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Fan Y, Zhu L, Wang H, Song A. Synthesize Personalized Training for Robot-Assisted Upper Limb Rehabilitation With Diversity Enhancement. IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2024; 30:5705-5718. [PMID: 37639418 DOI: 10.1109/tvcg.2023.3308940] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/31/2023]
Abstract
For upper limb rehabilitation, the robot-assisted technique in combination with serious games requires well-specified training plans. For the best quality of the rehabilitation process, customized game levels for each user are desired, while it is labor-intensive to design and adjust game levels for different individuals. We work on generating training content for a desktop end-effector rehabilitation robot and propose a method to automatically generate individualized training plans. By modeling the search of the training motions as finding optimal hand paths and trajectories, we introduce solving the design problem with a multi-objective optimization (MO) solver. We further improve the MO solver to enhance the diversity of the solutions. With the proposed approach, our system is capable of automatically generating various training plans considering the training intensity and dexterity of each joint in the upper limb. In addition, the enhanced diversity avoids repeated training plans, which helps motivate the user in the rehabilitation. We test our method with different requirements on the training plans and validate the solutions.
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Sauerzopf L, Luft AR, Baldissera A, Frey S, Klamroth-Marganska V, Spiess MR. Remotely Assessing Motor Function and Activity of the Upper Extremity After Stroke: A Systematic Review of Validity and Clinical Utility of Tele-Assessments. Clin Rehabil 2024:2692155241258867. [PMID: 38839104 DOI: 10.1177/02692155241258867] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/07/2024]
Abstract
OBJECTIVE The aim of this systematic review is to identify currently available tele-assessments for motor impairments of the upper extremity in adults after a stroke and to assess their psychometric properties and clinical utility. DATA SOURCES We searched for studies describing the psychometric properties of tele-assessments for the motor function of the upper extremity. A systematic search was conducted in the Cumulative Index to Nursing and Allied Health Literature, Medline via OVID, Embase, The Cochrane Library, Scopus, Web of Science and Institute of Electrical and Electronics Engineers Xplore from inception until 30 April 2024. REVIEW METHODS The quality assessment for the included studies and the rating of the psychometric properties were performed using the COSMIN Risk of Bias Checklist for systematic reviews of patient-reported outcome measures. RESULTS A total of 12 studies (N = 3912) describing 11 tele-assessments met the predefined inclusion criteria. The included assessments were heterogeneous in terms of quality and psychometric properties and risk of bias. None of the tele-assessments currently meets the criteria of clinical utility to be recommended for clinical practice without restriction. CONCLUSION The quality and clinical utility of tele-assessments varied widely, suggesting a cautious consideration for immediate clinical practice application. There is potential for tele-assessments in clinical practice, but the clinical benefits need to be improved by simplifying the complexity of tele-assessments. REGISTRATION NUMBER CRD42022335035.
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Affiliation(s)
- Lena Sauerzopf
- ZHAW School of Health Sciences, Institute of Occupational Therapy, Winterthur, Switzerland
- Faculty of Medicine, University of Zurich, Zurich, Switzerland
| | - Andreas R Luft
- Department of Neurology, Division of Vascular Neurology and Neurorehabilitation, University of Zurich, Zürich, Switzerland
| | | | - Sara Frey
- ZHAW School of Health Sciences, Institute of Occupational Therapy, Winterthur, Switzerland
| | | | - Martina R Spiess
- ZHAW School of Health Sciences, Institute of Occupational Therapy, Winterthur, Switzerland
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Wang Z, Zhang T, Fan J, Gu F, Yu Q, Wang H, Yang J, Zhu Q. Clinical validation of automated depth camera-based measurement of the Fugl-Meyer assessment for upper extremity. Clin Rehabil 2024:2692155241251434. [PMID: 38693881 DOI: 10.1177/02692155241251434] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/03/2024]
Abstract
OBJECTIVE Depth camera-based measurement has demonstrated efficacy in automated assessment of upper limb Fugl-Meyer Assessment for paralysis rehabilitation. However, there is a lack of adequately sized studies to provide clinical support. Thus, we developed an automated system utilizing depth camera and machine learning, and assessed its feasibility and validity in a clinical setting. DESIGN Validation and feasibility study of a measurement instrument based on single cross-sectional data. SETTING Rehabilitation unit in a general hospital. PARTICIPANTS Ninety-five patients with hemiparesis admitted for inpatient rehabilitation unit (2021-2023). MAIN MEASURES Scores for each item, excluding those related to reflexes, were computed utilizing machine learning models trained on participant videos and readouts from force test devices, while the remaining reflex scores were derived through regression algorithms. Concurrent criterion validity was evaluated using sensitivity, specificity, percent agreement and Cohen's Kappa coefficient for ordinal scores of individual items, as well as correlations and intraclass correlation coefficients for total scores. Video-based manual assessment was also conducted and compared to the automated tools. RESULT The majority of patients completed the assessment without therapist intervention. The automated scoring models demonstrated superior validity compared to video-based manual assessment across most items. The total scores derived from the automated assessment exhibited a high coefficient of 0.960. However, the validity of force test items utilizing force sensing resistors was relatively low. CONCLUSION The integration of depth camera technology and machine learning models for automated Fugl-Meyer Assessment demonstrated acceptable validity and feasibility, suggesting its potential as a valuable tool in rehabilitation assessment.
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Affiliation(s)
- Zhaoyang Wang
- Department of Microsurgery, Orthopedic Trauma and Hand Surgery, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Tao Zhang
- Department of Rehabilitation Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Jingyuan Fan
- Department of Microsurgery, Orthopedic Trauma and Hand Surgery, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Fanbin Gu
- Department of Microsurgery, Orthopedic Trauma and Hand Surgery, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Qiuhua Yu
- Department of Rehabilitation Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Honggang Wang
- Department of Microsurgery, Orthopedic Trauma and Hand Surgery, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
- Guangdong Provincial Key Laboratory for Orthopedics and Traumatology, Guangzhou, China
| | - Jiantao Yang
- Department of Microsurgery, Orthopedic Trauma and Hand Surgery, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
- Guangdong Provincial Key Laboratory for Orthopedics and Traumatology, Guangzhou, China
| | - Qingtang Zhu
- Department of Microsurgery, Orthopedic Trauma and Hand Surgery, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
- Guangdong Provincial Key Laboratory for Orthopedics and Traumatology, Guangzhou, China
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Abedi A, Colella TJF, Pakosh M, Khan SS. Artificial intelligence-driven virtual rehabilitation for people living in the community: A scoping review. NPJ Digit Med 2024; 7:25. [PMID: 38310158 PMCID: PMC10838287 DOI: 10.1038/s41746-024-00998-w] [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: 04/14/2023] [Accepted: 01/03/2024] [Indexed: 02/05/2024] Open
Abstract
Virtual Rehabilitation (VRehab) is a promising approach to improving the physical and mental functioning of patients living in the community. The use of VRehab technology results in the generation of multi-modal datasets collected through various devices. This presents opportunities for the development of Artificial Intelligence (AI) techniques in VRehab, namely the measurement, detection, and prediction of various patients' health outcomes. The objective of this scoping review was to explore the applications and effectiveness of incorporating AI into home-based VRehab programs. PubMed/MEDLINE, Embase, IEEE Xplore, Web of Science databases, and Google Scholar were searched from inception until June 2023 for studies that applied AI for the delivery of VRehab programs to the homes of adult patients. After screening 2172 unique titles and abstracts and 51 full-text studies, 13 studies were included in the review. A variety of AI algorithms were applied to analyze data collected from various sensors and make inferences about patients' health outcomes, most involving evaluating patients' exercise quality and providing feedback to patients. The AI algorithms used in the studies were mostly fuzzy rule-based methods, template matching, and deep neural networks. Despite the growing body of literature on the use of AI in VRehab, very few studies have examined its use in patients' homes. Current research suggests that integrating AI with home-based VRehab can lead to improved rehabilitation outcomes for patients. However, further research is required to fully assess the effectiveness of various forms of AI-driven home-based VRehab, taking into account its unique challenges and using standardized metrics.
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Affiliation(s)
- Ali Abedi
- KITE Research Institute, Toronto Rehabilitation Institute, University Health Network, Toronto, ON, Canada.
| | - Tracey J F Colella
- KITE Research Institute, Toronto Rehabilitation Institute, University Health Network, Toronto, ON, Canada
| | - Maureen Pakosh
- Library & Information Services, Toronto Rehabilitation Institute, University Health Network, Toronto, ON, Canada
| | - Shehroz S Khan
- KITE Research Institute, Toronto Rehabilitation Institute, University Health Network, Toronto, ON, Canada
- Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada
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Oubre B, Lee SI. Detection and Assessment of Point-to-Point Movements During Functional Activities Using Deep Learning and Kinematic Analyses of the Stroke-Affected Wrist. IEEE J Biomed Health Inform 2024; 28:1022-1030. [PMID: 38015679 DOI: 10.1109/jbhi.2023.3337156] [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: 11/30/2023]
Abstract
Stoke is a leading cause of long-term disability, including upper-limb hemiparesis. Frequent, unobtrusive assessment of naturalistic motor performance could enable clinicians to better assess rehabilitation effectiveness and monitor patients' recovery trajectories. We therefore propose and validate a two-phase data analytic pipeline to estimate upper-limb impairment based on the naturalistic performance of activities of daily living (ADLs). Eighteen stroke survivors were equipped with an inertial sensor on the stroke-affected wrist and performed up to four ADLs in a naturalistic manner. Continuous inertial time series were segmented into sliding windows, and a machine-learned model identified windows containing instances of point-to-point (P2P) movements. Using kinematic features extracted from the detected windows, a subsequent model was used to estimate upper-limb motor impairment, as measured by the Fugl-Meyer Assessment (FMA). Both models were evaluated using leave-one-subject-out cross-validation. The P2P movement detection model had an area under the precision-recall curve of 0.72. FMA estimates had a normalized root mean square error of 18.8% with R2=0.72. These promising results support the potential to develop seamless, ecologically valid measures of real-world motor performance.
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Sheng B, Chen X, Cheng J, Zhang Y, Xie SSQ, Tao J, Duan C. A novel scoring approach for the Wolf Motor Function Test in stroke survivors using motion-sensing technology and machine learning: A preliminary study. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 243:107887. [PMID: 37913714 DOI: 10.1016/j.cmpb.2023.107887] [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: 12/08/2022] [Revised: 10/23/2023] [Accepted: 10/24/2023] [Indexed: 11/03/2023]
Abstract
BACKGROUND AND OBJECTIVE Human-administered clinical scales, such as the Functional Ability Scale of the Wolf Motor Function Test (WMFT-FAS), are widely utilized to evaluate upper-limb motor function in stroke survivors. However, these scales are generally subjective and labor-intensive. To end this, we proposed a novel scoring approach for the motor function assessment. METHODS The proposed novel scoring approach mainly contained one Microsoft Kinect v2, one customized motion tracking system, and one customized intelligent scoring system. Specifically, the Kinect v2 was used to capture stroke survivors' functional movements, the motion tracking system was developed for recording the gathered movement data, and the intelligent scoring system (kernel: feed-forward neural network, FFNN) was developed to evaluate movement quality and provide corresponding WMFT-FAS scores. Several methods have been applied to enhance the approach's usability, such as singular spectrum analysis and multi-ReliefF method. RESULTS Sixteen stroke survivors and ten healthy subjects were recruited for validation. Inspiring results of the proposed approach were achieved when compared with the clinical scores provided by a physiotherapist: 0.924 ± 0.027 for accuracy, 0.875 ± 0.063 for F1-score, 0.915 ± 0.051 for sensitivity, 0.969 ± 0.013 for specificity, 0.952 ± 0.038 for AUC, 0.098 ± 0.037 for mean absolute error, and 0.214 ± 0.078 for root mean squared error. CONCLUSIONS The results indicate that the proposed novel scoring approach can provide objective and accurate assessment scores, which can help physiotherapists make individualized treatment decisions.
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Affiliation(s)
- Bo Sheng
- School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, 200444, China
| | - Xiaohui Chen
- School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, 200444, China
| | - Jian Cheng
- School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, 200444, China
| | - Yanxin Zhang
- Department of Exercise Sciences, The University of Auckland, Auckland, 1010, New Zealand
| | - Shane Sheng Quan Xie
- School of Electronic and Electrical Engineering, University of Leeds, Leeds, LS2 9JT, United Kingdom
| | - Jing Tao
- School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, 200444, China
| | - Chaoqun Duan
- School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, 200444, China.
