WiHF: Gesture and User Recognition With WiFi
Autor: | Manni Liu, Chenning Li Li, Zhichao Cao |
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Rok vydání: | 2022 |
Předmět: |
Artificial neural network
Computer Networks and Communications Computer science business.industry 020206 networking & telecommunications Collaborative learning 02 engineering and technology Motion (physics) Rendering (computer graphics) Identification (information) Seam carving Gesture recognition 0202 electrical engineering electronic engineering information engineering Computer vision Artificial intelligence Electrical and Electronic Engineering business Software Gesture |
Zdroj: | IEEE Transactions on Mobile Computing. 21:757-768 |
ISSN: | 2161-9875 1536-1233 |
Popis: | User identified gesture recognition is a fundamental step towards ubiquitous WiFi based sensing. We propose WiHF, which first simultaneously enables cross-domain gesture recognition and user identification using commodity WiFi in a real-time manner. The basic idea of WiHF is to derive a domain-independent motion change pattern of arm gestures from WiFi signals, rendering the unique gesture characteristics and the personalized user performing styles. To extract the motion change pattern in real time, we develop an efficient method based on the seam carving algorithm. Moreover, taking as input the motion change pattern, a Deep Neural Network (DNN) is adopted for both gesture recognition and user identification tasks. In DNN, we apply splitting and splicing schemes to optimize collaborative learning for dual tasks. We implement WiHF and extensively evaluate its performance on a public dataset including 6 users and 6 gestures performed across 5 locations and 5 orientations in 3 environments. Experimental results show that WiHF achieves 97.65% and 96.74% for in-domain gesture recognition and user identification accuracy, respectively. The cross-domain gesture recognition accuracy is comparable with the state-of-the-art method, but the processing time is reduced by 30×. |
Databáze: | OpenAIRE |
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