Ankle Joint Torque Prediction Based on Surface Electromyographic and Angular Velocity Signals

• Published in: IEEE access 2020, Vol.8, p.217681-217687
• Main Authors: Su, Chanjuan, Chen, Shurong, Jiang, Haiyan, Chen, Yan
• Format: Article
• Language: English
• Subjects: Accelerometers; Adaptive control; Angular velocity; angular velocity Signals; Ankle; artificial neural network; Artificial neural networks; Correlation coefficients; Electromyography; Exoskeletons; Force; Gyroscopes; Joint force; Joints (anatomy); Muscles; Neural networks; Real time; Real-time systems; Rehabilitation; Robot control; Sensors; Signal processing; Support vector machines; surface electromyography (sEMG); Time lag; Torque
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2020.3040820

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.