction research has a number of limitations, which are identified in the discussion. These include issues with cueing devices, dataset constraints, ethical and privacy concerns, financial and accessibility restrictions, and the requirement for multidisciplinary collaboration. Future research avenues center on refining explainability, expanding and diversifying datasets, adhering to user requirements, and increasing detection and prediction accuracy. The findings contribute to advancing the understanding of FOG and offer valuable guidance for the development of more effective detection and prediction methodologies, ultimately benefiting individuals affected by PD.
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Affiliation(s)
- Hagar Elbatanouny
- Department of Electrical Engineering, University of Sharjah, Sharjah 27272, United Arab Emirates; (H.D.); (E.A.); (S.M.)
| | | | - Hayssam Dahrouj
- Department of Electrical Engineering, University of Sharjah, Sharjah 27272, United Arab Emirates; (H.D.); (E.A.); (S.M.)
| | - Sundus Alusi
- The Walton Centre NHS Foundation Trust, Liverpool L9 7LJ, UK;
| | - Eqab Almajali
- Department of Electrical Engineering, University of Sharjah, Sharjah 27272, United Arab Emirates; (H.D.); (E.A.); (S.M.)
| | - Soliman Mahmoud
- Department of Electrical Engineering, University of Sharjah, Sharjah 27272, United Arab Emirates; (H.D.); (E.A.); (S.M.)
- University of Khorfakkan, Khorfakkan, Sharjah 18119, United Arab Emirates
| | - Abir Hussain
- Department of Electrical Engineering, University of Sharjah, Sharjah 27272, United Arab Emirates; (H.D.); (E.A.); (S.M.)
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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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Wu X, Ma L, Wei P, Shan Y, Chan P, Wang K, Zhao G. Wearable sensor devices can automatically identify the ON-OFF status of patients with Parkinson's disease through an interpretable machine learning model. Front Neurol 2024; 15:1387477. [PMID: 38751881 PMCID: PMC11094303 DOI: 10.3389/fneur.2024.1387477] [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: 02/17/2024] [Accepted: 04/12/2024] [Indexed: 05/18/2024] Open
Abstract
Introduction Accurately and objectively quantifying the clinical features of Parkinson's disease (PD) is crucial for assisting in diagnosis and guiding the formulation of treatment plans. Therefore, based on the data on multi-site motor features, this study aimed to develop an interpretable machine learning (ML) model for classifying the "OFF" and "ON" status of patients with PD, as well as to explore the motor features that are most associated with changes in clinical symptoms. Methods We employed a support vector machine with a recursive feature elimination (SVM-RFE) algorithm to select promising motion features. Subsequently, 12 ML models were constructed based on these features, and we identified the model with the best classification performance. Then, we used the SHapley Additive exPlanations (SHAP) and the Local Interpretable Model agnostic Explanations (LIME) methods to explain the model and rank the importance of those motor features. Results A total of 96 patients were finally included in this study. The naive Bayes (NB) model had the highest classification performance (AUC = 0.956; sensitivity = 0.8947, 95% CI 0.6686-0.9870; accuracy = 0.8421, 95% CI 0.6875-0.9398). Based on the NB model, we analyzed the importance of eight motor features toward the classification results using the SHAP algorithm. The Gait: range of motion (RoM) Shank left (L) (degrees) [Mean] might be the most important motor feature for all classification horizons. Conclusion The symptoms of PD could be objectively quantified. By utilizing suitable motor features to construct ML models, it became possible to intelligently identify whether patients with PD were in the "ON" or "OFF" status. The variations in these motor features were significantly correlated with improvement rates in patients' quality of life. In the future, they might act as objective digital biomarkers to elucidate the changes in symptoms observed in patients with PD and might be used to assist in the diagnosis and treatment of patients with PD.
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Affiliation(s)
- Xiaolong Wu
- Department of Neurosurgery, Xuanwu Hospital of Capital Medical University, Beijing, China
- International Neuroscience Institute (China-INI), Beijing, China
| | - Lin Ma
- Department of Neurorehabilitation, Rehabilitation Medicine of Capital Medical University, China Rehabilitation Research Centre, Beijing, China
| | - Penghu Wei
- Department of Neurosurgery, Xuanwu Hospital of Capital Medical University, Beijing, China
- International Neuroscience Institute (China-INI), Beijing, China
| | - Yongzhi Shan
- Department of Neurosurgery, Xuanwu Hospital of Capital Medical University, Beijing, China
- International Neuroscience Institute (China-INI), Beijing, China
| | - Piu Chan
- Department of Neurology and Neurobiology, Xuanwu Hospital of Capital Medical University, Beijing, China
| | - Kailiang Wang
- Department of Neurosurgery, Xuanwu Hospital of Capital Medical University, Beijing, China
- International Neuroscience Institute (China-INI), Beijing, China
| | - Guoguang Zhao
- Department of Neurosurgery, Xuanwu Hospital of Capital Medical University, Beijing, China
- International Neuroscience Institute (China-INI), Beijing, China
- Beijing Municipal Geriatric Medical Research Center, Beijing, China
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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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Kamo H, Oyama G, Yamasaki Y, Nagayama T, Nawashiro R, Hattori N. A proof of concept: digital diary using 24-hour monitoring using wearable device for patients with Parkinson's disease in nursing homes. Front Neurol 2024; 15:1356042. [PMID: 38660090 PMCID: PMC11041395 DOI: 10.3389/fneur.2024.1356042] [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: 12/14/2023] [Accepted: 02/26/2024] [Indexed: 04/26/2024] Open
Abstract
Introduction In the advanced stages of Parkinson's disease (PD), motor complications such as wearing-off and dyskinesia are problematic and vary daily. These symptoms need to be monitored precisely to provide adequate care for patients with advanced PD. Methods This study used wearable devices to explore biomarkers for motor complications by measuring multiple biomarkers in patients with PD residing in facilities and combining them with lifestyle and clinical assessments. Data on the pulse rate and activity index (metabolic equivalents) were collected from 12 patients over 30 days. Results The pulse rate and activity index during the off- and on-periods and dyskinesia were analyzed for two participants; the pulse rate and activity index did not show any particular trend in each participant; however, the pulse rate/activity index was significantly greater in the off-state compared to that in the dyskinesia and on-states, and this index in the dyskinesia state was significantly greater than that in the on-state in both participants. Conclusion These results suggest the pulse rate and activity index combination would be a useful indicator of wearing-off and dyskinesia and that biometric information from wearable devices may function as a digital diary. Accumulating more cases and collecting additional data are necessary to verify our findings.
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Affiliation(s)
- Hikaru Kamo
- Department of Neurology, Juntendo University School of Medicine, Tokyo, Japan
| | - Genko Oyama
- Department of Neurology, Juntendo University School of Medicine, Tokyo, Japan
- Department of Home Medical Care System, Based on Information and Communication Technology, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Department of Drug Development for Parkinson's Disease, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Department of PRO-Based Integrated Data Analysis in Neurological Disorders, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Research and Therapeutics for Movement Disorders, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Yui Yamasaki
- Sunwels Company Limited, Chiyoda-ku, Tokyo, Japan
| | | | | | - Nobutaka Hattori
- Department of Neurology, Juntendo University School of Medicine, Tokyo, Japan
- Department of Home Medical Care System, Based on Information and Communication Technology, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Department of Drug Development for Parkinson's Disease, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Department of PRO-Based Integrated Data Analysis in Neurological Disorders, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Research and Therapeutics for Movement Disorders, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Research Institute of Disease of Old Age, Graduate School of Medicine, Juntendo University, Tokyo, Japan
- Neurodegenerative Disorders Collaborative Laboratory, RIKEN Center for Brain Science, Saitama, Japan
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Abumalloh RA, Nilashi M, Samad S, Ahmadi H, Alghamdi A, Alrizq M, Alyami S. Parkinson's disease diagnosis using deep learning: A bibliometric analysis and literature review. Ageing Res Rev 2024; 96:102285. [PMID: 38554785 DOI: 10.1016/j.arr.2024.102285] [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: 08/03/2023] [Revised: 03/20/2024] [Accepted: 03/24/2024] [Indexed: 04/02/2024]
Abstract
Parkinson's Disease (PD) is a progressive neurodegenerative illness triggered by decreased dopamine secretion. Deep Learning (DL) has gained substantial attention in PD diagnosis research, with an increase in the number of published papers in this discipline. PD detection using DL has presented more promising outcomes as compared with common machine learning approaches. This article aims to conduct a bibliometric analysis and a literature review focusing on the prominent developments taking place in this area. To achieve the target of the study, we retrieved and analyzed the available research papers in the Scopus database. Following that, we conducted a bibliometric analysis to inspect the structure of keywords, authors, and countries in the surveyed studies by providing visual representations of the bibliometric data using VOSviewer software. The study also provides an in-depth review of the literature focusing on different indicators of PD, deployed approaches, and performance metrics. The outcomes indicate the firm development of PD diagnosis using DL approaches over time and a large diversity of studies worldwide. Additionally, the literature review presented a research gap in DL approaches related to incremental learning, particularly in relation to big data analysis.
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Affiliation(s)
- Rabab Ali Abumalloh
- Department of Computer Science and Engineering, Qatar University, Doha 2713, Qatar
| | - Mehrbakhsh Nilashi
- Institute of Research and Development, Duy Tan University, Da Nang, Vietnam; School of Computer Science, Duy Tan University, Da Nang, Vietnam; UCSI Graduate Business School, UCSI University, No. 1 Jalan Menara Gading, UCSI Heights, Cheras, Kuala Lumpur 56000, Malaysia; Centre for Global Sustainability Studies (CGSS), Universiti Sains Malaysia, Penang 11800, Malaysia.
| | - Sarminah Samad
- Faculty of Business, UNITAR International University, Tierra Crest, Jalan SS6/3, Petaling Jaya, Selangor 47301, Malaysia
| | - Hossein Ahmadi
- Centre for Health Technology, Faculty of Health, University of Plymouth, Plymouth PL4 8AA, UK
| | - Abdullah Alghamdi
- Information Systems Dept., College of Computer Science and Information Systems, Najran University, Najran, Saudi Arabia; AI Lab, Scientific and Engineering Research Center (SERC), Najran University, Najran, Saudi Arabia
| | - Mesfer Alrizq
- Information Systems Dept., College of Computer Science and Information Systems, Najran University, Najran, Saudi Arabia; AI Lab, Scientific and Engineering Research Center (SERC), Najran University, Najran, Saudi Arabia
| | - Sultan Alyami
- AI Lab, Scientific and Engineering Research Center (SERC), Najran University, Najran, Saudi Arabia; Computer Science Dept., College of Computer Science and Information Systems, Najran University, Najran, Saudi Arabia
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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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Chalitsios C, Nikodelis T, Mavrommatis G, Kollias I. Subject-specific sensitivity of several biomechanical features to fatigue during an exhaustive treadmill run. Sci Rep 2024; 14:1004. [PMID: 38200137 PMCID: PMC10781943 DOI: 10.1038/s41598-024-51296-0] [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: 05/25/2023] [Accepted: 01/03/2024] [Indexed: 01/12/2024] Open
Abstract
The aim of the present study was to examine the sensitivity of several movement features during running to exhaustion in a subject-specific setup adopting a cross-sectional design and a machine learning approach. Thirteen recreational runners, that systematically trained and competed, performed an exhaustive running protocol on an instrumented treadmill. Respiratory data were collected to establish the second ventilatory threshold (VT2) in order to obtain a reference point regarding the gradual accumulation of fatigue. A machine learning approach was adopted to analyze kinetic and kinematic data recorded for each participant, using a random forest classifier for the region pre and post the second ventilatory threshold. SHapley Additive exPlanations (SHAP) analysis was used to explain the models' predictions and to provide insight about the most important variables. The classification accuracy value of the models adopted ranged from 0.853 to 0.962. The most important feature in six out of thirteen participants was the angular range in AP axis of upper trunk C7 (RTAPu) followed by maximum loading rate (RFDmaxD) and the angular range in the LT axis of the C7. SHAP dependence plots also showed an increased dispersion of predictions in stages around the second ventilatory threshold which is consistent with feature interactions. These results showed that each runner used the examined features differently to cope with the increase in fatigue and mitigate its effects in order to maintain a proper motor pattern.
