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Communication Dans Un Congrès 2020 19th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN) Année : 2020

Robust dynamic hand gesture interaction using LTE terminals

Résumé

Device-free hand gesture is one of the most natural ways to interact with everyday objects. However, existing WiFi-based gesture recognition solutions are typically restricted to indoor environments due to limited outdoor coverage. Furthermore, to achieve high sampling rates, they may interfere with normal data transmissions. In this paper, we aim to develop a robust dynamic gesture interaction system that can be ubiquitously deployed using Long-term Evolution (LTE) mobile terminals. Through both empirical studies and in-depth analysis using the Fresnel zone model, we reveal the key factors that contribute to the repeatability and discernibility of gestures. We show that the optimal location and orientation to perform gestures indeed exist and can be identified without prior knowledge of the position of LTE base stations (BSs) relative to a terminal. Guided by the design principles derived from Fresnel zone characteristics around a 4G terminal, we design highly repeatable and discernible gestures with salient received signal profiles. A gesture interaction system has been developed and implemented to achieve robust recognition with this careful design. Extensive experiments have been conducted in both indoor and outdoor environments, for different relative placements of mobile terminal and BS, and with different users. The proposed system can automatically identify the direction of BSs with a median error of less than 15 degrees and achieve gesture recognition accuracy as high as 98% in all scenarios without the need to acquire any training data.
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Dates et versions

hal-03002712 , version 1 (12-11-2020)

Identifiants

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Weiyan Chen, Kai Niu, Deng Zhao, Rong Zheng, Dan Wu, et al.. Robust dynamic hand gesture interaction using LTE terminals. IPSN 2020: 19th ACM/IEEE International Conference on Information Processing in Sensor Networks, Apr 2020, Sydney (online), Australia. pp.109-120, ⟨10.1109/ipsn48710.2020.00017⟩. ⟨hal-03002712⟩
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