湖北民族大学学报（自然科学版）2025，Vol.43Issue(2)：253-258,6.DOI:10.13501/j.cnki.42-1908/n.2025.03.021
基于改进Transformer模型的电工手部动作识别与分类方法
Method for Recognizing and Classifying Electricians'Hand Movements Based on Improved Transformer Model
摘要
Abstract
In order to fill the gaps in the application of motion recognition technology of wearable devices in the field of electrician operations,a method for recognizing and classifying electricians' hand movements based on improved Transformer model was proposed.Firstly,this method recognized and classified electricians' hand movements through multi-sensor data fusion,utilizing flex sensors and surface electromyography(sEMG)electrode patches to collect finger bending data and electromyography.Secondly,low-pass filters and time warping techniques were employed for preprocessing to address data interference and instability.Finally,multi-head attention(MHA)mechanism capturing global features and single-head attention(SHA)mechanism enhancing local features were combined to address the issue of insufficient feature depth in MHA and improve the accuracy of motion recognition.The results demonstrated that the recognition accuracy of the improved Transformer model increased by 5.21 percentage points compared to the classic Transformer model,effectively capturing and recognizing common electrician hand movements.This research could provide technical support for virtual reality-based electrician training,and enhance training efficiency and operational safety.关键词
动作分类/手指弯曲度/表面肌电图/变换器/电工操作/虚拟现实/数据融合Key words
motion classification/finger curvature/surface electromyography/Transformer/electrician operations/virtual reality/data fusion分类
信息技术与安全科学引用本文复制引用
魏欣然,何昕怡,杨秀秀,谭寒钟,朱黎,谭建军..基于改进Transformer模型的电工手部动作识别与分类方法[J].湖北民族大学学报（自然科学版）,2025,43(2):253-258,6.基金项目
国家自然科学基金项目(61961017) （61961017）
中央引导地方科技发展资金项目(2022BGE235) （2022BGE235）
湖北民族大学硒食品营养与健康智能技术湖北省工程研究中心开放课题项目(PT082302). （PT082302）
