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

Document Type

Article

Publication Date

12-8-2022

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.

Identifier

85146656186 (Scopus)

Publication Title

ACM Transactions on Sensor Networks

External Full Text Location

https://doi.org/10.1145/3532094

e-ISSN

15504867

ISSN

15504859

Issue

1

Volume

19

Grant

2020KW-004

Fund Ref

National Natural Science Foundation of China

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