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Stulberg EL, Sachdev PS, Murray AM, Cramer SC, Sorond FA, Lakshminarayan K, Sabayan B. Post-Stroke Brain Health Monitoring and Optimization: A Narrative Review. J Clin Med 2023; 12:7413. [PMID: 38068464 PMCID: PMC10706919 DOI: 10.3390/jcm12237413] [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: 09/13/2023] [Revised: 11/10/2023] [Accepted: 11/21/2023] [Indexed: 01/22/2024] Open
Abstract
Significant advancements have been made in recent years in the acute treatment and secondary prevention of stroke. However, a large proportion of stroke survivors will go on to have enduring physical, cognitive, and psychological disabilities from suboptimal post-stroke brain health. Impaired brain health following stroke thus warrants increased attention from clinicians and researchers alike. In this narrative review based on an open timeframe search of the PubMed, Scopus, and Web of Science databases, we define post-stroke brain health and appraise the body of research focused on modifiable vascular, lifestyle, and psychosocial factors for optimizing post-stroke brain health. In addition, we make clinical recommendations for the monitoring and management of post-stroke brain health at major post-stroke transition points centered on four key intertwined domains: cognition, psychosocial health, physical functioning, and global vascular health. Finally, we discuss potential future work in the field of post-stroke brain health, including the use of remote monitoring and interventions, neuromodulation, multi-morbidity interventions, enriched environments, and the need to address inequities in post-stroke brain health. As post-stroke brain health is a relatively new, rapidly evolving, and broad clinical and research field, this narrative review aims to identify and summarize the evidence base to help clinicians and researchers tailor their own approach to integrating post-stroke brain health into their practices.
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Affiliation(s)
- Eric L. Stulberg
- Department of Neurology, University of Utah, Salt Lake City, UT 84112, USA;
| | - Perminder S. Sachdev
- Centre for Healthy Brain Ageing (CHeBA), University of New South Wales, Sydney, NSW 2052, Australia;
- Neuropsychiatric Institute, Prince of Wales Hospital, Sydney, NSW 2031, Australia
| | - Anne M. Murray
- Berman Center for Outcomes and Clinical Research, Minneapolis, MN 55415, USA;
- Department of Medicine, Geriatrics Division, Hennepin Healthcare Research Institute, Minneapolis, MN 55404, USA
| | - Steven C. Cramer
- Department of Neurology, University of California Los Angeles, Los Angeles, CA 90095, USA;
- California Rehabilitation Institute, Los Angeles, CA 90067, USA
| | - Farzaneh A. Sorond
- Department of Neurology, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA;
| | - Kamakshi Lakshminarayan
- Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN 55455, USA;
| | - Behnam Sabayan
- Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN 55455, USA;
- Department of Neurology, Hennepin Healthcare Research Institute, Minneapolis, MN 55404, USA
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Zadeh SM, MacDermid J, Johnson J, Birmingham TB, Shafiee E. Applications of wearable sensors in upper extremity MSK conditions: a scoping review. J Neuroeng Rehabil 2023; 20:158. [PMID: 37980497 PMCID: PMC10656914 DOI: 10.1186/s12984-023-01274-w] [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: 02/18/2023] [Accepted: 10/30/2023] [Indexed: 11/20/2023] Open
Abstract
PURPOSE This scoping review uniquely aims to map the current state of the literature on the applications of wearable sensors in people with or at risk of developing upper extremity musculoskeletal (UE-MSK) conditions, considering that MSK conditions or disorders have the highest rate of prevalence among other types of conditions or disorders that contribute to the need for rehabilitation services. MATERIALS AND METHODS The preferred reporting items for systematic reviews and meta-analysis (PRISMA) extension for scoping reviews guideline was followed in this scoping review. Two independent authors conducted a systematic search of four databases, including PubMed, Embase, Scopus, and IEEEXplore. We included studies that have applied wearable sensors on people with or at risk of developing UE-MSK condition published after 2010. We extracted study designs, aims, number of participants, sensor placement locations, sensor types, and number, and outcome(s) of interest from the included studies. The overall findings of our scoping review are presented in tables and diagrams to map an overview of the existing applications. RESULTS The final review encompassed 80 studies categorized into clinical population (31 studies), workers' population (31 studies), and general wearable design/performance studies (18 studies). Most were observational, with 2 RCTs in workers' studies. Clinical studies focused on UE-MSK conditions like rotator cuff tear and arthritis. Workers' studies involved industrial workers, surgeons, farmers, and at-risk healthy individuals. Wearable sensors were utilized for objective motion assessment, home-based rehabilitation monitoring, daily activity recording, physical risk characterization, and ergonomic assessments. IMU sensors were prevalent in designs (84%), with a minority including sEMG sensors (16%). Assessment applications dominated (80%), while treatment-focused studies constituted 20%. Home-based applicability was noted in 21% of the studies. CONCLUSION Wearable sensor technologies have been increasingly applied to the health care field. These applications include clinical assessments, home-based treatments of MSK disorders, and monitoring of workers' population in non-standardized areas such as work environments. Assessment-focused studies predominate over treatment studies. Additionally, wearable sensor designs predominantly use IMU sensors, with a subset of studies incorporating sEMG and other sensor types in wearable platforms to capture muscle activity and inertial data for the assessment or rehabilitation of MSK conditions.
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Affiliation(s)
- Sohrob Milani Zadeh
- Biomedical Engineering, Physical Therapy, Western University, London, ON, Canada.
| | - Joy MacDermid
- Physical Therapy and Surgery, Western University, London, ON, Canada
- Clinical Research Lab, Hand and Upper Limb Center, St. Joseph's Health Center, London, ON, Canada
- Rehabilitation Science McMaster University, Hamilton, ON, Canada
| | - James Johnson
- Roth-McFarlane Hand and Upper Limb Centre, St. Joseph's Health Care, London, ON, Canada
| | - Trevor B Birmingham
- Biomedical Engineering, Physical Therapy, Western University, London, ON, Canada
| | - Erfan Shafiee
- School of Rehabilitation Therapy, Queen's University, Kingston, ON, Canada
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Szabo DA, Neagu N, Teodorescu S, Apostu M, Predescu C, Pârvu C, Veres C. The Role and Importance of Using Sensor-Based Devices in Medical Rehabilitation: A Literature Review on the New Therapeutic Approaches. SENSORS (BASEL, SWITZERLAND) 2023; 23:8950. [PMID: 37960649 PMCID: PMC10648494 DOI: 10.3390/s23218950] [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: 08/31/2023] [Revised: 10/22/2023] [Accepted: 10/31/2023] [Indexed: 11/15/2023]
Abstract
Due to the growth of sensor technology, more affordable integrated circuits, and connectivity technologies, the usage of wearable equipment and sensing devices for monitoring physical activities, whether for wellness, sports monitoring, or medical rehabilitation, has exploded. The current literature review was performed between October 2022 and February 2023 using PubMed, Web of Science, and Scopus in accordance with P.R.I.S.M.A. criteria. The screening phase resulted in the exclusion of 69 articles that did not fit the themes developed in all subchapters of the study, 41 articles that dealt exclusively with rehabilitation and orthopaedics, 28 articles whose abstracts were not visible, and 10 articles that dealt exclusively with other sensor-based devices and not medical ones; the inclusion phase resulted in the inclusion of 111 articles. Patients who utilise sensor-based devices have several advantages due to rehabilitating a missing component, which marks the accomplishment of a fundamental goal within the rehabilitation program. As technology moves faster and faster forward, the field of medical rehabilitation has to adapt to the time we live in by using technology and intelligent devices. This means changing every part of rehabilitation and finding the most valuable and helpful gadgets that can be used to regain lost functions, keep people healthy, or prevent diseases.
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Affiliation(s)
- Dan Alexandru Szabo
- Department of Human Movement Sciences, George Emil Palade University of Medicine, Pharmacy, Science, and Technology of Targu Mures, 540139 Targu Mures, Romania;
- Department ME1, Faculty of Medicine in English, George Emil Palade University of Medicine, Pharmacy, Science, and Technology of Targu Mures, 540139 Targu Mures, Romania
| | - Nicolae Neagu
- Department of Human Movement Sciences, George Emil Palade University of Medicine, Pharmacy, Science, and Technology of Targu Mures, 540139 Targu Mures, Romania;
| | - Silvia Teodorescu
- Department of Doctoral Studies, National University of Physical Education and Sports, 060057 Bucharest, Romania;
| | - Mihaela Apostu
- Department of Special Motor and Rehabilitation Medicine, National University of Physical Education and Sports, 060057 Bucharest, Romania; (M.A.); (C.P.)
| | - Corina Predescu
- Department of Special Motor and Rehabilitation Medicine, National University of Physical Education and Sports, 060057 Bucharest, Romania; (M.A.); (C.P.)
| | - Carmen Pârvu
- Faculty of Physical Education and Sports, “Dunărea de Jos” University, 63-65 Gării Street, 337347 Galati, Romania;
| | - Cristina Veres
- Department of Industrial Engineering and Management, University of Medicine, Pharmacy, Science and Technology of Targu Mures, 540142 Targu Mures, Romania;
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Carmona C, Sullivan JE, Arceo R, Drogos J, Besser S, Gutierrez S, Jeteric Z, Wyman J, Yao J. Development and Preliminary Validity Study of a Modified Version of the Upper Extremity Fugl-Meyer Assessment for Use in Telerehabilitation. J Neurol Phys Ther 2023; 47:208-216. [PMID: 37314323 PMCID: PMC10487354 DOI: 10.1097/npt.0000000000000447] [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] [Indexed: 06/15/2023]
Abstract
BACKGROUND/PURPOSE The Upper Extremity Fugl-Meyer Assessment (UEFMA, maximum 66) is widely used in clinics and research studies to examine poststroke upper extremity (UE) impairment. This study aimed to develop and provide pilot data to support the validity of a remote version of the UEFMA to examine UE impairment after stroke through telerehabilitation. METHODS Team members developed a remote version of the UEFMA for telerehabilitation (tUEFMA, maximum 44) using subscales II to IV and VII of the UEFMA. Twenty-two participants with moderate to severe arm impairment (UEFMA, median = 19) and chronic stroke (>1 year post) were evaluated using the UEFMA (face-to-face) and the tUEFMA (remotely). A prediction equation was used to identify the function to predict the UEFMA based on the tUEFMA. Intraclass correlation (ICC) was used to test the absolute agreement between the subscales included in the UEFMA and the tUEFMA, and between their 2 normalized total scores. RESULTS A strong and significant agreement was found between the total scores of the UEFMA and the projected value based on the tUEFMA (ICC = 0.79, P < 0.05). The ICC test also reported a good agreement in subscales II to IV and a poor agreement in subscale VII between the UEFMA and the tUEFMA using a real-time video link. DISCUSSION AND CONCLUSIONS The study findings suggest that the tUEFMA is a promising tool to remotely examine UE impairment in individuals with chronic stroke and moderate to severe arm impairment. Future research should evaluate additional psychometric properties and clinical utility of the tUEFMA across stroke participants with a broad range of arm impairments.Video Abstract available for more insights from the authors (see the Video, Supplemental Digital Content 1, available at: http://links.lww.com/JNPT/A441 ).
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Affiliation(s)
- Carolina Carmona
- Department of Physical Therapy and Human Movement Sciences, Northwestern University, Chicago, Illinois (C.C,. J.E.S., R.A., J.D., S.B., S.G., Z.J., J.W., J.Y.); and Northwestern University Interdepartmental Neuroscience, Northwestern University, and Department of Biomedical Engineering, Northwestern University, Chicago, Illinois (J.Y.)
| | - Jane E. Sullivan
- Department of Physical Therapy and Human Movement Sciences, Northwestern University, Chicago, Illinois (C.C,. J.E.S., R.A., J.D., S.B., S.G., Z.J., J.W., J.Y.); and Northwestern University Interdepartmental Neuroscience, Northwestern University, and Department of Biomedical Engineering, Northwestern University, Chicago, Illinois (J.Y.)
| | - Riegele Arceo
- Department of Physical Therapy and Human Movement Sciences, Northwestern University, Chicago, Illinois (C.C,. J.E.S., R.A., J.D., S.B., S.G., Z.J., J.W., J.Y.); and Northwestern University Interdepartmental Neuroscience, Northwestern University, and Department of Biomedical Engineering, Northwestern University, Chicago, Illinois (J.Y.)
| | - Justin Drogos
- Department of Physical Therapy and Human Movement Sciences, Northwestern University, Chicago, Illinois (C.C,. J.E.S., R.A., J.D., S.B., S.G., Z.J., J.W., J.Y.); and Northwestern University Interdepartmental Neuroscience, Northwestern University, and Department of Biomedical Engineering, Northwestern University, Chicago, Illinois (J.Y.)
| | - Sofie Besser
- Department of Physical Therapy and Human Movement Sciences, Northwestern University, Chicago, Illinois (C.C,. J.E.S., R.A., J.D., S.B., S.G., Z.J., J.W., J.Y.); and Northwestern University Interdepartmental Neuroscience, Northwestern University, and Department of Biomedical Engineering, Northwestern University, Chicago, Illinois (J.Y.)
| | - Susana Gutierrez
- Department of Physical Therapy and Human Movement Sciences, Northwestern University, Chicago, Illinois (C.C,. J.E.S., R.A., J.D., S.B., S.G., Z.J., J.W., J.Y.); and Northwestern University Interdepartmental Neuroscience, Northwestern University, and Department of Biomedical Engineering, Northwestern University, Chicago, Illinois (J.Y.)
| | - Zineyra Jeteric
- Department of Physical Therapy and Human Movement Sciences, Northwestern University, Chicago, Illinois (C.C,. J.E.S., R.A., J.D., S.B., S.G., Z.J., J.W., J.Y.); and Northwestern University Interdepartmental Neuroscience, Northwestern University, and Department of Biomedical Engineering, Northwestern University, Chicago, Illinois (J.Y.)
| | - James Wyman
- Department of Physical Therapy and Human Movement Sciences, Northwestern University, Chicago, Illinois (C.C,. J.E.S., R.A., J.D., S.B., S.G., Z.J., J.W., J.Y.); and Northwestern University Interdepartmental Neuroscience, Northwestern University, and Department of Biomedical Engineering, Northwestern University, Chicago, Illinois (J.Y.)
| | - Jun Yao
- Department of Physical Therapy and Human Movement Sciences, Northwestern University, Chicago, Illinois (C.C,. J.E.S., R.A., J.D., S.B., S.G., Z.J., J.W., J.Y.); and Northwestern University Interdepartmental Neuroscience, Northwestern University, and Department of Biomedical Engineering, Northwestern University, Chicago, Illinois (J.Y.)
