Zhuravchak, A, Kapshii, O and Pournaras, E orcid.org/0000-0003-3900-2057 (2022) Human Activity Recognition based on Wi-Fi CSI Data -A Deep Neural Network Approach. In: Procedia Computer Science. 11th International Conference on Current and Future Trends of Information and Communication Technologies in Healthcare, 01-04 Nov 2021, Leuven, Belgium. Elsevier , pp. 59-66.
Abstract
Using Wi-Fi Channel State Information (CSI) is a novel way of environmental sensing and human activity recognition (HAR). These methods can be used for several safety and security applications by (re)using Wi-Fi routers without the need for additional costly hardware required for vision-based approaches, known also to be particularly privacy-intrusive. This work introduces a full pipeline of a Wi-Fi CSI-based system for human activity recognition that assesses and compares two deep learning methods. We analyze how different hardware configurations affect WiFi CSI signals. We contribute a novel and more realistic data collection process, in which human activity recognition is seamlessly integrated in real-life, resulting in more reliable assessments of the model classification performance. We analyze how InceptionTime and LSTM-based classification models perform in human activity recognition. The source code and collected dataset are made publicly available for reproducibility and encouraging further research in the community.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license. |
Keywords: | Wi-Fi; channel state information; human activity recognition; machine learning; digital signal processing |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 03 Mar 2022 14:17 |
Last Modified: | 03 Mar 2022 14:17 |
Status: | Published |
Publisher: | Elsevier |
Identification Number: | 10.1016/j.procs.2021.12.211 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:184207 |