School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China.
School of Aerospace Engineering and Mechanics, Tongji University, Shanghai 200092, China.
Sensors (Basel). 2019 Jul 18;19(14):3170. doi: 10.3390/s19143170.
In recent years, surface electromyography (sEMG) signals have been increasingly used in pattern recognition and rehabilitation. In this paper, a real-time hand gesture recognition model using sEMG is proposed. We use an armband to acquire sEMG signals and apply a sliding window approach to segment the data in extracting features. A feedforward artificial neural network (ANN) is founded and trained by the training dataset. A test method is used in which the gesture will be recognized when recognized label times reach the threshold of activation times by the ANN classifier. In the experiment, we collected real sEMG data from twelve subjects and used a set of five gestures from each subject to evaluate our model, with an average recognition rate of 98.7% and an average response time of 227.76 ms, which is only one-third of the gesture time. Therefore, the pattern recognition system might be able to recognize a gesture before the gesture is completed.
近年来，表面肌电图（sEMG）信号在模式识别和康复领域的应用越来越多。本文提出了一种基于 sEMG 的实时手势识别模型。我们使用臂带采集 sEMG 信号，并采用滑动窗口方法对数据进行分割，以提取特征。通过训练数据集，建立了前馈人工神经网络（ANN）并进行了训练。采用测试方法，当 ANN 分类器的识别标签次数达到激活次数的阈值时，将识别手势。在实验中，我们从十二名受试者中采集了真实的 sEMG 数据，并使用每个受试者的一组五个手势来评估我们的模型，平均识别率为 98.7%，平均响应时间为 227.76 毫秒，仅为手势时间的三分之一。因此，模式识别系统可能能够在手势完成之前识别手势。
