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Robust dynamic hand gesture interaction using LTE terminals

  • Weiyan Chen
  • , Kai Niu
  • , Deng Zhao
  • , Rong Zheng
  • , Dan Wu
  • , Wei Wang
  • , Leye Wang
  • , Daqing Zhang
  • Tsinghua University
  • China University of Geosciences, Beijing
  • McMaster University
  • Nanjing University

Résultats de recherche: Le chapitre dans un livre, un rapport, une anthologie ou une collectionContribution à une conférenceRevue par des pairs

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.

langue originaleAnglais
titreProceedings - 2020 19th ACM/IEEE International Conference on Information Processing in Sensor Networks, IPSN 2020
EditeurInstitute of Electrical and Electronics Engineers Inc.
Pages109-120
Nombre de pages12
ISBN (Electronique)9781728154978
Les DOIs
étatPublié - 1 avr. 2020
Modification externeOui
Evénement19th ACM/IEEE International Conference on Information Processing in Sensor Networks, IPSN 2020 - Sydney, Australie
Durée: 21 avr. 202024 avr. 2020

Série de publications

NomProceedings - 2020 19th ACM/IEEE International Conference on Information Processing in Sensor Networks, IPSN 2020

Une conférence

Une conférence19th ACM/IEEE International Conference on Information Processing in Sensor Networks, IPSN 2020
Pays/TerritoireAustralie
La villeSydney
période21/04/2024/04/20

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