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Wei S, Wu Z. The Application of Wearable Sensors and Machine Learning Algorithms in Rehabilitation Training: A Systematic Review. SENSORS (BASEL, SWITZERLAND) 2023; 23:7667. [PMID: 37765724 PMCID: PMC10537628 DOI: 10.3390/s23187667] [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: 08/05/2023] [Revised: 08/24/2023] [Accepted: 09/04/2023] [Indexed: 09/29/2023]
Abstract
The integration of wearable sensor technology and machine learning algorithms has significantly transformed the field of intelligent medical rehabilitation. These innovative technologies enable the collection of valuable movement, muscle, or nerve data during the rehabilitation process, empowering medical professionals to evaluate patient recovery and predict disease development more efficiently. This systematic review aims to study the application of wearable sensor technology and machine learning algorithms in different disease rehabilitation training programs, obtain the best sensors and algorithms that meet different disease rehabilitation conditions, and provide ideas for future research and development. A total of 1490 studies were retrieved from two databases, the Web of Science and IEEE Xplore, and finally 32 articles were selected. In this review, the selected papers employ different wearable sensors and machine learning algorithms to address different disease rehabilitation problems. Our analysis focuses on the types of wearable sensors employed, the application of machine learning algorithms, and the approach to rehabilitation training for different medical conditions. It summarizes the usage of different sensors and compares different machine learning algorithms. It can be observed that the combination of these two technologies can optimize the disease rehabilitation process and provide more possibilities for future home rehabilitation scenarios. Finally, the present limitations and suggestions for future developments are presented in the study.
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Affiliation(s)
- Suyao Wei
- College of Furnishings and Industrial Design, Nanjing Forestry University, Nanjing 210037, China
| | - Zhihui Wu
- College of Furnishings and Industrial Design, Nanjing Forestry University, Nanjing 210037, China
- Co-Innovation Center of Efficient Processing and Utilization of Forest Resources, Nanjing Forestry University, Nanjing 210037, China
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12
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Zamin SA, Tang K, Stevens EA, Howard M, Parker DM, Jiang X, Savitz S, Seals A, Shams S. aBnormal motION capture In aCute Stroke (BIONICS): A Low-Cost Tele-Evaluation Tool for Automated Assessment of Upper Extremity Function in Stroke Patients. Neurorehabil Neural Repair 2023; 37:591-602. [PMID: 37592867 PMCID: PMC10602593 DOI: 10.1177/15459683231184186] [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] [Indexed: 08/19/2023]
Abstract
BACKGROUND The incidence of stroke and stroke-related hemiparesis has been steadily increasing and is projected to become a serious social, financial, and physical burden on the aging population. Limited access to outpatient rehabilitation for these stroke survivors further deepens the healthcare issue and estranges the stroke patient demographic in rural areas. However, new advances in motion detection deep learning enable the use of handheld smartphone cameras for body tracking, offering unparalleled levels of accessibility. METHODS In this study we want to develop an automated method for evaluation of a shortened variant of the Fugl-Meyer assessment, the standard stroke rehabilitation scale describing upper extremity motor function. We pair this technology with a series of machine learning models, including different neural network structures and an eXtreme Gradient Boosting model, to score 16 of 33 (49%) Fugl-Meyer item activities. RESULTS In this observational study, 45 acute stroke patients completed at least 1 recorded Fugl-Meyer assessment for the training of the auto-scorers, which yielded average accuracies ranging from 78.1% to 82.7% item-wise. CONCLUSION In this study, an automated method was developed for the evaluation of a shortened variant of the Fugl-Meyer assessment, the standard stroke rehabilitation scale describing upper extremity motor function. This novel method is demonstrated with potential to conduct telehealth rehabilitation evaluations and assessments with accuracy and availability.
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Affiliation(s)
- Syed A Zamin
- Louisiana State University Health New Orleans School of Medicine, LA
| | | | - Emily A Stevens
- Department of Neurology, McGovern School of Medicine, UTHealth, TX
| | - Melissa Howard
- Department of Neurology, McGovern School of Medicine, UTHealth, TX
- Institute for Stroke and Cerebrovascular Disease
| | - Dorothea M Parker
- Department of Neurology, McGovern School of Medicine, UTHealth, TX
- Institute for Stroke and Cerebrovascular Disease
| | - Xiaoqian Jiang
- School of Biomedical Informatics, UTHealth, TX
- Institute for Stroke and Cerebrovascular Disease
| | - Sean Savitz
- Department of Neurology, McGovern School of Medicine, UTHealth, TX
- Institute for Stroke and Cerebrovascular Disease
| | | | - Shayan Shams
- School of Biomedical Informatics, UTHealth, TX
- Institute for Stroke and Cerebrovascular Disease
- Applied Data Science Department, San Jose State University, CA
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13
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Bandara DSV, Arata J. Active Range of Motion Measurement System Using an Optical Sensor to Evaluate Hand Functions. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38083126 DOI: 10.1109/embc40787.2023.10340729] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Impairment of hand function greatly affects the independence of a human being. Proper assessment of hand function before and after any treatment for functional restoration is important to decide better treatment strategies. Despite traditional techniques of hand function evaluation, individual joint based assessment is vital to better track the details of the hand function. Current clinical assessments with goniometers are labour intensive, cumbersome and highly depend on the skill level of the practitioner. This study introduces an active range of motion (AROM) measurement system to measure individual range of motion of finger joints using an optical sensor. The proposed method is highly efficient, and the results demonstrated that the measurements are instant, repeatable and can successfully be employed in a clinical setup for patient evaluations.Clinical Relevance-Closely working with clinician to develop rehabilitation systems, we have identified that the assessment of patient hand functions is time consuming, and accuracy can be depended on the skill level of the practitioner in measuring joint range of motions (ROM). System introduced in this study can measure the joint AROMs instantly and independent of the practitioner's skill level and hence can provide a reliable, repeatable assessment of patient's hand function.
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14
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Razfar N, Kashef R, Mohammadi F. Automatic Post-Stroke Severity Assessment Using Novel Unsupervised Consensus Learning for Wearable and Camera-Based Sensor Datasets. SENSORS (BASEL, SWITZERLAND) 2023; 23:5513. [PMID: 37420682 DOI: 10.3390/s23125513] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Revised: 05/30/2023] [Accepted: 06/02/2023] [Indexed: 07/09/2023]
Abstract
Stroke survivors often suffer from movement impairments that significantly affect their daily activities. The advancements in sensor technology and IoT have provided opportunities to automate the assessment and rehabilitation process for stroke survivors. This paper aims to provide a smart post-stroke severity assessment using AI-driven models. With the absence of labelled data and expert assessment, there is a research gap in providing virtual assessment, especially for unlabeled data. Inspired by the advances in consensus learning, in this paper, we propose a consensus clustering algorithm, PSA-NMF, that combines various clusterings into one united clustering, i.e., cluster consensus, to produce more stable and robust results compared to individual clustering. This paper is the first to investigate severity level using unsupervised learning and trunk displacement features in the frequency domain for post-stroke smart assessment. Two different methods of data collection from the U-limb datasets-the camera-based method (Vicon) and wearable sensor-based technology (Xsens)-were used. The trunk displacement method labelled each cluster based on the compensatory movements that stroke survivors employed for their daily activities. The proposed method uses the position and acceleration data in the frequency domain. Experimental results have demonstrated that the proposed clustering method that uses the post-stroke assessment approach increased the evaluation metrics such as accuracy and F-score. These findings can lead to a more effective and automated stroke rehabilitation process that is suitable for clinical settings, thus improving the quality of life for stroke survivors.
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Affiliation(s)
- Najmeh Razfar
- Department of Electrical, Computer, and Biomedical Engineering, Faculty of Engineering and Architectural Science, Toronto Metropolitan University, Toronto, ON M5B 2K3, Canada
| | - Rasha Kashef
- Department of Electrical, Computer, and Biomedical Engineering, Faculty of Engineering and Architectural Science, Toronto Metropolitan University, Toronto, ON M5B 2K3, Canada
| | - Farah Mohammadi
- Department of Electrical, Computer, and Biomedical Engineering, Faculty of Engineering and Architectural Science, Toronto Metropolitan University, Toronto, ON M5B 2K3, Canada
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15
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Ueyama Y, Takebayashi T, Takeuchi K, Yamazaki M, Hanada K, Okita Y, Shimada S. Attempt to Make the Upper-Limb Item of Objective Fugl-Meyer Assessment Using 9-Axis Motion Sensors. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23115213. [PMID: 37299941 DOI: 10.3390/s23115213] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Revised: 05/11/2023] [Accepted: 05/28/2023] [Indexed: 06/12/2023]
Abstract
The Fugl-Meyer Assessment (FMA) has been used as a functional assessment of upper-limb function in stroke patients. This study aimed to create a more objective and standardized evaluation based on an FMA of the upper-limb items. A total of 30 first-ever stroke patients (65.3 ± 10.3 years old) and 15 healthy participants (35.4 ± 13.4 years old) admitted to Itami Kousei Neurosurgical Hospital were included. A nine-axis motion sensor was attached to the participants, and the joint angles of 17 upper-limb items (excluding fingers) and 23 FMA upper-limb items (excluding reflexes and fingers) were measured. From the measurement results, we analyzed the time-series data of each movement and obtained the correlation between the joint angles of each part. Discriminant analysis showed that 17 and 6 items had a concordance rate of ≥80% (80.0~95.6%) and <80% (64.4~75.6%), respectively. In the multiple regression analysis of continuous variables of FMA, a good regression model was obtained to predict the FMA with three to five joint angles. The discriminant analysis for 17 evaluation items suggests the possibility of roughly calculating FMA scores from joint angles.
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Affiliation(s)
- Yusuke Ueyama
- Department of Occupational Therapy and Rehabilitation, Itami Kousei Neurosurgical Hospital, Itami City 664-0028, Japan
| | - Takashi Takebayashi
- Department of Occupational Therapy, School of Comprehensive Rehabilitation, Osaka Metropolitan University, Habikino City 558-8555, Japan
| | - Kenta Takeuchi
- Department of Occupational Therapy and Rehabilitation, Itami Kousei Neurosurgical Hospital, Itami City 664-0028, Japan
| | - Makoto Yamazaki
- Department of Physical Medicine and Rehabilitation, Itami Kousei Neurosurgical Hospital, Itami City 664-0028, Japan
| | - Keisuke Hanada
- Faculty of Rehabilitation, Shijonawate Gakuen University, Daitou City 574-0011, Japan
| | - Yuho Okita
- Faculty of Health, Arts and Design, Swinburne University of Technology, Melbourne, VIC 3122, Australia
| | - Shinichi Shimada
- Department of Neurosurgery, Itami Kousei Neurosurgical Hospital, Itami City 664-0028, Japan
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Facciorusso S, Spina S, Reebye R, Turolla A, Calabrò RS, Fiore P, Santamato A. Sensor-Based Rehabilitation in Neurological Diseases: A Bibliometric Analysis of Research Trends. Brain Sci 2023; 13:brainsci13050724. [PMID: 37239196 DOI: 10.3390/brainsci13050724] [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: 03/03/2023] [Revised: 04/20/2023] [Accepted: 04/24/2023] [Indexed: 05/28/2023] Open
Abstract
BACKGROUND As the field of sensor-based rehabilitation continues to expand, it is important to gain a comprehensive understanding of its current research landscape. This study aimed to conduct a bibliometric analysis to identify the most influential authors, institutions, journals, and research areas in this field. METHODS A search of the Web of Science Core Collection was performed using keywords related to sensor-based rehabilitation in neurological diseases. The search results were analyzed with CiteSpace software using bibliometric techniques, including co-authorship analysis, citation analysis, and keyword co-occurrence analysis. RESULTS Between 2002 and 2022, 1103 papers were published on the topic, with slow growth from 2002 to 2017, followed by a rapid increase from 2018 to 2022. The United States was the most active country, while the Swiss Federal Institute of Technology had the highest number of publications among institutions. Sensors published the most papers. The top keywords included rehabilitation, stroke, and recovery. The clusters of keywords comprised machine learning, specific neurological conditions, and sensor-based rehabilitation technologies. CONCLUSIONS This study provides a comprehensive overview of the current state of sensor-based rehabilitation research in neurological diseases, highlighting the most influential authors, journals, and research themes. The findings can help researchers and practitioners to identify emerging trends and opportunities for collaboration and can inform the development of future research directions in this field.
