Analysis of Typical Features Using an Angle-to-sEMG Decoder Model

Current prosthetic control methods mainly rely on extensive data collection and the training of classification models. Nevertheless, achieving a balance between learning efficiency and classification accuracy remains a persistent challenge. In this paper, we introduce a novel prosthetic control approach that divides the processing of surface electromyography (sEMG) signals into two stages to predict corresponding joint angles. Initially, a pretrained model is utilized to infer the associated joint angles based on typical features. This methodology necessitates the mapping of individual sEMG signals to these typical features through a compact mapping network during application. we think that it will enhance hand motion recognition accuracy while minimizing learning duration though a pre-training approach. This study represents the initial phase, which involves the acquisition and evaluation of the similarity among typical features linked to joint angles. To this end, we constructed angle-to-sEMG models (decoders) and input the same unprocessed continuous joint angle data into the decoders of five participants. Subsequently, we utilized the resultant typical features as inputs for the encoders of different individuals. The findings indicate that, notwithstanding interindividual variability, there exists a degree of resemblance in the typical features associated with joint angles, thus enabling the generation of angle data resembling the true angles even across diverse subjects.

I. Introduction

Numerous individuals have experienced limb loss due to ac-cidents or other causes [1]. Consequently, a range of methods has emerged in the market to operate prosthetic hands, aiming to enhance the quality of life for people with disabilities. Fig. 1.

Part 1 is a small volume model that maps individual sEMG to typical features, and part 2 is a pre-trained model that outputs the corresponding joint angles from typical features.

Fig. 2.

The upper half is the encoder from the prior study; the lower half is the angle-to-sEMG decoder to be constructed in this study.