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These systems allow for treatment beyond clinical settings and support preventive monitoring. Wearable systems have become essential tools for health monitoring, but they focus mainly on physiological data, overlooking motor data evaluation. The World Health Organization reports that 1.71 billion people globally suffer from musculoskeletal conditions, marked by pain and limited mobility. (2) Methods: To gain a deeper understanding of wearables for the motor rehabilitation, monitoring, and prediction of the progression and\/or degradation of symptoms directly associated with upper-limb pathologies, this study was conducted. Thus, all articles indexed in the Web of Science database containing the terms \u201cwearable\u201d, \u201cupper limb\u201d, and (\u201crehabilitation\u201d or \u201cmonitor\u201d or \u201cpredict\u201d) between 2019 and 2023 were flagged for analysis. (3) Results: Out of 391 papers identified, 148 were included and analyzed, exploring pathologies, technologies, and their interrelationships. Technologies were categorized by typology and primary purpose. (4) Conclusions: The study identified essential sensory units and actuators in wearable systems for upper-limb physiotherapy and analyzed them based on treatment methods and targeted pathologies.<\/jats:p>","DOI":"10.3390\/s24247973","type":"journal-article","created":{"date-parts":[[2024,12,13]],"date-time":"2024-12-13T10:34:12Z","timestamp":1734086052000},"page":"7973","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Trends and Innovations in Wearable Technology for Motor Rehabilitation, Prediction, and Monitoring: A Comprehensive Review"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7990-1343","authenticated-orcid":false,"given":"Pedro","family":"Lobo","sequence":"first","affiliation":[{"name":"2AI, School of Technology, IPCA, 4750-810 Barcelos, Portugal"},{"name":"LIFE Research Institute, TUS\u2014Technological University of the Shannon, V94 EC5T Limerick, Ireland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1995-7879","authenticated-orcid":false,"given":"Pedro","family":"Morais","sequence":"additional","affiliation":[{"name":"2AI, School of Technology, IPCA, 4750-810 Barcelos, Portugal"},{"name":"LASI\u2014Associate Laboratory of Intelligent Systems, 4800-058 Guimar\u00e3es, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6349-7761","authenticated-orcid":false,"given":"Patrick","family":"Murray","sequence":"additional","affiliation":[{"name":"LIFE Research Institute, TUS\u2014Technological University of the Shannon, V94 EC5T Limerick, Ireland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4196-5357","authenticated-orcid":false,"given":"Jo\u00e3o L.","family":"Vila\u00e7a","sequence":"additional","affiliation":[{"name":"2AI, School of Technology, IPCA, 4750-810 Barcelos, Portugal"},{"name":"LASI\u2014Associate Laboratory of Intelligent Systems, 4800-058 Guimar\u00e3es, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2024,12,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1097\/MOG.0000000000000845","article-title":"The future of telemedicine and wearable technology in IBD","volume":"38","author":"Rowan","year":"2022","journal-title":"Curr. Opin. Gastroenterol."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Kamecka, K., Foti, C., Gawi\u0144ski, \u0141., Matejun, M., Rybarczyk-Szwajkowska, A., Kilja\u0144ski, M., Krochmalski, M., Koz\u0142owski, R., and Marczak, M. (2022). Telemedicine Technologies Selection for the Posthospital Patient Care Process after Total Hip Arthroplasty. Int. J. Environ. Res. Public Health, 19.","DOI":"10.3390\/ijerph191811521"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"e28724","DOI":"10.2196\/28724","article-title":"Telemedicine Acceptance among Older Adult Patients with Cancer: Scoping Review","volume":"24","author":"Pang","year":"2022","journal-title":"J. Med. Internet Res."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"9047","DOI":"10.1109\/JSEN.2019.2925638","article-title":"Energy Harvesting for Wearable Devices: A Review","volume":"19","author":"Chong","year":"2019","journal-title":"IEEE Sens. J."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"625","DOI":"10.1093\/geronb\/gby085","article-title":"Feeling Old, Body and Soul: The Effect of Aging Body Reminders on Age Identity","volume":"75","author":"Barrett","year":"2020","journal-title":"J. Gerontol. Ser. B"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"e13080","DOI":"10.1111\/acel.13080","article-title":"Measuring biological aging in humans: A quest","volume":"19","author":"Ferrucci","year":"2020","journal-title":"Aging Cell"},{"key":"ref_7","first-page":"1061","article-title":"Wearable and telemedicine innovations for Olympic events and elite sport","volume":"61","author":"Angeloudis","year":"2021","journal-title":"J. Sports Med. Phys. Fit."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"403","DOI":"10.31083\/j.rcm2202046","article-title":"Chronic disease management in heart failure: Focus on telemedicine and remote monitoring","volume":"22","author":"Alvarez","year":"2021","journal-title":"Rev. Cardiovasc. Med."},{"key":"ref_9","first-page":"817","article-title":"Telemedicine: A promising solution for heart failure?","volume":"204","author":"Desnos","year":"2020","journal-title":"Bull. Acad. Natl. Med."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"R41","DOI":"10.1530\/ERP-18-0081","article-title":"Application of mobile health, telemedicine and artificial intelligence to echocardiography","volume":"6","author":"Seetharam","year":"2019","journal-title":"Echo Res. Pract."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"319","DOI":"10.1097\/JCN.0000000000000957","article-title":"Self-care Practices of Patients With Heart Failure Using Wearable Electronic Devices","volume":"38","author":"Patel","year":"2023","journal-title":"J. Cardiovasc. Nurs."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1372","DOI":"10.1093\/europace\/euac052","article-title":"Wearables, telemedicine, and artificial intelligence in arrhythmias and heart failure: Proceedings of the European Society of Cardiology Cardiovascular Round Table","volume":"24","author":"Leclercq","year":"2022","journal-title":"Europace"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"969","DOI":"10.1016\/j.trechm.2021.09.001","article-title":"Laser-engraved graphene for flexible and wearable electronics","volume":"3","author":"Wang","year":"2021","journal-title":"Trends Chem."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Deng, Z., Guo, L., Chen, X., and Wu, W. (2023). Smart Wearable Systems for Health Monitoring. Sensors, 23.","DOI":"10.3390\/s23052479"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"18923","DOI":"10.1039\/C9NR05532K","article-title":"Graphene-based wearable sensors","volume":"11","author":"Qiao","year":"2019","journal-title":"Nanoscale"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"3079","DOI":"10.1039\/D2NR05447G","article-title":"Wearable chemical sensors based on 2D materials for healthcare applications","volume":"15","author":"Zhang","year":"2023","journal-title":"Nanoscale"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Hashim, F.F., Mahadi, W.N.L.B., Latef, T.B.A., and Othman, M.B. (2022). Key Factors in the Implementation of Wearable Antennas for WBNs and ISM Applications: A Review WBNs and ISM Applications: A Review. Electronics, 11.","DOI":"10.3390\/electronics11152470"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Devi, D.H., Duraisamy, K., Armghan, A., Alsharari, M., Aliqab, K., Sorathiya, V., Das, S., and Rashid, N. (2023). 