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题名: 基于单通道sEMG分解的手部动作识别方法
其他题名: Classification of Hand Gestures Based on Single-Channel sEMG Decomposition
作者: 熊安斌; 丁其川; 赵新刚; 韩建达; 刘光军
作者部门: 机器人学研究室
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
刊名: 机械工程学报
ISSN号: 0577-6686
出版日期: 2016
卷号: 52, 期号:7, 页码:6-13
收录类别: EI ; CSCD
产权排序: 1
项目资助者: 国家高技术研究发展计划(863 计划, 2015AA042302) ; 国家自然科学基金(61273355, 61503374, 61573340) ; 机器人学国家重点实验室自主课题(2015-z06)资助
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.
语种: 中文
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内容类型: 期刊论文
URI标识: http://ir.sia.cn/handle/173321/17580
Appears in Collections:机器人学研究室_期刊论文

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Recommended Citation:
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.
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