Tracking Human Behavioural Consistency by Analysing Periodicity of Household Water Consumption

TL;DR

This paper demonstrates how analyzing periodicity in household water consumption data collected via IoT sensors over 8 months can track behavioral consistency and detect significant life or health-related changes, supporting remote healthcare.

Contribution

It introduces a scalable method using periodicity analysis of water usage data for monitoring behavioral regularity and detecting health-related changes in households.

Findings

Water usage patterns can indicate behavioral consistency.

Longitudinal analysis detects significant life events.

IoT-based data collection is feasible for large-scale deployment.

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.