Improving the Robustness of Human-Machine Interactive Control for Myoelectric Prosthetic Hand During Arm Position Changing

Ang Ke; Jian Huang; Jing Wang; Jiping He

doi:10.3389/fnbot.2022.853773

Improving the Robustness of Human-Machine Interactive Control for Myoelectric Prosthetic Hand During Arm Position Changing

Front Neurorobot. 2022 Jun 7:16:853773. doi: 10.3389/fnbot.2022.853773. eCollection 2022.

Authors

Ang Ke¹, Jian Huang^{1

2}, Jing Wang¹, Jiping He³

Affiliations

¹ Key Laboratory of Ministry of Education for Image Processing and Intelligent Control, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, China.
² Shenzhen Huazhong University of Science and Technology Research Institute, Shenzhen, China.
³ Department of Intelligent Robots and Systems, Beijing Institute of Technology, Beijing, China.

Abstract

Robust classification of natural hand grasp type based on electromyography (EMG) still has some shortcomings in the practical prosthetic hand control, owing to the influence of dynamic arm position changing during hand actions. This study provided a framework for robust hand grasp type classification during dynamic arm position changes, improving both the "hardware" and "algorithm" components. In the hardware aspect, co-located synchronous EMG and force myography (FMG) signals are adopted as the multi-modal strategy. In the algorithm aspect, a sequential decision algorithm is proposed by combining the RNN-based deep learning model with a knowledge-based post-processing model. Experimental results showed that the classification accuracy of multi-modal EMG-FMG signals was increased by more than 10% compared with the EMG-only signal. Moreover, the classification accuracy of the proposed sequential decision algorithm improved the accuracy by more than 4% compared with other baseline models when using both EMG and FMG signals.

Keywords: EMG-FMG control; arm movement; gesture recognition; post-processing; robustness.