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Tracking Human Behavioural Consistency by Analysing Periodicity of Household Water Consumption

Quinn, Sean orcid logoORCID: 0000-0003-0807-1076, Murphy, Noel and Smeaton, Alan F. orcid logoORCID: 0000-0003-1028-8389 (2019) Tracking Human Behavioural Consistency by Analysing Periodicity of Household Water Consumption. In: International Conference on Sensors, Signal and Image Processing (SSIP2019), 8-10 Oct 2019, Prague, Czech Republic. ISBN 978-1-4503-7243-5

Abstract
People are living longer than ever due to advances in healthcare, and this has prompted many healthcare providers to look towards remote patient care as a means to meet the needs of the future. It is now a priority to enable people to reside in their own homes rather than in overburdened facilities whenever possible. The increasing maturity of IoT technologies and the falling costs of connected sensors has made the deployment of remote healthcare at scale an increasingly attractive prospect. In this work we demonstrate that we can measure the consistency and regularity of the behaviour of a household using sensor readings generated from interaction with the home environment. We show that we can track changes in this behaviour regularity longitudinally and detect changes that may be related to significant life events or trends that may be medically significant. We achieve this using periodicity analysis on water usage readings sampled from the main household water meter every 15 minutes for over 8 months. We utilise an IoT Application Enablement Platform in conjunction with low cost LoRa enabled sensors and a Low Power Wide Area Network in order to validate a data collection methodology that could be deployed at large scale in future. We envision the statistical methods described here being applied to data streams from the homes of elderly and at-risk groups, both as a means of early illness detection and for monitoring the well-being of those with known illnesses.
Metadata
Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Refereed:Yes
Uncontrolled Keywords:Home Monitoring; Sensor Networks; Sensor Applications; Internet of Things; Ambient Assisted Living
Subjects:Engineering > Signal processing
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Research Initiatives and Centres > INSIGHT Centre for Data Analytics
Published in: International Conference on Sensors, Signal and Image Processing, Proceedings. . Association for Computing Machinery (ACM). ISBN 978-1-4503-7243-5
Publisher:Association for Computing Machinery (ACM)
Official URL:https://doi.org/10.1145/3365245.3365246
Copyright Information:© 2019 Association for Computing Machinery
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:Science Foundation Ireland (SFI) SFI/12/RC/2289, SFI Enable Spoke 16/SP/3804
ID Code:23840
Deposited On:17 Oct 2019 12:02 by Sean Quinn . Last Modified 05 Jan 2022 14:22
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