{"title":"Autonomous Training Assistant: A System and Framework for Guided At-Home Motor Learning","authors":"Ramin Tadayon, T. McDaniel, S. Panchanathan","doi":"10.1145/2982142.2982192","DOIUrl":null,"url":null,"abstract":"We present a novel framework and system for at-home rehabilitative exercise in the absence of a physical therapist. The framework includes metrics for assessing motor performance on a wide variety of exercises. We present our system, the Autonomous Training Assistant, which utilizes this framework and a low-cost accessible exercise device called the Intelligent Stick to deliver feedback as a user trains at home. We evaluated the system's multimodal feedback mechanism in a case study whose results indicate that individual preference may have a significant effect on modality assignment for optimal learning. We conclude with ideas for future work.","PeriodicalId":306165,"journal":{"name":"Proceedings of the 18th International ACM SIGACCESS Conference on Computers and Accessibility","volume":"20 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 18th International ACM SIGACCESS Conference on Computers and Accessibility","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2982142.2982192","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
We present a novel framework and system for at-home rehabilitative exercise in the absence of a physical therapist. The framework includes metrics for assessing motor performance on a wide variety of exercises. We present our system, the Autonomous Training Assistant, which utilizes this framework and a low-cost accessible exercise device called the Intelligent Stick to deliver feedback as a user trains at home. We evaluated the system's multimodal feedback mechanism in a case study whose results indicate that individual preference may have a significant effect on modality assignment for optimal learning. We conclude with ideas for future work.