WiGeR: WiFi-Based Gesture Recognition System

Recently, researchers around the world have been striving to develop and modernize human-computer interaction systems by exploiting advances in modern communication systems. The priority in this field involves exploiting radio signals so human-computer interaction will require neither special device...

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Bibliographic Details
Published in:ISPRS international journal of geo-information 2016-06, Vol.5 (6), p.92
Main Authors: Al-qaness, Mohammed, Li, Fangmin
Format: Article
Language:English
Subjects:
Algorithms
Classification
Communication
Communications systems
CSI
device-free
Duration
Gesture recognition
Human-computer interaction
Human-computer interface
human–computer interfaces
Interfaces
Mathematical models
Modernization
Radio signals
Segmentation
Telecommunications
Variation
Wavelet analysis
WiFi
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Summary:Recently, researchers around the world have been striving to develop and modernize human-computer interaction systems by exploiting advances in modern communication systems. The priority in this field involves exploiting radio signals so human-computer interaction will require neither special devices nor vision-based technology. In this context, hand gesture recognition is one of the most important issues in human-computer interfaces. In this paper, we present a novel device-free WiFi-based gesture recognition system (WiGeR) by leveraging the fluctuations in the channel state information (CSI) of WiFi signals caused by hand motions. We extract CSI from any common WiFi router and then filter out the noise to obtain the CSI fluctuation trends generated by hand motions. We design a novel and agile segmentation and windowing algorithm based on wavelet analysis and short-time energy to reveal the specific pattern associated with each hand gesture and detect duration of the hand motion. Furthermore, we design a fast dynamic time warping algorithm to classify our system’s proposed hand gestures. We implement and test our system through experiments involving various scenarios. The results show that WiGeR can classify gestures with high accuracy, even in scenarios where the signal passes through multiple walls.
ISSN:2220-9964
2220-9964
DOI:10.3390/ijgi5060092