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Affiliation(s)
- Salvatore Facciorusso
- Department of Medical and Surgical Specialties and Dentistry, University of Campania "Luigi Vanvitelli", 80138 Naples, Italy
- Spasticity and Movement Disorders "ReSTaRt", Unit Physical Medicine and Rehabilitation Section, Department of Medical and Surgical Sciences, University of Foggia, 71122 Foggia, Italy
| | - Stefania Spina
- Spasticity and Movement Disorders "ReSTaRt", Unit Physical Medicine and Rehabilitation Section, Department of Medical and Surgical Sciences, University of Foggia, 71122 Foggia, Italy
| | - Rajiv Reebye
- Division of Physical Medicine and Rehabilitation, Faculty of Medicine, University of British Columbia, Vancouver, BC V5Z 2G9, Canada
| | - Andrea Turolla
- Department of Biomedical and Neuromotor Sciences-DIBINEM, Alma Mater Studiorum Università di Bologna, 40138 Bologna, Italy
| | | | - Pietro Fiore
- Neurorehabilitation Unit, Institute of Bari, Istituti Clinici Scientifici Maugeri IRCCS, 70124 Bari, Italy
| | - Andrea Santamato
- Spasticity and Movement Disorders "ReSTaRt", Unit Physical Medicine and Rehabilitation Section, Department of Medical and Surgical Sciences, University of Foggia, 71122 Foggia, Italy
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17
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Lin S, Wang D, Sang H, Xiao H, Yan K, Wang D, Zhang Y, Yi L, Shao G, Shao Z, Yang A, Zhang L, Sun J. Predicting poststroke dyskinesia with resting-state functional connectivity in the motor network. NEUROPHOTONICS 2023; 10:025001. [PMID: 37025568 PMCID: PMC10072005 DOI: 10.1117/1.nph.10.2.025001] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Accepted: 03/17/2023] [Indexed: 06/19/2023]
Abstract
SIGNIFICANCE Motor function evaluation is essential for poststroke dyskinesia rehabilitation. Neuroimaging techniques combined with machine learning help decode a patient's functional status. However, more research is needed to investigate how individual brain function information predicts the dyskinesia degree of stroke patients. AIM We investigated stroke patients' motor network reorganization and proposed a machine learning-based method to predict the patients' motor dysfunction. APPROACH Near-infrared spectroscopy (NIRS) was used to measure hemodynamic signals of the motor cortex in the resting state (RS) from 11 healthy subjects and 31 stroke patients, 15 with mild dyskinesia (Mild), and 16 with moderate-to-severe dyskinesia (MtS). The graph theory was used to analyze the motor network characteristics. RESULTS The small-world properties of the motor network were significantly different between groups: (1) clustering coefficient, local efficiency, and transitivity: MtS > Mild > Healthy and (2) global efficiency: MtS < Mild < Healthy. These four properties linearly correlated with patients' Fugl-Meyer Assessment scores. Using the small-world properties as features, we constructed support vector machine (SVM) models that classified the three groups of subjects with an accuracy of 85.7%. CONCLUSIONS Our results show that NIRS, RS functional connectivity, and SVM together constitute an effective method for assessing the poststroke dyskinesia degree at the individual level.
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Affiliation(s)
- Shuoshu Lin
- Foshan University, School of Mechatronic Engineering and Automation, Foshan, China
| | - Dan Wang
- Beijing Rehabilitation Hospital of Capital Medical University, Department of Traditional Chinese Medicine, Beijing, China
| | - Haojun Sang
- Chinese Institute for Brain Research, Beijing, China
| | - Hongjun Xiao
- Foshan University, School of Mechatronic Engineering and Automation, Foshan, China
| | - Kecheng Yan
- Foshan University, School of Mechatronic Engineering and Automation, Foshan, China
| | - Dongyang Wang
- Foshan University, School of Mechatronic Engineering and Automation, Foshan, China
| | - Yizheng Zhang
- Foshan University, School of Mechatronic Engineering and Automation, Foshan, China
| | - Li Yi
- Foshan University, School of Mechatronic Engineering and Automation, Foshan, China
| | - Guangjian Shao
- Foshan University, School of Mechatronic Engineering and Automation, Foshan, China
| | - Zhiyong Shao
- Foshan University, School of Mechatronic Engineering and Automation, Foshan, China
| | - Aoran Yang
- Beijing Rehabilitation Hospital of Capital Medical University, Department of Traditional Chinese Medicine, Beijing, China
| | - Lei Zhang
- Chinese Institute for Brain Research, Beijing, China
- Capital Medical University, School of Biomedical Engineering, Beijing, China
| | - Jinyan Sun
- Foshan University, School of Medicine, Foshan, China
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18
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Chen X, Pan X, Ji T, Yu S, Sun Y. Fusion classification of stroke patients' biosignals by weighted cross-validation-based feature selection (W-CVFS) method. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104282] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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19
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Kadirvelu B, Gavriel C, Nageshwaran S, Chan JPK, Nethisinghe S, Athanasopoulos S, Ricotti V, Voit T, Giunti P, Festenstein R, Faisal AA. A wearable motion capture suit and machine learning predict disease progression in Friedreich's ataxia. Nat Med 2023; 29:86-94. [PMID: 36658420 PMCID: PMC9873563 DOI: 10.1038/s41591-022-02159-6] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Accepted: 11/29/2022] [Indexed: 01/21/2023]
Abstract
Friedreich's ataxia (FA) is caused by a variant of the Frataxin (FXN) gene, leading to its downregulation and progressively impaired cardiac and neurological function. Current gold-standard clinical scales use simplistic behavioral assessments, which require 18- to 24-month-long trials to determine if therapies are beneficial. Here we captured full-body movement kinematics from patients with wearable sensors, enabling us to define digital behavioral features based on the data from nine FA patients (six females and three males) and nine age- and sex-matched controls, who performed the 8-m walk (8-MW) test and 9-hole peg test (9 HPT). We used machine learning to combine these features to longitudinally predict the clinical scores of the FA patients, and compared these with two standard clinical assessments, Spinocerebellar Ataxia Functional Index (SCAFI) and Scale for the Assessment and Rating of Ataxia (SARA). The digital behavioral features enabled longitudinal predictions of personal SARA and SCAFI scores 9 months into the future and were 1.7 and 4 times more precise than longitudinal predictions using only SARA and SCAFI scores, respectively. Unlike the two clinical scales, the digital behavioral features accurately predicted FXN gene expression levels for each FA patient in a cross-sectional manner. Our work demonstrates how data-derived wearable biomarkers can track personal disease trajectories and indicates the potential of such biomarkers for substantially reducing the duration or size of clinical trials testing disease-modifying therapies and for enabling behavioral transcriptomics.
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Affiliation(s)
- Balasundaram Kadirvelu
- Brain & Behaviour Lab, Department of Bioengineering, Imperial College London, London, UK
- Brain & Behaviour Lab, Department of Computing, Imperial College London, London, UK
| | - Constantinos Gavriel
- Brain & Behaviour Lab, Department of Bioengineering, Imperial College London, London, UK
- Brain & Behaviour Lab, Department of Computing, Imperial College London, London, UK
| | - Sathiji Nageshwaran
- Epigenetic Mechanisms and Disease Group, Department of Brain Sciences, Imperial College London, London, UK
| | - Jackson Ping Kei Chan
- Epigenetic Mechanisms and Disease Group, Department of Brain Sciences, Imperial College London, London, UK
| | - Suran Nethisinghe
- NIHR Great Ormond Street Hospital Biomedical Research Centre, UCL Great Ormond Street Institute of Child Health, London, UK
| | - Stavros Athanasopoulos
- Epigenetic Mechanisms and Disease Group, Department of Brain Sciences, Imperial College London, London, UK
| | - Valeria Ricotti
- NIHR Great Ormond Street Hospital Biomedical Research Centre, UCL Great Ormond Street Institute of Child Health, London, UK
- Great Ormond Street Hospital for Children, NHS Foundation Trust, London, UK
| | - Thomas Voit
- NIHR Great Ormond Street Hospital Biomedical Research Centre, UCL Great Ormond Street Institute of Child Health, London, UK
- Great Ormond Street Hospital for Children, NHS Foundation Trust, London, UK
| | - Paola Giunti
- Institute of Neurology, UCL, National Hospital for Neurology and Neurosurgery (UCLH), London, UK
| | - Richard Festenstein
- Epigenetic Mechanisms and Disease Group, Department of Brain Sciences, Imperial College London, London, UK
- Institute of Neurology, UCL, National Hospital for Neurology and Neurosurgery (UCLH), London, UK
- MRC London Institute of Medical Sciences, London, UK
| | - A Aldo Faisal
- Brain & Behaviour Lab, Department of Bioengineering, Imperial College London, London, UK.
- Brain & Behaviour Lab, Department of Computing, Imperial College London, London, UK.
- MRC London Institute of Medical Sciences, London, UK.
- Behaviour Analytics Lab, Data Science Institute, Imperial College London, London, UK.
- Brain & Behaviour Lab, Institute for Artificial and Human Intelligence, University of Bayreuth, Bayreuth, Germany.
- Chair in Digital Health, Faculty of Life Sciences, University of Bayreuth, Bayreuth, Germany.
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20
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Guo L, Zhang B, Wang J, Wu Q, Li X, Zhou L, Xiong D. Wearable Intelligent Machine Learning Rehabilitation Assessment for Stroke Patients Compared with Clinician Assessment. J Clin Med 2022; 11:jcm11247467. [PMID: 36556083 PMCID: PMC9783419 DOI: 10.3390/jcm11247467] [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: 11/21/2022] [Revised: 12/10/2022] [Accepted: 12/13/2022] [Indexed: 12/23/2022] Open
Abstract
In order to solve the shortcomings of the current clinical scale assessment for stroke patients, such as excessive time consumption, strong subjectivity, and coarse grading, this study designed an intelligent rehabilitation assessment system based on wearable devices and a machine learning algorithm and explored the effectiveness of the system in assessing patients’ rehabilitation outcomes. The accuracy and effectiveness of the intelligent rehabilitation assessment system were verified by comparing the consistency and time between the designed intelligent rehabilitation assessment system scores and the clinical Fugl−Meyer assessment (FMA) scores. A total of 120 stroke patients from two hospitals participated as volunteers in the trial study, and statistical analyses of the two assessment methods were performed. The results showed that the R2 of the total score regression analysis for both methods was 0.9667, 95% CI 0.92−0.98, p < 0.001, and the mean of the deviation was 0.30, 95% CI 0.57−1.17. The percentages of deviations/relative deviations falling within the mean ± 1.96 SD of deviations/relative deviations were 92.50% and 95.83%, respectively. The mean time for system assessment was 35.00% less than that for clinician assessment, p < 0.05. Therefore, wearable intelligent machine learning rehabilitation assessment has a strong and significant correlation with clinician assessment, and the time spent is significantly reduced, which provides an accurate, objective, and effective solution for clinical rehabilitation assessment and remote rehabilitation without the presence of physicians.
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Affiliation(s)
- Liquan Guo
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230052, China
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
| | - Bochao Zhang
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230052, China
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
| | - Jiping Wang
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230052, China
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
| | - Qunqiang Wu
- Department of Rehabilitation Medicine, Tangdu Hospital Airforce Medicine University, Xi’an 710032, China
| | - Xinming Li
- Department of Rehabilitation Medicine, Xi’an Gaoxin Hospital, Xi’an 710065, China
| | - Linfu Zhou
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
- Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital, Nanjing Medical University, Nanjing 210029, China
- Correspondence: (L.Z.); (D.X.); Tel.: +86-18662576055 (D.X.)
| | - Daxi Xiong
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230052, China
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
- Correspondence: (L.Z.); (D.X.); Tel.: +86-18662576055 (D.X.)
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21
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Li Y, Li C, Shu X, Sheng X, Jia J, Zhu X. A Novel Automated RGB-D Sensor-Based Measurement of Voluntary Items of the Fugl-Meyer Assessment for Upper Extremity: A Feasibility Study. Brain Sci 2022; 12:brainsci12101380. [PMID: 36291314 PMCID: PMC9599696 DOI: 10.3390/brainsci12101380] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Revised: 10/02/2022] [Accepted: 10/05/2022] [Indexed: 11/19/2022] Open
Abstract
Motor function assessment is essential for post-stroke rehabilitation, while the requirement for professional therapists’ participation in current clinical assessment limits its availability to most patients. By means of sensors that collect the motion data and algorithms that conduct assessment based on such data, an automated system can be built to optimize the assessment process, benefiting both patients and therapists. To this end, this paper proposed an automated Fugl-Meyer Assessment (FMA) upper extremity system covering all 30 voluntary items of the scale. RGBD sensors, together with force sensing resistor sensors were used to collect the patients’ motion information. Meanwhile, both machine learning and rule-based logic classification were jointly employed for assessment scoring. Clinical validation on 20 hemiparetic stroke patients suggests that this system is able to generate reliable FMA scores. There is an extremely high correlation coefficient (r = 0.981, p < 0.01) with that yielded by an experienced therapist. This study offers guidance and feasible solutions to a complete and independent automated assessment system.
