Improved Long Short-term Memory Network for Gesture Recognition
作者机构：Software CollegeShenyang Normal UniversityShenyang 110034China
出 版 物：《IJLAI Transactions on Science and Engineering》 (IJLAI科学与工程学报汇刊（英文）)
年 卷 期：2024年第2卷第2期
页 面：5-12页
主 题：Surface EMG Human-computer interaction Gesture recognition Long short-term memory network
摘 要：Surface EMG contains a lot of physiological information reflecting the intention of human *** recognition by surface EMG has been widely concerned in the field of human-computer interaction and *** present,most studies on gesture recognition based on surface EMG signal are obtained by discrete separation method,ignoring continuous natural motion.A gesture recognition method of surface EMG based on improved long short-term memory network is *** sensors are rationally arranged according to physiological structure and muscle *** this paper,the finger curvature is used to describe the gesture state,and the gesture at every moment can be represented by the set of different finger curvature,so as to realize continuous gesture ***,the proposed gesture recognition model is tested on Ninapro(a large gesture recognition database).The results show that the proposed method can effectively improve the representation mining ability of surface EMG signal,and provide reference for deep learning modeling of human gesture recognition.
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