Conference Publication Details
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Prabhu G.;Kuklyte J.;Gualano L.;Venkataraman K.;Ahmadi A.;Duff O.;Walsh D.;Woods C.;O¿Connor N.;Moran K.
Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
Design and development of the medFit app: A mobile application for cardiovascular disease rehabilitation
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Cardiovascular disease Mobile application Repetition counting Support vector machine Wearable sensors
© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2018. Rehabilitation from cardiovascular disease (CVD) usually requires lifestyle changes, especially an increase in exercise and physical activity. However, uptake and adherence to exercise is low for community-based programmes. We propose a mobile application that allows users to choose the type of exercise and compete it at a convenient time in the comfort of their own home. Grounded in a behaviour change framework, the application provides feedback and encouragement to continue exercising and to improve on previous results. The application also utilizes wearable wireless technologies in order to provide highly personalized feedback. The application can accurately detect if a specific exercise is being done, and count the associated number of repetitions utilizing accelerometer or gyroscope signals Machine learning models are employed to recognize individual local muscular endurance (LME) exercises, achieving overall accuracy of more than 98%. This technology allows providing a near real-time personalized feedback which mimics the feedback that the user might expect from an instructor. This is provided to motivate users to continue the recovery process.
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