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Affiliation(s)
- Yue Li
- State Key Laboratory of Machanical System and Vibration, Shanghai Jiao Tong University, Shanghai 200040, China
| | - Chong Li
- Department of Rehabilitation Medicine, Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Xiaokang Shu
- State Key Laboratory of Machanical System and Vibration, Shanghai Jiao Tong University, Shanghai 200040, China
| | - Xinjun Sheng
- State Key Laboratory of Machanical System and Vibration, Shanghai Jiao Tong University, Shanghai 200040, China
- Correspondence: (X.S.); (J.J.); Tel.: +86-021-34206547 (X.S.); +86-13617722357 (J.J.)
| | - Jie Jia
- Department of Rehabilitation Medicine, Huashan Hospital, Fudan University, Shanghai 200040, China
- Correspondence: (X.S.); (J.J.); Tel.: +86-021-34206547 (X.S.); +86-13617722357 (J.J.)
| | - Xiangyang Zhu
- State Key Laboratory of Machanical System and Vibration, Shanghai Jiao Tong University, Shanghai 200040, China
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22
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Kim GJ, Parnandi A, Eva S, Schambra H. The use of wearable sensors to assess and treat the upper extremity after stroke: a scoping review. Disabil Rehabil 2022; 44:6119-6138. [PMID: 34328803 PMCID: PMC9912423 DOI: 10.1080/09638288.2021.1957027] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2021] [Revised: 06/25/2021] [Accepted: 07/13/2021] [Indexed: 01/27/2023]
Abstract
PURPOSE To address the gap in the literature and clarify the expanding role of wearable sensor data in stroke rehabilitation, we summarized the methods for upper extremity (UE) sensor-based assessment and sensor-based treatment. MATERIALS AND METHODS The guideline outlined by the preferred reporting items for systematic reviews and meta-analysis extension for scoping reviews was used to complete this scoping review. Information pertaining to participant demographics, sensory information, data collection, data processing, data analysis, and study results were extracted from the studies for analysis and synthesis. RESULTS We included 43 articles in the final review. We organized the results into assessment and treatment categories. The included articles used wearable sensors to identify UE functional motion, categorize motor impairment/activity limitation, and quantify real-world use. Wearable sensors were also used to augment UE training by triggering sensory cues or providing instructional feedback about the affected UE. CONCLUSIONS Sensors have the potential to greatly expand assessment and treatment beyond traditional clinic-based approaches. This capability could support the quantification of rehabilitation dose, the nuanced assessment of impairment and activity limitation, the characterization of daily UE use patterns in real-world settings, and augment UE training adherence for home-based rehabilitation.IMPLICATIONS FOR REHABILITATIONSensor data have been used to assess UE functional motion, motor impairment/activity limitation, and real-world use.Sensor-assisted treatment approaches are emerging, and may be a promising tool to augment UE adherence in home-based rehabilitation.Wearable sensors may extend our ability to objectively assess UE motion beyond supervised clinical settings, and into home and community settings.
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Affiliation(s)
- Grace J. Kim
- Department of Occupational Therapy, Steinhardt School of Culture, Education and Human Development, New York University, New York, NY, USA
| | - Avinash Parnandi
- Department of Neurology, NYU Langone Grossman School of Medicine, New York, NY, USA
| | - Sharon Eva
- Department of Occupational Therapy, Nova Southeastern University, Fort Lauderdale, FL, USA
| | - Heidi Schambra
- Department of Neurology, NYU Langone Grossman School of Medicine, New York, NY, USA
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Gopal A, Hsu WY, Allen DD, Bove R. Remote Assessments of Hand Function in Neurological Disorders: Systematic Review. JMIR Rehabil Assist Technol 2022; 9:e33157. [PMID: 35262502 PMCID: PMC8943610 DOI: 10.2196/33157] [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: 08/25/2021] [Revised: 01/17/2022] [Accepted: 01/26/2022] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Loss of fine motor skills is observed in many neurological diseases, and remote monitoring assessments can aid in early diagnosis and intervention. Hand function can be regularly assessed to monitor loss of fine motor skills in people with central nervous system disorders; however, there are challenges to in-clinic assessments. Remotely assessing hand function could facilitate monitoring and supporting of early diagnosis and intervention when warranted. OBJECTIVE Remote assessments can facilitate the tracking of limitations, aiding in early diagnosis and intervention. This study aims to systematically review existing evidence regarding the remote assessment of hand function in populations with chronic neurological dysfunction. METHODS PubMed and MEDLINE, CINAHL, Web of Science, and Embase were searched for studies that reported remote assessment of hand function (ie, outside of traditional in-person clinical settings) in adults with chronic central nervous system disorders. We excluded studies that included participants with orthopedic upper limb dysfunction or used tools for intervention and treatment. We extracted data on the evaluated hand function domains, validity and reliability, feasibility, and stage of development. RESULTS In total, 74 studies met the inclusion criteria for Parkinson disease (n=57, 77% studies), stroke (n=9, 12%), multiple sclerosis (n=6, 8%), spinal cord injury (n=1, 1%), and amyotrophic lateral sclerosis (n=1, 1%). Three assessment modalities were identified: external device (eg, wrist-worn accelerometer), smartphone or tablet, and telerehabilitation. The feasibility and overall participant acceptability were high. The most common hand function domains assessed included finger tapping speed (fine motor control and rigidity), hand tremor (pharmacological and rehabilitation efficacy), and finger dexterity (manipulation of small objects required for daily tasks) and handwriting (coordination). Although validity and reliability data were heterogeneous across studies, statistically significant correlations with traditional in-clinic metrics were most commonly reported for telerehabilitation and smartphone or tablet apps. The most readily implementable assessments were smartphone or tablet-based. CONCLUSIONS The findings show that remote assessment of hand function is feasible in neurological disorders. Although varied, the assessments allow clinicians to objectively record performance in multiple hand function domains, improving the reliability of traditional in-clinic assessments. Remote assessments, particularly via telerehabilitation and smartphone- or tablet-based apps that align with in-clinic metrics, facilitate clinic to home transitions, have few barriers to implementation, and prompt remote identification and treatment of hand function impairments.
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Affiliation(s)
- Arpita Gopal
- Weill Institute of Neurosciences, University of California San Francisco, San Francisco, CA, United States
| | - Wan-Yu Hsu
- Weill Institute of Neurosciences, University of California San Francisco, San Francisco, CA, United States
| | - Diane D Allen
- Department of Physical Therapy and Rehabilitation Science, University of California San Francisco/San Francisco State University, San Francisco, CA, United States
| | - Riley Bove
- Weill Institute of Neurosciences, University of California San Francisco, San Francisco, CA, United States
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24
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Wearable sensors and machine learning in post-stroke rehabilitation assessment: A systematic review. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103197] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
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25
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Dutta D, Aruchamy S, Mandal S, Sen S. Poststroke Grasp Ability Assessment using an Intelligent Data Glove based on Action Research Arm Test: Development, Algorithms, and Experiments. IEEE Trans Biomed Eng 2021; 69:945-954. [PMID: 34495824 DOI: 10.1109/tbme.2021.3110432] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Growing impact of poststroke upper extremity (UE) functional limitations entails newer dimensions in assessment methodologies. This has compelled researchers to think way beyond traditional stroke assessment scales during the out-patient rehabilitation phase. In concurrence with this, sensor-driven quantitative evaluation of poststroke UE functional limitations has become a fertile field of research. Here, we have emphasized an instrumented wearable for systematic monitoring of stroke patients with right-hemiparesis for evaluating their grasp abilities deploying intelligent algorithms. An instrumented glove housing 6 flex sensors, 3 force sensors, and a motion processing unit was developed to administer 19 activities of Action Research Arm Test (ARAT) while experimenting on 20 voluntarily participating subjects. After necessary signal conditioning, meaningful features were extracted, and subsequently the most appropriate ones were selected using the ReliefF algorithm. An optimally tuned support vector classifier was employed to classify patients with different degrees of disability and an accuracy of 92% was achieved supported by a high area under the receiver operating characteristic score. Furthermore, selected features could provide additional information that revealed the causes of grasp limitations. This would assist physicians in planning more effective poststroke rehabilitation strategies. Results of the one-way ANOVA test conducted on actual and predicted ARAT scores of the subjects indicated remarkable prospects of the proposed glove-based method in poststroke grasp ability assessment and rehabilitation.
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Lee SH, Hwang YJ, Lee HJ, Kim YH, Ogrinc M, Burdet E, Kim JH. Proof-of-Concept of a Sensor-Based Evaluation Method for Better Sensitivity of Upper-Extremity Motor Function Assessment. SENSORS (BASEL, SWITZERLAND) 2021; 21:5926. [PMID: 34502816 PMCID: PMC8434647 DOI: 10.3390/s21175926] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Revised: 08/20/2021] [Accepted: 08/27/2021] [Indexed: 11/18/2022]
Abstract
In rehabilitation, the Fugl-Meyer assessment (FMA) is a typical clinical instrument to assess upper-extremity motor function of stroke patients, but it cannot measure fine changes of motor function (both in recovery and deterioration) due to its limited sensitivity. This paper introduces a sensor-based automated FMA system that addresses this limitation with a continuous rating algorithm. The system consists of a depth sensor (Kinect V2) and an algorithm to rate the continuous FM scale based on fuzzy inference. Using a binary logic based classification method developed from a linguistic scoring guideline of FMA, we designed fuzzy input/output variables, fuzzy rules, membership functions, and a defuzzification method for several representative FMA tests. A pilot trial with nine stroke patients was performed to test the feasibility of the proposed approach. The continuous FM scale from the proposed algorithm exhibited a high correlation with the clinician rated scores and the results showed the possibility of more sensitive upper-extremity motor function assessment.
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Affiliation(s)
| | - Ye-Ji Hwang
- School of Mechanical Engineering, Sungkyunkwan University, Suwon 16419, Korea;
| | - Hwang-Jae Lee
- Center for Prevention & Rehabilitation, Heart Vascular and Stroke, Samsung Medical Center, Department of Physical and Rehabilitation Medicine, Sungkyunkwan University School of Medicine, Seoul 06351, Korea; (H.-J.L.); (Y.-H.K.)
| | - Yun-Hee Kim
- Center for Prevention & Rehabilitation, Heart Vascular and Stroke, Samsung Medical Center, Department of Physical and Rehabilitation Medicine, Sungkyunkwan University School of Medicine, Seoul 06351, Korea; (H.-J.L.); (Y.-H.K.)
| | - Matjaž Ogrinc
- Department of Bioengineering, Imperial College London, London SW72AZ, UK; (M.O.); (E.B.)
- GripAble Limited, Thornton House, 39 Thornton Road, London, SW19 4NQ, UK
| | - Etienne Burdet
- Department of Bioengineering, Imperial College London, London SW72AZ, UK; (M.O.); (E.B.)
| | - Jong-Hyun Kim
- School of Mechanical Engineering, Sungkyunkwan University, Suwon 16419, Korea;
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Li Q, Liu Y, Zhu J, Chen Z, Liu L, Yang S, Zhu G, Zhu B, Li J, Jin R, Tao J, Chen L. Upper-Limb Motion Recognition Based on Hybrid Feature Selection: Algorithm Development and Validation. JMIR Mhealth Uhealth 2021; 9:e24402. [PMID: 34473067 PMCID: PMC8446846 DOI: 10.2196/24402] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2020] [Revised: 04/30/2021] [Accepted: 07/15/2021] [Indexed: 02/05/2023] Open
Abstract
Background For rehabilitation training systems, it is essential to automatically record and recognize exercises, especially when more than one type of exercise is performed without a predefined sequence. Most motion recognition methods are based on feature engineering and machine learning algorithms. Time-domain and frequency-domain features are extracted from original time series data collected by sensor nodes. For high-dimensional data, feature selection plays an important role in improving the performance of motion recognition. Existing feature selection methods can be categorized into filter and wrapper methods. Wrapper methods usually achieve better performance than filter methods; however, in most cases, they are computationally intensive, and the feature subset obtained is usually optimized only for the specific learning algorithm. Objective This study aimed to provide a feature selection method for motion recognition of upper-limb exercises and improve the recognition performance. Methods Motion data from 5 types of upper-limb exercises performed by 21 participants were collected by a customized inertial measurement unit (IMU) node. A total of 60 time-domain and frequency-domain features were extracted from the original sensor data. A hybrid feature selection method by combining filter and wrapper methods (FESCOM) was proposed to eliminate irrelevant features for motion recognition of upper-limb exercises. In the filter stage, candidate features were first selected from the original feature set according to the significance for motion recognition. In the wrapper stage, k-nearest neighbors (kNN), Naïve Bayes (NB), and random forest (RF) were evaluated as the wrapping components to further refine the features from the candidate feature set. The performance of the proposed FESCOM method was verified using experiments on motion recognition of upper-limb exercises and compared with the traditional wrapper method. Results Using kNN, NB, and RF as the wrapping components, the classification error rates of the proposed FESCOM method were 1.7%, 8.9%, and 7.4%, respectively, and the feature selection time in each iteration was 13 seconds, 71 seconds, and 541 seconds, respectively. Conclusions The experimental results demonstrated that, in the case of 5 motion types performed by 21 healthy participants, the proposed FESCOM method using kNN and NB as the wrapping components achieved better recognition performance than the traditional wrapper method. The FESCOM method dramatically reduces the search time in the feature selection process. The results also demonstrated that the optimal number of features depends on the classifier. This approach serves to improve feature selection and classification algorithm selection for upper-limb motion recognition based on wearable sensor data, which can be extended to motion recognition of more motion types and participants.
