Using Entropy Measures for Monitoring the Evolution of Activity Patterns

TL;DR

This paper demonstrates how entropy measures derived from information theory can effectively monitor and detect changes in daily activity patterns, aiding in early healthcare event detection for dementia care using IoT data.

Contribution

It introduces the application of Shannon's entropy, entropy rates for Markov chains, and entropy production rate to in-home activity time-series data for healthcare monitoring.

Findings

Entropy measures can indicate healthcare-related events in activity data.

Combining multiple entropy measures improves detection accuracy.

Different individuals may show varied entropy patterns for the same events.

Abstract

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.