Real-time isometric finger extension force estimation based on motor unit discharge information.

Abstract

OBJECTIVE\nThe goal of this study was to perform real-time estimation of isometric finger extension force using discharge information of motor units (MUs). Approach: A real-time electromyogram (EMG) decomposition method based on the fast independent component analysis (FastICA) algorithm was developed to extract MU discharge events from high-density (HD) EMG recordings. The decomposition was first performed offline during an initialization period, and the obtained separation matrix was then applied to new data samples in real-time. Since MU pool discharge probability reflects the neural drive to spinal motoneurons, individual finger forces were estimated based on a firing rate-force model established during the initialization, termed neural-drive method. Conventional EMG amplitude-based method was used to estimate the forces as a comparison, termed EMG-amplitude method. Simulated HD-EMG signals were first used to evaluate the accuracy of the real-time decomposition. Experimental EMG recordings of 5-minute isometric finger extension with pseudorandom force levels were used to assess the performance of force estimation over time. Main Results: The simulation results showed that the accuracy of real-time decomposition was 86%, compared with an offline accuracy of 94%. However, the real-time decomposition accuracy was stable over time. Experimental results showed that the neural-drive method had a significantly smaller root mean square error (RMSE) of the force estimation compared with the EMG-amplitude method, and the estimation delay was also smaller, which was consistent across fingers. Additionally, the RMSE of the neural-drive method was stable until 230 s, while the RMSE of the EMG-amplitude method increased progressively over time. Significance: The neural-drive method on real-time finger force estimation was more accurate over time compared with the conventional EMG-amplitude method during prolonged muscle contractions. The outcomes can potentially offer a more accurate and robust neural interface technique for reliable neural-machine interactions based on MU pool discharge information. .