Dynamic gripping force estimation and reconstruction in EMG-based human-machine interaction. (February 2023)

• Record Type: Journal Article
• Authors: Xue, Jiaqi; Lai, King Wai Chiu
• Is Part Of: Biomedical signal processing and control. Volume 80(2023)Part 1
• Publication Date: 2023-02
• Subjects: Electromyography -- HMI system -- Dynamic force -- Deep learning; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2022.104216
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: An efficient and fluent interactive system for EMG-based force estimation. Dynamic and continuous finger gripping force decoding through 3-channel EMG signal. Reduced preliminary work of feature design and selection. Simultaneous gripper action control based on motion intentions captured from EMG. Abstract: Electromyography (EMG) can reveal the state of muscle activity in advance, therefore, it has been widely used in human–machine interaction (HMI) to predict human intention. Force estimation from EMG signals is acknowledged as an important research topic in HMI. In order to develop a simple and smooth HMI system, it is necessary to estimate the dynamic force effectively and smoothly from a small number of EMG electrodes. In this paper, we have proposed an EMG-based dynamic force reconstruction scheme applied in HMI system. A deep neural prediction network using one-dimensional convolutional structure has been proposed to learn the complex EMG features automatically from three-channel EMG signals. This model was applied in our interactive system to estimate dynamic force and reconstruct it on a robotic gripper for precise EMG-based robot control. Our proposed model outperformed the two-dimensional convolutional neural network (CNN) method and feature-based linear regression. And it can meet the requirement of online interaction. The offline and online tests have shown good estimation performance with R 2 of 0.99 and 0.83, respectively. The average prediction speed has reached 115.5 μs per sample. The system has avoided tedious feature extraction process and has demonstrated dynamic recognition in real time which can further advance various prosthesis and assistive robotic applications in the future.