Training Explainable and Effective Multi-DoF EMG Decoder Using Additive 1-DoF EMG

Abstract

Human hands can execute intricate and dexterous control of diverse objects. Decoding hand motions, especially estimating the force of each individual finger via surface electromyography (sEMG), is an essential step in intuitive and dexterous control of prosthetics, exoskeletons and more various human-machine systems. Previous sEMG decoders lack explainability and show degraded performances in decoding finger forces with multiple degrees-of-freedom (DoFs). When developing a multi-DoF EMG decoder, the combinations of various forces levels exerted by different fingers are too numerous to be exhaustively enumerate. In our work, we utilized the data of 1-DoF finger activation to generate synthetic N-DoF sEMG data with a straightforward additive mixup data augmentation approach, which overlays 1-DoF sEMG signals and finger force labels. The basic assumption of our method is the additive property of sEMG associated with different DoFs. With the synthetic N-DoF sEMG data, we then developed N-DoF EMG-force models via the highly explainable deep forest built on simple and transparent decision trees. With data augmentation using only 1-DoF sEMG data, the regression error reduced by ~20% of the baseline level (without data augmentation). More significantly, the explainability of the deep forest suggested that, the crucial electrodes in the decision making process of the 2-DoF deep forest are essentially a linear superposition of the counterparts in the 1-DoF deep forest.