Article: Recognition of basketball movement sEMG signals based on multi-channel feature fusion network Journal: International Journal of Information and Communication Technology (IJICT) 2025 Vol.26 No.18 pp.65 - 85 Abstract: This study addresses the underutilisation of multi-channel surface electromyography (sEMG) features in basketball motion recognition by proposing a spatiotemporal fusion network. Multi-channel sEMG signals from athletes' key muscles were collected and synchronised with motion capture data, followed by preprocessing to reduce individual variations. The dual-branch architecture integrates time-frequency feature extraction using convolutional-recurrent networks with graph-based modelling of inter-muscle spatial correlations. An adaptive attention mechanism fuses temporal dynamics and spatial synergies for classification. Experimental results demonstrate superior recognition performance compared to conventional machine learning and single-channel deep learning approaches, with ablation studies confirming the critical roles of spatial modelling and feature fusion. The framework provides an effective solution for analysing complex sports motions through multi-channel physiological signals, offering applications in athletic training optimisation and injury risk prevention. Inderscience Publishers - linking academia, business and industry through research

Title: Recognition of basketball movement sEMG signals based on multi-channel feature fusion network

Authors: Xi Fu; Yao Hu

Addresses: Faculty of Humanities and International Education, Sichuan Vocational College of Cultural Industries, Chengdu 610213, China ' Ministry of Basic Education, Chongqing Medical and Pharmaceutical College, Chongqing 4013313, China

Abstract: This study addresses the underutilisation of multi-channel surface electromyography (sEMG) features in basketball motion recognition by proposing a spatiotemporal fusion network. Multi-channel sEMG signals from athletes' key muscles were collected and synchronised with motion capture data, followed by preprocessing to reduce individual variations. The dual-branch architecture integrates time-frequency feature extraction using convolutional-recurrent networks with graph-based modelling of inter-muscle spatial correlations. An adaptive attention mechanism fuses temporal dynamics and spatial synergies for classification. Experimental results demonstrate superior recognition performance compared to conventional machine learning and single-channel deep learning approaches, with ablation studies confirming the critical roles of spatial modelling and feature fusion. The framework provides an effective solution for analysing complex sports motions through multi-channel physiological signals, offering applications in athletic training optimisation and injury risk prevention.

Keywords: sEMG; multi-channel feature fusion; basketball movement recognition; graph convolutional network; GCN; attention mechanism.

DOI: 10.1504/IJICT.2025.146693

International Journal of Information and Communication Technology, 2025 Vol.26 No.18, pp.65 - 85

Received: 15 Apr 2025
Accepted: 30 Apr 2025
Published online: 13 Jun 2025 *

Title: Recognition of basketball movement sEMG signals based on multi-channel feature fusion network

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