Abstract
The use of a smartwatches to enable human activity recognition has brought forth immersive applications. This paper presents an end-to-end approach using deep learning to recognise physical exercises from a commercially available smartwatch. The exercises are recognised based on two different settings namely; constrained and unconstrained workouts in the form of High-Intensity Interval Training. The model reported a 97.35% accuracy for constrained exercise recognition, and a 82.29% accuracy for unconstrained exercise recognition. This method is capable of recognising 18 High-Intensity Interval Training exercises. The model was deployed to Google Cloud Platform to recognise exercises in real-time settings. The method will be further expanded to operate as a real-time 'Fitness Coach', which could automatically suggest optimal workout plans for users and monitor their health conditions during workout sessions.
Original language | English |
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Title of host publication | Proceedings - 2020 IEEE EMBS Conference on Biomedical Engineering and Sciences, IECBES 2020 |
Publisher | IEEE, Institute of Electrical and Electronics Engineers |
Pages | 206-211 |
Number of pages | 6 |
ISBN (Electronic) | 9781728142456 |
DOIs | |
Publication status | Published - 2021 |
Event | IEEE-EMBS International Conference on Biomedical Engineering and Sciences (IECBES) 2020 - Virtual, Langkawi Island, Malaysia Duration: 1 Mar 2021 → 3 Mar 2021 https://ieeexplore.ieee.org/xpl/conhome/9398728/proceeding (Proceedings) |
Conference
Conference | IEEE-EMBS International Conference on Biomedical Engineering and Sciences (IECBES) 2020 |
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Abbreviated title | ICEBES 2020 |
Country/Territory | Malaysia |
City | Virtual, Langkawi Island |
Period | 1/03/21 → 3/03/21 |
Internet address |