Whole-Field Action Sensing via Wearable Single-Channel EMG Sensors and Resource-Efficient Motion Network
Xuanming Jiang, Dingyu Nie, Baoyi An, Yuzhe Zheng, Yichuan Mao, Jialie Shen, Xueming Qian, Zhiwen Jin
Abstract
The proliferation of collaborative training and multi-person sports has underscored the necessity for concurrent whole-field action sensing. However, Electromyography (EMG) recognition, which plays a pivotal role in Wearable Human Activity Recognition (WHAR) for analyzing muscle activity and decoding action intent, still faces challenges in achieving a balance between performance, cost, and efficiency in multi-person scenarios. Unlike current channel-expansion solutions, we propose a wireless wearable Single-Dimensional Sparse EMG (2SEMG) Sensor for efficient personal sampling. These action-unaffected sensors leverage the proposed lightweight One-Dimensional Motion Network (OMONet) to facilitate concurrent action sensing. Experiments demonstrate that OMONet achieves leading performance and efficiency in action signal recognition, and two real-world badminton matches further confirm the performance, robustness, and real-time efficiency of the whole-field action sensing network constructed via 2SEMG Sensors and OMONet.
BibTeX
@inproceedings{aaai2026_wholefieldaction,
title = {Whole-Field Action Sensing via Wearable Single-Channel EMG Sensors and Resource-Efficient Motion Network},
author = {Xuanming Jiang and Dingyu Nie and Baoyi An and Yuzhe Zheng and Yichuan Mao and Jialie Shen and Xueming Qian and Zhiwen Jin and Wei Lan and Guoshuai Zhao},
booktitle = {AAAI 2026},
year = {2026}
}