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Analysis of patient domestic activity in recovery from hip or knee replacement surgery: modelling wrist-worn wearable RSSI and accelerometer data in the wild

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Original languageEnglish
Title of host publicationProceedings of the 3rd International Workshop on Knowledge Discovery in Healthcare Data
Subtitle of host publicationco-located with the 27th International Joint Conference on Artificial Intelligence and the 23rd European Conference on Artificial Intelligence (IJCAI-ECAI 2018)
Publisher or commissioning bodyCEUR Workshop Proceedings
Number of pages8
DateAccepted/In press - 29 May 2018
DatePublished (current) - 13 Jul 2018

Publication series

NameCEUR Workshop Proceedings
ISSN (Print)1613-0073


The UK health service sees around 160,000 total hip or knee replacements every year and this number is expected to rise. Expectations of surgical outcome are changing alongside demographic trends, whilst aftercare may be fractured as a result of resource limitation or other factors. Conventional assessments of health outcomes must evolve to keep up with these changing trends. In practice, patients may visit a health care professional to discuss recovery and will provide survey feedback to clinicians using standardised instruments, such as the Oxford Hip & Knee score, in the months following surgery. To aid clinicians in providing accurate assessment of patient recovery a continuous home health care monitoring system would be beneficial. In this paper the authors explore how the SPHERE sensor network can be used to automatically generate measures of recovery from arthroplasty to facilitate continuous monitoring of behaviour, including location, room transitions, movement and activity; in terms of frequency and duration; in a domestic environment. The authors present a case study of data collected from a home equipped with the SPHERE sensor network. Machine learning algorithms are applied to a week of continuous observational data to generate insights into the domestic routine of the occupant. Testing of models shows that location and activity are classified with 86% and 63% precision, respectively.

    Structured keywords

  • Digital Health

    Research areas

  • Predictive analyses of home healthcare data, Internet of Things, Domestic Sensor Networks, Machine Learning, Indoor Localisation, Movement Classification, Activity Classification, Wearable Technology

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  • Full-text PDF (accepted author manuscript)

    Rights statement: This is the author accepted manuscript (AAM). The final published version (version of record) is available online via CEUR-WS at . Please refer to any applicable terms of use of the publisher.

    Accepted author manuscript, 395 KB, PDF document

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