In a robot-assisted surgery, a skillful surgeon can perform the operation excellently through flexible wrist motions and rich experience. However, there are little researches about the relationship between the wrist motion and electromyography (EMG) signal of surgeon. To this end, we introduce a classification framework of wrist motion to recognize the common wrist motion of the surgeon based on EMG signals. Generally, surface electromyogram (sEMG) signal has been widely used in prosthetic hand control and medical clinical application. Hence, in this paper, we utilize sEMG signals to evaluate the wrist motions. Eight channels of sEMG signals are captured through a MYO armband from the forearm of the subject. Different kinds of features based on EMG signal, root-mean-square, waveform length, and autoregressive are used to recognize wrist motion through linear discriminant analysis method. We test the impacts on recognition performance from the different sEMG features and different sampling moving window's length. Experimental results have verified the recognition performance of the presented approach. It is validated that the RMS feature can achieve best recognition performance with all different sampling moving window's length in comparison with the WL feature and AR feature. GRAPHICAL ABSTRACT

中文翻译：

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一种基于肌电信号的运动识别方法

在机器人辅助手术中，熟练的外科医生可以通过灵活的手腕动作和丰富的经验来出色地完成手术。然而，关于腕部运动与外科医生肌电信号（EMG）之间关系的研究较少。为此，我们引入了腕部运动的分类框架，以基于 EMG 信号识别外科医生的常见腕部运动。通常，表面肌电（sEMG）信号已广泛应用于假手控制和医学临床应用。因此，在本文中，我们利用 sEMG 信号来评估手腕运动。八个通道的 sEMG 信号通过 MYO 臂带从受试者的前臂捕获。基于肌电信号、均方根、波形长度、和自回归用于通过线性判别分析方法识别手腕运动。我们测试了不同 sEMG 特征和不同采样移动窗口长度对识别性能的影响。实验结果验证了所提出方法的识别性能。与WL特征和AR特征相比，RMS特征在所有不同的采样移动窗口长度下都能达到最佳识别性能。图形概要 与WL特征和AR特征相比，RMS特征在所有不同的采样移动窗口长度下都能达到最佳识别性能。图形概要 与WL特征和AR特征相比，RMS特征在所有不同的采样移动窗口长度下都能达到最佳识别性能。图形概要