Gesture action intent recognition based on surface electromyography: a systematic review
10.3969/j.issn.1006-9771.2022.09.005
- VernacularTitle:基于表面肌电图手势动作意图识别的系统综述
- Author:
Xu ZHU
1
;
Jing LIU
1
;
Zeping DONG
1
;
Dawei QIU
1
Author Information
1. College of Intelligent and Information Engineering, Shandong University of Traditional Chinese Medicine, Ji'nan, Shandong 250355, China
- Publication Type:Journal Article
- Keywords:
human-machine interaction;
surface electromyography;
gesture recognition;
machine learning;
systematic review
- From:
Chinese Journal of Rehabilitation Theory and Practice
2022;28(9):1032-1038
- CountryChina
- Language:Chinese
-
Abstract:
ObjectiveTo systematicly review the researches of gesture action intent recognition based on surface electromyography (sEMG). MethodsExperimental researches on gesture action intention recognition based on sEMG were retrieved from CNKI, Wanfang Data, PubMed and Web of Science. The literatures were screened, and the classification methods and other related factors were summarized. ResultsA total of 735 researches were returned, and 25 researches were finally included. The publication time was mainly from 2012 to 2021. The subjects were healthy people or amputees. The classification model included traditional machine learning models and deep learning models. Other related factors included acquisition, noise interference and sliding window size. ConclusionTraditional machine learning models based on sEMG signals have been maturely applied, and gesture recognition with deep learning models are of great potential. The individual differences of subjects, the real-time requirements of gesture classification and the stability requirements of sEMG devices still need to be addressed.
