Bio-signal based elbow angle and torque simultaneous prediction during isokinetic contraction

Abstract

It is of great importance to decode motion dynamics of the human limbs such as the joint angle and torque in order to improve the functionality and provide more intuitive control in human-machine collaborative systems. In order to achieve feasible prediction, both the surface electromyography (sEMG) and A-mode ultrasound were applied to detect muscle deformation and motor intent. Six abled subjects were recruited to perform five trails elbow isokinetic flexion and extension, and each trail contained five repetitions, with muscle deformation and sEMG signals recorded simultaneously. The experimental datasets were categorized as: the ultrasound-EMG combined datasets, ultrasound-only datasets and EMG-only datasets. The support vector machine (SVM) regression model was developed for both elbow joint angle and torque prediction, based on the above three kinds of datasets. The root-mean-square error (RMSE) and the correlation coefficients (R) were applied to evaluate the prediction accuracy. The results across all the subjects for different datasets indicated that the combined datasets and the ultrasound datasets were superior to the sEMG datasets both on elbow joint angle and torque prediction, and there were no significant differences between the combined datasets and the ultrasound datasets. It turns out that elbow angle and torque can be reconstructed by A-mode ultrasound, and the significant findings pave the way towards the application of musculature-driven human-machine collaborative systems.