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Affiliation(s)
- Christos Chalitsios
- Biomechanics Laboratory, Department of Physical Education and Sports Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece.
| | - Thomas Nikodelis
- Biomechanics Laboratory, Department of Physical Education and Sports Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Georgios Mavrommatis
- Department of Physical Education and Sports Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Iraklis Kollias
- Biomechanics Laboratory, Department of Physical Education and Sports Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece
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Bibbo D, De Marchis C, Schmid M, Ranaldi S. Machine learning to detect, stage and classify diseases and their symptoms based on inertial sensor data: a mapping review. Physiol Meas 2023; 44:12TR01. [PMID: 38061062 DOI: 10.1088/1361-6579/ad133b] [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: 06/19/2023] [Accepted: 12/07/2023] [Indexed: 12/27/2023]
Abstract
This article presents a systematic review aimed at mapping the literature published in the last decade on the use of machine learning (ML) for clinical decision-making through wearable inertial sensors. The review aims to analyze the trends, perspectives, strengths, and limitations of current literature in integrating ML and inertial measurements for clinical applications. The review process involved defining four research questions and applying four relevance assessment indicators to filter the search results, providing insights into the pathologies studied, technologies and setups used, data processing schemes, ML techniques applied, and their clinical impact. When combined with ML techniques, inertial measurement units (IMUs) have primarily been utilized to detect and classify diseases and their associated motor symptoms. They have also been used to monitor changes in movement patterns associated with the presence, severity, and progression of pathology across a diverse range of clinical conditions. ML models trained with IMU data have shown potential in improving patient care by objectively classifying and predicting motor symptoms, often with a minimally encumbering setup. The findings contribute to understanding the current state of ML integration with wearable inertial sensors in clinical practice and identify future research directions. Despite the widespread adoption of these technologies and techniques in clinical applications, there is still a need to translate them into routine clinical practice. This underscores the importance of fostering a closer collaboration between technological experts and professionals in the medical field.
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Affiliation(s)
- Daniele Bibbo
- Department of Industrial, Electronic and Mechanical Engineering, Roma Tre University, Rome, Italy
| | | | - Maurizio Schmid
- Department of Industrial, Electronic and Mechanical Engineering, Roma Tre University, Rome, Italy
| | - Simone Ranaldi
- Department of Industrial, Electronic and Mechanical Engineering, Roma Tre University, Rome, Italy
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11
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Conde CI, Lang C, Baumann CR, Easthope CA, Taylor WR, Ravi DK. Triggers for freezing of gait in individuals with Parkinson's disease: a systematic review. Front Neurol 2023; 14:1326300. [PMID: 38187152 PMCID: PMC10771308 DOI: 10.3389/fneur.2023.1326300] [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: 10/23/2023] [Accepted: 12/07/2023] [Indexed: 01/09/2024] Open
Abstract
Background Freezing of Gait (FOG) is a motor symptom frequently observed in advanced Parkinson's disease. However, due to its paroxysmal nature and diverse presentation, assessing FOG in a clinical setting can be challenging. Before FOG can be fully investigated, it is critical that a reliable experimental setting is established in which FOG can be evoked in a standardized manner, but the efficacy of various gait tasks and triggers for eliciting FOG remains unclear. Objectives This study aimed to conduct a systematic review of the existing literature and evaluate the available evidence for the relationship between specific motor tasks, triggers, and FOG episodes in individuals with Parkinson's disease (PwPD). Methods We conducted a literature search on four online databases (PubMed, Web of Science, EMBASE, and Cochrane Library) using the keywords "Parkinson's disease," "Freezing of Gait", "triggers" and "tasks". A total of 128 articles met the inclusion criteria and were included in our analysis. Results The review found that a wide range of gait tasks were employed in studies assessing FOG among PD patients. However, three tasks (turning, dual tasking, and straight walking) emerged as the most frequently used. Turning (28%) appears to be the most effective trigger for eliciting FOG in PwPD, followed by walking through a doorway (14%) and dual tasking (10%). Conclusion This review thereby supports the utilisation of turning, especially a 360-degree turn, as a reliable trigger for FOG in PwPD. This finding could be beneficial to clinicians conducting clinical evaluations and researchers aiming to assess FOG in a laboratory environment.
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Affiliation(s)
| | - Charlotte Lang
- Institute for Biomechanics, ETH Zürich, Zürich, Switzerland
| | - Christian R. Baumann
- Department of Neurology, University Hospital Zurich, Zürich, Switzerland
- The LOOP Zurich – Medical Research Center, Zürich, Switzerland
| | - Chris A. Easthope
- The LOOP Zurich – Medical Research Center, Zürich, Switzerland
- Lake Lucerne Institute, Vitznau, Switzerland
- creneo Foundation – Center for Interdisciplinary Research, Vitznau, Switzerland
| | - William R. Taylor
- Institute for Biomechanics, ETH Zürich, Zürich, Switzerland
- The LOOP Zurich – Medical Research Center, Zürich, Switzerland
| | - Deepak K. Ravi
- Institute for Biomechanics, ETH Zürich, Zürich, Switzerland
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12
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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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13
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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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14
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Gong NJ, Clifford GD, Esper CD, Factor SA, McKay JL, Kwon H. Classifying Tremor Dominant and Postural Instability and Gait Difficulty Subtypes of Parkinson's Disease from Full-Body Kinematics. SENSORS (BASEL, SWITZERLAND) 2023; 23:8330. [PMID: 37837160 PMCID: PMC10575216 DOI: 10.3390/s23198330] [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: 08/11/2023] [Revised: 10/03/2023] [Accepted: 10/07/2023] [Indexed: 10/15/2023]
Abstract
Characterizing motor subtypes of Parkinson's disease (PD) is an important aspect of clinical care that is useful for prognosis and medical management. Although all PD cases involve the loss of dopaminergic neurons in the brain, individual cases may present with different combinations of motor signs, which may indicate differences in underlying pathology and potential response to treatment. However, the conventional method for distinguishing PD motor subtypes involves resource-intensive physical examination by a movement disorders specialist. Moreover, the standardized rating scales for PD rely on subjective observation, which requires specialized training and unavoidable inter-rater variability. In this work, we propose a system that uses machine learning models to automatically and objectively identify some PD motor subtypes, specifically Tremor-Dominant (TD) and Postural Instability and Gait Difficulty (PIGD), from 3D kinematic data recorded during walking tasks for patients with PD (MDS-UPDRS-III Score, 34.7 ± 10.5, average disease duration 7.5 ± 4.5 years). This study demonstrates a machine learning model utilizing kinematic data that identifies PD motor subtypes with a 79.6% F1 score (N = 55 patients with parkinsonism). This significantly outperformed a comparison model using classification based on gait features (19.8% F1 score). Variants of our model trained to individual patients achieved a 95.4% F1 score. This analysis revealed that both temporal, spectral, and statistical features from lower body movements are helpful in distinguishing motor subtypes. Automatically assessing PD motor subtypes simply from walking may reduce the time and resources required from specialists, thereby improving patient care for PD treatments. Furthermore, this system can provide objective assessments to track the changes in PD motor subtypes over time to implement and modify appropriate treatment plans for individual patients as needed.
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Affiliation(s)
- N. Jabin Gong
- School of Computer Science, College of Computing, Georgia Institute of Technology, Atlanta, GA 30332, USA;
| | - Gari D. Clifford
- Department of Biomedical Informatics, School of Medicine, Emory University, Atlanta, GA 30322, USA (J.L.M.)
- Department of Biomedical Engineering, Emory University and 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; (C.D.E.); (S.A.F.)
| | - 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; (C.D.E.); (S.A.F.)
| | - J. Lucas McKay
- Department of Biomedical Informatics, School of Medicine, Emory University, Atlanta, GA 30322, USA (J.L.M.)
| | - Hyeokhyen Kwon
- Department of Biomedical Informatics, School of Medicine, Emory University, Atlanta, GA 30322, USA (J.L.M.)
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15
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Chakka K, Wu R, Belko S, Annaswamy T. Determining Gaps in Current Physiatric Tele-Physical Assessments via a Needs Assessment Survey. Am J Phys Med Rehabil 2023; 102:682-686. [PMID: 36927980 DOI: 10.1097/phm.0000000000002175] [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: 03/18/2023]
Abstract
OBJECTIVES Physical examinations are essential for in-person patient visits but remain difficult to replicate during virtual encounters. This work aims to identify gaps in the current state of telemedicine-based physical assessments by surveying physical medicine and rehabilitation physicians who perform physical examinations. DESIGN A survey of 29 Likert-scale questions and five open-ended questions was distributed to practicing physical medicine and rehabilitation physicians. The Likert-scale questions covered remote physical assessment, access, perception/engagement, implementation/effectiveness, and administrative concerns. RESULTS Fifty-three participants completed the survey. More than 80% of respondents suggested that while telemedicine was universally well accepted, they could not effectively perform telemedicine-based physical assessments, especially the musculoskeletal and neurological components. Remote assessment of upper and lower limb strength, reflexes, and sensation were examples of key unmet needs. Responses to open-ended questions suggested that telemedicine-based physical assessments can reduce the burden of travel and increase adherence to follow-up visits, but complex technology setup can pose difficulty for older patients and patients with cognitive deficits. CONCLUSIONS These findings suggest that current telemedicine technology is insufficient to meet physical medicine and rehabilitation physicians' telemedicine-based physical assessments needs. Despite high levels of provider and patient engagement with telemedicine, numerous deficits remain in performing musculoskeletal and neurological examinations. These results can inform future technology developments that address these identified telemedicine-based physical assessments gaps.
