Simultaneous estimation of grip force and wrist angles by surface electromyography and acceleration signals. (January 2023)

• Record Type: Journal Article
• Authors: Mao, He; Zheng, Yue; Ma, Chenfei; Wu, Kai; Li, Guanglin; Fang, Peng
• Is Part Of: Biomedical signal processing and control. Volume 79(2023)Part 1
• Publication Date: 2023-01
• Subjects: Myoelectric control -- Electromyography -- Acceleration signals -- Movement estimation -- Force estimation; 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.2022.104088
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: User's active control of grip force and wrist angles is essential for prosthetic hands. We experiment with force-position varying movements in free space. Continuous grip force and wrist angles are estimated using sEMG and ACC signals. The multi-modal approach shows higher accuracy and robustness. Abstract: In myoelectric control, simultaneous and proportional (SP) control of multiple degrees of freedom (DOFs) can realize a high level of dexterity. This study proposed a new control scheme that simultaneously estimates continuous grip force and wrist angles using a combination of surface electromyography (sEMG) and acceleration (ACC) signals. Four popular EMG features were utilized to study the regression accuracy. Nine intact subjects were recruited. They performed six force-position varying movements which involved simultaneous activation of four DOFs (wrist flexion/extension, ulnar/radial deviation, pronation/supination, and grip). The estimation performance was evaluated by the Pearson Correlation Coefficient ( cc ). The highest cc value was 92.12 ± 1.51%, 96.68 ± 1.16%, and 97.18 ± 0.69% for the sEMG-only, ACC-only, and multi-modal methods, respectively. Results indicate that the multi-modal approach can significantly improve regression accuracy of concurrent grip force and wrist angles, especially in pronation/supination. Besides, the multi-modal method showed higher robustness when the available number of sensors decreased and better generalization ability between movements. In conclusion, the proposed multi-modal approach has the potential for SP control of future hand prostheses.