SPATIO-TEMPORAL CONVOLUTIONAL NETWORKS FOR MYOELECTRIC CONTROL
DOI:
https://doi.org/10.57922/mec.2510Abstract
Utilising both within-channel temporal and between-channel spatial dependencies of the surface Electromyographic (sEMG) signals improves the accuracy of machine learning-based models of myoelectric control. Here, we introduce the Spatio-Temporal Convolutional Network (STCN) to decode five hand gestures from eight EMG signals recorded from the forearm of eight able-bodied subjects. We compared our proposed STCN model with a combination of a conventional convolutional neural network (CNN) and a Long Short-Term Memory (LSTM) deep learning model, as well as the Linear Discriminant Analysis (LDA). The results show that STCN model can outperform both CNN-LSTM and LDA methods at a much lower computational complexity.
Downloads
Published
2024-08-15
How to Cite
[1]
M. Jabbari and K. Nazarpour, “SPATIO-TEMPORAL CONVOLUTIONAL NETWORKS FOR MYOELECTRIC CONTROL”, MEC Symposium, Aug. 2024.
Conference Proceedings Volume
Section
Myoelectric Control Algorithms
