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Hand Gesture Recognition by a MMG-based Wearable Device
IEEE Sensors Journal ( IF 4.3 ) Pub Date : 2020-12-15 , DOI: 10.1109/jsen.2020.3011825
Meng-Kun Liu , Yu-Ting Lin , Zhao-Wei Qiu , Chao-Kuang Kuo , Chi-Kang Wu

A novel wearable human machine interface based on mechanomyogram (MMG) signals was presented in this study. A three-axis accelerometer was fixed to a customized watch strap to measure the MMG signals that were generated by the end of the extensor digitorum muscle. Eight gaming gestures, including clapping, index figure flicking, finger snapping, coin flipping, shooting, wrist extension, wrist flexion and fist-making, were identified in real time. This study extracted the features from both the time signals and the coefficients of the wavelet packet decomposition (WPD), and sequential forward selection (SFS) was used to identify the significant features to improve the classification accuracy and reduce the processing time. The performances of the classifiers such as the k-nearest neighbors (KNN), the support vector machine (SVM), linear discriminant analysis (LDA), and deep neural network (DNN) were compared. After testing the system on 35 subjects aged from 16 to 55 years old, the proposed system has advantages with respect to its convenient portability, stable signal acquisition, low power consumption, and high classification accuracy.

中文翻译:

基于 MMG 的可穿戴设备的手势识别

本研究提出了一种基于肌力图 (MMG) 信号的新型可穿戴人机界面。三轴加速度计固定在定制的表带上,以测量由伸指肌末端产生的 MMG 信号。实时识别拍手、轻弹食指、弹指、掷硬币、射击、伸腕、屈腕、握拳等8种游戏手势。本研究从时间信号和小波包分解(WPD)的系数中提取特征,并使用顺序前向选择(SFS)识别重要特征,以提高分类精度并减少处理时间。k-近邻(KNN)、支持向量机(SVM)等分类器的性能 比较了线性判别分析(LDA)和深度神经网络(DNN)。该系统在 35 名 16 至 55 岁的受试者上进行测试后,该系统具有携带方便、信号采集稳定、功耗低、分类准确率高等优点。
更新日期:2020-12-15
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