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Dynamic Hand Gesture Detection and Recognition with WiFi Signal Based on 1D-CNN

机译：基于1D-CNN的WiFi信号动态手势检测与识别

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摘要

Due to the rapid development of Internet of Things (IoT) technology and artificial intelligence, there is an urgent need for human-computer interaction (HCI) applications. The dynamic hand gesture recognition technology based on WiFi signal plays an important role. However, although the gesture recognition system using Channel State Information (CSI) has made great progress, we have observed that in the current research, most commercial network cards can not directly extract such signals, and the easily acquired received signal strength (RSS) can only recognize simple gestures. Therefore, in this paper, we present a universal framework to achieve dynamic hand gesture detection and recognition with RSS. We use RSS from multiple independent WiFi nodes to increase the upper limit of recognize capability, enabling the system to recognize seven complex dynamic hand gestures. The false trigger detection algorithm effectively eliminates the false triggers, and the detection accuracy is close to 91.38%. The system uses a state machine and a linear scale algorithm to accommodate different hand gesture speed with durations ranging from 0.9s to 5.4s. Furthermore, we analyze the errors of the detection algorithm and propose a recognition architecture based on One Dimension Convolutional Neural Network (1D-CNN) and two data collection strategies: gesture extending and gesture shifting. The proposed 1D-CNN effectively overcomes the error caused by hand gesture detection algorithm and the recognition accuracy reaches 86.91%. Combined with the gesture shifting strategy, the recognition accuracy is further improved to 93.03%.
机译：由于物联网（IoT）技术和人工智能的快速发展，迫切需要人机交互（HCI）应用程序。基于WiFi信号的动态手势识别技术起着重要的作用。但是，尽管使用信道状态信息（CSI）的手势识别系统已经取得了很大的进步，但我们已经观察到，在当前的研究中，大多数商用网卡无法直接提取此类信号，而容易获得的接收信号强度（RSS）可以只识别简单的手势。因此，在本文中，我们提出了一个通用框架，以实现RSS的动态手势检测和识别。我们使用来自多个独立WiFi节点的RSS来提高识别能力的上限，从而使系统能够识别七个复杂的动态手势。错误触发检测算法有效消除了错误触发，检测精度接近91.38％。该系统使用状态机和线性比例算法来适应不同的手势速度，持续时间范围从0.9s到5.4s。此外，我们分析了检测算法的错误，并提出了基于一维卷积神经网络（1D-CNN）和两种数据收集策略的手势识别结构：手势扩展和手势移动。提出的一维神经网络有效地克服了手势检测算法带来的误差，识别精度达到86.91％。结合手势转移策略，识别精度进一步提高到93.03％。

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