In this work, we apply information theory inspired methods to quantify
changes in daily activity patterns. We use in-home movement monitoring data and
show how they can help indicate the occurrence of healthcare-related events.
Three different types of entropy measures namely Shannon's entropy, entropy
rates for Markov chains, and entropy production rate have been utilised. The
measures are evaluated on a large-scale in-home monitoring dataset that has
been collected within our dementia care clinical study. The study uses Internet
of Things (IoT) enabled solutions for continuous monitoring of in-home
activity, sleep, and physiology to develop care and early intervention
solutions to support people living with dementia (PLWD) in their own homes. Our
main goal is to show the applicability of the entropy measures to time-series
activity data analysis and to use the extracted measures as new engineered
features that can be fed into inference and analysis models. The results of our
experiments show that in most cases the combination of these measures can
indicate the occurrence of healthcare-related events. We also find that
different participants with the same events may have different measures based
on one entropy measure. So using a combination of these measures in an
inference model will be more effective than any of the single measures.