5G Technology in Healthcare and Wearable Devices: A Review. Sensors, 23.","DOI":"10.3390\/s23052519"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"646","DOI":"10.1177\/0040517520932230","article-title":"Electro-textile wearable antennas in wireless body area networks: Materials, antenna design, manufacturing techniques, and human body consideration\u2014A review","volume":"91","author":"Almohammed","year":"2021","journal-title":"Text. Res. J."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Qureshi, H.N., Manalastas, M., Ijaz, A., Imran, A., Liu, Y., and Al Kalaa, M.O. (2022). Communication Requirements in 5G-Enabled Healthcare Applications: Review and Considerations. Healthcare, 10.","DOI":"10.3390\/healthcare10020293"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"986","DOI":"10.1089\/tmj.2022.0280","article-title":"The Impact of Consumer Wearable Devices on Physical Activity and Adherence to Physical Activity in Patients with Cardiovascular Disease: A Systematic Review of Systematic Reviews and Meta-Analyses","volume":"29","author":"Alam","year":"2023","journal-title":"Telemed. e-Health"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"e11819","DOI":"10.2196\/11819","article-title":"Consumer-based wearable activity trackers increase physical activity participation: Systematic review and meta-analysis","volume":"7","author":"Brickwood","year":"2019","journal-title":"JMIR mHealth uHealth"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"418","DOI":"10.1177\/0890117119895204","article-title":"Changing the Physical Activity Behavior of Adults With Fitness Trackers: A Systematic Review and Meta-Analysis","volume":"34","author":"Lynch","year":"2020","journal-title":"Am. J. Health Promot."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"100523","DOI":"10.1016\/j.mser.2019.100523","article-title":"Reviews of wearable healthcare systems: Materials, devices and system integration","volume":"140","author":"Lou","year":"2020","journal-title":"Mater. Sci. Eng. R Rep."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1266","DOI":"10.1111\/ene.15235","article-title":"Technology outcome measures in neuromuscular disorders: A systematic review","volume":"29","author":"Bortolani","year":"2022","journal-title":"Eur. J. Neurol."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Escobar-Linero, E., Mu\u00f1oz-Saavedra, L., Luna-Perej\u00f3n, F., Sevillano, J.L., and Dom\u00ednguez-Morales, M. (2023). Wearable Health Devices for Diagnosis Support: Evolution and Future Tendencies. Sensors, 23.","DOI":"10.3390\/s23031678"},{"key":"ref_27","first-page":"1","article-title":"Wearable technology in stroke rehabilitation: Towards improved diagnosis and treatment of upper-limb motor impairment","volume":"16","author":"Popa","year":"2019","journal-title":"J. Neuroeng. Rehabil."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"S173","DOI":"10.1080\/10790268.2021.1920787","article-title":"Perspectives and recommendations of individuals with tetraplegia regarding wearable cameras for monitoring hand function at home: Insights from a community-based study","volume":"44","author":"Bandini","year":"2021","journal-title":"J. Spinal Cord. Med."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"748","DOI":"10.1109\/TNSRE.2020.2968912","article-title":"An Effective and Efficient Method for Detecting Hands in Egocentric Videos for Rehabilitation Applications","volume":"28","author":"Visee","year":"2020","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1177\/15459683231159663","article-title":"Validity of Novel Outcome Measures for Hand Function Performance After Stroke Using Egocentric Video","volume":"37","author":"Tsai","year":"2023","journal-title":"Neurorehabil. Neural Repair."},{"key":"ref_31","unstructured":"Bandini, A., Dousty, M., and Zariffa, J. (2023, November 21). A Wearable Vision-Based System for Detecting Hand-Object Interactions in Individuals with Cervical Spinal Cord Injury: First Results in the Home Environment. Available online: https:\/\/www.webofscience.com\/wos\/woscc\/full-record\/WOS:000621592202121."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1463","DOI":"10.1109\/JBHI.2020.3003643","article-title":"Tenodesis Grasp Detection in Egocentric Video","volume":"25","author":"Dousty","year":"2021","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Tsai, M.F., Wang, R.H., and Zariffa, J. (2020, January 20\u201324). Generalizability of Hand-Object Interaction Detection in Egocentric Video across Populations with Hand Impairment. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, Montreal, QC, Canada.","DOI":"10.1109\/EMBC44109.2020.9176154"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"2100510","DOI":"10.1109\/JTEHM.2021.3072347","article-title":"Identifying Hand Use and Hand Roles after Stroke Using Egocentric Video","volume":"9","author":"Tsai","year":"2021","journal-title":"IEEE J. Transl. Eng. Health Med."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1697","DOI":"10.1089\/neu.2022.0156","article-title":"Measuring Hand Use in the Home after Cervical Spinal Cord Injury Using Egocentric Video","volume":"39","author":"Bandini","year":"2022","journal-title":"J. Neurotrauma"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1186\/s12984-019-0557-1","article-title":"Egocentric video: A new tool for capturing hand use of individuals with spinal cord injury at home","volume":"16","author":"Likitlersuang","year":"2019","journal-title":"J. Neuroeng. Rehabil."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"878","DOI":"10.1080\/17483107.2022.2129851","article-title":"Perspectives and expectations of stroke survivors using egocentric cameras for monitoring hand function at home: A mixed methods study","volume":"19","author":"Tsai","year":"2022","journal-title":"Disabil. Rehabil. Assist. Technol."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Battraw, M.A., Young, P.R., Welner, M.E., Joiner, W.M., and Schofield, J.S. (2022, January 25\u201329). Characterizing Pediatric Hand Grasps During Activities of Daily Living to Inform Robotic Rehabilitation and Assistive Technologies. Proceedings of the IEEE International Conference on Rehabilitation Robotics, Rotterdam, The Netherlands.","DOI":"10.1109\/ICORR55369.2022.9896512"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Ham, Y., Yang, D.S., Choi, Y., and Shin, J.H. (2022). The feasibility of mixed reality-based upper extremity self-training for patients with stroke\u2014A pilot study. Front. Neurol., 13.","DOI":"10.3389\/fneur.2022.994586"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Ozgur, A.G., Khodr, H., Bruno, B., Gandar, N., Wessel, M.J., Hummel, F.C., and Dillenbourg, P. (2021, January 8\u201312). Detecting compensatory motions and providing informative feedback during a tangible robot assisted game for post-stroke rehabilitation. Proceedings of the 2021 30th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2021, Vancouver, BC, Canada.","DOI":"10.1109\/RO-MAN50785.2021.9515325"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"831","DOI":"10.1007\/s00415-022-11410-6","article-title":"Video augmented mirror therapy for upper extremity rehabilitation after stroke: A randomized controlled trial","volume":"270","author":"Kim","year":"2023","journal-title":"J. Neurol."