Continuous Wrist Angle Estimation Under Different Resistance Based on Dynamic EMG Decomposition.

Estimating wrist movements through neural drives is crucial in human-machine interface (HMI). However, studies on wrist movements mostly focused on isometric contractions, while research on dynamic EMG decomposition during non-stationary movements is notably scarce. Moreover, the impact of different resistance on the motor unit (MU) decomposition and wrist angle estimation remains unexplored. To address these gaps, this paper proposed a novel framework to decode neural drives from EMG signals during dynamic wrist movements. Specifically, the EMG signals were divided into short segments firstly. Next, progressive FastICA peel-off (PFP) algorithm was utilized to decompose each EMG segment into motor unit spike trains (MUST). Then, a linear window function was applied to track the motor units (MU) to obtain complete MUSTs. Three resistance levels were investigated during wrist flexion and extension: 20%, 40%, and 60% maximum voluntary contraction (MVC). Multiple linear regression (LR) and convolutional neural network (CNN) were used to estimate wrist angles within a range of ± 20° based on neural drives. Results showed the proposed framework could effectively identify MUs at these three resistance levels, with an average global pulse-to-noise ratio (PNR) above 20 dB. The determination coefficients of LR model were 0.92 ± 0.06, 0.91 ± 0.07, and 0.85 ± 0.13 at the three resistance levels, respectively, while those of CNN were 0.88 ± 0.10, 0.88 ± 0.11, and 0.81 ± 0.17. This study demonstrates it is feasible to estimate wrist angles based on decomposed neural drives at different resistance levels, and has significant implications for HMI development.