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Modelling Patient Behaviour Using IoT Sensor Data: a Case Study to Evaluate Techniques for Modelling Domestic Behaviour in Recovery from Total Hip Replacement Surgery

Holmes, M., Perello Nieto, M., Song, H., Tonkin, E., Grant, Sabrina ORCID: https://orcid.org/0000-0003-0148-9103 and Flach, P. (2020) Modelling Patient Behaviour Using IoT Sensor Data: a Case Study to Evaluate Techniques for Modelling Domestic Behaviour in Recovery from Total Hip Replacement Surgery. Journal of Healthcare Informatics Research. ISSN Print: 2509-4971 Online: 2509-498X (In Press)

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Abstract

The UK health service sees around 160,000 total hip or knee replacements every year and this number is expected to rise with an ageing population. Expectations of surgical outcomes are changing alongside demographic trends, whilst aftercare may be fractured as a result of resource limitations. Conventional assessments of health outcomes must evolve to keep up with these changing trends. Health outcomes may be assessed largely by self-report using Patient Reported Outcome Measures (PROMs), such as the Oxford Hip or Oxford Knee Score, in the months up to and following surgery. Though widely used, many PROMs have methodological limitations and there is debate about how to interpret results and definitions of clinically meaningful change. With the development of a home-monitoring system, there is opportunity to characterise the relationship between PROMs and behaviour in a natural setting and to develop methods of passive monitoring of outcome and recovery after surgery. In this paper, we discuss the motivation and technology used in long-term continuous observation of movement, sleep and domestic routine for healthcare applications, such as the HEmiSPHERE project for hip and knee replacement patients. In this case study, we evaluate trends evident in data of two patients, collected over a 3-month observation period post-surgery, by comparison with scores from PROMs for sleep and movement quality, and by comparison with a third control home. We find that accelerometer and indoor localisation data correctly highlight long-term trends in sleep and movement quality and can be used to predict sleep and wake times and measure sleep and wake routine variance over time, whilst indoor localisation provides context for the domestic routine and mobility of the patient. Finally, we discuss a visual method of sharing findings with healthcare professionals.

Item Type: Article
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This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommonshorg/licenses/by/4.0/.

Uncontrolled Discrete Keywords: internet of things, wearable sensors, indoor localisation, sleep mobility, hip replacement surgery, actigraphy
Subjects: R Medicine > R Medicine (General)
Divisions: College of Health, Life and Environmental Sciences > School of Nursing and Midwifery
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Copyright Info: Open access article
Depositing User: Sabrina Grant
Date Deposited: 26 May 2020 10:15
Last Modified: 17 Jun 2020 17:36
URI: https://eprints.worc.ac.uk/id/eprint/9452

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