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Affiliation(s)
- Keerthana Chakka
- From the University of Texas Southwestern Medical Center, Dallas, Texas (KC, RW); Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, Pennsylvania (SB); and Penn State Health Milton S. Hershey Medical Center; Penn State College of Medicine, Hershey, Pennsylvania (TA)
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16
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Hu K, Mei S, Wang W, Martens KAE, Wang L, Lewis SJG, Feng DD, Wang Z. Multi-Level Adversarial Spatio-Temporal Learning for Footstep Pressure Based FoG Detection. IEEE J Biomed Health Inform 2023; 27:4166-4177. [PMID: 37227913 DOI: 10.1109/jbhi.2023.3272902] [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: 05/27/2023]
Abstract
Freezing of gait (FoG) is one of the most common symptoms of Parkinson's disease, which is a neurodegenerative disorder of the central nervous system impacting millions of people around the world. To address the pressing need to improve the quality of treatment for FoG, devising a computer-aided detection and quantification tool for FoG has been increasingly important. As a non-invasive technique for collecting motion patterns, the footstep pressure sequences obtained from pressure sensitive gait mats provide a great opportunity for evaluating FoG in the clinic and potentially in the home environment. In this study, FoG detection is formulated as a sequential modelling task and a novel deep learning architecture, namely Adversarial Spatio-temporal Network (ASTN), is proposed to learn FoG patterns across multiple levels. ASTN introduces a novel adversarial training scheme with a multi-level subject discriminator to obtain subject-independent FoG representations, which helps to reduce the over-fitting risk due to the high inter-subject variance. As a result, robust FoG detection can be achieved for unseen subjects. The proposed scheme also sheds light on improving subject-level clinical studies from other scenarios as it can be integrated with many existing deep architectures. To the best of our knowledge, this is one of the first studies of footstep pressure-based FoG detection and the approach of utilizing ASTN is the first deep neural network architecture in pursuit of subject-independent representations. In our experiments on 393 trials collected from 21 subjects, the proposed ASTN achieved an AUC 0.85, clearly outperforming conventional learning methods.
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17
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Wu P, Cao B, Liang Z, Wu M. The advantages of artificial intelligence-based gait assessment in detecting, predicting, and managing Parkinson's disease. Front Aging Neurosci 2023; 15:1191378. [PMID: 37502426 PMCID: PMC10368956 DOI: 10.3389/fnagi.2023.1191378] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Accepted: 06/05/2023] [Indexed: 07/29/2023] Open
Abstract
Background Parkinson's disease is a neurological disorder that can cause gait disturbance, leading to mobility issues and falls. Early diagnosis and prediction of freeze episodes are essential for mitigating symptoms and monitoring the disease. Objective This review aims to evaluate the use of artificial intelligence (AI)-based gait evaluation in diagnosing and managing Parkinson's disease, and to explore the potential benefits of this technology for clinical decision-making and treatment support. Methods A thorough review of published literature was conducted to identify studies, articles, and research related to AI-based gait evaluation in Parkinson's disease. Results AI-based gait evaluation has shown promise in preventing freeze episodes, improving diagnosis, and increasing motor independence in patients with Parkinson's disease. Its advantages include higher diagnostic accuracy, continuous monitoring, and personalized therapeutic interventions. Conclusion AI-based gait evaluation systems hold great promise for managing Parkinson's disease and improving patient outcomes. They offer the potential to transform clinical decision-making and inform personalized therapies, but further research is needed to determine their effectiveness and refine their use.
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Affiliation(s)
- Peng Wu
- College of Acupuncture and Orthopedics, Hubei University of Chinese Medicine, Wuhan, Hubei, China
| | - Biwei Cao
- Hubei Provincial Hospital of Traditional Chinese Medicine, Wuhan, China
- Affiliated Hospital of Hubei University of Chinese Medicine, Wuhan, Hubei, China
- Hubei Academy of Traditional Chinese Medicine, Wuhan, Hubei, China
| | - Zhendong Liang
- College of Acupuncture and Orthopedics, Hubei University of Chinese Medicine, Wuhan, Hubei, China
| | - Miao Wu
- Hubei Provincial Hospital of Traditional Chinese Medicine, Wuhan, China
- Affiliated Hospital of Hubei University of Chinese Medicine, Wuhan, Hubei, China
- Hubei Academy of Traditional Chinese Medicine, Wuhan, Hubei, China
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Guerra A, D'Onofrio V, Ferreri F, Bologna M, Antonini A. Objective measurement versus clinician-based assessment for Parkinson's disease. Expert Rev Neurother 2023; 23:689-702. [PMID: 37366316 DOI: 10.1080/14737175.2023.2229954] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 06/18/2023] [Accepted: 06/22/2023] [Indexed: 06/28/2023]
Abstract
INTRODUCTION Although clinician-based assessment through standardized clinical rating scales is currently the gold standard for quantifying motor impairment in Parkinson's disease (PD), it is not without limitations, including intra- and inter-rater variability and a degree of approximation. There is increasing evidence supporting the use of objective motion analyses to complement clinician-based assessment. Objective measurement tools hold significant potential for improving the accuracy of clinical and research-based evaluations of patients. AREAS COVERED The authors provide several examples from the literature demonstrating how different motion measurement tools, including optoelectronics, contactless and wearable systems allow for both the objective quantification and monitoring of key motor symptoms (such as bradykinesia, rigidity, tremor, and gait disturbances), and the identification of motor fluctuations in PD patients. Furthermore, they discuss how, from a clinician's perspective, objective measurements can help in various stages of PD management. EXPERT OPINION In our opinion, sufficient evidence supports the assertion that objective monitoring systems enable accurate evaluation of motor symptoms and complications in PD. A range of devices can be utilized not only to support diagnosis but also to monitor motor symptom during the disease progression and can become relevant in the therapeutic decision-making process.
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Affiliation(s)
- Andrea Guerra
- Parkinson and Movement Disorder Unit, Study Center on Neurodegeneration (CESNE), Department of Neuroscience, University of Padua, Padua, Italy
| | | | - Florinda Ferreri
- Unit of Neurology, Unit of Clinical Neurophysiology, Study Center of Neurodegeneration (CESNE), Department of Neuroscience, University of Padua, Padua, Italy
- Department of Clinical Neurophysiology, Kuopio University Hospital, University of Eastern Finland, Kuopio, Finland
| | - Matteo Bologna
- Department of Human Neurosciences, Sapienza University of Rome, Rome, Italy
- IRCCS Neuromed, Pozzilli, Italy
| | - Angelo Antonini
- Parkinson and Movement Disorder Unit, Study Center on Neurodegeneration (CESNE), Department of Neuroscience, University of Padua, Padua, Italy
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Koltermann K, Jung W, Blackwell G, Pinney A, Chen M, Cloud L, Pretzer-Aboff I, Zhou G. FoG-Finder: Real-time Freezing of Gait Detection and Treatment. ...IEEE...INTERNATIONAL CONFERENCE ON CONNECTED HEALTH: APPLICATIONS, SYSTEMS AND ENGINEERING TECHNOLOGIES. IEEE INTERNATIONAL CONFERENCE ON CONNECTED HEALTH: APPLICATIONS, SYSTEMS AND ENGINEERING TECHNOLOGIES 2023; 2023:22-33. [PMID: 37736618 PMCID: PMC10513482] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 09/23/2023]
Abstract
Freezing of gait is a serious symptom of Parkinson's disease that increases the risk of injury through falling, and reduces quality of life. Current clinical freezing of gait treatments fail to adequately address the fall risk posed by freezing of gait symptoms, and current real-time treatment systems have high false positive rates. To address this problem, we designed a closed-loop, non-intrusive, and real-time freezing of gait detection and treatment system, FoG-Finder, that automatically detects and treats freezing of gait. To evaluate FoG-Finder, we first collected 716 freezing of gait events from 11 patients. We then compared FoG-Finder against other real-time systems with our dataset. Our system was able to achieve a 13.4% higher F1 score and a 10.7% higher overall accuracy while achieving a reduction of 85.8% in the false positive treatment rate compared with other validated real-time freezing of gait detection and treatment systems. Additionally, FoG-Finder achieved an average treatment latency of 427ms and 615ms for subject-dependent and leave-one-subject-out settings, respectively, making it a viable system to treat freezing of gait in the real-world.
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Affiliation(s)
| | - Woosub Jung
- Department of Computer Science, William & Mary
| | | | | | | | - Leslie Cloud
- Department of Neurology, Virginia Commonwealth University
| | | | - Gang Zhou
- Department of Computer Science, William & Mary
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ZhuParris A, de Goede AA, Yocarini IE, Kraaij W, Groeneveld GJ, Doll RJ. Machine Learning Techniques for Developing Remotely Monitored Central Nervous System Biomarkers Using Wearable Sensors: A Narrative Literature Review. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23115243. [PMID: 37299969 DOI: 10.3390/s23115243] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 05/23/2023] [Accepted: 05/26/2023] [Indexed: 06/12/2023]
Abstract
BACKGROUND Central nervous system (CNS) disorders benefit from ongoing monitoring to assess disease progression and treatment efficacy. Mobile health (mHealth) technologies offer a means for the remote and continuous symptom monitoring of patients. Machine Learning (ML) techniques can process and engineer mHealth data into a precise and multidimensional biomarker of disease activity. OBJECTIVE This narrative literature review aims to provide an overview of the current landscape of biomarker development using mHealth technologies and ML. Additionally, it proposes recommendations to ensure the accuracy, reliability, and interpretability of these biomarkers. METHODS This review extracted relevant publications from databases such as PubMed, IEEE, and CTTI. The ML methods employed across the selected publications were then extracted, aggregated, and reviewed. RESULTS This review synthesized and presented the diverse approaches of 66 publications that address creating mHealth-based biomarkers using ML. The reviewed publications provide a foundation for effective biomarker development and offer recommendations for creating representative, reproducible, and interpretable biomarkers for future clinical trials. CONCLUSION mHealth-based and ML-derived biomarkers have great potential for the remote monitoring of CNS disorders. However, further research and standardization of study designs are needed to advance this field. With continued innovation, mHealth-based biomarkers hold promise for improving the monitoring of CNS disorders.
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Affiliation(s)
- Ahnjili ZhuParris
- Centre for Human Drug Research (CHDR), Zernikedreef 8, 2333 CL Leiden, The Netherlands
- Leiden Institute of Advanced Computer Science (LIACS), Snellius Gebouw, Niels Bohrweg 1, 2333 CA Leiden, The Netherlands
- Leiden University Medical Center (LUMC), Albinusdreef 2, 2333 ZA Leiden, The Netherlands
| | - Annika A de Goede
- Centre for Human Drug Research (CHDR), Zernikedreef 8, 2333 CL Leiden, The Netherlands
| | - Iris E Yocarini
- Leiden Institute of Advanced Computer Science (LIACS), Snellius Gebouw, Niels Bohrweg 1, 2333 CA Leiden, The Netherlands
| | - Wessel Kraaij
- Leiden Institute of Advanced Computer Science (LIACS), Snellius Gebouw, Niels Bohrweg 1, 2333 CA Leiden, The Netherlands
- The Netherlands Organisation for Applied Scientific Research (TNO), Anna van Buerenplein 1, 2595 DA, Den Haag, The Netherlands
| | - Geert Jan Groeneveld
- Centre for Human Drug Research (CHDR), Zernikedreef 8, 2333 CL Leiden, The Netherlands
- Leiden Institute of Advanced Computer Science (LIACS), Snellius Gebouw, Niels Bohrweg 1, 2333 CA Leiden, The Netherlands
| | - Robert Jan Doll
- Centre for Human Drug Research (CHDR), Zernikedreef 8, 2333 CL Leiden, The Netherlands
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Borzì L, Sigcha L, Olmo G. Context Recognition Algorithms for Energy-Efficient Freezing-of-Gait Detection in Parkinson's Disease. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23094426. [PMID: 37177629 PMCID: PMC10181532 DOI: 10.3390/s23094426] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Revised: 04/27/2023] [Accepted: 04/28/2023] [Indexed: 05/15/2023]
Abstract
Freezing of gait (FoG) is a disabling clinical phenomenon of Parkinson's disease (PD) characterized by the inability to move the feet forward despite the intention to walk. It is one of the most troublesome symptoms of PD, leading to an increased risk of falls and reduced quality of life. The combination of wearable inertial sensors and machine learning (ML) algorithms represents a feasible solution to monitor FoG in real-world scenarios. However, traditional FoG detection algorithms process all data indiscriminately without considering the context of the activity during which FoG occurs. This study aimed to develop a lightweight, context-aware algorithm that can activate FoG detection systems only under certain circumstances, thus reducing the computational burden. Several approaches were implemented, including ML and deep learning (DL) gait recognition methods, as well as a single-threshold method based on acceleration magnitude. To train and evaluate the context algorithms, data from a single inertial sensor were extracted using three different datasets encompassing a total of eighty-one PD patients. Sensitivity and specificity for gait recognition ranged from 0.95 to 0.96 and 0.80 to 0.93, respectively, with the one-dimensional convolutional neural network providing the best results. The threshold approach performed better than ML- and DL-based methods when evaluating the effect of context awareness on FoG detection performance. Overall, context algorithms allow for discarding more than 55% of non-FoG data and less than 4% of FoG episodes. The results indicate that a context classifier can reduce the computational burden of FoG detection algorithms without significantly affecting the FoG detection rate. Thus, implementation of context awareness can present an energy-efficient solution for long-term FoG monitoring in ambulatory and free-living settings.
