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.

What problem does this paper address?

In-home IoT sensors produce long activity sequences, but raw activity alone does not explicitly describe routine, uncertainty, or changes in transitions between rooms. The paper studies interpretable entropy features for identifying changes that may coincide with healthcare-related events in people living with dementia.

Main contributions

• Application of Shannon entropy, first-order Markov-chain entropy rate, and neural entropy-production estimation to in-home activity data.
• Evaluation on 9,370 person-days collected by a privacy-aware dementia-care monitoring platform.
• Separate analysis of daytime and nighttime activity changes in relation to clinically recorded events.

Key results

• In most evaluated cases, a combination of entropy measures indicated healthcare-related events more effectively than any single measure.
• Participants experiencing the same event could show changes in different entropy measures, supporting a multi-feature inference approach.

Relevant research topics

• Activity Pattern Monitoring
• Entropy Measures
• Internet of Things
• Dementia Care
• In-Home Sensing
• Health Monitoring
• Time-Series Feature Engineering

Preferred citation

@inproceedings{huang2022entropy,
  author    = {Yushan Huang and Yuchen Zhao and Hamed Haddadi and Payam Barnaghi},
  title     = {Using Entropy Measures for Monitoring the Evolution of Activity Patterns},
  booktitle = {2022 IEEE 8th World Forum on Internet of Things (WF-IoT)},
  year      = {2022},
  pages     = {1--6},
  doi       = {10.1109/WF-IoT54382.2022.10152050}
}