Title: Competitive physical action recognition via inertial sensing and graph convolutional transformer

Authors: Bingjie Sun; Rongjiao Hu

Addresses: Department of Physical Education, Guangdong Pharmaceutical University, Guangzhou, 510006, China ' Department of Physical Education, Guangdong Pharmaceutical University, Guangzhou, 510006, China

Abstract: Based on wearable-sensing internet of things technology, this paper proposes a complete solution encompassing data acquisition, processing, and intelligent recognition for the identification of athletic physical movements. A wireless inertial measurement unit sensor network is deployed across key body segments to capture motion data synchronously. After preprocessing, including low-pass filtering and sensor calibration, a spatio-temporal graph structure is constructed and fed into an innovative graph convolutional-transformer fusion model. This architecture fully leverages graph convolution to extract spatial correlation features from multiple sensors, while the transformer component effectively captures long-range temporal dependencies within movement sequences. Experimental results on the publicly available University of California, Irvine Human Activity Recognition dataset demonstrate that our method achieves 94.2% recognition accuracy. This performance confirms its practical value in wearable-sensing internet of things systems and provides an effective technological pathway for advancing smart sports training platforms.

Keywords: inertial sensing; graph convolution; transformer; action recognition.

DOI: 10.1504/IJSNET.2026.152194

International Journal of Sensor Networks, 2026 Vol.50 No.3, pp.174 - 185

Received: 15 Oct 2025
Accepted: 17 Oct 2025
Published online: 10 Mar 2026 *

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