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Affiliation(s)
- Luigi Borzì
- Data Analytics and Technologies for Health Lab (ANTHEA), Department of Control and Computer Engineering, Politecnico di Torino, 10129 Turin, Italy
| | - Luis Sigcha
- Data-Driven Computer Engineering (D2iCE) Group, Department of Electronic and Computer Engineering, University of Limerick, V94 T9PX Limerick, Ireland
| | - Gabriella Olmo
- Data Analytics and Technologies for Health Lab (ANTHEA), Department of Control and Computer Engineering, Politecnico di Torino, 10129 Turin, Italy
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Hu K, Wang Z, Martens KAE, Hagenbuchner M, Bennamoun M, Tsoi AC, Lewis SJG. Graph Fusion Network-Based Multimodal Learning for Freezing of Gait Detection. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2023; 34:1588-1600. [PMID: 34464270 DOI: 10.1109/tnnls.2021.3105602] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
Freezing of gait (FoG) is identified as a sudden and brief episode of movement cessation despite the intention to continue walking. It is one of the most disabling symptoms of Parkinson's disease (PD) and often leads to falls and injuries. Many computer-aided FoG detection methods have been proposed to use data collected from unimodal sources, such as motion sensors, pressure sensors, and video cameras. However, there are limited efforts of multimodal-based methods to maximize the value of all the information collected from different modalities in clinical assessments and improve the FoG detection performance. Therefore, in this study, a novel end-to-end deep architecture, namely graph fusion neural network (GFN), is proposed for multimodal learning-based FoG detection by combining footstep pressure maps and video recordings. GFN constructs multimodal graphs by treating the encoded features of each modality as vertex-level inputs and measures their adjacency patterns to construct complementary FoG representations, thus reducing the representation redundancy among different modalities. In addition, since GFN is devised to process multimodal graphs of arbitrary structures, it is expected to achieve superior performance with inputs containing missing modalities, compared to the alternative unimodal methods. A multimodal FoG dataset was collected, which included clinical assessment videos and footstep pressure sequences of 340 trials from 20 PD patients. Our proposed GFN demonstrates a great promise of multimodal FoG detection with an area under the curve (AUC) of 0.882. To the best of our knowledge, this is one of the first studies to utilize multimodal learning for automated FoG detection, which offers significant opportunities for better patient assessments and clinical trials in the future.
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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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Feasibility of a wearable inertial sensor to assess motor complications and treatment in Parkinson's disease. PLoS One 2023; 18:e0279910. [PMID: 36730238 PMCID: PMC9894418 DOI: 10.1371/journal.pone.0279910] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Accepted: 12/18/2022] [Indexed: 02/03/2023] Open
Abstract
BACKGROUND Wearable sensors-based systems have emerged as a potential tool to continuously monitor Parkinson's Disease (PD) motor features in free-living environments. OBJECTIVES To analyse the responsivity of wearable inertial sensor (WIS) measures (On/Off-Time, dyskinesia, freezing of gait (FoG) and gait parameters) after treatment adjustments. We also aim to study the ability of the sensor in the detection of MF, dyskinesia, FoG and the percentage of Off-Time, under ambulatory conditions of use. METHODS We conducted an observational, open-label study. PD patients wore a validated WIS (STAT-ONTM) for one week (before treatment), and one week, three months after therapeutic changes. The patients were analyzed into two groups according to whether treatment changes had been indicated or not. RESULTS Thirty-nine PD patients were included in the study (PD duration 8 ± 3.5 years). Treatment changes were made in 29 patients (85%). When comparing the two groups (treatment intervention vs no intervention), the WIS detected significant changes in the mean percentage of Off-Time (p = 0.007), the mean percentage of On-Time (p = 0.002), the number of steps (p = 0.008) and the gait fluidity (p = 0.004). The mean percentage of Off-Time among the patients who decreased their Off-Time (79% of patients) was -7.54 ± 5.26. The mean percentage of On-Time among the patients that increased their On-Time (59% of patients) was 8.9 ± 6.46. The Spearman correlation between the mean fluidity of the stride and the UPDRS-III- Factor I was 0.6 (p = <0.001). The system detected motor fluctuations (MF) in thirty-seven patients (95%), whilst dyskinesia and FoG were detected in fifteen (41%), and nine PD patients (23%), respectively. However, the kappa agreement analysis between the UPDRS-IV/clinical interview and the sensor was 0.089 for MF, 0.318 for dyskinesia and 0.481 for FoG. CONCLUSIONS It's feasible to use this sensor for monitoring PD treatment under ambulatory conditions. This system could serve as a complementary tool to assess PD motor complications and treatment adjustments, although more studies are required.
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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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Borzì L, Sigcha L, Rodríguez-Martín D, Olmo G. Real-time detection of freezing of gait in Parkinson's disease using multi-head convolutional neural networks and a single inertial sensor. Artif Intell Med 2023; 135:102459. [PMID: 36628783 DOI: 10.1016/j.artmed.2022.102459] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Revised: 11/17/2022] [Accepted: 11/18/2022] [Indexed: 11/25/2022]
Abstract
BACKGROUND Freezing of gait (FOG) is one of the most disabling symptoms of Parkinson's disease (PD), contributing to poor quality of life and increased risk of falls. Wearable sensors represent a valuable means for detecting FOG in the home environment. Moreover, real-time feedback has proven to help reduce the duration of FOG episodes. This work proposes a robust real-time FOG detection algorithm, which is easy to implement in stand-alone devices working in non-supervised conditions. METHOD Data from three different data sets were used in this study, with two employed as independent test sets. Acceleration recordings from 118 PD patients and 21 healthy elderly subjects were collected while they performed simulated daily living activities. A single inertial sensor was attached to the waist of each subject. More than 17 h of valid data and a total number of 1110 FOG episodes were analyzed in this study. The implemented algorithm consisted of a multi-head convolutional neural network, which exploited different spatial resolutions in the analysis of inertial data. The architecture and the model parameters were designed to provide optimal performance while reducing computational complexity and testing time. RESULTS The developed algorithm demonstrated good to excellent classification performance, with more than 50% (30%) of FOG episodes predicted on average 3.1 s (1.3 s) before the actual onset in the main (independent) data set. Around 50% of FOG was detected with an average delay of 0.8 s (1.1 s) in the main (independent) data set. Moreover, a specificity above 88% (93%) was obtained when testing the algorithm on the main (independent) test set, while 100% specificity was obtained on healthy elderly subjects. CONCLUSION The algorithm proved robust, with low computational complexity and processing time, thus paving the way to a real-time implementation in a stand-alone device that can be used in non-supervised environments.
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Affiliation(s)
- Luigi Borzì
- Department of Control and Computer Engineering, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Turin, Italy.
| | - Luis Sigcha
- Instrumentation and Applied Acoustics Research Group (I2A2), Universidad Politecnica de Madrid, Ctra. Valencia, Km 7, 28031 Madrid, Spain; ALGORITMI Research Center, School of Engineering, University of Minho, 4800-058 Guimaraes, Portugal.
| | - Daniel Rodríguez-Martín
- Sense4Care S.L., Cornellà de Llobregat, 08940 Barcelona, Spain; Technical Research Centre for Dependency Care and Autonomous Living (CETPD), Universitat Politecnica de Catalunya, 08800 Vilanova i la Geltrù, Spain.
| | - Gabriella Olmo
- Department of Control and Computer Engineering, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Turin, Italy.
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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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Ramesh V, Bilal E. Detecting motor symptom fluctuations in Parkinson's disease with generative adversarial networks. NPJ Digit Med 2022; 5:138. [PMID: 36085350 PMCID: PMC9463161 DOI: 10.1038/s41746-022-00674-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Accepted: 08/08/2022] [Indexed: 11/09/2022] Open
Abstract
Parkinson's disease is a neurodegenerative disorder characterized by several motor symptoms that develop gradually: tremor, bradykinesia, limb rigidity, and gait and balance problems. While there is no cure, levodopa therapy has been shown to mitigate symptoms. A patient on levodopa experiences cycles in the severity of their symptoms, characterized by an ON state-when the drug is active-and an OFF state-when symptoms worsen as the drug wears off. The longitudinal progression of the disease is monitored using episodic assessments performed by trained physicians in the clinic, such as the Unified Parkinson's Disease Rating Scale (UPDRS). Lately, there has been an effort in the field to develop continuous, objective measures of motor symptoms based on wearable sensors and other remote monitoring devices. In this work, we present an effort towards such a solution that uses a single wearable inertial sensor to automatically assess the postural instability and gait disorder (PIGD) of a Parkinson's disease patient. Sensor data was collected from two independent studies of subjects performing the UPDRS test and then used to train and validate a convolutional neural network model. Given the typical limited size of such studies we also employed the use of generative adversarial networks to improve the performance of deep-learning models that usually require larger amounts of data for training. We show that for a 2-min walk test, our method's predicted PIGD scores can be used to identify a patient's ON/OFF states better than a physician evaluated on the same criteria. This result paves the way for more reliable, continuous tracking of Parkinson's disease symptoms.
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Affiliation(s)
- Vishwajith Ramesh
- Department of Biomedical Informatics, University of California, San Diego, CA, USA.
| | - Erhan Bilal
- T.J. Watson Research Center, IBM Research, Yorktown Heights, NY, USA
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Li W, Chen X, Zhang J, Lu J, Zhang C, Bai H, Liang J, Wang J, Du H, Xue G, Ling Y, Ren K, Zou W, Chen C, Li M, Chen Z, Zou H. Recognition of Freezing of Gait in Parkinson’s Disease Based on Machine Vision. Front Aging Neurosci 2022; 14:921081. [PMID: 35912091 PMCID: PMC9329960 DOI: 10.3389/fnagi.2022.921081] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Accepted: 06/21/2022] [Indexed: 11/13/2022] Open
Abstract
BackgroundFreezing of gait (FOG) is a common clinical manifestation of Parkinson’s disease (PD), mostly occurring in the intermediate and advanced stages. FOG is likely to cause patients to fall, resulting in fractures, disabilities and even death. Currently, the pathogenesis of FOG is unclear, and FOG detection and screening methods have various defects, including subjectivity, inconvenience, and high cost. Due to limited public healthcare and transportation resources during the COVID-19 pandemic, there are greater inconveniences for PD patients who need diagnosis and treatment.ObjectiveA method was established to automatically recognize FOG in PD patients through videos taken by mobile phone, which is time-saving, labor-saving, and low-cost for daily use, which may overcome the above defects. In the future, PD patients can undergo FOG assessment at any time in the home rather than in the hospital.MethodsIn this study, motion features were extracted from timed up and go (TUG) test and the narrow TUG (Narrow) test videos of 50 FOG-PD subjects through a machine learning method; then a motion recognition model to distinguish between walking and turning stages and a model to recognize FOG in these stages were constructed using the XGBoost algorithm. Finally, we combined these three models to form a multi-stage FOG recognition model.ResultsWe adopted the leave-one-subject-out (LOSO) method to evaluate model performance, and the multi-stage FOG recognition model achieved a sensitivity of 87.5% sensitivity and a specificity of 79.82%.ConclusionA method to realize remote PD patient FOG recognition based on mobile phone video is presented in this paper. This method is convenient with high recognition accuracy and can be used to rapidly evaluate FOG in the home environment and remotely manage FOG-PD, or screen patients in large-scale communities.
