Generative sEMG Deep Learning for Early Prediction of Locomotion with Small Training Datasets

Generative sEMG Deep Learning for Early Prediction of Locomotion with Small Training Datasets

IECON 2023- 49th Annual Conference of the IEEE Industrial Electronics Society

10.1109/IECON51785.2023.10312696

http://dx.doi.org/10.1109/iecon51785.2023.10312696

Zhanfeng Huang, Zhiping Lin, Haihong Zhang, Chuanchu Wang, Soon Huat Ng, Christina Ka Yin Tang, Kai Keng Ang

16 November 2023

Huang, Z., Lin, Z., Zhang, H., Wang, C., NG, S. H., Yin Tang, C. K., & Ang, K. K. (2023, October 16). Generative sEMG Deep Learning for Early Prediction of Locomotion with Small Training Datasets. IECON 2023- 49th Annual Conference of the IEEE Industrial Electronics Society. https://doi.org/10.1109/iecon51785.2023.10312696

Early detection of locomotion intention is highly relevant to the development of intelligent rehabilitation/assistive robotics. While surface electromyography(sEMG) has been a promising tool, it is often challenged by the shear variability of sEMG patterns in contrast to only a handful of sEMG training samples per discrete motion intention class for each individual user to begin with. To address this issue, we introduce a deep convolutional generative adversarial networks (DCGANs), including dynamic time warping (DTW) and fast Fourier transform mean square error (FFT MSE) for artificial signal quality assessment. On a preliminary sEMG data set of 3-class directional lower-limb movement, the proposed method yielded an average accuracy rate of 89.31%±6.52. While this is a feasibility study using healthy human subjects only, the result warrants extended study to further establish the generative adversarial network learning for EMG intention detection in real-world rehabilitation/assistive system applications.

Publisher Copyright

This research / project is supported by the National Robotics Programme - Assistive Robotics Programme
Grant Reference no. : M22NBK0074

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