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Song, X., Ding, L., Zhao, J., Jia, J., and Shull, P. (2019, January 19\u201322). Cellphone augmented reality game-based rehabilitation for improving motor function and mental state after stroke. Proceedings of the 2019 IEEE 16th International Conference on Wearable and Implantable Body Sensor Networks, BSN 2019-Proceedings, Chicago, IL, USA.","DOI":"10.1109\/BSN.2019.8771093"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Campo-Prieto, P., Cancela-Carral, J.M., and Rodr\u00edguez-Fuentes, G. (2022). Wearable Immersive Virtual Reality Device for Promoting Physical Activity in Parkinson\u2019s Disease Patients. Sensors, 22.","DOI":"10.3390\/s22093302"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Franz\u00f2, M., Pica, A., Pascucci, S., Serrao, M., Marinozzi, F., and Bini, F. (2023). A Proof of Concept Combined Using Mixed Reality for Personalized Neurorehabilitation of Cerebellar Ataxic Patients. Sensors, 23.","DOI":"10.3390\/s23031680"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"984","DOI":"10.1109\/TNSRE.2019.2909287","article-title":"Design of virtual guiding tasks with haptic feedback for assessing the wrist motor function of patients with upper motor neuron lesions","volume":"27","author":"Liu","year":"2019","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1186\/s12984-021-00927-y","article-title":"Clinicians\u2019 perceptions of a potential wearable device for capturing upper limb activity post-stroke: A qualitative focus group study","volume":"18","author":"Simpson","year":"2021","journal-title":"J. Neuroeng. Rehabil."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1186\/s12984-023-01197-6","article-title":"Perspectives of users for a future interactive wearable system for upper extremity rehabilitation following stroke: A qualitative study","volume":"20","author":"Yang","year":"2023","journal-title":"J. Neuroeng. Rehabil."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1097\/NPT.0000000000000413","article-title":"Improvement in the Capacity for Activity Versus Improvement in Performance of Activity in Daily Life During Outpatient Rehabilitation","volume":"47","author":"Lang","year":"2023","journal-title":"J. Neurol. Phys. Ther."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1392","DOI":"10.1080\/17483107.2023.2183993","article-title":"Requirements for home-based upper extremity rehabilitation using wearable motion sensors for stroke patients: A user-centred approach","volume":"19","author":"Langerak","year":"2023","journal-title":"Disabil. Rehabil. Assist. Technol."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"21473","DOI":"10.1109\/JSEN.2021.3103803","article-title":"Tremor Class Scaling for Parkinson Disease Patients Using an Array X-Band Microwave Doppler-Based Upper Limb Movement Quantizer","volume":"21","author":"Lin","year":"2021","journal-title":"IEEE Sens. J."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Brown, R., Pearse, J.E., Nappey, T., Jackson, D., Edmonds, G., Guan, Y., and Basu, A.P. (2022). Wrist-Worn devices to encourage affected upper limb movement in unilateral cerebral palsy: Participatory design workshops. Front. Rehabil. Sci., 3.","DOI":"10.3389\/fresc.2022.1021760"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1480","DOI":"10.1080\/09638288.2022.2065542","article-title":"Task selection for a sensor-based, wearable, upper limb training device for stroke survivors: A multi-stage approach","volume":"45","author":"Turk","year":"2023","journal-title":"Disabil. Rehabil."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"David, A., Subash, T., Varadhan, S.K.M., Melendez-Calderon, A., and Balasubramanian, S. (2021). A Framework for Sensor-Based Assessment of Upper-Limb Functioning in Hemiparesis. Front. Hum. Neurosci., 15.","DOI":"10.3389\/fnhum.2021.667509"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"138690","DOI":"10.1109\/ACCESS.2020.3010674","article-title":"A Longitudinal Investigation of the Efficacy of Supported In-Home Post-Stroke Rehabilitation","volume":"8","author":"Fang","year":"2020","journal-title":"IEEE Access"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"75901","DOI":"10.1109\/ACCESS.2019.2921978","article-title":"Motion Velocity, Acceleration and Energy Expenditure Estimation Using Micro Flow Sensor","volume":"7","author":"Zhang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"2387378","DOI":"10.1155\/2020\/2387378","article-title":"Characteristics Associated with the Differential Activity of Nondominant and Dominant Affected Hands in Patients with Poststroke Right Hemiparesis","volume":"2020","author":"Lee","year":"2020","journal-title":"Occup. Ther. Int."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1186\/s12984-019-0499-7","article-title":"Actigraph assessment for measuring upper limb activity in unilateral cerebral palsy","volume":"16","author":"Beani","year":"2019","journal-title":"J. Neuroeng. Rehabil."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Hughes, C.M.L., Louie, A., Sun, S., Gordon-Murer, C., Belay, G.J., Baye, M., and Zhang, X. (2019). Development of a Post-stroke Upper Limb Rehabilitation Wearable Sensor for Use in Sub-Saharan Africa: A Pilot Validation Study. Front. Bioeng. Biotechnol., 7.","DOI":"10.3389\/fbioe.2019.00322"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"805","DOI":"10.1109\/TNSRE.2020.2972285","article-title":"Automated Scoring of Hemiparesis in Acute Stroke from Measures of Upper Limb Co-Ordination Using Wearable Accelerometry","volume":"28","author":"Datta","year":"2020","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Datta, S., Karmakar, H.K., Yan, B., and Palaniswami, M. (2020, January 20\u201324). Poincar\u00e9 Descriptors for Identifying Hemiparesis in Acute Stroke using Wearable Accelerometry. Proceedings of the 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Montreal, QC, Canada.","DOI":"10.1109\/EMBC44109.2020.9175847"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Datta, S., Karmakar, C.K., Yan, B., and Palaniswami, M. (2020, January 20\u201324). Analyzing Distance Measures for Upper Limb Activity Measurement in Hemiparetic Stroke Patients. Proceedings of the 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Montreal, QC, Canada.","DOI":"10.1109\/EMBC44109.2020.9175758"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"1964","DOI":"10.1109\/JBHI.2020.3024589","article-title":"Novel Measures of Similarity and Asymmetry in Upper Limb Activities for Identifying Hemiparetic Severity in Stroke Survivors","volume":"25","author":"Datta","year":"2021","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1080\/01942638.2023.2207635","article-title":"Construct Validity of the Both Hands Assessment Using Wrist-Worn Accelerometers","volume":"44","author":"Burgess","year":"2023","journal-title":"Phys. Occup. Ther. Pediatr."},{"key":"ref_64","first-page":"205566832110196","article-title":"Quantification of the relative arm use in patients with hemiparesis using inertial measurement units","volume":"8","author":"David","year":"2021","journal-title":"J. Rehabil. Assist. Technol. Eng."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"929","DOI":"10.1177\/15459683211041312","article-title":"Real-World Functional Grasping Activity in Individuals with Stroke and Healthy Controls Using a Novel Wearable Wrist Sensor","volume":"35","author":"Yang","year":"2021","journal-title":"Neurorehabil. Neural Repair."