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dc.contributor.authorGu, Yiwenen_US
dc.contributor.authorPandit, Shreyaen_US
dc.contributor.authorSaraee, Elhamen_US
dc.contributor.authorNordahl, Timothyen_US
dc.contributor.authorEllis, Terryen_US
dc.contributor.authorBetke, Margriten_US
dc.coverage.spatialSeoul, Koreaen_US
dc.date.accessioned2020-05-15T15:50:40Z
dc.date.available2020-05-15T15:50:40Z
dc.date.issued2019-10-28
dc.identifierhttp://openaccess.thecvf.com/content_ICCVW_2019/papers/ACVR/Gu_Home-Based_Physical_Therapy_with_an_Interactive_Computer_Vision_System_ICCVW_2019_paper.pdf
dc.identifier.citationYiwen Gu, Shreya Pandit, Elham Saraee, Timothy Nordahl, Terry Ellis, Margrit Betke. 2019. "Home-based Physical Therapy with an Interactive Computer Vision System." International Conference on Computer Vision Workshop on Assistive Computer Vision and Robotics. Seoul, Korea,
dc.identifier.urihttps://hdl.handle.net/2144/40915
dc.description.abstractIn this paper, we present ExerciseCheck. ExerciseCheck is an interactive computer vision system that is sufficiently modular to work with different sources of human pose estimates, i.e., estimates from deep or traditional models that interpret RGB or RGB-D camera input. In a pilot study, we first compare the pose estimates produced by four deep models based on RGB input with those of the MS Kinect based on RGB-D data. The results indicate a performance gap that required us to choose the MS Kinect when we tested ExerciseCheck with Parkinson’s disease patients in their homes. ExerciseCheck is capable of customizing exercises, capturing exercise information, evaluating patient performance, providing therapeutic feedback to the patient and the therapist, checking the progress of the user over the course of the physical therapy, and supporting the patient throughout this period. We conclude that ExerciseCheck is a user-friendly computer vision application that can assist patients by providing motivation and guidance to ensure correct execution of the required exercises. Our results also suggest that while there has been considerable progress in the field of pose estimation using deep learning, current deep learning models are not fully ready to replace RGB-D sensors, especially when the exercises involved are complex, and the patient population being accounted for has to be carefully tracked for its “active range of motion.”en_US
dc.format.extent10 pages.en_US
dc.language.isoen_US
dc.titleHome-based physical therapy with an interactive computer vision systemen_US
dc.typeConference materialsen_US
dc.description.versionPublished versionen_US
pubs.elements-sourcemanual-entryen_US
pubs.notesEmbargo: Not knownen_US
pubs.organisational-groupBoston Universityen_US
pubs.organisational-groupBoston University, College of Arts & Sciencesen_US
pubs.organisational-groupBoston University, College of Arts & Sciences, Department of Computer Scienceen_US
pubs.organisational-groupBoston University, College of Health & Rehabilitation Sciences: Sargent Collegeen_US
pubs.organisational-groupBoston University, College of Health & Rehabilitation Sciences: Sargent College, Physical Therapy and Athletic Trainingen_US
pubs.publication-statusPublisheden_US
dc.identifier.orcid0000-0002-4491-6868 (Betke, Margrit)
dc.identifier.mycv547431


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