Modem Myoelectric Control - Is it Time to Change the Algorithmic Focus?

This paper explores the evolving landscape of Electromyogram (EMG) signal analysis, focusing on the growing prominence of deep learning (DL) algorithms for hand, wrist, and finger movement recognition. Such algorithms often come with high computational costs, potentially limiting the clinical translation on resource-limited devices and igniting more research on reduced complexity models. This prompts the question: is it time to shift the algorithmic focus in EMG pattern recognition, given the reported performance of some light-weight traditional or hybrid methods emphasizing synergy between different EMG signals? A comparative study is implemented between state-of-the-art deep learning extension for time series classification, denoted as Random Convolutional Kernel Transform (ROCKET), and simple, yet effective pattern recognition methods tailored to exploit basic forms of EMG signal synergies- Waveform Length Phasors (WLPHASOR), Root-Mean-Squared Phasor (RMSPHASOR), and the proposed novel Multi-Signal Waveform Length (MSWL). Tests are conducted on EMG data from 22 participants performing 11 hand and wrist movements using two EMG armbands (10 and 8 channels, respectively), utilizing the open-source LibEMG toolbox. Preliminary findings suggest that, while DL algorithms exhibit formidable capabilities, the performance gap with traditional EMG feature extraction methods may not be as substantial as anticipated. The observations of this study revealed no significant differences in average accuracies between ROCKET, WLPHASOR, and RMSPHASOR (87% average across participants). Furthermore, MSWL significantly enhances performance to 90%, and the combination of ROCKET+MSWL achieves 91% on average across all subjects. These findings challenge the narrative of DL dominance in EMG pattern recognition, urging a re-evaluation of the algorithmic focus and contributing valuable insights to the debate on information extraction from EMG signals.