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"3643","DOI":"10.1161\/STROKEAHA.119.026921","article-title":"Preliminary examination of the ability of a new wearable device to capture functional hand activity after stroke","volume":"50","author":"Simpson","year":"2019","journal-title":"Stroke"},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Schwarz, A., Bhagubai, M.M.C., Wolterink, G., Held, J.P.O., Luft, A.R., and Veltink, P.H. (2020). Assessment of upper limb movement impairments after stroke using wearable inertial sensing. Sensors, 20.","DOI":"10.3390\/s20174770"},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Palani, P., Panigrahi, S., Jammi, S.A., and Thondiyath, A. (2022, January 7\u20139). Real-time Joint Angle Estimation using Mediapipe Framework and Inertial Sensors. Proceedings of the 2022 IEEE 22nd International Conference on Bioinformatics and Bioengineering (BIBE), Taichung, Taiwan.","DOI":"10.1109\/BIBE55377.2022.00035"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"7293","DOI":"10.1109\/ACCESS.2020.3048645","article-title":"Instrumented Ergonomic Risk Assessment Using Wearable Inertial Measurement Units: Impact of Joint Angle Convention","volume":"9","author":"Humadi","year":"2021","journal-title":"IEEE Access"},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Rahman, M.M., Gan, K.B., Aziz, N.A.A., Huong, A., and You, H.W. (2023). Upper Limb Joint Angle Estimation Using Wearable IMUs and Personalized Calibration Algorithm. Mathematics, 11.","DOI":"10.3390\/math11040970"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"3777","DOI":"10.1109\/JSEN.2019.2960320","article-title":"Wearable Inertial Sensors for Range of Motion Assessment","volume":"20","author":"Rajkumar","year":"2020","journal-title":"IEEE Sens. J."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"7168","DOI":"10.1109\/JSEN.2022.3233344","article-title":"An Inertial-Based Upper-Limb Motion Assessment Model: Performance Validation Across Various Motion Tasks","volume":"23","author":"Meng","year":"2023","journal-title":"IEEE Sens. J."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"20552076231153737","DOI":"10.1177\/20552076231153737","article-title":"Usability of a wearable device for home-based upper limb telerehabilitation in persons with stroke: A mixed-methods study","volume":"9","author":"Toh","year":"2023","journal-title":"Digit. Health"},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"El Khoury, G., Penta, M., Barbier, O., Libouton, X., Thonnard, J.L., and Lef\u00e8vre, P. (2021). Recognizing manual activities using wearable inertial measurement units: Clinical application for outcome measurement. Sensors, 21.","DOI":"10.3390\/s21093245"},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"\u0160lajpah, S., \u010ceba\u0161ek, E., Munih, M., and Mihelj, M. (2023). Time-Based and Path-Based Analysis of Upper-Limb Movements during Activities of Daily Living. Sensors, 23.","DOI":"10.3390\/s23031289"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1186\/s12984-023-01182-z","article-title":"Wearable accelerometers for measuring and monitoring the motor behaviour of infants with brain damage during CareToy-Revised training","volume":"20","author":"Cavalieri","year":"2023","journal-title":"J. Neuroeng. Rehabil."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"604","DOI":"10.1002\/pmrj.12780","article-title":"Quantifying real-world upper limb activity via patient-initiated spontaneous movement in neonatal brachial plexus palsy","volume":"15","author":"Gatward","year":"2023","journal-title":"PMR"},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Schneider, S., Popp, W.L., Brogioli, M., Albisser, U., Ortmann, S., Velstra, I.M., Demko, L., Gassert, R., and Curt, A. (2019, January 24\u201328). Predicting upper limb compensation during prehension tasks in tetraplegic spinal cord injured patients using a single wearable sensor. Proceedings of the 2019 IEEE 16th International Conference on Rehabilitation Robotics (ICORR), Toronto, ON, Canada.","DOI":"10.1109\/ICORR.2019.8779561"},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/j.comcom.2020.01.074","article-title":"Design on a wearable armband device for assessing the motion function of upper limbs","volume":"153","author":"Gao","year":"2020","journal-title":"Comput. Commun."},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Jung, H.T., Kim, Y., Lee, J., Lee, S.I., and Choe, E.K. (2022). Envisioning the use of in-situ arm movement data in stroke rehabilitation: Stroke survivors\u2019 and occupational therapists\u2019 perspectives. PLoS ONE, 17.","DOI":"10.1371\/journal.pone.0274142"},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"1158","DOI":"10.1109\/TNSRE.2021.3089613","article-title":"Quantification of Motor Function Post-Stroke Using Novel Combination of Wearable Inertial and Mechanomyographic Sensors","volume":"29","author":"Formstone","year":"2021","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"Tran, H., Nguyen, K.D., Pathirana, P.N., Horne, M., Power, L., and Szmulewicz, D.J. (2020, January 20\u201324). Multimodal Data Acquisition for the Assessment of Cerebellar Ataxia via Ballistic Tracking. Proceedings of the 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Montreal, QC, Canada.","DOI":"10.1109\/EMBC44109.2020.9176379"},{"key":"ref_83","first-page":"3109","article-title":"Development of a Wearable Wireless Sensing Device for Characterization of Hand Tremors Through Vibration Frequency Analysis","volume":"1","author":"Yousef","year":"2022","journal-title":"J. Vib. Eng. Technol."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"905","DOI":"10.1002\/mds.28462","article-title":"Motor Onset Topography and Progression in Parkinson\u2019s Disease: The Upper Limb Is First","volume":"36","author":"Monje","year":"2021","journal-title":"Mov. Disord."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"903","DOI":"10.1177\/15459683211041302","article-title":"Upper Limb Performance in Daily Life Approaches Plateau Around Three to Six Weeks Post-stroke","volume":"35","author":"Lang","year":"2021","journal-title":"Neurorehabil. Neural Repair."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1080\/17518423.2023.2193630","article-title":"Action Observation Training to Improve Upper Limb Function in Infants with Unilateral Brain Lesion\u2014A Feasibility Study","volume":"26","author":"Botros","year":"2023","journal-title":"Dev. Neurorehabil."},{"key":"ref_87","doi-asserted-by":"crossref","unstructured":"Vanmechelen, I., Bekteshi, S., Haberfehlner, H., Feys, H., Desloovere, K., Aerts, J.-M., and Monbaliu, E. (2023). Reliability and Discriminative Validity of Wearable Sensors for the Quantification of Upper Limb Movement Disorders in Individuals with Dyskinetic Cerebral Palsy. Sensors, 23.","DOI":"10.3390\/s23031574"},{"key":"ref_88","unstructured":"Leuenberger, K., and Gassert, R. (September, January 30). Low-Power Sensor Module for Long-Term Activity Monitoring. Proceedings of the 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Boston, MA, USA."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"e772","DOI":"10.1002\/hsr2.772","article-title":"Assessing the validity of inertial measurement units for shoulder kinematics using a commercial sensor-software system: A validation study","volume":"5","author":"Henschke","year":"2022","journal-title":"Health Sci. Rep."