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• W3204350215 abstract "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 WiFi-based 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." @default.
• W3204350215 created "2021-10-11" @default.
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• W3204350215 date "2021-11-01" @default.
• W3204350215 modified "2023-10-17" @default.
• W3204350215 title "Just‐in‐Time Human Gesture Recognition Using WiFi Signals" @default.
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• W3204350215 doi "https://doi.org/10.1049/cje.2021.07.022" @default.
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