  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.
