A Computerized Recognition System for the Home-Based Physiotherapy Exercises Using an RGBD Camera

dc.contributor.authorAr, İlktan
dc.contributor.authorAkgül, Yusuf Sinan
dc.date.accessioned2019-06-27T08:02:45Z
dc.date.available2019-06-27T08:02:45Z
dc.date.issued2014
dc.departmentFakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.description.abstractComputerized recognition of the home based physiotherapy exercises has many benefits and it has attracted considerable interest among the computer vision community. However most methods in the literature view this task as a special case of motion recognition. In contrast we propose to employ the three main components of a physiotherapy exercise (the motion patterns the stance knowledge and the exercise object) as different recognition tasks and embed them separately into the recognition system. The low level information about each component is gathered using machine learning methods. Then we use a generative Bayesian network to recognize the exercise types by combining the information from these sources at an abstract level which takes the advantage of domain knowledge for a more robust system. Finally a novel postprocessing step is employed to estimate the exercise repetitions counts. The performance evaluation of the system is conducted with a new dataset which contains RGB (red green and blue) and depth videos of home-based exercise sessions for commonly applied shoulder and knee exercises. The proposed system works without any body-part segmentation bodypart tracking joint detection and temporal segmentation methods. In the end favorable exercise recognition rates and encouraging results on the estimation of repetition counts are obtained.en_US]
dc.identifier.citation34
dc.identifier.doi10.1109/TNSRE.2014.2326254en_US
dc.identifier.endpage1171
dc.identifier.issn1534-4320en_US
dc.identifier.issn1558-0210en_US
dc.identifier.issn1534-4320
dc.identifier.issn1558-0210
dc.identifier.issue6
dc.identifier.pmid24860037en_US
dc.identifier.scopus2-s2.0-84912142902en_US
dc.identifier.scopusqualityQ1
dc.identifier.startpage1160en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12469/678
dc.identifier.urihttps://doi.org/10.1109/TNSRE.2014.2326254
dc.identifier.volume22en_US
dc.identifier.wosWOS:000345573500007en_US
dc.identifier.wosqualityQ1
dc.institutionauthorAr, İlktanen_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.relation.journalIEEE Transactions on Neural Systems and Rehabilitation Engineeringen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectBayesian networken_US
dc.subjectEstimation of repetition counten_US
dc.subjectExercise recognitionen_US
dc.subjectHome-based physiotherapyen_US
dc.titleA Computerized Recognition System for the Home-Based Physiotherapy Exercises Using an RGBD Cameraen_US
dc.typeArticleen_US
dspace.entity.typePublication

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