Gesture recognition has become increasingly important in human-computer interaction and can support different applications such as smart home, VR, and gaming. Traditional approaches usually rely on dedicated sensors that are worn by the user or cameras that require line of sight. In this paper, we present fine-grained finger gesture recognition by using commodity WiFi without requiring user to wear any sensors. Our system takes advantages of the fine-grained Channel State Information available from commodity WiFi devices and the prevalence of WiFi network infrastructures. It senses and identifies subtle movements of finger gestures by examining the unique patterns exhibited in the detailed CSI. We devise environmental noise removal mechanism to mitigate the effect of signal dynamic due to the environment changes. Moreover, we propose to capture the intrinsic gesture behavior to deal with individual diversity and gesture inconsistency. Lastly, we utilize multiple WiFi links and larger bandwidth at 5GHz to achieve finger gesture recognition under multi-user scenario. Our experimental evaluation in different environments demonstrates that our system can achieve over 90% recognition accuracy and is robust to both environment changes and individual diversity. Results also show that our system can provide accurate gesture recognition under different scenarios.
翻译:在人-计算机互动中,定位定位越来越重要,可以支持智能家庭、VR和赌博等不同应用。传统方法通常依赖用户或摄影机所穿戴的专用传感器,这些传感器需要视线。在本文中,我们通过使用商品WiFi来显示精细的手指手势识别,而不需要用户穿戴任何传感器。我们的系统利用了商品WiFi设备提供的精细的频道国家信息以及WiFi网络基础设施的普及性。它通过审查详细的 CSI所展示的独特模式来感知和识别手指手势的微妙移动。我们设计了环境噪音清除机制,以减轻环境变化带来的信号动态效应。此外,我们提议捕捉内在的动作行为,处理个人多样性和动作不一致问题。最后,我们在5Ghz使用多种WiFi链接和更大的带宽,以便在多用户的情景下实现手指手势识别。我们在不同环境中的实验性评估表明,我们的系统能够达到90%以上的识别准确度,并且能够适应环境变化和个人多样性。结果还表明,我们的系统可以在不同的情景下提供准确的姿态识别。