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Affiliation(s)
- Qiaoqin Li
- Knowledge and Data Engineering Laboratory of Chinese Medicine, School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Yongguo Liu
- Knowledge and Data Engineering Laboratory of Chinese Medicine, School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Jiajing Zhu
- Knowledge and Data Engineering Laboratory of Chinese Medicine, School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Zhi Chen
- Knowledge and Data Engineering Laboratory of Chinese Medicine, School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Lang Liu
- Knowledge and Data Engineering Laboratory of Chinese Medicine, School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Shangming Yang
- Knowledge and Data Engineering Laboratory of Chinese Medicine, School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Guanyi Zhu
- College of Electrical and Information Engineering, Hunan University, Changsha, China
| | - Bin Zhu
- Chengdu Chronic Diseases Hospital, Chengdu, China
| | - Juan Li
- College of Health Preservation and Rehabilitation, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Rongjiang Jin
- College of Health Preservation and Rehabilitation, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Jing Tao
- College of Rehabilitation Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou, China
| | - Lidian Chen
- College of Rehabilitation Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou, China
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Hooyman A, Talboom JS, DeBoth MD, Ryan L, Huentelman M, Schaefer SY. Remote, Unsupervised Functional Motor Task Evaluation in Older Adults across the United States Using the MindCrowd Electronic Cohort. Dev Neuropsychol 2021; 46:435-446. [PMID: 34612107 PMCID: PMC8671381 DOI: 10.1080/87565641.2021.1979005] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Revised: 09/01/2021] [Accepted: 09/04/2021] [Indexed: 10/20/2022]
Abstract
COVID-19 has impacted the ability to evaluate motor function in older adults, as motor assessments typically require face-to-face interaction. One hundred seventy-seven older adults nationwide completed an unsupervised functional upper-extremity assessment at home. Data were compared to data from an independent sample of community-dwelling older adults (N = 250) assessed in lab. The effect of age on performance was similar between the in-lab and at-home groups. Practice effects were also similar. Assessing upper-extremity motor function remotely is feasible and reliable in community-dwelling older adults. This test offers a practical solution for telehealth practice and other research involving remote or geographically isolated individuals.
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Affiliation(s)
- Andrew Hooyman
- School of Biological and Health Systems Engineering, Arizona State University, Tempe, AZ, USA
- The Arizona Alzheimer’s Consortium, Phoenix, AZ, USA
| | - Joshua S. Talboom
- The Arizona Alzheimer’s Consortium, Phoenix, AZ, USA
- Neurogenomics Division, The Translational Genomics Research Institute (TGen), Phoenix, AZ, USA
| | - Matthew D. DeBoth
- The Arizona Alzheimer’s Consortium, Phoenix, AZ, USA
- Neurogenomics Division, The Translational Genomics Research Institute (TGen), Phoenix, AZ, USA
| | - Lee Ryan
- The Arizona Alzheimer’s Consortium, Phoenix, AZ, USA
- Evelyn F. McKnight Brain Institute, University of Arizona, Tucson, AZ, USA
| | - Matt Huentelman
- The Arizona Alzheimer’s Consortium, Phoenix, AZ, USA
- Neurogenomics Division, The Translational Genomics Research Institute (TGen), Phoenix, AZ, USA
| | - Sydney Y. Schaefer
- School of Biological and Health Systems Engineering, Arizona State University, Tempe, AZ, USA
- The Arizona Alzheimer’s Consortium, Phoenix, AZ, USA
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Abbas M, Somme D, Le Bouquin Jeannès R. D-SORM: A digital solution for remote monitoring based on the attitude of wearable devices. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 208:106247. [PMID: 34260971 DOI: 10.1016/j.cmpb.2021.106247] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Accepted: 06/16/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND AND OBJECTIVE E-health is a growing research topic, especially with the expansion of the Internet of Things (IoT). Miniaturized wearable sensors are auspicious tools for biomedicine and healthcare systems. In this paper, we present D-SORM, a sensor fusion-based digital solution intended to assist clinicians and improve their diagnosis by providing objective measurements and automatic recognition. The aim is to supply an interface for remote monitoring to the medical staff. METHODS D-SORM platform estimates the wearable device attitude based on its acquired data, and visualizes it in real-time using a graphical user interface (GUI). It also integrates two modules which serve two different medical applications. The first one is arm tele-rehabilitation, where sessions are done online. The practitioner gives the instructions while wearing the device, and the patient has to reproduce the gestures. A processing unit is dedicated to compute statistical features and calculate the success rate. The second one is human motion tracking for elderly care. A novel machine learning architecture is proposed, based on feature fusion, to predict the activities of daily living. RESULTS The rehabilitation mechanism was tested under supervised conditions, by performing a set of movements. D-SORM provides extra information and objective measurements, thus facilitates the diagnosis of clinicians. The human activity recognition is also validated using a public dataset. With D-SORM, an efficiency ranging from 97.7% to 99.65% is ensured under unsupervised conditions. CONCLUSIONS The proposed design constitutes a digital clinical tool for medical teams allowing remote health monitoring. It overcomes geographical barriers while providing faster and highly accurate assessment.
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Affiliation(s)
- Manuel Abbas
- Univ Rennes, Inserm, LTSI - UMR 1099, F-35000 Rennes, France.
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Ye F, Yang B, Nam C, Xie Y, Chen F, Hu X. A Data-Driven Investigation on Surface Electromyography Based Clinical Assessment in Chronic Stroke. Front Neurorobot 2021; 15:648855. [PMID: 34335219 PMCID: PMC8320436 DOI: 10.3389/fnbot.2021.648855] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2021] [Accepted: 06/14/2021] [Indexed: 12/23/2022] Open
Abstract
Background: Surface electromyography (sEMG) based robot-assisted rehabilitation systems have been adopted for chronic stroke survivors to regain upper limb motor function. However, the evaluation of rehabilitation effects during robot-assisted intervention relies on traditional manual assessments. This study aimed to develop a novel sEMG data-driven model for automated assessment. Method: A data-driven model based on a three-layer backpropagation neural network (BPNN) was constructed to map sEMG data to two widely used clinical scales, i.e., the Fugl-Meyer Assessment (FMA) and the Modified Ashworth Scale (MAS). Twenty-nine stroke participants were recruited in a 20-session sEMG-driven robot-assisted upper limb rehabilitation, which consisted of hand reaching and withdrawing tasks. The sEMG signals from four muscles in the paretic upper limbs, i.e., biceps brachii (BIC), triceps brachii (TRI), flexor digitorum (FD), and extensor digitorum (ED), were recorded before and after the intervention. Meanwhile, the corresponding clinical scales of FMA and MAS were measured manually by a blinded assessor. The sEMG features including Mean Absolute Value (MAV), Zero Crossing (ZC), Slope Sign Change (SSC), Root Mean Square (RMS), and Wavelength (WL) were adopted as the inputs to the data-driven model. The mapped clinical scores from the data-driven model were compared with the manual scores by Pearson correlation. Results: The BPNN, with 15 nodes in the hidden layer and sEMG features, i.e., MAV, ZC, SSC, and RMS, as the inputs to the model, was established to achieve the best mapping performance with significant correlations (r > 0.9, P < 0.001), according to the FMA. Significant correlations were also obtained between the mapped and manual FMA subscores, i.e., FMA-wrist/hand and FMA-shoulder/elbow, before and after the intervention (r > 0.9, P < 0.001). Significant correlations (P < 0.001) between the mapped and manual scores of MASs were achieved, with the correlation coefficients r = 0.91 at the fingers, 0.88 at the wrist, and 0.91 at the elbow after the intervention. Conclusion: An sEMG data-driven BPNN model was successfully developed. It could evaluate upper limb motor functions in chronic stroke and have potential application in automated assessment in post-stroke rehabilitation, once validated with large sample sizes. Clinical Trial Registration: www.ClinicalTrials.gov, identifier: NCT02117089.
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Affiliation(s)
- Fuqiang Ye
- Department of Biomedical Engineering, The Hong Kong Polytechnic University, Hong Kong, China
- Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen, China
| | - Bibo Yang
- Department of Biomedical Engineering, The Hong Kong Polytechnic University, Hong Kong, China
| | - Chingyi Nam
- Department of Biomedical Engineering, The Hong Kong Polytechnic University, Hong Kong, China
| | - Yunong Xie
- Department of Biomedical Engineering, The Hong Kong Polytechnic University, Hong Kong, China
| | - Fei Chen
- Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen, China
| | - Xiaoling Hu
- Department of Biomedical Engineering, The Hong Kong Polytechnic University, Hong Kong, China
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Davarinia F, Maleki A. Automated estimation of clinical parameters by recurrence quantification analysis of surface EMG for agonist/antagonist muscles in amputees. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102740] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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NE-Motion: Visual Analysis of Stroke Patients Using Motion Sensor Networks. SENSORS 2021; 21:s21134482. [PMID: 34208996 PMCID: PMC8271972 DOI: 10.3390/s21134482] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Revised: 06/20/2021] [Accepted: 06/24/2021] [Indexed: 11/16/2022]
Abstract
A large number of stroke survivors suffer from a significant decrease in upper extremity (UE) function, requiring rehabilitation therapy to boost recovery of UE motion. Assessing the efficacy of treatment strategies is a challenging problem in this context, and is typically accomplished by observing the performance of patients during their execution of daily activities. A more detailed assessment of UE impairment can be undertaken with a clinical bedside test, the UE Fugl-Meyer Assessment, but it fails to examine compensatory movements of functioning body segments that are used to bypass impairment. In this work, we use a graph learning method to build a visualization tool tailored to support the analysis of stroke patients. Called NE-Motion, or Network Environment for Motion Capture Data Analysis, the proposed analytic tool handles a set of time series captured by motion sensors worn by patients so as to enable visual analytic resources to identify abnormalities in movement patterns. Developed in close collaboration with domain experts, NE-Motion is capable of uncovering important phenomena, such as compensation while revealing differences between stroke patients and healthy individuals. The effectiveness of NE-Motion is shown in two case studies designed to analyze particular patients and to compare groups of subjects.
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Lee SI, Adans-Dester CP, OBrien AT, Vergara-Diaz GP, Black-Schaffer R, Zafonte R, Dy JG, Bonato P. Predicting and Monitoring Upper-Limb Rehabilitation Outcomes Using Clinical and Wearable Sensor Data in Brain Injury Survivors. IEEE Trans Biomed Eng 2021; 68:1871-1881. [PMID: 32997621 PMCID: PMC8723794 DOI: 10.1109/tbme.2020.3027853] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
OBJECTIVE Rehabilitation specialists have shown considerable interest for the development of models, based on clinical data, to predict the response to rehabilitation interventions in stroke and traumatic brain injury survivors. However, accurate predictions are difficult to obtain due to the variability in patients' response to rehabilitation interventions. This study aimed to investigate the use of wearable technology in combination with clinical data to predict and monitor the recovery process and assess the responsiveness to treatment on an individual basis. METHODS Gaussian Process Regression-based algorithms were developed to estimate rehabilitation outcomes (i.e., Functional Ability Scale scores) using either clinical or wearable sensor data or a combination of the two. RESULTS The algorithm based on clinical data predicted rehabilitation outcomes with a Pearson's correlation of 0.79 compared to actual clinical scores provided by clinicians but failed to model the variability in responsiveness to the intervention observed across individuals. In contrast, the algorithm based on wearable sensor data generated rehabilitation outcome estimates with a Pearson's correlation of 0.91 and modeled the individual responses to rehabilitation more accurately. Furthermore, we developed a novel approach to combine estimates derived from the clinical data and the sensor data using a constrained linear model. This approach resulted in a Pearson's correlation of 0.94 between estimated and clinician-provided scores. CONCLUSION This algorithm could enable the design of patient-specific interventions based on predictions of rehabilitation outcomes relying on clinical and wearable sensor data. SIGNIFICANCE This is important in the context of developing precision rehabilitation interventions.
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Thomas SM, Delanni E, Christophe B, Connolly ES. Systematic review of novel technology-based interventions for ischemic stroke. Neurol Sci 2021; 42:1705-1717. [PMID: 33604762 DOI: 10.1007/s10072-021-05126-0] [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: 11/13/2020] [Accepted: 02/09/2021] [Indexed: 10/22/2022]
Abstract
PURPOSE To identify novel technologies pertinent to the prevention, diagnosis, treatment, and rehabilitation of ischemic stroke, and recommend the technologies that show the most promise in advancing ischemic stroke care. METHOD A systematic literature search on PubMed and Medscape was performed. Articles were assessed based on pre-determined criteria. Included journal articles were evaluated for specific characteristics and reviewed according to a structured paradigm. A search on www.clinicaltrials.gov was performed to identify pre-clinical ischemic stroke technological interventions. All clinical trial results were included. An additional search on PubMed was conducted to identify studies on robotic neuroendovascular procedures. RESULTS Thirty journal articles and five clinical trials were analyzed. Articles were categorized as follows: six studies pertinent to pre-morbidity and prevention of ischemic stroke, three studies relevant to the diagnosis of ischemic stroke, 16 studies about post-ischemic stroke rehabilitation, and five studies on robotic neuroendovascular interventions. CONCLUSIONS Novel technologies across the spectrum of ischemic stroke care were identified, and the ones that appear to have the most clinical utility are recommended. Future investigation of the feasibility and long-term efficacy of the recommended technologies in clinical settings is warranted.
