Continuous Multi-DoF Wrist Kinematics Estimation Based on a Human-Machine Interface With Electrical-Impedance-Tomography

Enhao Zheng; Jingzhi Zhang; Qining Wang; Hong Qiao

doi:10.3389/fnbot.2021.734525

Continuous Multi-DoF Wrist Kinematics Estimation Based on a Human-Machine Interface With Electrical-Impedance-Tomography

Front Neurorobot. 2021 Sep 30:15:734525. doi: 10.3389/fnbot.2021.734525. eCollection 2021.

Authors

Enhao Zheng¹, Jingzhi Zhang^{1

2}, Qining Wang³, Hong Qiao¹

Affiliations

¹ The State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
² School of General Engineering, Beihang University, Beijing, China.
³ Department of Advanced Manufacturing and Robotics, College of Engineering, Peking University, Beijing, China.

Abstract

This study proposed a multiple degree-of-freedom (DoF) continuous wrist angle estimation approach based on an electrical impedance tomography (EIT) interface. The interface can inspect the spatial information of deep muscles with a soft elastic fabric sensing band, extending the measurement scope of the existing muscle-signal-based sensors. The designed estimation algorithm first extracted the mutual correlation of the EIT regions with a kernel function, and second used a regularization procedure to select the optimal coefficients. We evaluated the method with different features and regression models on 12 healthy subjects when they performed six basic wrist joint motions. The average root-mean-square error of the 3-DoF estimation task was 7.62°, and the average R ² was 0.92. The results are comparable to state-of-the-art with sEMG signals in multi-DoF tasks. Future endeavors will be paid in this new direction to get more promising results.

Keywords: Lasso; electrical-impedance-tomography; human-machine interface; multi-DoF; wrist angle estimation.