[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117527-en":3,"doc-seo-117527-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},117527,687197207057,"Sage","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","A Survey of Wearable Sensors and Machine Learning Algorithms for Automated Stroke Rehabilitation","Stroke is a leading cause of disability among older adults and a major global public health burden, with functional impairment driven by post-stroke motor dysfunction. Rehabilitation outcomes depend on effective therapy for restoring pre-stroke mobility and on continuous assessment of affected patients during daily activities to support recovery tracking. This survey explains how wearable devices paired with machine learning create new pathways for automated rehabilitation, enabling fine-grained movement capture and predictive modeling from wearable signals. It reviews literature findings, key research challenges, and future directions for monitoring training, assessment, and remote care, and how these methods can improve monitoring quality in post-stroke therapies.","A Survey of Wearable Sensors and Machine Learning Algorithms for Automated Stroke Rehabilitation  \nAuthor  \nSengupta , Nandini , Rao , Aravinda S , Yan , Bernard , Palaniswami , Marimuthu  \nPublished 2024  \nJournal Title IEEE Access  \nVersion  \nVersion of Record (VoR)  \nDOI  \n10. 1109/access.2024.3373910  \nRights statement  \n© 2024 The Authors. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see [https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)  \nDownloaded from  \n[https://hdl.handle.net/10072/435858](https://hdl.handle.net/10072/435858)  \nGriffith Research Online  \n[https://research-repository.griffith.edu.au](https://research-repository.griffith.edu.au)  \nReceived 17 January 2024, accepted 27 February 2024, date of publication 5 March 2024, date of current version 12 March 2024. Digital Object Identifier 10.1109/ACCESS.2024.3373910  \nA Survey of Wearable Sensors and Machine Learning Algorithms for Automated Stroke Rehabilitation  \nNANDINI SENGUPTA1, ARAVINDA S. RAO1,(Senior Member, IEEE), BERNARD YAN2, AND MARIMUTHU PALANISWAMI1,(Life Fellow, IEEE)  \n1Department of Electrical and Electronic Engineering, The University of Melbourne, Parkville, Melbourne, VIC 3010, Australia  \n2The Royal Melbourne Hospital, Parkville, VIC 3052, Australia  \nCorresponding author: Nandini Sengupta ([nsengupta@student.unimelb.edu.au](nsengupta@student.unimelb.edu.au))  \nThis work was supported in part by the Australian Government through the Australian Research Council’s Discovery Projects Funding Scheme under Project DP190101248 .  \nABSTRACT Stroke is one of the leading causes of disability among the elderly population and is a significant public health problem worldwide. The main impact of stroke is functional disabilities due to motor impairment after stroke. Advances in modern medicine and technology have significantly improved diagnosis and treatment; however, most post-stroke care is based on the effectiveness of rehabilitation. Stroke rehabilitation depends on two main components: (i) training (or therapy) to restore the patient to pre-stroke mobility and (ii) assessing motor functionality of affected patients performing activities to track motor recovery. This article highlights how combining wearable devices and machine learning (ML) produces new pathways for effective stroke rehabilitation. While wearable devices help capture patient movements at much finer time resolutions, ML allows us to build predictive models from wearable data to assist clinicians in diagnosis and treatments. Specifically, we expand on how wearable devices and ML can improve monitoring quality in training intervention, assessment, and remote monitoring. In addition, we provide our main findings from the literature, research challenges, and future directions in post-stroke therapies using wearable devices and ML.  \nINDEX TERMS Stroke rehabilitation, wearable devices, machine learning, interventions, remote monitoring.  \nI. INTRODUCTION  \nStroke is the second leading cause of death, and 17 million people worldwide suffer from stroke each year [1] . Stroke can have devastating effects, including death or severe disability, which can cause social and family burdens. Even thoughthe majority of the victims are older adults, the number of people 60 years of age or older is projected to increase from an estimated 488 million in 1990 to nearly 1,363 million in 2030 [2] . Since stroke is the leading cause of adult disability in the world and 70-85% of stroke patients have hemiplegia after the first stroke [3], motor recovery is one of the most crucial aspects for stroke victims. Wearable devices, when  \nThe associate editor coordinating the review of this manuscript and approving it for publication was Chan Hwang See.  \npaired with machine learning (ML), enable us to continuously monitor subjects and ascertain the progressive course towards enhanced motor recovery. It is vital to monitor","cbCaigzhWLlKJUDD","https://ap.wps.com/l/cbCaigzhWLlKJUDD","pdf",4494077,1,30,"English","en",105,"# Introduction\n## Wearable-device-assisted remote monitoring concept\n# Abstract and index terms\n## Motivation and rehabilitation workflow","[{\"question\":\"Why is stroke rehabilitation so important to monitor motor recovery?\",\"answer\":\"Stroke frequently leads to severe disability due to motor impairment, making motor recovery a crucial rehabilitation goal. Continuous monitoring supports tracking progression and functional improvements over time.\"},{\"question\":\"How do wearable devices contribute to automated stroke rehabilitation?\",\"answer\":\"Wearable sensors capture patient movement continuously with fine time resolution. The collected data is transferred to a phone/tablet and then uploaded to a cloud for clinician review and analysis.\"},{\"question\":\"What role does machine learning play alongside wearable sensors?\",\"answer\":\"Machine learning builds predictive models from wearable data to assist diagnosis and treatment. It also improves remote monitoring by enabling automated analysis of activity during training and assessment.\"}]","A Survey of Wearable Sensors and Machine Learning Algorithms for Automated Stroke Rehabilitation | PDF",1785676693,76,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"a-survey-of-wearable-sensors-and-machine-learning-algorithms-for-automated-stroke-rehabilitation","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/a-survey-of-wearable-sensors-and-machine-learning-algorithms-for-automated-stroke-rehabilitation/117527/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05","2026-08-02",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Why is stroke rehabilitation so important to monitor motor recovery?","Question",{"text":76,"@type":77},"Stroke frequently leads to severe disability due to motor impairment, making motor recovery a crucial rehabilitation goal. Continuous monitoring supports tracking progression and functional improvements over time.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How do wearable devices contribute to automated stroke rehabilitation?",{"text":81,"@type":77},"Wearable sensors capture patient movement continuously with fine time resolution. The collected data is transferred to a phone/tablet and then uploaded to a cloud for clinician review and analysis.",{"name":83,"@type":74,"acceptedAnswer":84},"What role does machine learning play alongside wearable sensors?",{"text":85,"@type":77},"Machine learning builds predictive models from wearable data to assist diagnosis and treatment. 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