Integrating Motor Unit Activity With Deep Learning for Real-Time, Simultaneous and Proportional Wrist Angle and Grasp Force Estimation.

Abstract

Objective: Myoelectric prostheses offer great promise in enabling amputees to perform daily activities independently. However, existing neural interfaces generally cannot simultaneously and proportionally decode kinematics and kinetics in real time, nor can they directly interpret neural commands. We thus propose a novel framework that integrates motor unit activity with deep learning and demonstrate its efficiency in the real-time, simultaneous, and proportional estimation of wrist angles and grasp forces.

Methods: This framework utilizes real-time high-density surface electromyography decomposition to identify motor neuron discharges, followed by neural drive computation integrated with a modular Long Short-Term Memory-based neural network. Ten subjects participated in the experiments involving wrist pronation/supination, flexion/extension, and abduction/adduction, with varying grasp force.

Results: The proposed framework significantly outperformed five baseline methods, achieving an nRMSE of 13.6% and 11.1% and an R² of 73.2% and 76.8% for wrist angle and grasp force, respectively. In addition, we further characterized the spatial distribution and recruitment patterns of motor units during movement generation.

Conclusion: These findings highlight the feasibility of integrating neural drive insights with deep learning methods to improve simultaneous and proportional estimation performance.

Significance: The proposed framework has the potential to enhance the independence and quality of life of prosthetic users by enabling them to perform a wider range of tasks with improved precision and control over both kinematics and kinetics.