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 Amode ultrasound, and the significant findings pave the way towards the application of musculature-driven human-machine collaborative systems.Angle, torque, surface electromyography (sEMG), ultrasound, support vector machine (SVM), regression, isokinetic contraction.

Investigation on the Sampling Frequency and Channel Number for Force Myography Based Hand Gesture Recognition

“…After downsampling, the RMS [ 10 , 11 , 16 ] feature is extracted from the signal using sliding window technology. As an alternative, the MEAN feature is also adopted in this study, which is the same as the mean absolute value (MAV) feature of EMG signal analysis [ 34 , 35 , 36 , 37 ], because all the FMG values are positive. The equations for feature extraction are as follows.…”

Study on the Control Method of Knee Joint Human–Exoskeleton Interactive System

“…To further improve the prediction effect, sEMG, joint angles, and plantar pressure signals [ 19 , 20 ] are introduced into the generalized regression neural network for training and prediction. Similarly, sEMG and A-mode ultrasound [ 21 ] have been combined and introduced to build a vector machine regression model. Although the above experimental results prove the effectiveness of the prediction method, the effectiveness of predicting the joint angle in advance to reduce the disturbance has yet to be verified.…”

Classifying Muscle States with One-Dimensional Radio-Frequency Signals from Single Element Ultrasound Transducers

“…to the recognition of the rest state [ 16 ]. Another publication from the same year found that angles and torques of elbows could be reconstructed from 1-D SMG signals using SVM models [ 17 ]. The A-Mode US signals of a commercially available system were examined in a 2016 study comprising 206 individuals and it was found that a reliable body fat percentage estimation could be performed on the basis of those signals [ 18 ].…”