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Wifi-based human activity recognition using Raspberry Pi.

Forbes, Glenn; Massie, Stewart; Craw, Susan

Authors

Glenn Forbes



Contributors

Miltos Alamaniotis
Editor

Shimei Pan
Editor

Abstract

Ambient, non-intrusive approaches to smart home health monitoring, while limited in capability, are preferred by residents. More intrusive methods of sensing, such as video and wearables, can offer richer data but at the cost of lower resident uptake, in part due to privacy concerns. A radio frequency-based approach to sensing, Channel State Information (CSI),can make use of low cost off-the-shelf WiFi hardware. We have implemented an activity recognition system on the Raspberry Pi 4, one of the world’s most popular embedded boards. We have implemented an classification system using the Pi to demonstrate its capability for activity recognition. This involves performing data collection, interpretation and windowing, before supplying the data to a classification model. In this paper, the capabilities of the Raspberry Pi 4 at performing activity recognition on CSI data are investigated. We have developed and publicly released a data interaction framework, capable of interpreting, processing and visualising data from a range of CSI-capable hardware. Furthermore, CSI data captured for these experiments during various activity performances have also been made publically available. We then train a Deep Convolutional LSTM model to classify the activities. Our experiments, performed in a small apartment, achieve 92% average accuracy on 11 activity classes.

Citation

FORBES, G., MASSIE, S. and CRAW, S. 2020. Wifi-based human activity recognition using Raspberry Pi. In Alamaniotis, M. and Pan, S. (eds.) Proceedings of Institute of Electrical and Electronics Engineers (IEEE) 32nd Tools with artificial intelligence international conference 2020 (ICTAI 2020), 9-11 Nov 2020, [virtual conference]. Piscataway: IEEE [online], pages 722-730. Available from: https://doi.org/10.1109/ICTAI50040.2020.00115

Conference Name Institute of Electrical and Electronics Engineers (IEEE) 32nd Tools with artificial intelligence international conference 2020 (ICTAI 2020)
Conference Location [virtual conference]
Start Date Nov 9, 2020
End Date Nov 11, 2020
Acceptance Date Sep 1, 2020
Online Publication Date Dec 24, 2020
Publication Date Dec 31, 2020
Deposit Date Sep 11, 2020
Publicly Available Date Sep 11, 2020
Publisher Institute of Electrical and Electronics Engineers
Pages 722-730
Series ISSN 2375-0197
Book Title Proceedings of IEEE 32nd Tools with artificial intelligence international conference (ICTAI 2020)
ISBN 9781728192284
DOI https://doi.org/10.1109/ICTAI50040.2020.00115
Keywords Activity recognition; Smart home; IoT; RF
Public URL https://rgu-repository.worktribe.com/output/966777

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