},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"30","DOI":"10.21037\/mhealth-22-7","article-title":"Assessment of shoulder range of motion using a commercially available wearable sensor\u2014A validation study","volume":"8","author":"Chan","year":"2022","journal-title":"mHealth"},{"key":"ref_91","doi-asserted-by":"crossref","unstructured":"Tran, B., Zhang, X., Modan, A., and Hughes, C.M.L. (2022, January 21\u201324). Design and Evaluation of an IMU Sensor-based System for the Rehabilitation of Upper Limb Motor Dysfunction. Proceedings of the IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics, Seoul, Republic of Korea.","DOI":"10.1109\/BioRob52689.2022.9925549"},{"key":"ref_92","doi-asserted-by":"crossref","unstructured":"Hughes, C.M.L., Tran, B., Modan, A., and Zhang, X. (2022). Accuracy and Validity of a Single Inertial Measurement Unit-Based System to Determine Upper Limb Kinematics for Medically Underserved Populations. Front. Bioeng. Biotechnol., 10.","DOI":"10.3389\/fbioe.2022.918617"},{"key":"ref_93","doi-asserted-by":"crossref","unstructured":"Milosevic, B., Leardini, A., and Farella, E. (2020). Kinect and wearable inertial sensors for motor rehabilitation programs at home: State of the art and an experimental comparison. Biomed. Eng. Online, 19.","DOI":"10.1186\/s12938-020-00762-7"},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"4","DOI":"10.21037\/mhealth-19-199","article-title":"Usability study of wearable inertial sensors for exergames (WISE) for movement assessment and exercise","volume":"7","author":"Rajkumar","year":"2021","journal-title":"mHealth"},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"811","DOI":"10.1007\/s12311-021-01247-6","article-title":"Decomposition of Reaching Movements Enables Detection and Measurement of Ataxia","volume":"20","author":"Oubre","year":"2021","journal-title":"Cerebellum"},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"601","DOI":"10.1109\/TNSRE.2020.2966950","article-title":"Estimating Upper-Limb Impairment Level in Stroke Survivors Using Wearable Inertial Sensors and a Minimally-Burdensome Motor Task","volume":"28","author":"Oubre","year":"2020","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_97","doi-asserted-by":"crossref","unstructured":"Oubre, B., and Lee, S.I. (2022, January 27\u201330). Estimating Post-Stroke Upper-Limb Impairment from Four Activities of Daily Living using a Single Wrist-Worn Inertial Sensor. Proceedings of the BHI-BSN 2022-IEEE-EMBS International Conference on Biomedical and Health Informatics and IEEE-EMBS International Conference on Wearable and Implantable Body Sensor Networks, Ioannina, Greece.","DOI":"10.1109\/BHI56158.2022.9926918"},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"2144","DOI":"10.1002\/mds.28631","article-title":"Detecting Sensitive Mobility Features for Parkinson\u2019s Disease Stages via Machine Learning","volume":"36","author":"Mirelman","year":"2021","journal-title":"Mov. Disord."},{"key":"ref_99","doi-asserted-by":"crossref","unstructured":"Rovini, E., Fiorini, L., Esposito, D., Maremmani, C., and Cavallo, F. (2019, January 24\u201328). Fine motor assessment with unsupervised learning for personalized rehabilitation in Parkinson disease. Proceedings of the 2019 IEEE 16th International Conference on Rehabilitation Robotics (ICORR), Toronto, ON, Canada.","DOI":"10.1109\/ICORR.2019.8779543"},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"2254","DOI":"10.1109\/TNSRE.2022.3197807","article-title":"An Auxiliary Diagnostic System for Parkinson\u2019s Disease Based on Wearable Sensors and Genetic Algorithm Optimized Random Forest","volume":"30","author":"Chen","year":"2022","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"3397","DOI":"10.1109\/TNSRE.2023.3306203","article-title":"A Wearable Multi-Segment Upper Limb Tremor Assessment System for Differential Diagnosis of Parkinson\u2019s Disease Versus Essential Tremor","volume":"31","author":"Li","year":"2023","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_102","doi-asserted-by":"crossref","unstructured":"de Vries, W.H.K., Amrein, S., Arnet, U., Mayrhuber, L., Ehrmann, C., and Veeger, H.E.J. (2022). Classification of Wheelchair Related Shoulder Loading Activities from Wearable Sensor Data: A Machine Learning Approach. Sensors, 22.","DOI":"10.3390\/s22197404"},{"key":"ref_103","doi-asserted-by":"crossref","unstructured":"Chen, C.H., Liu, K.C., Lu, T.Y., Chang, C.Y., Chan, C.T., and Tsao, Y. (2023, January 24\u201327). Wearable-based Pain Assessment in Patients with Adhesive Capsulitis Using Machine Learning. Proceedings of the International IEEE\/EMBS Conference on Neural Engineering, NER, Baltimore, MD, USA.","DOI":"10.1109\/NER52421.2023.10123790"},{"key":"ref_104","doi-asserted-by":"crossref","unstructured":"Gao, R., Yang, S., Yuan, M., Song, X., Suganthan, P.N., and Ang, W.T. (2023, January 18\u201323). Online ensemble deep random vector functional link for the assistive robots. Proceedings of the International Joint Conference on Neural Networks, Gold Coast, Australia.","DOI":"10.1109\/IJCNN54540.2023.10191330"},{"key":"ref_105","doi-asserted-by":"crossref","unstructured":"Yang, S., Gao, R., Li, L., and Ang, W.T. (2022, January 18\u201323). Deep Randomized Feed-forward Networks Based Prediction of Human Joint Angles Using Wearable Inertial Measurement Unit: Performance Comparison. Proceedings of the International Joint Conference on Neural Networks, Padua, Italy.","DOI":"10.1109\/IJCNN55064.2022.9892502"},{"key":"ref_106","doi-asserted-by":"crossref","unstructured":"Little, K., Pappachan, B.K., Yang, S., Noronha, B., Campolo, D., and Accoto, D. (2021). Elbow motion trajectory prediction using a multi-modal wearable system: A comparative analysis of machine learning techniques. Sensors, 21.","DOI":"10.3390\/s21020498"},{"key":"ref_107","doi-asserted-by":"crossref","unstructured":"Yang, S., Garg, N.P., Gao, R., Yuan, M., Noronha, B., Ang, W.T., and Accoto, D. (2023). Learning-Based Motion-Intention Prediction for End-Point Control of Upper-Limb-Assistive Robots. Sensors, 23.","DOI":"10.3390\/s23062998"},{"key":"ref_108","doi-asserted-by":"crossref","first-page":"3026","DOI":"10.1109\/TBME.2019.2899927","article-title":"Rehab-Net: Deep Learning Framework for Arm Movement Classification Using Wearable Sensors for Stroke Rehabilitation","volume":"66","author":"Panwar","year":"2019","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_109","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1038\/s41746-020-00328-w","article-title":"Enabling precision rehabilitation interventions using wearable sensors and machine learning to track motor recovery","volume":"3","author":"Hankov","year":"2020","journal-title":"npj Digit. Med."},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"e17216","DOI":"10.2196\/17216","article-title":"Development and clinical evaluation of a web-based upper limb home rehabilitation system using a smartwatch and machine learning model for chronic stroke survivors: Prospective comparative study","volume":"8","author":"Chae","year":"2020","journal-title":"JMIR mHealth uHealth"},{"key":"ref_111","doi-asserted-by":"crossref","unstructured":"Barth, J., Lohse, K.R., Konrad, J.D., Bland, M.D., and Lang, C.E. (2021). Sensor-Based Categorization of Upper Limb Performance in Daily Life of Persons With and Without Neurological Upper Limb Deficits. Front. Rehabil. Sci., 2.","DOI":"10.3389\/fresc.2021.741393"},{"key":"ref_112","doi-asserted-by":"crossref","first-page":"599","DOI":"10.1109\/JBHI.2018.2821136","article-title":"The Use of a Finger-Worn Accelerometer for Monitoring of Hand Use in Ambulatory Settings","volume":"23","author":"Liu","year":"2019","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_113","doi-asserted-by":"crossref","unstructured":"Subash, T., David, A., ReetaJanetSurekha, S., Gayathri, S., Samuelkamaleshkumar, S., Magimairaj, H.P., Malesevic, N., Antfolk, C., Skm, V., and Melendez-Calderon, A. (2022). Comparing algorithms for assessing upper limb use with inertial measurement units. Front. Physiol., 13.","DOI":"10.3389\/fphys.2022.1023589"},{"key":"ref_114","unstructured":"Ernesto, C., Parisi, F., Adans-Dester, C., O\u2019Brien, A., Vergara-Diaz, G., Black-Schaffer, R., Zafonte, R., Ferreira, H., and Bonato, P. (2022, January 17\u201319). Wearable Technology and Machine Learning to Monitor Upper-Limb Use in Brain Injury Survivors. Proceedings of the 2022 IEEE\/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies, CHASE 2022, Arlington, VA, USA."