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JAIT 2022 Vol.13(2): 132-138
doi: 10.12720/jait.13.2.132-138

Multimodal Wearable Sensing for Sport-Related Activity Recognition Using Deep Learning Networks

Sakorn Mekruksavanich 1 and Anuchit Jitpattanakul 2,3
1. Department of Computer Engineering, School of Information and Communication Technology, University of Phayao, Phayao, Thailand
2. Department of Mathematics, Faculty of Applied Science, King Mongkut’s University of Technology North Bangkok, Bangkok, Thailand
3. Intelligent and Nonlinear Dynamic Innovations Research Center, Science and Technology Research Institute, King Mongkut’s University of Technology North Bangkok, Bangkok, Thailand

Abstract—Wearable sensors using sensor-based Human Activity Recognition (S-HAR) are generally capable of regular simple actions (walking, sitting, or standing), but are indistinguishable from sophisticated activities, such as sports-related activities. Because these involve a more comprehensive, contextual, and fine-grained classification of complex human activities, simplex activity recognition systems are ineffective for growing real-world applications, for example remote rehabilitation observation and sport performance tracking. So, an S-HAR framework for recognizing sport-related activity utilizing multimodal wearable sensors in numerous body positions is proposed in this study. A public dataset named UCI-DSADS was used to investigate the recognition performance of five deep learning networks. According to the experimental results, the BiGRU recognition model surpasses other deep learning networks with a maximum accuracy of 99.62%.
 
Index Terms—deep learning, multimodal wearable sensor, human activity recognition, CNN, LSTM

Cite: Sakorn Mekruksavanich and Anuchit Jitpattanakul, "Multimodal Wearable Sensing for Sport-Related Activity Recognition Using Deep Learning Networks," Journal of Advances in Information Technology, Vol. 13, No. 2, pp. 132-138, April 2022.
 
Copyright © 2022 by the authors. This is an open access article distributed under the Creative Commons Attribution License (CC BY-NC-ND 4.0), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made.