High-intensity interval training exercise recognition using smartwatch

Saaveethya Sivakumar, Yong Jin Kun, Alpha Agape Gopalai

Research output: Chapter in Book/Report/Conference proceedingConference PaperOther

3 Citations (Scopus)

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 languageEnglish
Title of host publicationProceedings - 2020 IEEE EMBS Conference on Biomedical Engineering and Sciences, IECBES 2020
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages206-211
Number of pages6
ISBN (Electronic)9781728142456
DOIs
Publication statusPublished - 2021
EventIEEE-EMBS International Conference on Biomedical Engineering and Sciences (IECBES) 2020 - Virtual, Langkawi Island, Malaysia
Duration: 1 Mar 20213 Mar 2021
https://ieeexplore.ieee.org/xpl/conhome/9398728/proceeding (Proceedings)

Conference

ConferenceIEEE-EMBS International Conference on Biomedical Engineering and Sciences (IECBES) 2020
Abbreviated titleICEBES 2020
Country/TerritoryMalaysia
CityVirtual, Langkawi Island
Period1/03/213/03/21
Internet address

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