Recent advances in wireless sensing techniques have made it possible to recognize hand gestures using channel state information (CSI) in commodity WiFi devices. Existing WiFi-based gesture recognition systems mainly use learning-based pattern recognition methods to recognize different gestures, however, these methods fail to work well when the locations of transceivers, the relative location and orientation of the hand with respect to transceivers, and/or the hand gesturing size change, leading to inconsistent signal patterns caused by those factors. Although some recent efforts have been made to address the so-called “domain-dependent” gesture recognition problem, they either require prior knowledge on initial locations of the hand and WiFi devices or need to train several classifiers for the specific domains. Different from the state-of-the-art methods, we construct two distinct features from a hand-oriented view (rather than from a transceiver’s view), namely, the dynamic phase vector (DPV) and motion rotation variable (MRV), which are quite consistent in characterizing a big set of handwriting gestures, despite significant change in locations of transceivers, the relative location and orientation of the hand with respect to transceivers, and the drawing sizes. We further incorporate a hierarchical sensing framework and develop HandGest—a real-time handwriting gesture recognition system using commodity WiFi devices, to precisely recognize a great number of “in-the-air” handwritings based on the aforementioned two domain-independent features and a pipeline of specific features. Extensive experiments have been done in practical settings with 20 volunteers, evaluation results demonstrate that HandGest outperforms state-of-the-art methods on a large number of handwritings with different transceivers’ location, different initial hand locations and orientations, as well as different drawing sizes. Given its superior performance, we believe that HandGest paves a new way to enhance the real-world practicality of WiFi-based gesture recognition.

中文翻译：

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HandGest：使用商品 WiFi 设备进行强大的空中手写识别的分层传感

无线传感技术的最新进展使得使用商品 WiFi 设备中的信道状态信息 (CSI) 识别手势成为可能。现有的基于 WiFi 的手势识别系统主要使用基于学习的模式识别方法来识别不同的手势，然而，当收发器的位置、手相对于收发器的相对位置和方向、和/或手势大小的变化，导致这些因素导致的信号模式不一致。尽管最近已经做出了一些努力来解决所谓的“领域相关”手势识别问题，但它们要么需要关于手和 WiFi 设备的初始位置的先验知识，要么需要为特定领域训练多个分类器。与最先进的方法不同，我们从面向手的视图（而不是从收发器的视图）构建了两个不同的特征，即动态相位矢量（DPV）和运动旋转变量（MRV），它们尽管收发器的位置、手相对于收发器的相对位置和方向以及图纸尺寸发生了显着变化，但它们在表征大量手写手势方面非常一致。我们进一步结合了分层传感框架并开发了 HandGest——一个使用商品 WiFi 设备的实时手写手势识别系统，基于上述两个独立于域的特征和一个特定功能的管道。在实际环境中对 20 名志愿者进行了广泛的实验，评估结果表明，HandGest 在具有不同收发器位置、不同初始手部位置和方向以及不同绘图的大量笔迹上优于最先进的方法尺寸。鉴于其卓越的性能，我们相信 HandGest 为增强基于 WiFi 的手势识别的实际实用性铺平了新的道路。