Abstract
Joint torque prediction plays an important role in quantitative limb rehabilitation assessment and exoskeleton robot, and it is essential to acquire feedback or feedforward signal for adaptive functional electrical stimulation (FES) control. The Surface electromyography (sEMG) signal is one of the basic processing techniques to detect muscle activity, and also one favorable technique to estimate joint torque. In order to predict joint torque in a wide range of real time convenient applications, it is necessary to fuse sEMG signals with other convenient physical sensors such as accelerometers and gyroscopes, herein, we use a time delay artificial neural network to predict human joint force of ankle eversion and inversion based on sEMG and angular velocity signals. We testify our method on the data recorded from 8 subjects (5 healthy subjects and 3 patients) who are on isokinetic ankle eversion and inversion. The results show that the mean Cross-correlation coefficients ( $\rho$ ) and the mean normalized root-mean-square deviation (NRMSE %) calculated between the prediction and the real value for isokinetic contraction is 0.966± 0.019 and 7.9% ±0.026. Compared with artificial neural network (ANN) and support vector regression (SVR), the proposed method can predict the joint torque effectively. For the future application, the method has the potential to be employed to predict the ankle moments in real-time application for quantitative lower limb rehabilitation assessment and exoskeleton robot control.
Highlights
Joint torque prediction plays an important role in quantitative limb rehabilitation assessment and exoskeleton robot [1,2], especially in the closed-loop control of functional electrical stimulation (FES)[3] and the control of transhumeral prosthetic
The Surface electromyography signal is the sum of the action potentials generated by the active motor units and detected over the skin, the signal contains a wealth of information about muscle functions, and it is one of the basic processing techniques to detect muscle activity and provide the motion intentions of the user
Because of the nonlinear relationship between the recorded Surface electromyography (sEMG) signals and joint force, nonlinear torque estimation methods were introduced for the study of torque prediction
Summary
Joint torque prediction plays an important role in quantitative limb rehabilitation assessment and exoskeleton robot [1,2], especially in the closed-loop control of functional electrical stimulation (FES)[3] and the control of transhumeral prosthetic. Because of the significant advantages such as noninvasive, real-time, and multi-point measurement, sEMG has been widely used in medical [4,5], engineering studies[6,7], control of prosthesis[8], biomechanics and movement analysis[9,10,11], genomics and exoskeleton robot control. The recording and analysis of sEMG signals provide important information to the field of limb joint force prediction [12,13], used as an feedback prediction controller of FES[3] and provide continuous real-time control signal for human musculoskeletal system. Zhang Q et al [14] proposed an EMG-based torque prediction method for time-variant muscle fatigue tracking in spinal cord injured patients with surface FES, the results
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