WiFine: Real-Time Gesture Recognition Using Wi-Fi with Edge Intelligence

Tianzhang Xing, Qing Yang, Zhiping Jiang, Xinhua Fu, Junfeng Wang, Chase Q. Wu, Xiaojiang Chen

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

Gesture detection based on radio frequency signals has gained increasing popularity in recent years due to several benefits it has brought, such as eliminating the need to carry additional devices and providing better privacy. In traditional methods, significant breakthroughs have been made to improve recognition accuracy and scene robustness, but the limited computing power of edge devices (the first-level equipment to receive signals) and the requirement of fast response for detection have not been adequately addressed. In this article, we propose a lightweight Wi-Fi gesture recognition system, referred to as WiFine, which is designed and implemented for deployment on low-end edge devices without the use of any additional high-performance services in the process. Toward these goals, we first design algorithms for phase difference selection and amplitude enhancement, respectively, to tackle the problem of data drift caused by user change. Then, we design a cross-dimension fusion method to extract features of finer granularity from information of different dimensions, thus solving the precision problem of feature granularity. Finally, we design a lightweight neural network architecture by leveraging redundancy to reduce computational cost while ensuring satisfactory recognition accuracy. Extensive experimental results show that the proposed system achieves fast recognition of various actions with an accuracy up to 96.03% in 0.19 seconds.

Original languageEnglish (US)
Article number3532094
JournalACM Transactions on Sensor Networks
Volume19
Issue number1
DOIs
StatePublished - Dec 8 2022

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications

Keywords

  • Wi-Fi sensing
  • deep learning
  • edge devices

Fingerprint

Dive into the research topics of 'WiFine: Real-Time Gesture Recognition Using Wi-Fi with Edge Intelligence'. Together they form a unique fingerprint.

Cite this