基于单通道sEMG分解的手部动作识别方法
文献类型：期刊论文
| 作者 | 熊安斌 ; 丁其川; 赵新刚; 韩建达; 刘光军
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| 刊名 | 机械工程学报
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| 出版日期 | 2016 |
| 卷号 | 52期号:7页码:6-13 |
| 关键词 | 表面肌电信号 运动单元动作电位序列 分层聚类 主元分析支持向量机 |
| ISSN号 | 0577-6686 |
| 其他题名 | Classification of Hand Gestures Based on Single-Channel sEMG Decomposition |
| 产权排序 | 1 |
| 通讯作者 | 熊安斌 |
| 中文摘要 | 表面肌电信号(surface electromyography,s EMG)已广泛应用于手部动作识别。为提高动作识别精度,研究者往往需要采集多个通道s EMG信号,从而增加应用复杂性,针对这一情况,提出一种基于单通道s EMG分解的手部动作识别方法。使用单通道电极采集人体上臂肌肉s EMG,将其分解为6个运动单元动作电位序列,过程包括:二阶差分滤波、阈值计算、尖峰检测、分层聚类;然后,提取绝对值积分、最大值、非零中值、半窗能量等特征,并采用主元分析法降维;最后,利用支持向量机分类识别5种不同手部动作,精度达到80.4%。而采用未融合s EMG分解的传统方法,动作识别精度仅有约70%。 |
| 英文摘要 | Surface electromyography (sEMG) has been applied extensively in gestures recognition. In order to improve the recognition accuracy, multi-channel sEMG is conventionally sampled, which also increases the complexity of applications. To solve the problem, a novel gesture recognition method based on sEMG decomposition is proposed. Sampling sEMG signals from the muscle of human upper limb by a single-channel electrode; then decomposing the sEMG into six motor unit action potential trains (MUAPTs) and the decomposition process includes 2-order differential filtering, threshold calculation, spike detection and hierarchical clustering. Afterwards, the features, including integral of absolute value, maximum value, median of non-zero value and semi-window energy, are extracted to form a feature matrix, whose dimension is then reduced by the principal component analysis. Finally, support vector machine is employed to recognize five different hand gestures, and 80.4% of accuracy can be obtained, while only about 70% of recognition accuracy can be achieved by traditional methods without sEMG decomposition. |
| 收录类别 | EI ; CSCD |
| 语种 | 中文 |
| CSCD记录号 | CSCD:5677302 |
| 源URL | [http://ir.sia.cn/handle/173321/17580]  |
| 专题 | 沈阳自动化研究所_机器人学研究室 |
| 推荐引用方式 GB/T 7714 | 熊安斌,丁其川,赵新刚,等. 基于单通道sEMG分解的手部动作识别方法[J]. 机械工程学报,2016,52(7):6-13. |
| APA | 熊安斌,丁其川,赵新刚,韩建达,&刘光军.(2016).基于单通道sEMG分解的手部动作识别方法.机械工程学报,52(7),6-13. |
| MLA | 熊安斌,et al."基于单通道sEMG分解的手部动作识别方法".机械工程学报 52.7(2016):6-13. |
入库方式： OAI收割
来源：沈阳自动化研究所
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