posted on 2020-03-10, 09:53authored byMoran K., Ghanashyama Prabhu, Jogile Kuklyte, Leonardo Gualano, Kaushik Venkataraman, Amin Ahmadi, Orlaith Mairead Duff, Deirdre M.J. Walsh, Catherine B. Woods, Noel E. O'Connor, Kieran A. Moran
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.
History
Publication
MobiHealth 2017 – 7th EAI International Conference on Wireless Mobile Communication and Healthcare,;
Publisher
Springer
Note
peer-reviewed
Other Funding information
SFI
Rights
The original publication is available at www.springerlink.com