},{"key":"ref_115","doi-asserted-by":"crossref","unstructured":"Werner, C., Sch\u00f6nhammer, J.G., Steitz, M.K., Lambercy, O., Luft, A.R., Demk\u00f3, L., and Easthope, C.A. (2022). Using Wearable Inertial Sensors to Estimate Clinical Scores of Upper Limb Movement Quality in Stroke. Front. Physiol., 13.","DOI":"10.3389\/fphys.2022.877563"},{"key":"ref_116","doi-asserted-by":"crossref","first-page":"1871","DOI":"10.1109\/TBME.2020.3027853","article-title":"Predicting and Monitoring Upper-Limb Rehabilitation Outcomes Using Clinical and Wearable Sensor Data in Brain Injury Survivors","volume":"68","author":"Lee","year":"2021","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_117","doi-asserted-by":"crossref","first-page":"1248","DOI":"10.1002\/acs.2941","article-title":"A comparative study of motion recognition methods for efficacy assessment of upper limb function","volume":"33","author":"He","year":"2019","journal-title":"Int. J. Adapt. Control Signal Process."},{"key":"ref_118","doi-asserted-by":"crossref","first-page":"e24402","DOI":"10.2196\/24402","article-title":"Upper-limb motion recognition based on hybrid feature selection: Algorithm development and validation","volume":"9","author":"Li","year":"2021","journal-title":"JMIR mHealth uHealth"},{"key":"ref_119","doi-asserted-by":"crossref","unstructured":"Lee, K.S., Chae, S., and Park, H.S. (2019, January 24\u201328). Optimal time-window derivation for human-activity recognition based on convolutional neural networks of repeated rehabilitation motions. Proceedings of the 2019 IEEE 16th International Conference on Rehabilitation Robotics (ICORR), Toronto, ON, Canada.","DOI":"10.1109\/ICORR.2019.8779475"},{"key":"ref_120","doi-asserted-by":"crossref","unstructured":"Segal, A.D., Lesak, M.C., Silverman, A.K., and Petruska, A.J. (2020). A gesture-controlled rehabilitation robot to improve engagement and quantify movement performance. Sensors, 20.","DOI":"10.3390\/s20154269"},{"key":"ref_121","doi-asserted-by":"crossref","unstructured":"Lapresa, M., Tamantini, C., Scotto di Luzio, F., Cordella, F., Bravi, M., Miccinilli, S., and Zollo, L. (2020, January 3\u20135). A Smart Solution for Proprioceptive Rehabilitation through M-IMU Sensors. Proceedings of the 2020 IEEE International Workshop on Metrology for Industry 4.0 and IoT, MetroInd 4.0 and IoT 2020\u2014Proceedings, Rome, Italy.","DOI":"10.1109\/MetroInd4.0IoT48571.2020.9138193"},{"key":"ref_122","doi-asserted-by":"crossref","unstructured":"Franzo\u2019, M., Pascucci, S., Serrao, M., Marinozzi, F., and Bini, F. (2022, January 22\u201324). Exergaming in mixed reality for the rehabilitation of ataxic patients. Proceedings of the 2022 IEEE International Symposium on Medical Measurements and Applications, MeMeA 2022\u2014Conference Proceedings, Messina, Italy.","DOI":"10.1109\/MeMeA54994.2022.9856552"},{"key":"ref_123","doi-asserted-by":"crossref","first-page":"480","DOI":"10.1111\/dmcn.14762","article-title":"Virtual reality rehabilitation in children with brain injury: A randomized controlled trial","volume":"63","author":"Choi","year":"2021","journal-title":"Dev. Med. Child. Neurol."},{"key":"ref_124","doi-asserted-by":"crossref","unstructured":"Choi, J.Y., Yi, S.H., Shim, D., Yoo, B., Park, E.S., and Rha, D.W. (2023). Home-based virtual reality-enhanced upper limb training system in children with brain injury: A randomized controlled trial. Front. Pediatr., 11.","DOI":"10.3389\/fped.2023.1131573"},{"key":"ref_125","doi-asserted-by":"crossref","first-page":"741","DOI":"10.1007\/978-3-030-58820-5_53","article-title":"Wearable Device for Immersive Virtual Reality Control and Application in Upper Limbs Motor Rehabilitation","volume":"Volume 12255","author":"Jurioli","year":"2020","journal-title":"Computational Science and Its Applications\u2014ICCSA 2020"},{"key":"ref_126","doi-asserted-by":"crossref","first-page":"727","DOI":"10.3171\/2022.2.PEDS21478","article-title":"Quantifying long-term upper-limb activity using wearable motion sensors after nerve reconstruction for neonatal brachial plexus palsy","volume":"29","author":"Muhlestein","year":"2022","journal-title":"J. Neurosurg. Pediatr."},{"key":"ref_127","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1089\/biores.2019.0035","article-title":"Movement Analysis with Inertial Measurement Unit Sensor after Surgical Treatment for Distal Radius Fractures","volume":"9","author":"Zucchi","year":"2020","journal-title":"Biores Open Access"},{"key":"ref_128","doi-asserted-by":"crossref","first-page":"403","DOI":"10.1016\/j.jss.2019.09.029","article-title":"Sensor-Based Upper-Extremity Frailty Assessment for the Vascular Surgery Risk Stratification","volume":"246","author":"Yanquez","year":"2020","journal-title":"J. Surg. Res."},{"key":"ref_129","doi-asserted-by":"crossref","first-page":"5597","DOI":"10.1245\/s10434-021-10458-4","article-title":"Objective Assessment of Postoperative Morbidity After Breast Cancer Treatments with Wearable Activity Monitors: The \u2018BRACELET\u2019 Study","volume":"28","author":"Bakri","year":"2021","journal-title":"Ann. Surg. Oncol."},{"key":"ref_130","doi-asserted-by":"crossref","unstructured":"Vets, N., De Groef, A., Verbeelen, K., Devoogdt, N., Smeets, A., Van Assche, D., De Baets, L., and Emmerzaal, J. (2023). Assessing Upper Limb Function in Breast Cancer Survivors Using Wearable Sensors and Machine Learning in a Free-Living Environment. Sensors, 23.","DOI":"10.3390\/s23136100"},{"key":"ref_131","doi-asserted-by":"crossref","unstructured":"Feldner, H.A., Papazian, C., Peters, K., and Steele, K.M. (2020). \u2018It\u2019s All Sort of Cool and Interesting\u2026but What Do I Do With It?\u2019 A Qualitative Study of Stroke Survivors\u2019 Perceptions of Surface Electromyography. Front. Neurol., 11.","DOI":"10.3389\/fneur.2020.01037"},{"key":"ref_132","doi-asserted-by":"crossref","unstructured":"Bonifati, P., Baracca, M., Menolotto, M., Averta, G., and Bianchi, M. (2023). A Multi-Modal Under-Sensorized Wearable System for Optimal Kinematic and Muscular Tracking of Human Upper Limb Motion. Sensors, 23.","DOI":"10.3390\/s23073716"},{"key":"ref_133","doi-asserted-by":"crossref","first-page":"338","DOI":"10.1016\/j.procs.2021.05.043","article-title":"The Use of Wearable Sensors for the Classification of Electromyographic Signal Patterns based on Changes in the Elbow Joint Angle","volume":"185","author":"Rahman","year":"2021","journal-title":"Procedia Comput. Sci."},{"key":"ref_134","doi-asserted-by":"crossref","unstructured":"Mendez, V., Pollina, L., Artoni, F., and Micera, S. (2021, January 4\u20136). Deep learning with convolutional neural network for proportionals control of finger movements from surface EMG recordings. Proceedings of the 2021 10th International IEEE\/EMBS Conference on Neural Engineering (NER), Virtual.","DOI":"10.1109\/NER49283.2021.9441095"},{"key":"ref_135","doi-asserted-by":"crossref","unstructured":"Salinas, S.A., Elgalhud, M.A.T.A., Tambakis, L., Salunke, S.V., Patel, K., Ghenniwa, H., Ouda, A., McIsaac, K., Grolinger, K., and Trejos, A.L. (2022, January 25\u201329). Comparison of Machine Learning Techniques for Activities of Daily Living Classification with Electromyographic Data. Proceedings of the IEEE International Conference on Rehabilitation Robotics, Rotterdam, The Netherlands.","DOI":"10.1109\/ICORR55369.2022.9896565"},{"key":"ref_136","doi-asserted-by":"crossref","unstructured":"Zhao, S., Liu, J., Gong, Z., Lei, Y., OuYang, X., Chan, C.C., and Ruan, S. (2020). Wearable physiological monitoring system based on electrocardiography and electromyography for upper limb rehabilitation training. Sensors, 20.","DOI":"10.3390\/s20174861"},{"key":"ref_137","doi-asserted-by":"crossref","unstructured":"Farago, E., Chinchalkar, S., Lizotte, D.J., and Trejos, A.L. (2019). Development of an EMG-based muscle health model for elbow trauma patients. Sensors, 19.","DOI":"10.3390\/s19153309"},{"key":"ref_138","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1186\/s12984-019-0504-1","article-title":"Applying LDA-based pattern recognition to predict isometric shoulder and elbow torque generation in individuals with chronic stroke with moderate to severe motor impairment","volume":"16","author":"Kopke","year":"2019","journal-title":"J. Neuroeng. Rehabil."