Human Activity Recognition based on Collaboration of Vision and WiFi Signals

Li, S., Ge, Y., Shentu, M., Zhu, S., Imran, M. A. , Abbasi, Q. H. and Cooper, J. (2022) Human Activity Recognition based on Collaboration of Vision and WiFi Signals. In: 6th International Conference on UK-China Emerging Technologies (UCET 2021), Chengdu, China, 4-6 Nov 2021, pp. 204-208. ISBN 9781665495752 (doi: 10.1109/UCET54125.2021.9674970)

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Abstract

In the WiFi protocol, channel state information (CSI) is the modulated as the fine-grained data to assess the channel efficiency. Meanwhile, it contains the information about the environment change, including the movement of human in a specific environment. Therefore, the CSI data can be used to recognize the human activity. In this paper, we design a vision and WiFi collaboration-based human activity recognition scheme to classify the human activities. More specifically, we collect the CSI data from the WiFi signals and the human skeleton points from the video signals. Then, we construct a long-short-term Transformer network to build up the collaboration of the CSI data and the skeleton points. Based on this collaboration, we can use the CSI data to well recognize the human activities.

Item Type:Conference Proceedings
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Abbasi, Professor Qammer and Imran, Professor Muhammad and Li, Shibo
Authors: Li, S., Ge, Y., Shentu, M., Zhu, S., Imran, M. A., Abbasi, Q. H., and Cooper, J.
College/School:College of Science and Engineering > School of Engineering > Electronics and Nanoscale Engineering
College of Science and Engineering > School of Engineering > Systems Power and Energy
ISBN:9781665495752
Copyright Holders:Copyright © 2021 The Authors
First Published:First published in 2021 International Conference on UK-China Emerging Technologies (UCET), 2021, pp. 204-208
Publisher Policy:Reproduced in accordance with the publisher copyright policy
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