Department of Electronics Engineering, Incheon National University, Incheon 22012, Korea.
Sensors (Basel). 2021 Dec 29;22(1):225. doi: 10.3390/s22010225.
Electromyogram (EMG) signals have been increasingly used for hand and finger gesture recognition. However, most studies have focused on the wrist and whole-hand gestures and not on individual finger (IF) gestures, which are considered more challenging. In this study, we develop EMG-based hand/finger gesture classifiers based on fixed electrode placement using machine learning methods. Ten healthy subjects performed ten hand/finger gestures, including seven IF gestures. EMG signals were measured from three channels, and six time-domain (TD) features were extracted from each channel. A total of 18 features was used to build personalized classifiers for ten gestures with an artificial neural network (ANN), a support vector machine (SVM), a random forest (RF), and a logistic regression (LR). The ANN, SVM, RF, and LR achieved mean accuracies of 0.940, 0.876, 0.831, and 0.539, respectively. One-way analyses of variance and F-tests showed that the ANN achieved the highest mean accuracy and the lowest inter-subject variance in the accuracy, respectively, suggesting that it was the least affected by individual variability in EMG signals. Using only TD features, we achieved a higher ratio of gestures to channels than other similar studies, suggesting that the proposed method can improve the system usability and reduce the computational burden.
肌电图 (EMG) 信号在手部和手指手势识别中的应用越来越广泛。然而，大多数研究都集中在手腕和整个手部的手势上，而不是单个手指 (IF) 的手势，因为后者被认为更具挑战性。在这项研究中，我们使用机器学习方法基于固定电极位置开发基于 EMG 的手/手指手势分类器。十位健康受试者执行了十种手/手指手势，包括七种 IF 手势。EMG 信号由三个通道测量，每个通道提取六个时域 (TD) 特征。总共使用 18 个特征来为十个手势构建个性化分类器，使用人工神经网络 (ANN)、支持向量机 (SVM)、随机森林 (RF) 和逻辑回归 (LR)。ANN、SVM、RF 和 LR 的平均准确率分别为 0.940、0.876、0.831 和 0.539。单向方差分析和 F 检验表明，ANN 分别在准确率方面具有最高的平均准确率和最低的个体间方差，这表明它受 EMG 信号个体变异性的影响最小。仅使用 TD 特征，我们实现了比其他类似研究更高的手势与通道比，这表明所提出的方法可以提高系统的可用性并降低计算负担。
