Department of Biomedical Engineering, Yonsei University, Wonju 26493, Korea.
Sensors (Basel). 2021 Nov 18;21(22):7681. doi: 10.3390/s21227681.
Surface electromyography (sEMG)-based gesture recognition systems provide the intuitive and accurate recognition of various gestures in human-computer interaction. In this study, an sEMG-based hand posture recognition algorithm was developed, considering three main problems: electrode shift, feature vectors, and posture groups. The sEMG signal was measured using an armband sensor with the electrode shift. An artificial neural network classifier was trained using 21 feature vectors for seven different posture groups. The inter-session and inter-feature Pearson correlation coefficients (PCCs) were calculated. The results indicate that the classification performance improved with the number of training sessions of the electrode shift. The number of sessions necessary for efficient training was four, and the feature vectors with a high inter-session PCC ( > 0.7) exhibited high classification accuracy. Similarities between postures in a posture group decreased the classification accuracy. Our results indicate that the classification accuracy could be improved with the addition of more electrode shift training sessions and that the PCC is useful for selecting the feature vector. Furthermore, hand posture selection was as important as feature vector selection. These findings will help in optimizing the sEMG-based pattern recognition algorithm more easily and quickly.
基于表面肌电（sEMG）的手势识别系统为人机交互中各种手势的直观和准确识别提供了可能。在这项研究中，考虑到三个主要问题：电极移位、特征向量和姿势组，开发了一种基于 sEMG 的手姿势识别算法。使用带有电极移位的臂带传感器测量 sEMG 信号。使用 21 个特征向量对七个不同姿势组的人工神经网络分类器进行训练。计算了组间和组内 Pearson 相关系数（PCC）。结果表明，分类性能随着电极移位的训练次数的增加而提高。有效训练所需的训练次数为四，具有高组间 PCC（>0.7）的特征向量表现出较高的分类准确性。姿势组内姿势的相似性降低了分类准确性。我们的结果表明，通过增加更多的电极移位训练，可以提高分类准确性，并且 PCC 有助于选择特征向量。此外，手姿势选择与特征向量选择同样重要。这些发现将有助于更轻松、更快速地优化基于 sEMG 的模式识别算法。