},{"key":"ref_139","doi-asserted-by":"crossref","unstructured":"Merlo, A., and Campanini, I. (2023). Automatic Identification of Involuntary Muscle Activity in Subacute Patients with Upper Motor Neuron Lesion at Rest\u2014A Validation Study. Sensors, 23.","DOI":"10.3390\/s23020866"},{"key":"ref_140","doi-asserted-by":"crossref","unstructured":"Teh, Y., and Hargrove, L.J. (2021, January 4\u20136). Using latent representations of muscle activation patterns to mitigate myoelectric interface noise. Proceedings of the 2021 10th International IEEE\/EMBS Conference on Neural Engineering (NER), Virtual.","DOI":"10.1109\/NER49283.2021.9441396"},{"key":"ref_141","doi-asserted-by":"crossref","unstructured":"Baraka, A., Shaban, H., El-Nasr, M.A., and Attallah, O. (2019). Wearable accelerometer and sEMG-based upper limb BSN for tele-rehabilitation. Appl. Sci., 9.","DOI":"10.3390\/app9142795"},{"key":"ref_142","doi-asserted-by":"crossref","unstructured":"Song, X., van de Ven, S.S., Chen, S., Kang, P., Gao, Q., Jia, J., and Shull, P.B. (2022). Proposal of a Wearablse Multimodal Sensing-Based Serious Games Approach for Hand Movement Training After Stroke. Front. Physiol., 13.","DOI":"10.3389\/fphys.2022.811950"},{"key":"ref_143","doi-asserted-by":"crossref","unstructured":"Zhou, H., Zhang, Q., Zhang, M., Shahnewaz, S., Wei, S., Ruan, J., Zhang, X., and Zhang, L. (2021). Toward Hand Pattern Recognition in Assistive and Rehabilitation Robotics Using EMG and Kinematics. Front. Neurorobot., 15.","DOI":"10.3389\/fnbot.2021.659876"},{"key":"ref_144","doi-asserted-by":"crossref","unstructured":"Abass, Z., Meng, W., Xie, S.Q., and Zhang, Z. (2019, January 8\u201312). A robust, practical upper limb electromyography interface using dry 3D printed electrodes. Proceedings of the 2019 IEEE\/ASME International Conference on Advanced Intelligent Mechatronics (AIM), Hong Kong, China.","DOI":"10.1109\/AIM.2019.8868500"},{"key":"ref_145","doi-asserted-by":"crossref","unstructured":"Kanoga, S., Hoshino, T., and Asoh, H. (2021). Semi-supervised style transfer mapping-based framework for sEMG-based pattern recognition with 1- or 2-DoF forearm motions. Biomed. Signal Process Control, 68.","DOI":"10.1016\/j.bspc.2021.102817"},{"key":"ref_146","doi-asserted-by":"crossref","unstructured":"Lee, Y., Liu, X., Gummeson, J., and Lee, S.I. (2019, January 19\u201322). A Wearable RFID system to monitor hand use for individuals with upper limb paresis. Proceedings of the 2019 IEEE 16th International Conference on Wearable and Implantable Body Sensor Networks, BSN 2019\u2014Proceedings, Chicago, IL, USA.","DOI":"10.1109\/BSN.2019.8771099"},{"key":"ref_147","doi-asserted-by":"crossref","unstructured":"Bharadwaj, R., and Koul, S.K. (2019, January 6\u20138). Monitoring of Limb Movement Activities during Physical Exercises using UWB Channel Parameters. Proceedings of the IEEE MTT-S 2019 International Microwave Biomedical Conference, IMBioC 2019\u2014Proceedings, Nanjing, China.","DOI":"10.1109\/IMBIOC.2019.8777814"},{"key":"ref_148","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1186\/s12984-023-01199-4","article-title":"Quantitative measurement of finger usage in stroke hemiplegia using ring-shaped wearable devices","volume":"20","author":"Yamamoto","year":"2023","journal-title":"J. Neuroeng. Rehabil."},{"key":"ref_149","doi-asserted-by":"crossref","first-page":"35726","DOI":"10.1109\/ACCESS.2023.3265898","article-title":"A Versatile Embedded Platform for Implementation of Biocooperative Control in Upper-Limb Neuromotor Rehabilitation Scenarios","volume":"11","author":"Cisnal","year":"2023","journal-title":"IEEE Access"},{"key":"ref_150","doi-asserted-by":"crossref","unstructured":"Badesa, F.J., Diez, J.A., Catalan, J.M., Trigili, E., Cordella, F., Nann, M., Crea, S., Soekadar, S.R., Zollo, L., and Vitiello, N. (2019). Physiological responses during hybrid BNCI control of an upper-limb exoskeleton. Sensors, 19.","DOI":"10.3390\/s19224931"},{"key":"ref_151","doi-asserted-by":"crossref","unstructured":"Ogata, K., Kanazawa, S., Tanaka, H., and Kurata, T. (2022, January 23\u201327). Upper Limb Movement Estimation and Function Evaluation of the Shoulder Girdle by Multi-Sensing Flexible Sensor Wear. Proceedings of the 2022 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Kyoto, Japan.","DOI":"10.1109\/IROS47612.2022.9982102"},{"key":"ref_152","doi-asserted-by":"crossref","first-page":"1922","DOI":"10.1109\/TNSRE.2023.3263227","article-title":"Bending-Sensitive Optical Waveguide Sensor With Carbon-Fiber Layer for Monitoring Grip Strength","volume":"31","author":"Zhang","year":"2023","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_153","doi-asserted-by":"crossref","first-page":"724","DOI":"10.1109\/TNSRE.2019.2905658","article-title":"Hand Gesture Recognition and Finger Angle Estimation via Wrist-Worn Modified Barometric Pressure Sensing","volume":"27","author":"Shull","year":"2019","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_154","doi-asserted-by":"crossref","first-page":"2434","DOI":"10.1109\/TBME.2019.2962499","article-title":"Comparative Analysis of Wearable A-Mode Ultrasound and sEMG for Muscle-Computer Interface","volume":"67","author":"Yang","year":"2020","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_155","doi-asserted-by":"crossref","unstructured":"Xiao, Z.G., and Menon, C. (2019). An investigation on the sampling frequency of the upper-limb force myographic signals. Sensors, 19.","DOI":"10.3390\/s19112432"},{"key":"ref_156","doi-asserted-by":"crossref","unstructured":"Mariani, G., Taborri, J., Mileti, I., Bagordo, G., Palermo, E., Patan\u00e8, F., and Rossi, S. (2022, January 7\u20139). Design and characterization of a smart fabric sensor to recognize human intention for robotic applications. Proceedings of the 2022 IEEE International Workshop on Metrology for Industry 4.0 and IoT, MetroInd 4.0 and IoT 2022\u2014Proceedings, Trento, Italy.","DOI":"10.1109\/MetroInd4.0IoT54413.2022.9831554"},{"key":"ref_157","doi-asserted-by":"crossref","unstructured":"Stefanou, T., Chance, G., Assaf, T., and Dogramadzi, S. (2019). Tactile Signatures and Hand Motion Intent Recognition for Wearable Assistive Devices. Front. Robot. AI, 6.","DOI":"10.3389\/frobt.2019.00124"},{"key":"ref_158","doi-asserted-by":"crossref","first-page":"1471","DOI":"10.1109\/TNSRE.2020.2992885","article-title":"Intent Prediction Based on Biomechanical Coordination of EMG and Vision-Filtered Gaze for End-Point Control of an Arm Prosthesis","volume":"28","author":"Krausz","year":"2020","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_159","doi-asserted-by":"crossref","first-page":"144144","DOI":"10.1186\/s12984-020-00771-6","article-title":"Immersive Virtual Environments and Wearable Haptic Devices in rehabilitation of children with neuromotor impairments: A single-blind randomized controlled crossover pilot study","volume":"17","author":"Bortone","year":"2020","journal-title":"J. Neuroeng. Rehabil."