Taha, A. , Ge, Y., Taylor, W., Zoha, A. , Abbasi, Q. H. and Imran, M. A. (2022) Indoor Activity Position and Direction Detection Using Software Defined Radios. In: EAI BODYNETS 2021 - 16th EAI International Conference on Body Area Networks: Smart IoT and big data for intelligent health management, Glasgow, Great Britain, 25-26 October 2021, pp. 15-27. ISBN 9783030955922 (doi: 10.1007/978-3-030-95593-9_2)
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Abstract
The next generation of health activity monitoring is greatly dependent on wireless sensing. By analysing variations in channel state information, several studies were capable of detecting activities in an indoor setting. This paper presents promising results of an experiment conducted to identify the activity performed by a subject and where it took place within the activity region. The system utilises two Universal Software Radio Peripheral (USRP) devices, operating as software-defined radios, to collect a total of 360 data samples that represent five different activities and an empty room. The five activities were performed in three different zones, resulting in 15 classes and a 16th class representing the room whilst it is empty. Using the Random Forest classifier, the system was capable of differentiating between the majority of activities, across the 16 classes, with an accuracy of almost 94%. Moreover, it was capable of detecting whether the room is occupied, with an accuracy of 100%, and identify the walking directions of a human subject in three different positions within the room, with an accuracy of 90%.
| Item Type: | Conference Proceedings |
|---|---|
| Status: | Published |
| Refereed: | Yes |
| Glasgow Author(s) Enlighten ID: | Zoha, Dr Ahmed and Taha, Dr Ahmad and Imran, Professor Muhammad and Abbasi, Dr Qammer |
| Authors: | Taha, A., Ge, Y., Taylor, W., Zoha, A., Abbasi, Q. H., and Imran, M. A. |
| 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: | 9783030955922 |
| Published Online: | 11 February 2022 |
| Copyright Holders: | Copyright © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2022 |
| First Published: | First published in Body Area Networks. Smart IoT and Big Data for Intelligent Health Management: 15-27 |
| Publisher Policy: | Reproduced in accordance with the publisher copyright policy |
| Related URLs: |
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