[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118621-en":3,"doc-seo-118621-105":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},118621,687197207919,"Theodora","https://ap-avatar.wpscdn.com/avatar/a000253d6f5f7c60be?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779446848396160552",8,"Research & Report","Machine Learning in Rehabilitation Training - Traditional and Deep Learning Approaches","Machine learning methods are reviewed for their role in rehabilitation training, with emphasis on motor function recovery after stroke. The framework distinguishes traditional machine learning and deep learning strategies, covering gait prediction, movement intention recognition from sEMG, and decision-tree support for training planning. Deep learning methods process EEG and time-series sEMG using DNNs, LSTM, and CNN-LSTM hybrids for skeletal motion recognition. Current systems achieve over 90% accuracy for standard movements, but face challenges in interpretability, generalization to rare movements, and deployment in low-resource settings. Future work prioritizes interpretable models, transfer learning, and model distillation for real-world rehabilitation systems.","Machine Learning in Rehabilitation Training: Traditional and Deep Learning Approaches  \nYanbo Li  \nCollege of Computer and Information Science & College of Software, Southwest University, Chongqing, China  \nAbstract. This study aims to review and analyze the application of machine learning techniques in rehabilitation training, particularly in motor function recovery after stroke. The methodological framework is classified into two primary categories: traditional machine learning techniques and deep learning approaches. Traditional machine learning methods include:  \nusing multiple linear regression to predict gait parameters, applying Support Vector Machines (SVM) combined with surface electromyography (sEMG) signals to recognize upper limb movement intentions, and employing decision trees to assist in developing rehabilitation training plans. Deep learning methods involve using Deep Neural Networks (DNNs) to process electroencephalogram (EEG) signals, Time-series sEMG signals can be effectively captured using Long ShortTerm Memory (LSTM) models, and CNN-LSTM hybrid models for skeletal motion sequence recognition. Some methods, such as SVM optimized with genetic algorithms and CNN-LSTM models incorporating attention mechanisms, have achieved recognition accuracy exceeding 90% without manual feature extraction. Although current models perform wellin recognizing standard rehabilitation movements, challenges remain in terms of poor interpretability, weak generalization to rare movements, and limited deployability in low-resource environments. Future efforts should focus on interpretable network design, transfer learning strategies, and the application of model distillation algorithms to facilitate the real-world deployment of rehabilitation training systems.  \n1 Introduction  \nRehabilitation encompasses structured interventions intended to improve physical and cognitive function while mitigating disability in individuals experiencing health-related challenges in interaction with their surroundings [1] . Rehabilitation Training (RT), a significant branch of rehabilitation, usually refers to training provided to injured individuals to help them return to their pre-injury fitness levels. Rehabilitation has been demonstrated to be beneficial for individuals. Specifically, rehabilitation has been found effective in improving trunk and lower extremity movement control, hip muscle strength, gait speed, and daily activities in stroke patients [2] . Additionally, significant improvements in insulin  \n[eric0823wtf@email.swu.edu.cn](eric0823wtf@email.swu.edu.cn)  \n© The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 ([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)).  \nprofile and lipid metabolism were observed when RT was combined with dietary interventions compared to dietary interventions alone [3] .  \nCurrently, the quality of rehabilitation training is universally emphasized during training sessions. Investing in quality rehabilitation education and training contributes to more efficient healthcare delivery, potentially reducing overall healthcare costs [1] . Traditional rehabilitation training, which relies solely on medical professionals, often depends primarily on intuitive judgments and estimations [4] . In contrast, the involvement of Artificial Intelligence (AI) algorithms has improved the quality of rehabilitation training today. By supporting tailored treatment strategies, AI-driven approaches contribute to improved rehabilitation efficacy and help bridge the gap between clinical rehabilitation and real-life functional recovery [5] . Introducing AI algorithms to guide rehabilitation training is a viable option for maintaining training quality in the absence of necessary equipment and medical professionals [6] .  \nThe formalization concept of rehabilitation training occurred in the 20th cen","cbCaihAwgV0gENiv","https://ap.wps.com/l/cbCaihAwgV0gENiv","pdf",266921,1,9,"English","en",105,"# Introduction\n## Rehabilitation training background\n## AI and machine learning in rehabilitation","[{\"question\":\"What is the main focus of the study on rehabilitation training?\",\"answer\":\"The study reviews and analyzes how machine learning techniques are used in rehabilitation training, especially for motor function recovery after stroke.\"},{\"question\":\"How do traditional machine learning methods contribute to rehabilitation training?\",\"answer\":\"Examples include using multiple linear regression for gait parameter prediction, SVM with sEMG signals for upper-limb movement intention recognition, and decision trees to help develop rehabilitation training plans.\"},{\"question\":\"What limitations remain for current models and what future directions are suggested?\",\"answer\":\"The document notes poor interpretability, weak generalization to rare movements, and limited deployability in low-resource environments, recommending interpretable network design, transfer learning, and model distillation for real-world deployment.\"}]","Machine Learning in Rehabilitation Training - Traditional and Deep Learning Approaches | PDF",1785684556,23,{"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},"machine-learning-in-rehabilitation-training-traditional-and-deep-learning-approaches","",{"@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/machine-learning-in-rehabilitation-training-traditional-and-deep-learning-approaches/118621/",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},"What is the main focus of the study on rehabilitation training?","Question",{"text":76,"@type":77},"The study reviews and analyzes how machine learning techniques are used in rehabilitation training, especially for motor function recovery after stroke.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How do traditional machine learning methods contribute to rehabilitation training?",{"text":81,"@type":77},"Examples include using multiple linear regression for gait parameter prediction, SVM with sEMG signals for upper-limb movement intention recognition, and decision trees to help develop rehabilitation training plans.",{"name":83,"@type":74,"acceptedAnswer":84},"What limitations remain for current models and what future directions are suggested?",{"text":85,"@type":77},"The document notes poor interpretability, weak generalization to rare movements, and limited deployability in low-resource environments, recommending interpretable network design, transfer learning, and model distillation for real-world deployment.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":107,"slug":138},19,"General","general"]