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Affiliation(s)
- Wendan Li
- Department of Neurosurgery, General Hospital of Southern Theater Command of PLA, Guangzhou, China
- Graduate School, Guangzhou University of Chinese Medicine, Guangzhou, China
| | | | - Jintao Zhang
- Department of Neurology, The 960th Hospital of PLA, Taian, China
| | - Jianjun Lu
- Department of Neurosurgery, Guangdong Second Provincial General Hospital, Guangzhou, China
| | - Chencheng Zhang
- Department of Neurosurgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Hongmin Bai
- Department of Neurosurgery, General Hospital of Southern Theater Command of PLA, Guangzhou, China
| | - Junchao Liang
- Department of Neurosurgery, General Hospital of Southern Theater Command of PLA, Guangzhou, China
| | - Jiajia Wang
- Department of Neurosurgery, General Hospital of Southern Theater Command of PLA, Guangzhou, China
| | - Hanqiang Du
- Department of Neurosurgery, General Hospital of Southern Theater Command of PLA, Guangzhou, China
| | - Gaici Xue
- Department of Neurosurgery, General Hospital of Southern Theater Command of PLA, Guangzhou, China
| | - Yun Ling
- GYENNO SCIENCE Co., LTD., Shenzhen, China
| | - Kang Ren
- GYENNO SCIENCE Co., LTD., Shenzhen, China
| | | | - Cheng Chen
- GYENNO SCIENCE Co., LTD., Shenzhen, China
| | - Mengyan Li
- Department of Neurology, Guangzhou First People’s Hospital, Guangzhou, China
- *Correspondence: Mengyan Li,
| | - Zhonglue Chen
- GYENNO SCIENCE Co., LTD., Shenzhen, China
- HUST-GYENNO CNS Intelligent Digital Medicine Technology Center, Wuhan, China
- Zhonglue Chen,
| | - Haiqiang Zou
- Department of Neurosurgery, General Hospital of Southern Theater Command of PLA, Guangzhou, China
- Branch of National Clinical Research Center for Geriatric Diseases, Chinese PLA General Hospital, Guangzhou, China
- Haiqiang Zou,
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30
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Maremmani C, Rovini E, Salvadori S, Pecori A, Pasquini J, Ciammola A, Rossi S, Berchina G, Monastero R, Cavallo F. Hands-feet wireless devices: Test-retest reliability and discriminant validity of motor measures in Parkinson's disease telemonitoring. Acta Neurol Scand 2022; 146:304-317. [PMID: 35788914 PMCID: PMC9541466 DOI: 10.1111/ane.13667] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Revised: 06/18/2022] [Accepted: 06/21/2022] [Indexed: 11/26/2022]
Abstract
BACKGROUND Telemonitoring, a branch of telemedicine, involves the use of technological tools to remotely detect clinical data and evaluate patients. Telemonitoring of patients with Parkinson's disease (PD) should be performed using reliable and discriminant motor measures. Furthermore, the method of data collection and transmission, and the type of subjects suitable for telemonitoring must be well defined. OBJECTIVE To analyze differences in patients with PD and healthy controls (HC) with the wearable inertial device SensHands-SensFeet (SH-SF), adopting a standardized acquisition mode, to verify if motor measures provided by SH-SF have a high discriminating capacity and high intraclass correlation coefficient (ICC). METHODS Altogether, 64 patients with mild-to-moderate PD and 50 HC performed 14 standardized motor activities for assessing bradykinesia, postural and resting tremors, and gait parameters. SH-SF inertial devices were used to acquire movements and calculate objective motor measures of movement (total: 75). For each motor task, five or more biomechanical parameters were measured twice. The results were compared between patients with PD and HC. RESULTS Fifty-eight objective motor measures significantly differed between patients with PD and HC; among these, 32 demonstrated relevant discrimination power (Cohen's d > 0.8). The test-retest reliability was excellent in patients with PD (median ICC = 0.85 right limbs, 0.91 left limbs) and HC (median ICC = 0.78 right limbs, 0.82 left limbs). CONCLUSION In a supervised environment, the SH-SF device provides motor measures with good results in terms of reliability and discriminant ability. The reliability of SH-SF measurements should be evaluated in an unsupervised home setting in future studies.
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Affiliation(s)
- Carlo Maremmani
- Unit of Neurology, Ospedale Apuane, Azienda USL Toscana Nord Ovest, Massa, Italy
| | - Erika Rovini
- Department of Industrial Engineering, University of Florence, Florence, Italy
| | - Stefano Salvadori
- Institute of Clinical Physiology, National Research Council (CNR), Pisa, Italy
| | - Alessandro Pecori
- Institute of Clinical Physiology, National Research Council (CNR), Pisa, Italy
| | - Jacopo Pasquini
- Department of Neurology - Stroke Unit and Laboratory of Neuroscience, IRCCS Istituto Auxologico Italiano, Milan, Italy.,Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy
| | - Andrea Ciammola
- Department of Neurology - Stroke Unit and Laboratory of Neuroscience, IRCCS Istituto Auxologico Italiano, Milan, Italy.,Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy
| | - Simone Rossi
- Department of Biomedical and Neuromotor Sciences University of Bologna, Bologna, Italy
| | - Giulia Berchina
- Unit of Neurology, Ospedale Apuane, Azienda USL Toscana Nord Ovest, Massa, Italy
| | - Roberto Monastero
- Department of Biomedicine, Neuroscience and Advanced Diagnostics, University of Palermo, Palermo, Italy
| | - Filippo Cavallo
- Department of Industrial Engineering, University of Florence, Florence, Italy.,The Biorobotics Institute, Scuola Superiore Sant'Anna, Pontedera, Pisa, Italy
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Revuelta GJ, Embry A, Elm JJ, Jenkins S, Lee P, Kautz S. A feasibility study of objective outcome measures used in clinical trials of freezing of gait. Pilot Feasibility Stud 2022; 8:137. [PMID: 35787816 PMCID: PMC9252072 DOI: 10.1186/s40814-022-01092-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Accepted: 06/15/2022] [Indexed: 11/21/2022] Open
Abstract
BACKGROUND Freezing of gait (FOG) is notoriously difficult to quantify, which has led to the use of multiple markers as outcomes for clinical trials. The instrumented timed up and go (TUG) and the many parameters that can be derived from it are commonly used as objective markers of FOG severity in clinical trials; however, it is unknown if they represent actual FOG severity. OBJECTIVE To determine the specificity and responsiveness of objective surrogate markers of FOG severity commonly utilized in FOG studies. METHODS Study design: We compared the specificity and responsiveness of commonly used markers in FOG clinical trials. Markers compared included velocity, step/stride length, step/stride length variability, TUG, and turn duration. Data was collected in four conditions (ON and OFF dopaminergic drugs, with and without a dual task). Unified Parkinson's Disease Rating Scale (UPDRS) was administered in the ON and OFF states. RESULTS Thirty-three subjects were recruited (17 PD subjects without FOG (PD-control) and 16 subjects with PD and dopa-responsive FOG PD-FOG). The UPDRS motor scores were 24.9 for the PD-control group in the ON state, 24.8 for the FOG group in the ON state, and 42.4 for the FOG group in the OFF state. Significant mean differences between the ON and OFF conditions were observed with all surrogate markers (p < 0.01). However, only dual task turn duration and step variability showed trends toward significance when comparing PD-control and ON-FOG (p = 0.08). Test-retest reliability was high (ICC > 0.90) for all markers except standard deviations. Step length variability was the only marker to show an area under the ROC curve analysis > 0.70 comparing ON-FOG vs. PD-control. CONCLUSIONS Multiple candidate surrogate markers for FOG severity showed responsiveness to levodopa challenge; however, most were not specific for FOG severity.
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Affiliation(s)
- Gonzalo J Revuelta
- Movement Disorders Division, Department of Neurology, College of Medicine, Medical University of South Carolina, 208B Rutledge Avenue, MSC 108, Charleston, SC, 29425, USA.
- Ralph H. Johnson VA Medical Center, Charleston, SC, USA.
| | - Aaron Embry
- Ralph H. Johnson VA Medical Center, Charleston, SC, USA
- Center for Rehabilitation Research in Neurological Conditions, Department of Health Sciences and Research, College of Health Professions, Medical University of South Carolina, Charleston, SC, USA
| | - Jordan J Elm
- Department of Public Health Sciences, College of Medicine, Medical University of South Carolina, Charleston, SC, USA
| | - Shonna Jenkins
- Movement Disorders Division, Department of Neurology, College of Medicine, Medical University of South Carolina, 208B Rutledge Avenue, MSC 108, Charleston, SC, 29425, USA
| | - Philip Lee
- Movement Disorders Division, Department of Neurology, College of Medicine, Medical University of South Carolina, 208B Rutledge Avenue, MSC 108, Charleston, SC, 29425, USA
- Department of Neurology, Virginia Commonwealth University, Richmond, VA, USA
| | - Steve Kautz
- Ralph H. Johnson VA Medical Center, Charleston, SC, USA
- Center for Rehabilitation Research in Neurological Conditions, Department of Health Sciences and Research, College of Health Professions, Medical University of South Carolina, Charleston, SC, USA
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Bo F, Yerebakan M, Dai Y, Wang W, Li J, Hu B, Gao S. IMU-Based Monitoring for Assistive Diagnosis and Management of IoHT: A Review. Healthcare (Basel) 2022; 10:healthcare10071210. [PMID: 35885736 PMCID: PMC9318359 DOI: 10.3390/healthcare10071210] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Revised: 06/20/2022] [Accepted: 06/23/2022] [Indexed: 01/22/2023] Open
Abstract
With the rapid development of Internet of Things (IoT) technologies, traditional disease diagnoses carried out in medical institutions can now be performed remotely at home or even ambient environments, yielding the concept of the Internet of Health Things (IoHT). Among the diverse IoHT applications, inertial measurement unit (IMU)-based systems play a significant role in the detection of diseases in many fields, such as neurological, musculoskeletal, and mental. However, traditional numerical interpretation methods have proven to be challenging to provide satisfying detection accuracies owing to the low quality of raw data, especially under strong electromagnetic interference (EMI). To address this issue, in recent years, machine learning (ML)-based techniques have been proposed to smartly map IMU-captured data on disease detection and progress. After a decade of development, the combination of IMUs and ML algorithms for assistive disease diagnosis has become a hot topic, with an increasing number of studies reported yearly. A systematic search was conducted in four databases covering the aforementioned topic for articles published in the past six years. Eighty-one articles were included and discussed concerning two aspects: different ML techniques and application scenarios. This review yielded the conclusion that, with the help of ML technology, IMUs can serve as a crucial element in disease diagnosis, severity assessment, characteristic estimation, and monitoring during the rehabilitation process. Furthermore, it summarizes the state-of-the-art, analyzes challenges, and provides foreseeable future trends for developing IMU-ML systems for IoHT.
