Classification of electromyographic hand gesture signals using machine learning techniques

计算机科学 人工智能 稳健性(进化) 模式识别(心理学) 卷积神经网络 分类器(UML) 语音识别 试验数据 手势 手势识别 隐马尔可夫模型 机器学习 生物化学 化学 基因 程序设计语言
作者
Guangyu Jia,Hak‐Keung Lam,Junkai Liao,Rong Wang
出处
期刊:Neurocomputing [Elsevier BV]
卷期号:401: 236-248 被引量:48
标识
DOI:10.1016/j.neucom.2020.03.009
摘要

The electromyogram (EMG) signals from an individual’s muscles can reflect the biomechanics of human movement. The accurate classification of individual and combined finger movements using surface EMG signals is able to support many applications such as dexterous prosthetic hand control. The existing research of EMG-based hand gesture classification faces the challenges of inaccurate classification, insufficient generalization ability and weak robustness. To address these problems, this paper proposes a deep learning model that combines convolutional auto-encoder and convolutional neural network (CAE+CNN) to classify an EMG dataset consisting of 10 classes of hand gestures. The proposed method shrinks the inputs into a smaller latent space representation using CAE and the resultant compressed features are served as inputs of CNN, which reduces the redundancy of EMG signals and improves the classification accuracy and training efficiency. Besides, to enhance the robustness and generalization ability for classification, a data processing approach is proposed which combines the windowing method and majority voting of the obtained results from the classifier. In addition, comprehensive comparative study is carried out with 8 widely applied and state-of-the-art classifiers in terms of classification accuracy, robustness subject to noise and statistical analysis (sensitivity, specificity, precision, F1 Score and Matthews correlation coefficient). The results demonstrates that the integration of windowing method, CAE+CNN and majority voting achieves the best performance (99.38% test accuracy for the data without adding noise, which is 3.78% higher than the best classifier used for comparison), strongest robustness (achieved 98.13% test accuracy when Gaussian noise of level 1e-5 is added to the raw dataset, which is 4.07% higher than the best classifier used for comparison) and statistical properties compared to other classifiers, which shows the potential for healthcare applications such as movement intention detection and dexterous prostheses control.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
nuantong1shy完成签到,获得积分10
刚刚
甜甜亦巧完成签到,获得积分10
1秒前
1秒前
1秒前
复杂语山发布了新的文献求助10
1秒前
勤奋尔丝完成签到 ,获得积分10
1秒前
jiejie完成签到,获得积分10
1秒前
赵淑敏完成签到,获得积分10
2秒前
xzy完成签到,获得积分10
2秒前
2秒前
ksu发布了新的文献求助10
2秒前
沈平灵完成签到,获得积分10
2秒前
666发布了新的文献求助10
3秒前
4秒前
Rez完成签到,获得积分10
4秒前
科研通AI5应助nteicu采纳,获得10
4秒前
4秒前
华仔应助忧郁盼夏采纳,获得10
5秒前
mark163完成签到,获得积分10
5秒前
量子星尘发布了新的文献求助10
5秒前
5秒前
科研通AI2S应助斯文以蓝采纳,获得10
5秒前
Qian发布了新的文献求助10
5秒前
ZL张莉发布了新的文献求助10
5秒前
Camellia完成签到,获得积分10
6秒前
6秒前
lovehao完成签到 ,获得积分10
6秒前
silstorm发布了新的文献求助10
6秒前
失眠夏山发布了新的文献求助20
6秒前
rich完成签到,获得积分10
6秒前
ANXU完成签到,获得积分20
7秒前
yangyang发布了新的文献求助10
7秒前
中岛悠斗完成签到,获得积分10
7秒前
香蕉发布了新的文献求助10
7秒前
雪原白鹿完成签到 ,获得积分10
7秒前
自信的网络完成签到 ,获得积分10
7秒前
Jin完成签到,获得积分10
7秒前
strangeliu完成签到,获得积分10
7秒前
落后紫夏完成签到,获得积分10
8秒前
8秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
Cognitive Neuroscience: The Biology of the Mind 1000
Technical Brochure TB 814: LPIT applications in HV gas insulated switchgear 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
Nucleophilic substitution in azasydnone-modified dinitroanisoles 500
不知道标题是什么 500
A Preliminary Study on Correlation Between Independent Components of Facial Thermal Images and Subjective Assessment of Chronic Stress 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3969033
求助须知:如何正确求助?哪些是违规求助? 3513900
关于积分的说明 11170818
捐赠科研通 3249256
什么是DOI,文献DOI怎么找? 1794708
邀请新用户注册赠送积分活动 875326
科研通“疑难数据库(出版商)”最低求助积分说明 804759