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Affiliation(s)
- Steven Mulackal Thomas
- Department of Neurological Surgery, Columbia University Irving Medical Center, 710 West 168th Street, New York, NY, 10032, USA.
| | - Ellie Delanni
- Department of Neurological Surgery, Columbia University Irving Medical Center, 710 West 168th Street, New York, NY, 10032, USA
| | - Brandon Christophe
- Department of Neurological Surgery, Columbia University Irving Medical Center, 710 West 168th Street, New York, NY, 10032, USA
| | - Edward Sander Connolly
- Department of Neurological Surgery, Columbia University Irving Medical Center, 710 West 168th Street, New York, NY, 10032, USA
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Abdel Majeed Y, Awadalla S, Patton JL. Effects of robot viscous forces on arm movements in chronic stroke survivors: a randomized crossover study. J Neuroeng Rehabil 2020; 17:156. [PMID: 33234156 PMCID: PMC7685605 DOI: 10.1186/s12984-020-00782-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2019] [Accepted: 10/29/2020] [Indexed: 11/26/2022] Open
Abstract
Background Our previous work showed that speed is linked to the ability to recover in chronic stroke survivors. Participants moving faster on the first day of a 3-week study had greater improvements on the Wolf Motor Function Test. Methods We examined the effects of three candidate speed-modifying fields in a crossover design: negative viscosity, positive viscosity, and a “breakthrough” force that vanishes after speed exceeds an individualized threshold. Results Negative viscosity resulted in a significant speed increase when it was on. No lasting after effects on movement speed were observed from any of these treatments, however, training with negative viscosity led to significant improvements in movement accuracy and smoothness. Conclusions Our results suggest that negative viscosity could be used as a treatment to augment the training process while still allowing participants to make their own volitional motions in practice. Trial registration This study was approved by the Institutional Review Boards at Northwestern University (STU00206579) and the University of Illinois at Chicago (2018-1251).
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Affiliation(s)
- Yazan Abdel Majeed
- Richard and Loan Hill Bioengineering Department, University of Illinois at Chicago, Morgan St, 60607, Chicago, USA.,Shirley Ryan AbilityLab, Erie St, 60611, Chicago, USA
| | - Saria Awadalla
- School of Public Health, University of Illinois at Chicago, Taylor St, 60612, Chicago, USA
| | - James L Patton
- Richard and Loan Hill Bioengineering Department, University of Illinois at Chicago, Morgan St, 60607, Chicago, USA. .,Shirley Ryan AbilityLab, Erie St, 60611, Chicago, USA.
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Clip-On IMU System for Assessing Age-Related Changes in Hand Functions. SENSORS 2020; 20:s20216313. [PMID: 33167512 PMCID: PMC7663935 DOI: 10.3390/s20216313] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/07/2020] [Revised: 10/30/2020] [Accepted: 11/03/2020] [Indexed: 11/17/2022]
Abstract
Hand functions affect the instrumental activities of daily living. While functional outcome measures, such as a targeted box and block test, have been widely used in clinical settings and provide a useful measure of overall performance, the advent of a wearable Inertial Measurement Unit(IMU)-based system enables the examination of the specific performance and kinematic parameters of hand movements. This study proposed a novel clip-on IMU system to facilitate the clinically fitted measurements of fine-motor finger and wrist joint movements. Clinical validation was conducted with the aim of characterising age-related changes in hand functions, namely grasping, transporting, and releasing blocks. Eighteen young (age 20–31) and sixteen healthy older adults (age 75–89) were evaluated during the box and block test. The results demonstrated that an older age was characterized by slower movements and higher variations and kinematic alterations in the hand functions, such as a larger range of motions at the fingers as well as kinematic trajectories. The proposed IMU system and subsequent validations highlight the value of the performance and kinematics parameters for a more comprehensive understanding of fine-motor finger and wrist movements that could shed light on further implementations in clinical and practical settings.
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Sirsat MS, Fermé E, Câmara J. Machine Learning for Brain Stroke: A Review. J Stroke Cerebrovasc Dis 2020; 29:105162. [DOI: 10.1016/j.jstrokecerebrovasdis.2020.105162] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2020] [Revised: 07/08/2020] [Accepted: 07/11/2020] [Indexed: 12/29/2022] Open
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Adans-Dester C, Hankov N, O’Brien A, Vergara-Diaz G, Black-Schaffer R, Zafonte R, Dy J, Lee SI, Bonato P. Enabling precision rehabilitation interventions using wearable sensors and machine learning to track motor recovery. NPJ Digit Med 2020; 3:121. [PMID: 33024831 PMCID: PMC7506010 DOI: 10.1038/s41746-020-00328-w] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2019] [Accepted: 08/12/2020] [Indexed: 01/19/2023] Open
Abstract
The need to develop patient-specific interventions is apparent when one considers that clinical studies often report satisfactory motor gains only in a portion of participants. This observation provides the foundation for "precision rehabilitation". Tracking and predicting outcomes defining the recovery trajectory is key in this context. Data collected using wearable sensors provide clinicians with the opportunity to do so with little burden on clinicians and patients. The approach proposed in this paper relies on machine learning-based algorithms to derive clinical score estimates from wearable sensor data collected during functional motor tasks. Sensor-based score estimates showed strong agreement with those generated by clinicians. Score estimates of upper-limb impairment severity and movement quality were marked by a coefficient of determination of 0.86 and 0.79, respectively. The application of the proposed approach to monitoring patients' responsiveness to rehabilitation is expected to contribute to the development of patient-specific interventions, aiming to maximize motor gains.
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Affiliation(s)
- Catherine Adans-Dester
- Department of Physical Medicine & Rehabilitation, Harvard Medical School, Spaulding Rehabilitation Hospital, Boston, MA USA
- School of Health & Rehabilitation Sciences, MGH Institute of Health Professions, Boston, MA USA
| | - Nicolas Hankov
- Department of Physical Medicine & Rehabilitation, Harvard Medical School, Spaulding Rehabilitation Hospital, Boston, MA USA
| | - Anne O’Brien
- Department of Physical Medicine & Rehabilitation, Harvard Medical School, Spaulding Rehabilitation Hospital, Boston, MA USA
| | - Gloria Vergara-Diaz
- Department of Physical Medicine & Rehabilitation, Harvard Medical School, Spaulding Rehabilitation Hospital, Boston, MA USA
| | - Randie Black-Schaffer
- Department of Physical Medicine & Rehabilitation, Harvard Medical School, Spaulding Rehabilitation Hospital, Boston, MA USA
| | - Ross Zafonte
- Department of Physical Medicine & Rehabilitation, Harvard Medical School, Spaulding Rehabilitation Hospital, Boston, MA USA
| | - Jennifer Dy
- Department of Electrical and Computer Engineering, Northeastern University, Boston, MA USA
| | - Sunghoon I. Lee
- College of Information and Computer Sciences, University of Massachusetts Amherst, Amherst, MA USA
| | - Paolo Bonato
- Department of Physical Medicine & Rehabilitation, Harvard Medical School, Spaulding Rehabilitation Hospital, Boston, MA USA
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA USA
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Abstract
Neuromuscular and sensorimotor degeneration caused by stroke or any other disease significantly reduce the physical, cognitive, and social well-being across the life span. Mostly, therapeutic interventions are employed in order to restore the lost degrees-of-freedom (DOF) caused by such impairments and automating these therapeutic tasks through exoskeletons/robots is becoming a common practice. However, aligning these robotic devices with the complex anatomical and geometrical motions of the joints is very challenging. At the same time, a good alignment is required in order to establish a better synergy of human-exoskeleton system for an effective intervention procedure. In this paper, a case study of an exoskeleton and shoulder joint alignment were studied through different size and orientation impairment models through motion capture data and musculoskeletal modeling in OpenSim. A preliminary result indicates that shoulder elevation is very sensitive to misalignment and varies with shoulder joint axes orientation; this is partly due to drastic displacement of the upper arm axes with respect to the shoulder joint origin during elevation. Additional study and analysis is required to learn any possible restraint on shoulder elevation that could potentially help in the exoskeleton development.
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Seo M, Shin MJ, Park TS, Park JH. Clinometric Gait Analysis Using Smart Insoles in Patients With Hemiplegia After Stroke: Pilot Study. JMIR Mhealth Uhealth 2020; 8:e22208. [PMID: 32909949 PMCID: PMC7516684 DOI: 10.2196/22208] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2020] [Revised: 08/10/2020] [Accepted: 08/18/2020] [Indexed: 12/18/2022] Open
Abstract
Background For effective rehabilitation after stroke, it is essential to conduct an objective assessment of the patient’s functional status. Several stroke severity scales have been used for this purpose, but such scales have various limitations. Objective Gait analysis using smart insole technology can be applied continuously, objectively, and quantitatively, thereby overcoming the shortcomings of other assessment tools. Methods To confirm the reliability of gait analysis using smart insole technology, normal healthy controls wore insoles in their shoes during the Timed Up and Go (TUG) test. The gait parameters were compared with the manually collected data. To determine the gait characteristics of patients with hemiplegia due to stroke, they were asked to wear insoles and take the TUG test; gait parameters were calculated and compared with those of control subjects. To investigate whether the gait analysis accurately reflected the patients’ clinical condition, we analyzed the relationships of 22 gait parameters on 4 stroke severity scales. Results The smart insole gait parameter data were similar to those calculated manually. Among the 18 gait parameters tested, 14 were significantly effective at distinguishing patients from healthy controls. The smart insole data revealed that the stance duration on both sides was longer in patients than controls, which has proven difficult to show using other methods. Furthermore, the sound side in patients showed a markedly longer stance duration. Regarding swing duration, that of the sound side was shorter in patients than controls, whereas that of the hemiplegic side was longer. We identified 10 significantly correlated gait parameters on the stroke severity scales. Notably, the difference in stance duration between the sound and hemiplegic sides was significantly correlated with the Fugl-Meyer Assessment (FMA) lower extremity score. Conclusions This study confirmed the feasibility and applicability of the smart insole as a device to assess the gait of patients with hemiplegia due to stroke. In addition, we demonstrated that the FMA score was significantly correlated with the smart insole data. Providing an environment where stroke patients can easily measure walking ability helps to maintain chronic functions as well as acute rehabilitation. Trial Registration UMIN Clinical Trials Registry UMIN000041646, https://upload.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000047538
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Affiliation(s)
- Minseok Seo
- School of Medicine, Pusan National University, Busan, Republic of Korea
| | - Myung-Jun Shin
- Department of Rehabilitation Medicine, School of Medicine, Pusan National University, Busan, Republic of Korea
| | - Tae Sung Park
- Biomedical Research Institute, Pusan National University Hospital, Busan, Republic of Korea
| | - Jong-Hwan Park
- Biomedical Research Institute, Pusan National University Hospital, Busan, Republic of Korea
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Liu SQ, Zhang JC, Zhu R. A Wearable Human Motion Tracking Device Using Micro Flow Sensor Incorporating a Micro Accelerometer. IEEE Trans Biomed Eng 2020; 67:940-948. [DOI: 10.1109/tbme.2019.2924689] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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Oubre B, Daneault JF, Jung HT, Whritenour K, Miranda JGV, Park J, Ryu T, Kim Y, Lee SI. Estimating Upper-Limb Impairment Level in Stroke Survivors Using Wearable Inertial Sensors and a Minimally-Burdensome Motor Task. IEEE Trans Neural Syst Rehabil Eng 2020; 28:601-611. [PMID: 31944983 DOI: 10.1109/tnsre.2020.2966950] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Upper-limb paresis is the most common motor impairment post stroke. Current solutions to automate the assessment of upper-limb impairment impose a number of critical burdens on patients and their caregivers that preclude frequent assessment. In this work, we propose an approach to estimate upper-limb impairment in stroke survivors using two wearable inertial sensors, on the wrist and the sternum, and a minimally-burdensome motor task. Twenty-three stroke survivors with no, mild, or moderate upper-limb impairment performed two repetitions of one-to-two minute-long continuous, random (i.e., patternless), voluntary upper-limb movements spanning the entire range of motion. The three-dimensional time-series of upper-limb movements were segmented into a series of one-dimensional submovements by employing a unique movement decomposition technique. An unsupervised clustering algorithm and a supervised regression model were used to estimate Fugl-Meyer Assessment (FMA) scores based on features extracted from these submovements. Our regression model estimated FMA scores with a normalized root mean square error of 18.2% ( r2=0.70 ) and needed as little as one minute of movement data to yield reasonable estimation performance. These results support the possibility of frequently monitoring stroke survivors' rehabilitation outcomes, ultimately enabling the development of individually-tailored rehabilitation programs.
