Volume 30 Issue 6
Nov.  2021
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MENG Xianjia, FENG Lin, CHEN Hao, et al., “Just-in-Time Human Gesture Recognition Using WiFi Signals,” Chinese Journal of Electronics, vol. 30, no. 6, pp. 1111-1119, 2021, doi: 10.1049/cje.2021.07.022
Citation: MENG Xianjia, FENG Lin, CHEN Hao, et al., “Just-in-Time Human Gesture Recognition Using WiFi Signals,” Chinese Journal of Electronics, vol. 30, no. 6, pp. 1111-1119, 2021, doi: 10.1049/cje.2021.07.022

Just-in-Time Human Gesture Recognition Using WiFi Signals

doi: 10.1049/cje.2021.07.022

This work is supported by National Natural Science Foundation of China (No.61702416, No.61602382, No.61802310, No.61672428, No.61772422), the Key R&D Foundation of Shaanxi Province (No.2018SF-369), the ShaanXi Science and Technology Innovation Team Support Project (No.2018TD-O26), the China University of Labor Relations (No.20XYJS007), Shaanxi International Joint Research Centre for the Battery-free Internet of Things (No.2018SD0011), and Research on Target Perception, Recognition and Imaging Based on Low-Cost Commercial Equipment Wireless Signal (No.2019KWZ-05).

  • Received Date: 2019-12-25
  • Rev Recd Date: 2020-06-18
  • Available Online: 2021-09-23
  • Publish Date: 2021-11-05
  • In-air gesture recognition using wireless signals acts as a key enabler for various applications including smart homes, remote healthcare, shared autopilot, etc. Although researchers have conducted extensive research on WiFi-based gesture recognition, it remains an open question of providing accurate, robust, and in-time recognition system with the commodity WiFi infrastructure. We present FaSee, a just-in-time WiFibased gesture recognition system by identifying the fine-grained Channel state information (CSI) features upon off-the-shelf WiFi devices. The core of FaSee is essentially a novel hybrid recognition algorithm, which combines the classical K-Means algorithm with Dynamic time warping (DTW) together, to transform the feature matching in traditional gesture recognition schemes into a hierarchical manner, thereby significantly improving the recognition efficiency. Experimental results show that FaSee recognizes 9 representative gestures with an average accuracy of 94.75% without tedious per-person training, while achieving 30% signal processing delay saving when compared with the state-of-the-arts gesture recognition schemes.
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