Gesture recognition system based on cross-domain CSI extracted from Wi-Fi devices combined with the 3D CNN

Gesture recognition offers a wide range of applications in human–computer interaction. Wi-Fi devices have been deployed almost everywhere in recent years, thanks to the rapid expansion of wireless communication, the Internet of Things, and the emergence of data about Wi-Fi channel state information(...

Full description

Saved in:
Bibliographic Details
Published in:Signal, image and video processing image and video processing, 2023-09, Vol.17 (6), p.3201-3209
Main Author: Bulugu, Isack
Format: Article
Language:English
Subjects:
Accuracy
Artificial neural networks
Computer Imaging
Computer Science
Domains
Gesture recognition
Image Processing and Computer Vision
Internet of Things
Learning
Multimedia Information Systems
Neural networks
Original Paper
Pattern Recognition and Graphics
Signal,Image and Speech Processing
Vision
Wireless communications
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Gesture recognition offers a wide range of applications in human–computer interaction. Wi-Fi devices have been deployed almost everywhere in recent years, thanks to the rapid expansion of wireless communication, the Internet of Things, and the emergence of data about Wi-Fi channel state information(CSI). Currently, most existing CSI gesture recognition studies solely focus on gesture recognition in a known domain. In the case of an unknown domain, new data from unknown scenes must be added for additional learning and training; otherwise, recognition accuracy will be significantly reduced, limiting practicality. To address this problem, a CSI cross-domain gesture recognition approach based on 3D convolutional neural networks is proposed. The method realizes cross-scene gesture recognition by extracting domain-independent features, and combining such features with the 3D convolutional neural network learning model. The experiment uses public datasets to verify the approach. The findings demonstrate that the technique achieves 89.67% recognition accuracy in known scenes and 86.34% recognition accuracy in unknown scenes, indicating that it can recognize cross-scene gestures recognition.
ISSN:1863-1703
1863-1711
DOI:10.1007/s11760-023-02545-8