论文标题
使用多通道SEMG信号的颞肌激活图的实时手势识别
Real-Time Hand Gesture Recognition Using Temporal Muscle Activation Maps of Multi-Channel sEMG Signals
论文作者
论文摘要
准确，实时的手势识别对于控制先进的手术至关重要。从前臂获得的表面肌电图（SEMG）信号被广泛用于此目的。在这里，我们介绍了一种新型的手势表示，称为颞肌激活（TMA）图，该图捕获了有关前臂肌肉激活模式的信息。基于这些地图，我们提出了一种算法，该算法可以使用卷积神经网络实时识别手势。该算法对8个健康受试者进行了测试，并从沿前臂圆周放置的8个电极获得了SEMG信号。该方法的平均分类精度为94％，与最新方法相媲美。预测的平均计算时间为5.5ms，这使该算法非常适合实时手势识别应用程序。
Accurate and real-time hand gesture recognition is essential for controlling advanced hand prostheses. Surface Electromyography (sEMG) signals obtained from the forearm are widely used for this purpose. Here, we introduce a novel hand gesture representation called Temporal Muscle Activation (TMA) maps which captures information about the activation patterns of muscles in the forearm. Based on these maps, we propose an algorithm that can recognize hand gestures in real-time using a Convolution Neural Network. The algorithm was tested on 8 healthy subjects with sEMG signals acquired from 8 electrodes placed along the circumference of the forearm. The average classification accuracy of the proposed method was 94%, which is comparable to state-of-the-art methods. The average computation time of a prediction was 5.5ms, making the algorithm ideal for the real-time gesture recognition applications.
