Title: Sports training fatigue recognition using surface electromyography signals on wearable devices

Authors: Jing Li; Wenwen Pan

Addresses: Qiqihar University, No. 42, Wenhua Street, Jianhua District, Qiqihar – 161006, Heilongjiang, China ' Qiqihar University, No. 42, Wenhua Street, Jianhua District, Qiqihar – 161006, Heilongjiang, China

Abstract: How to timely assess the fatigue level of athletes to avoid muscle injury is critical for sports daily training. However, it is impossible to use huge device to monitor muscle fatigue level of athletes during training. This paper designs a lightweight muscle fatigue estimation system using surface electromyography (sEMG) signals to tackle these issues. First, the sEMG signals are collected using wireless sEMG sensors worn by athletes. Then, the collected sEMG signals are transmitted to edge device which integrates real-time sEMG signal processing and the lightweight artificial intelligence model deployment. The former one extracts time domain features, frequency domain features and time-frequency features of sEMG signals. The later one adopts relative margin support vector ordinal regression which is sparse to reflect the ordinal relationship between different fatigue levels. The experimental results show the proposed scheme can reach least mean absolute error and satisfy the computing resource limits of edge nodes.

Keywords: surface electromyography; sEMG; edge computing; fatigue recognition; ordinal regression; wearable devices.

DOI: 10.1504/IJBIDM.2026.155237

International Journal of Business Intelligence and Data Mining, 2026 Vol.28 No.4/5/6, pp.290 - 303

Received: 23 Jun 2025
Accepted: 13 Jan 2026
Published online: 29 Jul 2026 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article