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Maceira-Elvira P, Popa T, Schmid AC, Hummel FC. Wearable technology in stroke rehabilitation: towards improved diagnosis and treatment of upper-limb motor impairment. J Neuroeng Rehabil 2019; 16:142. [PMID: 31744553 PMCID: PMC6862815 DOI: 10.1186/s12984-019-0612-y] [Citation(s) in RCA: 90] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2019] [Accepted: 10/24/2019] [Indexed: 01/19/2023] Open
Abstract
Stroke is one of the main causes of long-term disability worldwide, placing a large burden on individuals and society. Rehabilitation after stroke consists of an iterative process involving assessments and specialized training, aspects often constrained by limited resources of healthcare centers. Wearable technology has the potential to objectively assess and monitor patients inside and outside clinical environments, enabling a more detailed evaluation of the impairment and allowing the individualization of rehabilitation therapies. The present review aims to provide an overview of wearable sensors used in stroke rehabilitation research, with a particular focus on the upper extremity. We summarize results obtained by current research using a variety of wearable sensors and use them to critically discuss challenges and opportunities in the ongoing effort towards reliable and accessible tools for stroke rehabilitation. Finally, suggestions concerning data acquisition and processing to guide future studies performed by clinicians and engineers alike are provided.
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Affiliation(s)
- Pablo Maceira-Elvira
- Defitech Chair in Clinical Neuroengineering, Center for Neuroprosthetics (CNP) and Brain Mind Institute (BMI), Swiss Federal Institute of Technology (EPFL), 9, Chemin des Mines, 1202, Geneva, Switzerland
- Defitech Chair in Clinical Neuroengineering, Center for Neuroprosthetics (CNP) and Brain Mind Institute (BMI), Swiss Federal Institute of Technology (EPFL Valais), Clinique Romande de Réadaptation, 1951, Sion, Switzerland
| | - Traian Popa
- Defitech Chair in Clinical Neuroengineering, Center for Neuroprosthetics (CNP) and Brain Mind Institute (BMI), Swiss Federal Institute of Technology (EPFL), 9, Chemin des Mines, 1202, Geneva, Switzerland
- Defitech Chair in Clinical Neuroengineering, Center for Neuroprosthetics (CNP) and Brain Mind Institute (BMI), Swiss Federal Institute of Technology (EPFL Valais), Clinique Romande de Réadaptation, 1951, Sion, Switzerland
| | - Anne-Christine Schmid
- Defitech Chair in Clinical Neuroengineering, Center for Neuroprosthetics (CNP) and Brain Mind Institute (BMI), Swiss Federal Institute of Technology (EPFL), 9, Chemin des Mines, 1202, Geneva, Switzerland
- Defitech Chair in Clinical Neuroengineering, Center for Neuroprosthetics (CNP) and Brain Mind Institute (BMI), Swiss Federal Institute of Technology (EPFL Valais), Clinique Romande de Réadaptation, 1951, Sion, Switzerland
| | - Friedhelm C Hummel
- Defitech Chair in Clinical Neuroengineering, Center for Neuroprosthetics (CNP) and Brain Mind Institute (BMI), Swiss Federal Institute of Technology (EPFL), 9, Chemin des Mines, 1202, Geneva, Switzerland.
- Defitech Chair in Clinical Neuroengineering, Center for Neuroprosthetics (CNP) and Brain Mind Institute (BMI), Swiss Federal Institute of Technology (EPFL Valais), Clinique Romande de Réadaptation, 1951, Sion, Switzerland.
- Clinical Neuroscience, University of Geneva Medical School, 1202, Geneva, Switzerland.
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Miller A, Duff SV, Quinn L, Bishop L, Youdan G, Ruthrauff H, Wade E. Development of Sensor-Based Measures of Upper Extremity Interlimb Coordination. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2019; 2018:3160-3164. [PMID: 30441065 DOI: 10.1109/embc.2018.8512903] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The development of motor impairment after the onset of an injury such as stroke may result in long-term compensatory behaviors. Because compensation often evolves in ambient settings (outside the purview of monitoring clinicians), there is a need for quantitative tools capable of accurately detecting the subtleties of compensation and related reduction in interlimb coordination. Improvement in interlimb coordination may serve as a marker of recovery from stroke, and rehabilitation progress. The current study investigates measures of upper extremity interlimb coordination in persons post-stroke and healthy controls. It introduces a novel algorithm for objective characterization of interlimb coordination during the performance of real-world tasks.
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45
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Song X, Chen S, Jia J, Shull PB. Cellphone-Based Automated Fugl-Meyer Assessment to Evaluate Upper Extremity Motor Function After Stroke. IEEE Trans Neural Syst Rehabil Eng 2019; 27:2186-2195. [PMID: 31502981 DOI: 10.1109/tnsre.2019.2939587] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
The Fugl-Meyer Assessment (FMA) is a widely used evaluation tool for assessing upper extremity motor function during stroke rehabilitation. However, the FMA is a repetitive, time-consuming task that currently must be performed by therapists in a hospital or clinic. We thus propose an alternative automated approach in which patients perform FMA movements while holding a cellphone at the hand and receive automated FMA scores. In the proposed system, features are extracted from cellphone movement data and decision trees are used to automatically score FMA test items. Ten stroke patients with upper extremity dysfunction participated in a validation experiment to compare automated FMA scores with traditional FMA scores from a trained therapist. Results showed that FMA scores from the cellphone-based automated system were highly correlated with FMA scores from the trained therapist (r2 = 0.97), and that the average accuracy for individual FMA test items was 85%. These results demonstrate that such a portable, automated FMA system could potentially be used to assess upper extremity function during stroke rehabilitation to remove the repetitive, time-consuming burden from therapists and could potentially be performed in clinic or home-based settings.
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Lin BS, Lee IJ, Hsiao PC, Hwang YT. An Assessment System for Post-Stroke Manual Dexterity Using Principal Component Analysis and Logistic Regression. IEEE Trans Neural Syst Rehabil Eng 2019; 27:1626-1634. [DOI: 10.1109/tnsre.2019.2928719] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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47
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Nelson Z, Wade E. Relative Efficacy of Sensor Modalities for Estimating Post-Stroke Motor Impairment. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2018; 2018:2503-2506. [PMID: 30440916 DOI: 10.1109/embc.2018.8512818] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Wearable inertial sensing has been beneficial in the development of measures of motor impairment after stroke. While most early work focused on the use of accelerometry, recent work has increasingly shown that rate gyroscopes may provide complementary information. Differences in performance of accelerometers and gyroscopes in activity recognition may be due to the nature of the impairment. The current approach seeks to investigate the relative sensitivity of these sensor modalities to impairment by evaluating their classification accuracy for tasks adapted from the Fugl-Meyer Assessment. Our findings indicated that, for upper-extremity motion, classifiers trained using a combination of accelerometer and rate gyroscope data performed the best (accuracy of 73.1%). Classifiers trained using accelerometer data alone and rate gyroscope data alone performed slightly worse than the combined data classifier (70.2% and 65.7%, respectively).
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Halilaj E, Rajagopal A, Fiterau M, Hicks JL, Hastie TJ, Delp SL. Machine learning in human movement biomechanics: Best practices, common pitfalls, and new opportunities. J Biomech 2018; 81:1-11. [PMID: 30279002 DOI: 10.1016/j.jbiomech.2018.09.009] [Citation(s) in RCA: 187] [Impact Index Per Article: 31.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2018] [Accepted: 09/08/2018] [Indexed: 12/11/2022]
Abstract
Traditional laboratory experiments, rehabilitation clinics, and wearable sensors offer biomechanists a wealth of data on healthy and pathological movement. To harness the power of these data and make research more efficient, modern machine learning techniques are starting to complement traditional statistical tools. This survey summarizes the current usage of machine learning methods in human movement biomechanics and highlights best practices that will enable critical evaluation of the literature. We carried out a PubMed/Medline database search for original research articles that used machine learning to study movement biomechanics in patients with musculoskeletal and neuromuscular diseases. Most studies that met our inclusion criteria focused on classifying pathological movement, predicting risk of developing a disease, estimating the effect of an intervention, or automatically recognizing activities to facilitate out-of-clinic patient monitoring. We found that research studies build and evaluate models inconsistently, which motivated our discussion of best practices. We provide recommendations for training and evaluating machine learning models and discuss the potential of several underutilized approaches, such as deep learning, to generate new knowledge about human movement. We believe that cross-training biomechanists in data science and a cultural shift toward sharing of data and tools are essential to maximize the impact of biomechanics research.
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Affiliation(s)
- Eni Halilaj
- Department of Mechanical Engineering, Carnegie Mellon University, United States.
| | - Apoorva Rajagopal
- Department of Mechanical Engineering, Stanford University, United States
| | - Madalina Fiterau
- Department of Computer Science, Stanford University, United States
| | - Jennifer L Hicks
- Department of Bioengineering, Stanford University, United States
| | - Trevor J Hastie
- Department of Statistics, Stanford University, United States; Department of Health Research and Policy, Stanford University, United States
| | - Scott L Delp
- Department of Mechanical Engineering, Stanford University, United States; Department of Bioengineering, Stanford University, United States; Department of Orthopaedic Surgery, Stanford University, United States
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Wang Z, Xin J, Sun P, Lin Z, Yao Y, Gao X. Improved lung nodule diagnosis accuracy using lung CT images with uncertain class. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2018; 162:197-209. [PMID: 29903487 DOI: 10.1016/j.cmpb.2018.05.028] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/15/2017] [Revised: 05/17/2018] [Accepted: 05/18/2018] [Indexed: 06/08/2023]
Abstract
BACKGROUND AND OBJECTIVE Among all malignant tumors, lung cancer ranks in the top in mortality rate. Pulmonary nodule is the early manifestation of lung cancer, and plays an important role in its discovery, diagnosis and treatment. The technology of medical imaging has encountered a rapid development in recent years, thus the amount of pulmonary nodules can be discovered are on the raise, which means even tiny or minor changes in lung can be recorded by the CT images. This paper proposes a pulmonary nodule computer aided diagnosis (CAD) based on semi-supervised extreme learning machine(SS-ELM). METHODS First, the feature model based on the pulmonary nodules regions of lung CT images is established. After that, the same feature data sets have been put into ELM, support vector machine (SVM) methods, probabilistic neural network (PNN) and multilayer perceptron (MLP) so as to compare the performance of the methods. ELM turned out to have better performance in training time and testing accuracy compared with SVM, PNN and MLP. Then, we propose a pulmonary nodules computer aided diagnosis algorithm based on semi-supervised ELM (SS-ELM), which enables both certain class feature sets with labels and unlabeled feature sets to be input for training and computer aided diagnosing. This algorithm has provided a solution for the using of uncertain class data and improve the testing accuracy of benign and malignant diagnosis. RESULTS 1018 sets of thoracic CT images from the Lung Database Consortium and Image Database Resource Initiative (LIDC-IDRI) have been used in experiment in order to test the effectiveness of the algorithm. Compared with ELM, the pulmonary nodules CAD based on SS-ELM has better testing accuracy performance. CONCLUSIONS We have proposed a pulmonary nodule CAD system based on SS-ELM, which achieving better generalization performance at faster learning speed and higher testing accuracy than ELM, SVM, PNN and MLP. The SS-ELM based pulmonary nodules CAD has been proposed to solve the problem of uncertain class data using.
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Affiliation(s)
- Zhiqiong Wang
- Sino-Dutch Biomedical & Information Engineering School, Northeastern University, China
| | - Junchang Xin
- School of Computer Science & Engineering, Key Laboratory of Big Data Management and Analytics (Liaoning Province), Northeastern University, China.
| | - Peishun Sun
- Sino-Dutch Biomedical & Information Engineering School, Northeastern University, China
| | - Zhixiang Lin
- Sino-Dutch Biomedical & Information Engineering School, Northeastern University, China
| | - Yudong Yao
- Department of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, NJ, United States
| | - Xiaosong Gao
- Sino-Dutch Biomedical & Information Engineering School, Northeastern University, China
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50
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Fang Q, Mahmoud SS, Gu X, Fu J. A Novel Multistandard Compliant Hand Function Assessment Method Using an Infrared Imaging Device. IEEE J Biomed Health Inform 2018; 23:758-765. [PMID: 29994552 DOI: 10.1109/jbhi.2018.2837380] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Many post-stroke patients suffer varying degrees of hand function and fine motor skills impairment. Both passive and active hand rehabilitation training are beneficial in improving the strength and dexterity of the hands. However, hand rehabilitation programs should be prescribed based on an accurate assessment of hand function. In this paper, we propose a novel method for hand function assessment, which can accurately measure multiple joint angles of a hand simultaneously using a portable infrared based imaging device. Different from traditional assessment methods that are often based on a clinician's subjective observations and ordinal charts, this method provides an accurate, fast, and objective evaluation using infrared imaging sensors. Performance evaluation and benchmarking for the proposed measurement system were carried out using the correlation coefficient (CC) method, the root mean squared error, and the percentage residual difference method (PRD). A clinical trial involving 25 participants resulted in a higher correlation with CC of 0.9672 and PRD of 8.8%, which indicated that the developed assessment framework is compliant with multiple assessment standards such as Swanson impairment evaluation and Fugl-Meyer assessment. The new hand function assessment method can be used to replace traditional methods for fine hand function modeling and assessment in rehabilitation medicine and can also play an important role in precision post-stroke function analysis.
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