Surface EMG signal hand motion recognition algorithm based on combinated energy characteristics

SONG Jiaqiang; PEI Xiaomin; ZHAO Qiang; LIU Honghai

doi:10.13873/J.1000-9787(2020)06-0139-04

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Surface EMG signal hand motion recognition algorithm based on combinated energy characteristics

更新时间：2025-09-11

- Surface EMG signal hand motion recognition algorithm based on combinated energy characteristics
- Issue 6, Pages: 139-142(2020)
- 作者机构：
  
  1. 辽宁石油化工大学信息与控制工程学院
  2. 朴茨茅斯大学智能系统与生物医学机器人实验室
- 作者简介：
- 基金信息：
- DOI：10.13873/J.1000-9787(2020)06-0139-04
  CLC： R318;TN911.7
- Published：2020
- 稿件说明：
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SONG Jiaqiang, PEI Xiaomin, ZHAO Qiang, et al. Surface EMG signal hand motion recognition algorithm based on combinated energy characteristics[J]. 2020, (6): 139-142.
DOI：

SONG Jiaqiang, PEI Xiaomin, ZHAO Qiang, et al. Surface EMG signal hand motion recognition algorithm based on combinated energy characteristics[J]. 2020, (6): 139-142. DOI： 10.13873/J.1000-9787(2020)06-0139-04.

摘要

为了提高基于表面肌电信号(s EMG)的手势动作识别准确率

提出一种基于肌电信号组合能量特征的手势识别方法。首先

计算s EMG信号和其高阶差分信号的能量谱;然后

提取基于能量谱的组合特征;最后

用主成分分析(PCA)算法对多组能量特征维度约简

线性判决分析(LDA)分类器识别手势动作。利用肌电仪采集8组手势动作进行识别

基于组合能量特征的肌电信号手势识别方法对于手势动作识别的准确率可达97. 5%

比基于典型特征提取算法手势动作识别准确率更高;利用UCI数据库中的肌电信号进行实验

手势识别准确率可达94. 5%。实验表明:组合能量特征提取算法对不同的数据库具有普适性

所提取肌电信号组合能量特征能使不同手势动作的差异性更明显

整个手势识别方法准确率更高。

Abstract

In order to improve the accuracy of gesture recognition based on surface electromyography signal（ s EMG）

a gesture recognition method based on the combined energy characteristics of electromyography signal is proposed. Firstly

the energy spectrum of s EMG signal and its high-order differential signal are calculated. Then

the combined features based on energy spectrum are extracted. Finally

PCA algorithm is used to reduce the multigroup energy feature dimension

and the LDA classifier is used to recognize gestures. Eight groups of gestures are collected by electromyography for recognition

and the recognition accuracy of electromyography signal gesture recognition method based on combined energy features can reach 97. 5%. which is higher than that based on typical feature extraction algorithm. By using the EMG signal in UCI database

the accuracy of gesture recognition can reach 94. 5 %. Experiments show that the combined energy feature extraction algorithm is applicable to different databases

and the extracted EMG combined energy feature can make the difference between different gestures more obvious

and the accuracy of the whole gesture recognition method is higher.

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