Time Reversal Based Robust Gesture Recognition Using Wifi
Time Reversal Based Robust Gesture Recognition Using Wifi
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DOI：
10.1109/icassp40776.2020.9053420
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发表时间：
2020-05
期刊：
影响因子：
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通讯作者：
Sai Deepika Regani;Beibei Wang;Min Wu;K. Liu
中科院分区：
文献类型：
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作者：
Sai Deepika Regani;Beibei Wang;Min Wu;K. Liu
无线传感手势识别技术在人机交互领域有着广泛的应用前景。然而，大多数现有的工作是不健壮的，不需要可穿戴设备或繁琐的培训/校准。在这项工作中，我们提出了WiGRep，一个基于时间反转的手势识别方法，使用Wi-Fi，它可以识别不同的手势，通过计数重复的手势片段的数量。基于RF传输中的时间反转现象，时间反射谐振强度（TRRS）用于检测手势中的重复模式。提出了一种鲁棒的低复杂度算法，以适应手势和室内环境的可能变化。WiGRep的主要优点是无需校准，并且与位置和环境无关。在视线和非视线的情况下进行的实验表明，99.6%和99.4%的检测率，分别为5%的固定误报率。
Gesture recognition using wireless sensing opened a plethora of applications in the field of human-computer interaction. However, most existing works are not robust without requiring wearables or tedious training/calibration. In this work, we propose WiGRep, a time reversal based gesture recognition approach using Wi-Fi, which can recognize different gestures by counting the number of repeating gesture segments. Built upon the time reversal phenomenon in RF transmission, the Time Reversal Resonating Strength (TRRS) is used to detect repeating patterns in a gesture. A robust low-complexity algorithm is proposed to accommodate possible variations of gestures and indoor environments. The main advantages of WiGRep are that it is calibration-free and location and environment independent. Experiments performed in both line of sight and non-line-of-sight scenarios demonstrate a detection rate of 99.6% and 99.4%, respectively, for a fixed false alarm rate of 5%.