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Wearable armband for real time hand gesture recognition

机译:可穿戴臂架的实时手势识别

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This paper presents a framework for hand gesture recognition based on 3-channel electromyography (EMG) sensors. In the framework, the start and the end points of meaningful gesture segments are detected automatically by checking the cross points of EMG signals and their moving average curves. Then, a classifier combining k-Nearest Neighbor (kNN) and Decision Tree algorithms is used for achieving gesture recognition. For gesture-based control application, a real-time interactive system has been built up for household appliance using 10 kinds of hand gestures as control commands. Our proposed framework facilitates intelligent and natural control in gesture-based interaction.
机译:本文介绍了基于3通道电拍摄(EMG)传感器的手势识别框架。在框架中,通过检查EMG信号的交叉点及其移动平均曲线来自动检测有意义的手势段的开始和终点。然后,组合K-Collect邻居(KNN)和决策树算法的分类器用于实现手势识别。对于基于手势的控制应用,使用10种手势作为控制命令,为家用电器建立了实时交互式系统。我们所提出的框架促进了基于手势的互动中的智能和自然控制。

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