Attention-Enhanced Frequency-Split Convolution Block for sEMG Motion Classification: Experiments on Premier League and Ninapro Datasets

• Published in: IEEE sensors journal Vol. 24; no. 4; pp. 4821 - 4830
• Main Authors: Ergeneci, Mert, Bayram, Erkan, Binningsley, David, Carter, Daryl, Kosmas, Panagiotis
• Format: Journal Article
• Language: English
• Published: New York IEEE 15.02.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Adaptation models; Attention; Classification; convolutional block attention module (CBAM); convolutional neural network (CNN); Data models; Datasets; Deep learning; Electromyography; Feature extraction; Frequencies; gesture recognition; Kernel; motion classification; Muscles; neural networks; Sensors; Soccer; Sports; sports science; surface electromyography (sEMG); Zooming
• ISSN: 1530-437X; 1558-1748
• DOI: 10.1109/JSEN.2023.3345731

This article presents convolutional octave-band zooming-in with depth-kernel attention learning (COZDAL), a versatile deep learning model designed for surface electromyography (sEMG) motion classification. Specifically focusing on sports movements involving the hamstring muscle, the model employs attention mechanisms across various frequency bands, kernel sizes, and hidden layer depths. The proposed method has been extensively evaluated on the benchmark Ninapro dataset and a custom soccer dataset. The results demonstrate substantial improvements over the existing state-of-the-art models, with an accuracy of 95.30% on Ninapro DB2, outperforming the previous best by 3.29%, and an accuracy of 98.80% on Ninapro DB2-B, an 8.66% enhancement. Remarkably, COZDAL exhibits a performance accuracy of 96.30% on a soccer dataset gathered from 45 elite-level athletes representing two clubs in the English Premier League (EPL). This result, achieved without parameter tuning, highlights the model's adaptability and exceptional efficacy across diverse motion scenarios, sensors, subjects, and muscle types.