TY - GEN
T1 - Design and development of the medFit app
T2 - 7th International Conference on Wireless Mobile Communication and Healthcare, MobiHealth 2017
AU - Prabhu, Ghanashyama
AU - Kuklyte, Jogile
AU - Gualano, Leonardo
AU - Venkataraman, Kaushik
AU - Ahmadi, Amin
AU - Duff, Orlaith
AU - Walsh, Deirdre
AU - Woods, Catherine
AU - O’Connor, Noel E.
AU - Moran, Kieran
N1 - Funding Information:
We acknowledge financial support from SFI under the Insight Centre award, Grant Number SFI/12/RC/2289, and ACQUIS BI, an industrial partner of Insight Centre for Data Analytics, Dublin City University, Ireland.
Funding Information:
Acknowledgements. We acknowledge financial support from SFI under the Insight Centre award, Grant Number SFI/12/RC/2289, and ACQUIS BI, an industrial partner of Insight Centre for Data Analytics, Dublin City University, Ireland.
Publisher Copyright:
© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2018.
PY - 2018
Y1 - 2018
N2 - 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.
AB - 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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U2 - 10.1007/978-3-319-98551-0_3
DO - 10.1007/978-3-319-98551-0_3
M3 - Conference contribution
AN - SCOPUS:85053165360
SN - 9783319985503
T3 - Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
SP - 20
EP - 28
BT - Wireless Mobile Communication and Healthcare - 7th International Conference, MobiHealth 2017, Proceedings
A2 - Rahmani, Amir M.
A2 - TaheriNejad, Nima
A2 - Perego, Paolo
PB - Springer Verlag
Y2 - 14 November 2017 through 15 November 2017
ER -