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Affiliation(s)
- Fan Bo
- Smart Sensing Research and Development Center, Institute of Microelectronics, Chinese Academy of Sciences, Beijing 100029, China; (F.B.); (W.W.)
- School of Microelectronics, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Mustafa Yerebakan
- Department of Industrial and Systems Engineering, University of Florida, Gainesville, FL 32611, USA;
| | - Yanning Dai
- School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, China;
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Beihang University, Beijing 100191, China
| | - Weibing Wang
- Smart Sensing Research and Development Center, Institute of Microelectronics, Chinese Academy of Sciences, Beijing 100029, China; (F.B.); (W.W.)
- School of Microelectronics, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jia Li
- Smart Sensing Research and Development Center, Institute of Microelectronics, Chinese Academy of Sciences, Beijing 100029, China; (F.B.); (W.W.)
- School of Microelectronics, University of Chinese Academy of Sciences, Beijing 100049, China
- Correspondence: (J.L.); (B.H.); (S.G.)
| | - Boyi Hu
- Department of Industrial and Systems Engineering, University of Florida, Gainesville, FL 32611, USA;
- Correspondence: (J.L.); (B.H.); (S.G.)
| | - Shuo Gao
- School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, China;
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Beihang University, Beijing 100191, China
- Correspondence: (J.L.); (B.H.); (S.G.)
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Ren K, Chen Z, Ling Y, Zhao J. Recognition of freezing of gait in Parkinson's disease based on combined wearable sensors. BMC Neurol 2022; 22:229. [PMID: 35729546 PMCID: PMC9210754 DOI: 10.1186/s12883-022-02732-z] [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: 12/19/2021] [Accepted: 05/18/2022] [Indexed: 11/29/2022] Open
Abstract
Freezing of gait is a common gait disorder among patients with advanced Parkinson’s disease and is associated with falls. This paper designed the relevant experimental procedures to obtain FoG signals from PD patients. Accelerometers, gyroscopes, and force sensing resistor sensors were placed on the lower body of patients. On this basis, the research on the optimal feature extraction method, sensor configuration, and feature quantity selection in the FoG detection process is carried out. Thirteen typical features consisting of time domain, frequency domain and statistical features were extracted from the sensor signals. Firstly, we used the analysis of variance (ANOVA) to select features through comparing the effectiveness of two feature selection methods. Secondly, we evaluated the detection effects with different combinations of sensors to get the best sensors configuration. Finally, we selected the optimal features to construct FoG recognition model based on random forest. After comprehensive consideration of factors such as detection performance, cost, and actual deployment requirements, the 35 features obtained from the left shank gyro and accelerometer, and 78.39% sensitivity, 91.66% specificity, 88.09% accuracy, 77.58% precision and 77.98% f-score were achieved. This objective FoG recognition method has high recognition accuracy, which will be helpful for early FoG symptoms screening and treatment.
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Affiliation(s)
- Kang Ren
- System Informatics, Kobe University, Kobe, Hyogo, Japan. .,GYENNO SCIENCE CO., LTD., Shenzhen, Guangdong, China.
| | - Zhonglue Chen
- GYENNO SCIENCE CO., LTD., Shenzhen, Guangdong, China
| | - Yun Ling
- GYENNO SCIENCE CO., LTD., Shenzhen, Guangdong, China
| | - Jin Zhao
- Key Laboratory of Image Information Processing and Intelligent Control, Ministry of Education, and the School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, Hubei, China.
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Pérez-López C, Hernández-Vara J, Caballol N, Bayes À, Buongiorno M, Lopez-Ariztegui N, Gironell A, López-Sánchez J, Martínez-Castrillo JC, Sauco M A, López-Manzanares L, Escalante-Arroyo S, Pérez-Martínez DA, Rodríguez-Molinero A. Comparison of the Results of a Parkinson's Holter Monitor With Patient Diaries, in Real Conditions of Use: A Sub-analysis of the MoMoPa-EC Clinical Trial. Front Neurol 2022; 13:835249. [PMID: 35651347 PMCID: PMC9149269 DOI: 10.3389/fneur.2022.835249] [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: 12/14/2021] [Accepted: 04/04/2022] [Indexed: 11/13/2022] Open
Abstract
Background For specialists in charge of Parkinson's disease (PD), one of the most time-consuming tasks of the consultations is the assessment of symptoms and motor fluctuations. This task is complex and is usually based on the information provided by the patients themselves, which in most cases is complex and biased. In recent times, different tools have appeared on the market that allow automatic ambulatory monitoring. The MoMoPa-EC clinical trial (NCT04176302) investigates the effect of one of these tools—Sense4Care's STAT-ON—can have on routine clinical practice. In this sub-analysis the agreement between the Hauser diaries and the STAT-ON sensor is analyzed. Methods Eighty four patients from MoMoPa-EC cohort were included in this sub-analysis. The intraclass correlation coefficient was calculated between the patient diary entries and the sensor data. Results The intraclass correlation coefficient of both methods was 0.57 (95% CI: 0.3–0.73) for the OFF time (%), 0.48 (95% CI: 0.17–0.68) for the time in ON (%), and 0.65 (95% CI%: 0.44–0.78) for the time with dyskinesias (%). Furthermore, the Spearman correlations with the UPDRS scale have been analyzed for different parameters of the two methods. The maximum correlation found was −0.63 (p < 0.001) between Mean Fluidity (one of the variables offered by the STAT-dON) and factor 1 of the UPDRS. Conclusion This sub-analysis shows a moderate concordance between the two tools, it is clearly appreciated that the correlation between the different UPDRS indices is better with the STAT-ON than with the Hauser diary. Trial Registration https://clinicaltrials.gov/show/NCT04176302 (NCT04176302).
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Affiliation(s)
- Carlos Pérez-López
- Department of Investigation, Consorci Sanitari de l'Alt Penedès i Garraf, Sant Pere de Ribes, Spain
| | - Jorge Hernández-Vara
- Neurology Department, Hospital Universitari Vall D Hebron Neurodegenerative Diseases Research Group, Vall D Hebron Institut de Recerca Universidad Autónoma de Barcelona, Biomedical Research Network Center on Neurodegenerative Diseases (CIBERNED), Instituto de Salud Carlos III, Madrid, Spain
| | - Nuria Caballol
- Department of Neurology, Hospital de Sant Joan Despí Moisès Broggi, Sant Joan Despi, Spain.,Parkinson's and Movement Disorders Unit, Hospital Quirón-Teknon, Barcelona, Spain
| | - Àngels Bayes
- Parkinson's and Movement Disorders Unit, Hospital Quirón-Teknon, Barcelona, Spain
| | | | | | - Alexandre Gironell
- Department of Neurology, Hospital de la Santa Creu i Sant Pau, Barcelona, Spain
| | - José López-Sánchez
- Department of Neurology, Hospital Virgen de la Arrixaca de Murcia, Murcia, Spain
| | | | - Alvarez Sauco M
- Department of Neurology, Hospital General de Elche, Elche, Spain
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Rodríguez-Martín D, Cabestany J, Pérez-López C, Pie M, Calvet J, Samà A, Capra C, Català A, Rodríguez-Molinero A. A New Paradigm in Parkinson's Disease Evaluation With Wearable Medical Devices: A Review of STAT-ON TM. Front Neurol 2022; 13:912343. [PMID: 35720090 PMCID: PMC9202426 DOI: 10.3389/fneur.2022.912343] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Accepted: 04/22/2022] [Indexed: 11/13/2022] Open
Abstract
In the past decade, the use of wearable medical devices has been a great breakthrough in clinical practice, trials, and research. In the Parkinson's disease field, clinical evaluation is time limited, and healthcare professionals need to rely on retrospective data collected through patients' self-filled diaries and administered questionnaires. As this often leads to inaccurate evaluations, a more objective system for symptom monitoring in a patient's daily life is claimed. In this regard, the use of wearable medical devices is crucial. This study aims at presenting a review on STAT-ONTM, a wearable medical device Class IIa, which provides objective information on the distribution and severity of PD motor symptoms in home environments. The sensor analyzes inertial signals, with a set of validated machine learning algorithms running in real time. The device was developed for 12 years, and this review aims at gathering all the results achieved within this time frame. First, a compendium of the complete journey of STAT-ONTM since 2009 is presented, encompassing different studies and developments in funded European and Spanish national projects. Subsequently, the methodology of database construction and machine learning algorithms design and development is described. Finally, clinical validation and external studies of STAT-ONTM are presented.
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Affiliation(s)
| | - Joan Cabestany
- Technical Research Centre for Dependency Care and Autonomous Living, Universitat Politecnica de Catalunya, Barcelona, Spain
| | - Carlos Pérez-López
- Department of Investigation, Consorci Sanitari Alt Penedès - Garraf, Vilanova i la Geltrú, Spain
| | - Marti Pie
- Sense4Care S.L., Cornellà de Llobregat, Spain
| | - Joan Calvet
- Sense4Care S.L., Cornellà de Llobregat, Spain
| | - Albert Samà
- Sense4Care S.L., Cornellà de Llobregat, Spain
| | | | - Andreu Català
- Technical Research Centre for Dependency Care and Autonomous Living, Universitat Politecnica de Catalunya, Barcelona, Spain
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Personalised Gait Recognition for People with Neurological Conditions. SENSORS 2022; 22:s22113980. [PMID: 35684600 PMCID: PMC9183078 DOI: 10.3390/s22113980] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 05/12/2022] [Accepted: 05/18/2022] [Indexed: 11/29/2022]
Abstract
There is growing interest in monitoring gait patterns in people with neurological conditions. The democratisation of wearable inertial sensors has enabled the study of gait in free living environments. One pivotal aspect of gait assessment in uncontrolled environments is the ability to accurately recognise gait instances. Previous work has focused on wavelet transform methods or general machine learning models to detect gait; the former assume a comparable gait pattern between people and the latter assume training datasets that represent a diverse population. In this paper, we argue that these approaches are unsuitable for people with severe motor impairments and their distinct gait patterns, and make the case for a lightweight personalised alternative. We propose an approach that builds on top of a general model, fine-tuning it with personalised data. A comparative proof-of-concept evaluation with general machine learning (NN and CNN) approaches and personalised counterparts showed that the latter improved the overall accuracy in 3.5% for the NN and 5.3% for the CNN. More importantly, participants that were ill-represented by the general model (the most extreme cases) had the recognition of gait instances improved by up to 16.9% for NN and 20.5% for CNN with the personalised approaches. It is common to say that people with neurological conditions, such as Parkinson’s disease, present very individual motor patterns, and that in a sense they are all outliers; we expect that our results will motivate researchers to explore alternative approaches that value personalisation rather than harvesting datasets that are may be able to represent these differences.