},{"key":"ref_160","doi-asserted-by":"crossref","unstructured":"Wang, C., Sivan, M., Bao, T., Li, G., and Xie, S. (2021, January 4\u20136). 3D free reaching movement prediction of upper-limb based on deep neural networks. Proceedings of the 2021 10th International IEEE\/EMBS Conference on Neural Engineering (NER), Virtual.","DOI":"10.1109\/NER49283.2021.9441350"},{"key":"ref_161","first-page":"205566832091611","article-title":"New advances in mechanomyography sensor technology and signal processing: Validity and intrarater reliability of recordings from muscle","volume":"7","author":"Meagher","year":"2020","journal-title":"J. Rehabil. Assist. Technol. Eng."},{"key":"ref_162","doi-asserted-by":"crossref","first-page":"20","DOI":"10.4103\/iahs.iahs_53_19","article-title":"Effect of shoulder pain on energy expenditure among paraplegic individuals: Role of wearable device","volume":"7","author":"Krishnan","year":"2020","journal-title":"Int. Arch. Health Sci."},{"key":"ref_163","doi-asserted-by":"crossref","unstructured":"Kanazawa, S., and Ushijima, H. (2020). Development of a strain sensor matrix on mobilized flexible substrate for the imaging ofwind pressure distribution. Micromachines, 11.","DOI":"10.3390\/mi11020232"},{"key":"ref_164","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1109\/TOH.2016.2640291","article-title":"A 3-RSR Haptic Wearable Device for Rendering Fingertip Contact Forces","volume":"10","author":"Leonardis","year":"2017","journal-title":"IEEE Trans. Haptics"},{"key":"ref_165","doi-asserted-by":"crossref","first-page":"e17036","DOI":"10.2196\/17036","article-title":"Haptic nudges increase affected upper limb movement during inpatient stroke rehabilitation: Multiple-period randomized crossover study","volume":"8","author":"Signal","year":"2020","journal-title":"JMIR mHealth uHealth"},{"key":"ref_166","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1109\/TNSRE.2018.2882235","article-title":"\u2018Remind-to-Move\u2019 for Promoting Upper Extremity Recovery Using Wearable Devices in Subacute Stroke: A Multi-Center Randomized Controlled Study","volume":"27","author":"Wei","year":"2019","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_167","doi-asserted-by":"crossref","unstructured":"Mayrhuber, L., Lestoille, M., Andres, S.D., Held, J.P.O., Luft, A.R., Ryser, F., Gassert, R., Awai Easthope, C., and Lambercy, O. (2023, January 24\u201328). Movement Reminders to Encourage Arm Use During Daily Life in Stroke Patients. Proceedings of the IEEE International Conference on Rehabilitation Robotics, Singapore.","DOI":"10.1109\/ICORR58425.2023.10304727"},{"key":"ref_168","doi-asserted-by":"crossref","unstructured":"Wang, H., Ghazi, M., Chandrashekhar, R., Rippetoe, J., Duginski, G.A., Lepak, L.V., Milhan, L.R., and James, S.A. (2022). User Participatory Design of a Wearable Focal Vibration Device for Home-Based Stroke Rehabilitation. Sensors, 22.","DOI":"10.3390\/s22093308"},{"key":"ref_169","doi-asserted-by":"crossref","unstructured":"Ferrari, F., Shell, C.E., Thumser, Z.C., Clemente, F., Plow, E.B., Cipriani, C., and Marasco, P.D. (2021). Proprioceptive Augmentation With Illusory Kinaesthetic Sensation in Stroke Patients Improves Movement Quality in an Active Upper Limb Reach-and-Point Task. Front. Neurorobot., 15.","DOI":"10.3389\/fnbot.2021.610673"},{"key":"ref_170","doi-asserted-by":"crossref","first-page":"702","DOI":"10.1177\/15394492231151887","article-title":"Effect of Using TheraBracelet on Grasping versus Reaching in Poststroke Rehabilitation","volume":"43","author":"Pennington","year":"2023","journal-title":"Occup. Ther. J. Res."},{"key":"ref_171","doi-asserted-by":"crossref","unstructured":"Camardella, C., Chiaradia, D., Bortone, I., Frisoli, A., and Leonardis, D. (2023). Introducing wearable haptics for rendering velocity feedback in VR serious games for neuro-rehabilitation of children. Front. Virtual Real., 3.","DOI":"10.3389\/frvir.2022.1019302"},{"key":"ref_172","doi-asserted-by":"crossref","first-page":"238","DOI":"10.1080\/08990220.2023.2272971","article-title":"Dual muscle tendon vibration does not impede performance of a goal-directed aiming task","volume":"41","author":"Mortaza","year":"2023","journal-title":"Somatosens. Mot. Res."},{"key":"ref_173","doi-asserted-by":"crossref","first-page":"600","DOI":"10.1177\/1545968320926162","article-title":"A Novel Wearable Device for Motor Recovery of Hand Function in Chronic Stroke Survivors","volume":"34","author":"Choudhury","year":"2020","journal-title":"Neurorehabil. Neural Repair."},{"key":"ref_174","doi-asserted-by":"crossref","unstructured":"Ward, T., Grabham, N., Freeman, C., Wei, Y., Hughes, A.-M., Power, C., Tudor, J., and Yang, K. (2020). Multichannel biphasic muscle stimulation system for post stroke rehabilitation. Electronics, 9.","DOI":"10.3390\/electronics9071156"},{"key":"ref_175","doi-asserted-by":"crossref","first-page":"2142","DOI":"10.1109\/TNSRE.2021.3120293","article-title":"FITFES: A Wearable Myoelectrically Controlled Functional Electrical Stimulator Designed Using a User-Centered Approach","volume":"29","author":"Crepaldi","year":"2021","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_176","doi-asserted-by":"crossref","unstructured":"Yong, C.Y., and Sia, T.T.L. (2022, January 7\u20139). An IoT Rehab Device: HHI-based NMES System for Motion Stimulation. Proceedings of the 7th IEEE-EMBS Conference on Biomedical Engineering and Sciences, IECBES 2022\u2014Proceedings, Kuala Lumpur, Malaysia.","DOI":"10.1109\/IECBES54088.2022.10079442"},{"key":"ref_177","doi-asserted-by":"crossref","first-page":"2000111","DOI":"10.1109\/JTEHM.2020.2985058","article-title":"A wearable system for attenuating essential tremor based on peripheral nerve stimulation","volume":"8","author":"Kim","year":"2020","journal-title":"IEEE J Transl. Eng Health Med"},{"key":"ref_178","doi-asserted-by":"crossref","unstructured":"Seo, N.J., Woodbury, M.L., Bonilha, L., Ramakrishnan, V., Kautz, S.A., Downey, R.J., Dellenbach, B.H.S., Lauer, A.W., Roark, C.M., and Landers, L.E. (2024, January 30). TheraBracelet Stimulation During Task-Practice Therapy to Improve Upper Extremity Function After Stroke: A Pilot Randomized Controlled Study. Available online: https:\/\/academic.oup.com\/ptj\/article\/99\/3\/319\/5303730.","DOI":"10.1093\/ptj\/pzy143"},{"key":"ref_179","unstructured":"Camardella, C., Gabardi, M., Frisoli, A., and Leonardis, D. (2024, January 31). Wearable Haptics in a Modern VR Rehabilitation System: Design Comparison for Usability and Engagement. Available online: https:\/\/link.springer.com\/bookseries\/558."},{"key":"ref_180","doi-asserted-by":"crossref","first-page":"1378","DOI":"10.1007\/s42765-023-00270-y","article-title":"Calotropis gigantea Fiber-Based Sensitivity-Tunable Strain Sensors with Insensitive Response to Wearable Microclimate Changes","volume":"5","author":"Zhang","year":"2023","journal-title":"Adv. Fiber Mater."},{"key":"ref_181","doi-asserted-by":"crossref","first-page":"141628","DOI":"10.1016\/j.cej.2023.141628","article-title":"A negative-response strain sensor towards wearable microclimate changes for body area sensing networks","volume":"459","author":"Liu","year":"2023","journal-title":"Chem. Eng. J."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/24\/7973\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:54:48Z","timestamp":1760115288000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/24\/7973"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,13]]},"references-count":181,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["s24247973"],"URL":"https:\/\/doi.org\/10.3390\/s24247973","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,13]]}}}