TN911.6
特殊环境机器人技术四川省重点实验室开放基金项目（15kftk03）；西南科技大学校级创新基金资助项目（CX16-076）；西南科技大学校内基金项目（14zx1107，14zx7124）。
表面肌电信号（sEMG，surface electromyography）作为人体运动检测的主要信息源之一，已被广泛应用于康复训练福祉机器人领域。针对人体下肢动作识别的问题，提出了一种针对表面肌电信号进行小波变换的特征提取方法。在肌电信号的频域分布中，该方法选取小波子空间中活动段的平均功率组成特征向量。为验证所提出方法的有效性，设计实现了一种微型便携式多通道sEMG信号采集系统，并利用支持向量机（SVM，support vector machine）构建分类器对腿部动作进行识别。实验结果表明：该方法能有效识别腿部常见的4种动作，同一个体动作识别率能达到95%以上，不同个体识别率平均能达到85%，能够较好地应用于下肢运动障碍患者的日常康复训练。
Surface electromyography(sEMG) is one of the main information sources of human motion detection and has been widely used in the field of well-being of robots. We present a feature extraction method based on wavelet transform power for identifying the movement of human legs. The average power of the active segment in wavelet subspace is used to make up the feature vector according to the frequency domain distribution of the sEMG signal. In order to verify the effectiveness of the proposed method, we design and implement a small portable multi-channel sEMG signal acquisition system, and construct a classifier with support vector machine (SVM) to identify the leg movements. The results of the study show that the method can distinguish four kinds of common actions of the leg, the recognition rate of the same individual can reach more than 95%, and the recognition rate of different individuals can reach 85%, which can be applied to daily rehabilitation training of patients with lower limb movement disorders.
王坤朋,庞杰,石磊,屈剑锋.人体腿部表面肌电信号特征提取方法[J].重庆大学学报,2017,40(11):83-90.
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