TY - GEN
T1 - Estimation of Joint Angle Using sEMG Based on WOA-SVR Algorithm
AU - Zhang, Li
AU - Wang, Jianhua
AU - Liu, Jingmeng
AU - Chen, Weihai
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Aiming at the problem of estimating joint angle based on surface electromyography (sEMG) signals in the upper limb therapy of rehabilitation robots, a whale optimization algorithm (WOA) optimized support vector regression (SVR) is proposed to overcome the shortcomings of a single machine learning model with insufficient generalization ability. In this paper, the WOA-SVR model is established for the mapping relationship between the joint angle and sEMG signal. The inputs of the WOA-SVR model are time domain features extracted from sEMG signal, including root-mean-square and wavelength. In the experiment, two channels of sEMG signal were acquired respectively from five healthy participants, and the elbow joint angles were recorded simultaneously. The results show that, the WOA-SVR model has lower RMSE and higher R2 compared with SVR. The joint angle is successfully estimated with a RMSE error of down to 10.86(11.47±0.53). The R2 of WOA-SVR reaches 0.88(0.85±0.02) and the R2 of SVR is 0.61 on average for 5 subjects. It is found that WOA-SVR model has a good performance on estimating the joint angle. The algorithm would be used on the non-invasive and quantitative evaluation of upper-limb motor function in rehabilitation effect.
AB - Aiming at the problem of estimating joint angle based on surface electromyography (sEMG) signals in the upper limb therapy of rehabilitation robots, a whale optimization algorithm (WOA) optimized support vector regression (SVR) is proposed to overcome the shortcomings of a single machine learning model with insufficient generalization ability. In this paper, the WOA-SVR model is established for the mapping relationship between the joint angle and sEMG signal. The inputs of the WOA-SVR model are time domain features extracted from sEMG signal, including root-mean-square and wavelength. In the experiment, two channels of sEMG signal were acquired respectively from five healthy participants, and the elbow joint angles were recorded simultaneously. The results show that, the WOA-SVR model has lower RMSE and higher R2 compared with SVR. The joint angle is successfully estimated with a RMSE error of down to 10.86(11.47±0.53). The R2 of WOA-SVR reaches 0.88(0.85±0.02) and the R2 of SVR is 0.61 on average for 5 subjects. It is found that WOA-SVR model has a good performance on estimating the joint angle. The algorithm would be used on the non-invasive and quantitative evaluation of upper-limb motor function in rehabilitation effect.
KW - Joint angle estimation
KW - support vector regression
KW - surface electromyography
KW - whale optimization algorithm
UR - https://www.scopus.com/pages/publications/85173621653
U2 - 10.1109/ICIEA58696.2023.10241850
DO - 10.1109/ICIEA58696.2023.10241850
M3 - 会议稿件
AN - SCOPUS:85173621653
T3 - Proceedings of the 18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023
SP - 1674
EP - 1679
BT - Proceedings of the 18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023
A2 - Cai, Wenjian
A2 - Yang, Guilin
A2 - Qiu, Jun
A2 - Gao, Tingting
A2 - Jiang, Lijun
A2 - Zheng, Tianjiang
A2 - Wang, Xinli
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023
Y2 - 18 August 2023 through 22 August 2023
ER -