S. Pandit, San Tran, Yiwen Gu, E. Saraee, Frederick Jansen, Saurabh Singh, Shirene Cao, Arezoo Sadeghi, Eugenia Shandelman, T. Ellis, Margrit Betke
{"title":"ExerciseCheck: a scalable platform for remote physical therapy deployed as a hybrid desktop and web application","authors":"S. Pandit, San Tran, Yiwen Gu, E. Saraee, Frederick Jansen, Saurabh Singh, Shirene Cao, Arezoo Sadeghi, Eugenia Shandelman, T. Ellis, Margrit Betke","doi":"10.1145/3316782.3321537","DOIUrl":null,"url":null,"abstract":"ExerciseCheck is a scalable, accessible platform designed and developed for the remote monitoring and evaluation of physical therapy. Physical rehabilitation is an important aspect of a patient's recovery from injury and often requires the patient to perform a prescribed set of exercises over medium- to long-term periods. In the absence of a physical therapist, using a low-cost, non-intrusive solution can serve as a complement to in-clinic sessions and provide patients with valuable insights into their exercises. We present the design, implementation, and deployment of ExerciseCheck, a modular system that incorporates machine learning techniques with contemporary web technologies to enable a user-friendly experience for patients and physical therapists. Initially a proof-of-concept, the latest version of ExerciseCheck is now deployed as a hybrid desktop and web application at a Boston University rehabilitation clinic and has been employed by physical therapists in their sessions with individuals with Parkinson's disease. We provide insights into the usability requirements, architecture design, and implementation challenges of the development and deployment of a production-quality platform for remote physical therapy in a clinical setting.","PeriodicalId":264425,"journal":{"name":"Proceedings of the 12th ACM International Conference on PErvasive Technologies Related to Assistive Environments","volume":"21 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 12th ACM International Conference on PErvasive Technologies Related to Assistive Environments","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3316782.3321537","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4
Abstract
ExerciseCheck is a scalable, accessible platform designed and developed for the remote monitoring and evaluation of physical therapy. Physical rehabilitation is an important aspect of a patient's recovery from injury and often requires the patient to perform a prescribed set of exercises over medium- to long-term periods. In the absence of a physical therapist, using a low-cost, non-intrusive solution can serve as a complement to in-clinic sessions and provide patients with valuable insights into their exercises. We present the design, implementation, and deployment of ExerciseCheck, a modular system that incorporates machine learning techniques with contemporary web technologies to enable a user-friendly experience for patients and physical therapists. Initially a proof-of-concept, the latest version of ExerciseCheck is now deployed as a hybrid desktop and web application at a Boston University rehabilitation clinic and has been employed by physical therapists in their sessions with individuals with Parkinson's disease. We provide insights into the usability requirements, architecture design, and implementation challenges of the development and deployment of a production-quality platform for remote physical therapy in a clinical setting.