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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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Body-Worn Sensors for Parkinson’s disease: A qualitative approach with patients and healthcare professionals. PLoS One 2022; 17:e0265438. [PMID: 35511812 PMCID: PMC9070870 DOI: 10.1371/journal.pone.0265438] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Accepted: 03/01/2022] [Indexed: 11/29/2022] Open
Abstract
Body-Worn Sensors (BWS) provide reliable objective and continuous assessment of Parkinson’s disease (PD) motor symptoms, but their implementation in clinical routine has not yet become widespread. Users’ perceptions of BWS have not been explored. This study intended to evaluate the usability, user experience (UX), patients’ perceptions of BWS, and health professionals’ (HP) opinions on BWS monitoring. A qualitative analysis was performed from semi-structured interviews conducted with 22 patients and 9 HP experts in PD. Patients completed two interviews before and after the BWS one-week experiment, and they answered two questionnaires assessing the usability and UX. Patients rated the three BWS usability with high scores (SUS median [range]: 87.5 [72.5–100]). The UX across all dimensions of their interaction with the BWS was positive. During interviews, all patients and HP expressed interest in BWS monitoring. Patients’ hopes and expectations increased the more they learned about BWS. They manifested enthusiasm to wear BWS, which they imagined could improve their PD symptoms. HP highlighted needs for logistical support in the implementation of BWS in their practice. Both patients and HP suggested possible uses of BWS monitoring in clinical practice, for treatment adjustments for example, or for research purposes. Patients and HP shared ideas about the use of BWS monitoring, although patients may be more likely to integrate BWS into their disease follow-up compared to HP in their practice. This study highlights gaps that need to be fulfilled to facilitate BWS adoption and promote their potential.
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Development of an Area Scan Step Length Measuring System Using a Polynomial Estimate of the Heel Cloud Point. SIGNALS 2022. [DOI: 10.3390/signals3020011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Due to impaired mobility caused by aging, it is very important to employ early detection and monitoring of gait parameters to prevent the inevitable huge amount of medical cost at a later age. For gait training and potential tele-monitoring application outside clinical settings, low-cost yet highly reliable gait analysis systems are needed. This research proposes using a single LiDAR system to perform automatic gait analysis with polynomial fitting. The experimental setup for this study consists of two different walking speeds, fast walk and normal walk, along a 5-m straight line. There were ten test subjects (mean age 28, SD 5.2) who voluntarily participated in the study. We performed polynomial fitting to estimate the step length from the heel projection cloud point laser data as the subject walks forwards and compared the values with the visual inspection method. The results showed that the visual inspection method is accurate up to 6 cm while the polynomial method achieves 8 cm in the worst case (fast walking). With the accuracy difference estimated to be at most 2 cm, the polynomial method provides reliability of heel location estimation as compared with the observational gait analysis. The proposed method in this study presents an improvement accuracy of 4% as opposed to the proposed dual-laser range sensor method that reported 57.87 cm ± 10.48, an error of 10%. Meanwhile, our proposed method reported ±0.0633 m, a 6% error for normal walking.
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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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O’Day J, Lee M, Seagers K, Hoffman S, Jih-Schiff A, Kidziński Ł, Delp S, Bronte-Stewart H. Assessing inertial measurement unit locations for freezing of gait detection and patient preference. J Neuroeng Rehabil 2022; 19:20. [PMID: 35152881 PMCID: PMC8842967 DOI: 10.1186/s12984-022-00992-x] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Accepted: 01/13/2022] [Indexed: 12/28/2022] Open
Abstract
Background Freezing of gait, a common symptom of Parkinson’s disease, presents as sporadic episodes in which an individual’s feet suddenly feel stuck to the ground. Inertial measurement units (IMUs) promise to enable at-home monitoring and personalization of therapy, but there is a lack of consensus on the number and location of IMUs for detecting freezing of gait. The purpose of this study was to assess IMU sets in the context of both freezing of gait detection performance and patient preference. Methods Sixteen people with Parkinson’s disease were surveyed about sensor preferences. Raw IMU data from seven people with Parkinson’s disease, wearing up to eleven sensors, were used to train convolutional neural networks to detect freezing of gait. Models trained with data from different sensor sets were assessed for technical performance; a best technical set and minimal IMU set were identified. Clinical utility was assessed by comparing model- and human-rater-determined percent time freezing and number of freezing events. Results The best technical set consisted of three IMUs (lumbar and both ankles, AUROC = 0.83), all of which were rated highly wearable. The minimal IMU set consisted of a single ankle IMU (AUROC = 0.80). Correlations between these models and human raters were good to excellent for percent time freezing (ICC = 0.93, 0.89) and number of freezing events (ICC = 0.95, 0.86) for the best technical set and minimal IMU set, respectively. Conclusions Several IMU sets consisting of three IMUs or fewer were highly rated for both technical performance and wearability, and more IMUs did not necessarily perform better in FOG detection. We openly share our data and software to further the development and adoption of a general, open-source model that uses raw signals and a standard sensor set for at-home monitoring of freezing of gait. Supplementary Information The online version contains supplementary material available at 10.1186/s12984-022-00992-x.
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Salchow-Hömmen C, Skrobot M, Jochner MCE, Schauer T, Kühn AA, Wenger N. Review-Emerging Portable Technologies for Gait Analysis in Neurological Disorders. Front Hum Neurosci 2022; 16:768575. [PMID: 35185496 PMCID: PMC8850274 DOI: 10.3389/fnhum.2022.768575] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Accepted: 01/07/2022] [Indexed: 01/29/2023] Open
Abstract
The understanding of locomotion in neurological disorders requires technologies for quantitative gait analysis. Numerous modalities are available today to objectively capture spatiotemporal gait and postural control features. Nevertheless, many obstacles prevent the application of these technologies to their full potential in neurological research and especially clinical practice. These include the required expert knowledge, time for data collection, and missing standards for data analysis and reporting. Here, we provide a technological review of wearable and vision-based portable motion analysis tools that emerged in the last decade with recent applications in neurological disorders such as Parkinson's disease and Multiple Sclerosis. The goal is to enable the reader to understand the available technologies with their individual strengths and limitations in order to make an informed decision for own investigations and clinical applications. We foresee that ongoing developments toward user-friendly automated devices will allow for closed-loop applications, long-term monitoring, and telemedical consulting in real-life environments.
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Affiliation(s)
- Christina Salchow-Hömmen
- Department of Neurology With Experimental Neurology, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Matej Skrobot
- Department of Neurology With Experimental Neurology, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Magdalena C E Jochner
- Department of Neurology With Experimental Neurology, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Thomas Schauer
- Control Systems Group, Technische Universität Berlin, Berlin, Germany
| | - Andrea A Kühn
- Department of Neurology With Experimental Neurology, Charité-Universitätsmedizin Berlin, Berlin, Germany
- Berlin School of Mind and Brain, Charité-Universitätsmedizin Berlin, Berlin, Germany
- NeuroCure Clinical Research Centre, Charité-Universitätsmedizin Berlin, Berlin, Germany
- German Center for Neurodegenerative Diseases, DZNE, Berlin, Germany
| | - Nikolaus Wenger
- Department of Neurology With Experimental Neurology, Charité-Universitätsmedizin Berlin, Berlin, Germany
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Abstract
Internet-connected devices, including personal computers, smartphones, smartwatches, and voice assistants, have evolved into powerful multisensor technologies that billions of people interact with daily to connect with friends and colleagues, access and share information, purchase goods, play games, and navigate their environment. Digital phenotyping taps into the data streams captured by these devices to characterize and understand health and disease. The purpose of this article is to summarize opportunities for digital phenotyping in neurology, review studies using everyday technologies to obtain motor and cognitive information, and provide a perspective on how neurologists can embrace and accelerate progress in this emerging field.
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Affiliation(s)
- Anoopum S. Gupta
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA
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Shalin G, Pardoel S, Lemaire ED, Nantel J, Kofman J. Prediction and detection of freezing of gait in Parkinson's disease from plantar pressure data using long short-term memory neural-networks. J Neuroeng Rehabil 2021; 18:167. [PMID: 34838066 PMCID: PMC8626900 DOI: 10.1186/s12984-021-00958-5] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Accepted: 11/10/2021] [Indexed: 11/10/2022] Open
Abstract
Background Freezing of gait (FOG) is a walking disturbance in advanced stage Parkinson’s disease (PD) that has been associated with increased fall risk and decreased quality of life. Freezing episodes can be mitigated or prevented with external intervention such as visual or auditory cues, activated by FOG prediction and detection systems. While most research on FOG detection and prediction has been based on inertial measurement unit (IMU) and accelerometer data, plantar-pressure data may capture subtle weight shifts unique to FOG episodes. Different machine learning algorithms have been used for FOG detection and prediction; however, long short-term memory (LSTM) deep learning methods hold an advantage when dealing with time-series data, such as sensor data. This research aimed to determine if LSTM can be used to detect and predict FOG from plantar pressure data alone, specifically for use in a real-time wearable system. Methods Plantar pressure data were collected from pressure-sensing insole sensors worn by 11 participants with PD as they walked a predefined freeze-provoking path. FOG instances were labelled, 16 features were extracted, and the dataset was balanced and normalized (z-score). The resulting datasets were classified using long short-term memory neural-network models. Separate models were trained for detection and prediction. For prediction models, data before FOG were included in the target class. Leave-one-freezer-out cross validation was used for model evaluation. In addition, the models were tested on all non-freezer data to determine model specificity. Results The best FOG detection model had 82.1% (SD 6.2%) mean sensitivity and 89.5% (SD 3.6%) mean specificity for one-freezer-held-out cross validation. Specificity improved to 93.3% (SD 4.0%) when ignoring inactive state data (standing) and analyzing the model only on active states (turning and walking). The model correctly detected 95% of freeze episodes. The best FOG prediction method achieved 72.5% (SD 13.6%) mean sensitivity and 81.2% (SD 6.8%) mean specificity for one-freezer-held-out cross validation. Conclusions Based on FOG data collected in a laboratory, the results suggest that plantar pressure data can be used for FOG detection and prediction. However, further research is required to improve FOG prediction performance, including training with a larger sample of people who experience FOG.
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Affiliation(s)
- Gaurav Shalin
- Department of Systems Design Engineering, University of Waterloo, Waterloo, ON, Canada
| | - Scott Pardoel
- Department of Systems Design Engineering, University of Waterloo, Waterloo, ON, Canada
| | - Edward D Lemaire
- Faculty of Medicine, University of Ottawa and Ottawa Hospital Research Institute, Ottawa, ON, Canada
| | - Julie Nantel
- School of Human Kinetics, University of Ottawa, Ottawa, ON, Canada
| | - Jonathan Kofman
- Department of Systems Design Engineering, University of Waterloo, Waterloo, ON, Canada.
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Ashfaque Mostafa T, Soltaninejad S, McIsaac TL, Cheng I. A Comparative Study of Time Frequency Representation Techniques for Freeze of Gait Detection and Prediction. SENSORS (BASEL, SWITZERLAND) 2021; 21:6446. [PMID: 34640763 PMCID: PMC8512068 DOI: 10.3390/s21196446] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Revised: 09/13/2021] [Accepted: 09/24/2021] [Indexed: 11/30/2022]
Abstract
Freezing of Gait (FOG) is an impairment that affects the majority of patients in the advanced stages of Parkinson's Disease (PD). FOG can lead to sudden falls and injuries, negatively impacting the quality of life for the patients and their families. Rhythmic Auditory Stimulation (RAS) can be used to help patients recover from FOG and resume normal gait. RAS might be ineffective due to the latency between the start of a FOG event, its detection and initialization of RAS. We propose a system capable of both FOG prediction and detection using signals from tri-axial accelerometer sensors that will be useful in initializing RAS with minimal latency. We compared the performance of several time frequency analysis techniques, including moving windows extracted from the signals, handcrafted features, Recurrence Plots (RP), Short Time Fourier Transform (STFT), Discreet Wavelet Transform (DWT) and Pseudo Wigner Ville Distribution (PWVD) with Deep Learning (DL) based Long Short Term Memory (LSTM) and Convolutional Neural Networks (CNN). We also propose three Ensemble Network Architectures that combine all the time frequency representations and DL architectures. Experimental results show that our ensemble architectures significantly improve the performance compared with existing techniques. We also present the results of applying our method trained on a publicly available dataset to data collected from patients using wearable sensors in collaboration with A.T. Still University.
