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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.identifier.issn1534-4320
dc.identifier.issn1558-0210
dc.identifier.urihttps://hdl.handle.net/20.500.12469/678
dc.identifier.urihttps://doi.org/10.1109/TNSRE.2014.2326254
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.
dc.language.isoEnglish
dc.publisherIEEE
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectBayesian network
dc.subjectEstimation of repetition count
dc.subjectExercise recognition
dc.subjectHome-based physiotherapy
dc.titleA Computerized Recognition System for the Home-Based Physiotherapy Exercises Using an RGBD Camera
dc.typeArticle
dc.identifier.startpage1160
dc.identifier.endpage1171
dc.relation.journalIEEE Transactions on Neural Systems and Rehabilitation Engineering
dc.identifier.issue6
dc.identifier.volume22
dc.identifier.wosWOS:000345573500007
dc.identifier.doi10.1109/TNSRE.2014.2326254
dc.contributor.khasauthorAr, İlktan


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