The electromyography (EMG) signal has great potential to determine the hand gestures automatically before the actual move begins. However, parameters of the sliding window along with the EMG signal, such as window size and overlapping size, as well as the number of votes in post-processing, such as majority voting, can significantly influence the gesture recognition accuracy. These phenomena have been investigated only in a few studies on a small number of subjects. The aim of this study is two-fold. First, to determine the influence of different window and overlapping sizes on the machine-learning performance using a large database consists of forty healthy subjects. Second, to develop a novel multi-window scheme to accumulate a large number of votes compared to the conventional single-window majority voting to improve gesture recognition accuracy. A large publicly available EMG dataset was used in this study. The window and overlapping sizes were varied between 50ms and 500ms, and between 0% and 80%, respectively. Six machine-learning algorithms, including k-Nearest Neighbor, Linear Discriminant Analysis, Logistic Regression, Naïve Bayes, Support Vector Machine, and Random Forest were used to classify six different hand gestures. Results show that the overall classification accuracy can be substantially improved by increasing the window size, overlapping size, and the number of votes in the majority voting strategy (p < 0.05). The maximum accuracy was achieved using the Random Forest algorithm. The two-way repeated measure analysis of variance shows that the proposed multi-window scheme substantially improved the overall accuracy of the machine-learning algorithms compared to the conventional majority voting. The proposed method can be instrumental for efficient control of prosthetic or exoskeleton devices.

中文翻译：

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使用肌电信号提高手势识别精度的多窗口多数投票策略

肌电图（EMG）信号具有很大的潜力，可以在实际移动开始之前自动确定手势。但是，滑动窗口的参数以及EMG信号（例如窗口大小和重叠大小）以及后处理中的投票数（例如多数投票）会严重影响手势识别的准确性。仅在少数主题的少数研究中对这些现象进行了研究。这项研究的目的是双重的。首先，使用一个包含40名健康受试者的大型数据库，确定不同窗口和重叠大小对机器学习性能的影响。第二，与传统的单窗口多数投票相比，开发一种新颖的多窗口方案以累积大量选票，以提高手势识别的准确性。在这项研究中使用了一个大型的公开可用的EMG数据集。窗口大小和重叠大小分别在50ms和500ms之间以及0％和80％之间变化。六种机器学习算法（包括k最近邻，线性判别分析，逻辑回归，朴素贝叶斯，支持向量机和随机森林）用于对六个不同的手势进行分类。结果表明，通过增加窗口大小，重叠大小和多数表决策略中的投票数，可以显着提高总体分类准确性（p <0.05）。使用随机森林算法可以达到最大精度。对方差的双向重复测量分析表明，与传统的多数投票相比，所提出的多窗口方案显着提高了机器学习算法的整体准确性。所提出的方法对于有效控制假体或外骨骼设备可能是有用的。