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Affiliation(s)
- Tahjid Ashfaque Mostafa
- Multimedia Research Center, Department of Computing Science, University of Alberta, Edmonton, AB T6G 2E8, Canada;
| | - Sara Soltaninejad
- Multimedia Research Center, Department of Computing Science, University of Alberta, Edmonton, AB T6G 2E8, Canada;
| | - Tara L. McIsaac
- Arizona School of Health Sciences, A.T. Still University, 5850 E. Still Circle, Mesa, AZ 85206, USA;
- School of Pharmacy and Health Professions, Creighton University Health Sciences, 3100 N. Central Ave., Phoenix, AZ 85013, USA
| | - Irene Cheng
- Multimedia Research Center, Department of Computing Science, University of Alberta, Edmonton, AB T6G 2E8, Canada;
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Could New Generations of Sensors Reshape the Management of Parkinson’s Disease? CLINICAL AND TRANSLATIONAL NEUROSCIENCE 2021. [DOI: 10.3390/ctn5020018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Parkinson's disease (PD) is a chronic neurologic disease that has a great impact on the patient’s quality of life. The natural course of the disease is characterized by an insidious onset of symptoms, such as rest tremor, shuffling gait, bradykinesia, followed by improvement with the initiation of dopaminergic therapy. However, this “honeymoon period” gradually comes to an end with the emergence of motor fluctuations and dyskinesia. PD patients need long-term treatments and monitoring throughout the day; however, clinical examinations in hospitals are often not sufficient for optimal management of the disease. Technology-based devices are a new comprehensive assessment method of PD patient’s symptoms that are easy to use and give unbiased measurements. This review article provides an exhaustive overview of motor complications of advanced PD and new approaches to the management of the disease using sensors.
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Rodríguez-Molinero A, Hernández-Vara J, Miñarro A, Pérez-López C, Bayes-Rusiñol À, Martínez-Castrillo JC, Pérez-Martínez DA. Multicentre, randomised, single-blind, parallel group trial to compare the effectiveness of a Holter for Parkinson's symptoms against other clinical monitoring methods: study protocol. BMJ Open 2021; 11:e045272. [PMID: 34281918 PMCID: PMC8291311 DOI: 10.1136/bmjopen-2020-045272] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/29/2022] Open
Abstract
INTRODUCTION In recent years, multiple studies have aimed to develop and validate portable technological devices capable of monitoring the motor complications of Parkinson's disease patients (Parkinson's Holter). The effectiveness of these monitoring devices for improving clinical control is not known. METHODS AND ANALYSIS This is a single-blind, cluster-randomised controlled clinical trial. Neurologists from Spanish health centres will be randomly assigned to one of three study arms (1:1:1): (a) therapeutic adjustment using information from a Parkinson's Holter that will be worn by their patients for 7 days, (b) therapeutic adjustment using information from a diary of motor fluctuations that will be completed by their patients for 7 days and (c) therapeutic adjustment using clinical information collected during consultation. It is expected that 162 consecutive patients will be included over a period of 6 months.The primary outcome is the efficiency of the Parkinson's Holter compared with traditional clinical practice in terms of Off time reduction with respect to the baseline (recorded through a diary of motor fluctuations, which will be completed by all patients). As secondary outcomes, changes in variables related to other motor complications (dyskinesia and freezing of gait), quality of life, autonomy in activities of daily living, adherence to the monitoring system and number of doctor-patient contacts will be analysed. The noninferiority of the Parkinson's Holter against the diary of motor fluctuations in terms of Off time reduction will be studied as the exploratory objective.Ethics and dissemination approval for this study has been obtained from the Hospital Universitari de Bellvitge Ethics Committee. The results of this study will inform the practical utility of the objective information provided by a Parkinson's Holter and, therefore, the convenience of adopting this technology in clinical practice and in future clinical trials. We expect public dissemination of the results in 2022. TRIAL REGISTRATION NCT04176302; https://clinicaltrials.gov/show/NCT04176302.
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Affiliation(s)
| | - Jorge Hernández-Vara
- Department of Neurology, Hospital Universitari Vall d'Hebron and Neurodegenerative Diseases Research Group, Barcelona, Spain
| | - Antonio Miñarro
- Department of Genetics, Microbiology and Statistics, Faculty of Biology, Universitat de Barcelona, Barcelona, Spain
| | - Carlos Pérez-López
- Àrea de Recerca, Consorci Sanitari de l'Alt Penedès i Garraf, Vilafranca del Pendès, Spain
| | - Àngels Bayes-Rusiñol
- Parkinson's and Movement Disorders Unit, Hospital Quirón Teknon, Barcelona, Spain
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Tăuţan AM, Ionescu B, Santarnecchi E. Artificial intelligence in neurodegenerative diseases: A review of available tools with a focus on machine learning techniques. Artif Intell Med 2021; 117:102081. [PMID: 34127244 DOI: 10.1016/j.artmed.2021.102081] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Revised: 02/21/2021] [Accepted: 04/26/2021] [Indexed: 10/21/2022]
Abstract
Neurodegenerative diseases have shown an increasing incidence in the older population in recent years. A significant amount of research has been conducted to characterize these diseases. Computational methods, and particularly machine learning techniques, are now very useful tools in helping and improving the diagnosis as well as the disease monitoring process. In this paper, we provide an in-depth review on existing computational approaches used in the whole neurodegenerative spectrum, namely for Alzheimer's, Parkinson's, and Huntington's Diseases, Amyotrophic Lateral Sclerosis, and Multiple System Atrophy. We propose a taxonomy of the specific clinical features, and of the existing computational methods. We provide a detailed analysis of the various modalities and decision systems employed for each disease. We identify and present the sleep disorders which are present in various diseases and which represent an important asset for onset detection. We overview the existing data set resources and evaluation metrics. Finally, we identify current remaining open challenges and discuss future perspectives.
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Affiliation(s)
- Alexandra-Maria Tăuţan
- University "Politehnica" of Bucharest, Splaiul Independenţei 313, 060042 Bucharest, Romania.
| | - Bogdan Ionescu
- University "Politehnica" of Bucharest, Splaiul Independenţei 313, 060042 Bucharest, Romania.
| | - Emiliano Santarnecchi
- Berenson-Allen Center for Noninvasive Brain Stimulation, Harvard Medical School, 330 Brookline Avenue, Boston, United States.
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Fietzek UM, Schulz SJ, Ziegler K, Ceballos-Baumann AO. The Minimal Clinically Relevant Change of the FOG Score. JOURNAL OF PARKINSONS DISEASE 2021; 10:325-332. [PMID: 31868684 DOI: 10.3233/jpd-191783] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Freezing of gait is a highly disabling symptom in persons with Parkinson's disease (PwP). Despite its episodic character, freezing can be reliably evaluated using the FOG score. The description of the minimal clinically relevant change is a requirement for a meaningful interpretation of its results. OBJECTIVE To determine the minimal clinically relevant change of the FOG score. METHODS We evaluated video recordings of a standardized freezing-evoking gait parkour, i.e., the FOG score just before and 30 minutes after the intake of a regular levodopa dose in a randomized blinded fashion. The minimal clinically relevant response was considered a value of one or more on a 7-step Likert-type response scale [-3; +3] that served as the anchor. The minimal clinically relevant change was determined by ROC analysis. RESULTS 37 PwP (Hoehn & Yahr stages 2.5-4, 27 male, 10 female) were aged 68.2 years on average (range 45-80). Mean disease duration was 12.9 years (2-29 years). Minimum FOG score was 0 and Maximum FOG score was 29. Mean FOG scores before medication were 10.6, and 11.1 after medication intake, with changes ranging from -14.7 to +16.7. The minimal clinically relevant change (MCRC) for improvement based on expert clinician rating was three scale points with a sensitivity of 0.67 and a specificity of 0.96. CONCLUSIONS The FOG score is recognized as a useful clinical instrument for the evaluation of freezing in the clinical setting. Knowledge of the MCRC should help to define responses to interventions that are discernible and meaningful to the expert physician and to the patient.
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Affiliation(s)
- Urban M Fietzek
- Department of Neurology and Clinical Neurophysiology, Schön Klinik München Schwabing, Munich, Germany.,Department of Neurology, University of Munich, Munich, Germany
| | - Simon J Schulz
- Department of Neurology and Clinical Neurophysiology, Schön Klinik München Schwabing, Munich, Germany.,Department of Neurology, Technical University Munich, Munich, Germany
| | - Kerstin Ziegler
- Department of Neurology and Clinical Neurophysiology, Schön Klinik München Schwabing, Munich, Germany
| | - Andres O Ceballos-Baumann
- Department of Neurology and Clinical Neurophysiology, Schön Klinik München Schwabing, Munich, Germany.,Department of Neurology, Technical University Munich, Munich, Germany
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50
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Annaswamy TM, Pradhan GN, Chakka K, Khargonkar N, Borresen A, Prabhakaran B. Using Biometric Technology for Telehealth and Telerehabilitation. Phys Med Rehabil Clin N Am 2021; 32:437-449. [PMID: 33814068 DOI: 10.1016/j.pmr.2020.12.007] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Abstract
This article discusses the use of physical and biometric sensors in telerehabilitation. It also discusses synchronous tele-physical assessment using haptics and augmented reality and asynchronous physical assessment using remote pose estimation. The article additionally focuses on computational models that have the potential to monitor and evaluate changes in kinematic and kinetic properties during telerehabilitation using biometric sensors such as electromyography and other wearable and noncontact sensors based on force and speed. And finally, the article discusses how virtual reality environments can be facilitated in telerehabilitation.
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Affiliation(s)
- Thiru M Annaswamy
- PM&R Service, Department of PM&R, VA North Texas Health Care System, UT Southwestern Medical Center, 4500, South Lancaster Road, Dallas, TX-75216, USA.
| | - Gaurav N Pradhan
- Biomedical Informatics, Mayo Clinic College of Medicine, 13400, East Shea Boulevard, Scottsdale, AZ-85259, USA
| | - Keerthana Chakka
- UT Southwestern Medical School, 5323, Harry Hines Boulevard, Dallas, TX-75390, USA
| | - Ninad Khargonkar
- Department of Computer Science, University of Texas at Dallas, 800, West Campbell Road, Richardson, TX-75080, USA
| | - Aleks Borresen
- Department of Physical Medicine and Rehabilitation, University of Alabama at Birmingham, 157 Spain Rehabilitation Center, 1717 6th Avenue South, Birmingham, AL 35249, USA
| | - Balakrishnan Prabhakaran
- Department of Computer Science, University of Texas at Dallas, 800, West Campbell Road, Richardson, TX-75080, USA
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