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.
Highlights
The UK health service sees around 160,000 primary total hip and knee joint replacements performed every year within the National Health Service [18]
This paper presents an initial analysis of long-term observational data from three participant homes (Section 4) to evaluate whether IoT sensor data can be used to produce informative trends of patient behaviour during recovery from total hip replacement surgery, using statistical analysis and machine learning techniques
We present results for training of indoor localisation and activity classifiers using the methods described in Section 3.3 along side 3-month observational data and classifications for location, movement and posture activity
Summary
The UK health service sees around 160,000 primary total hip and knee joint replacements performed every year within the National Health Service [18]. This number is expected to increase with a growing more active population in the UK [21]. After a hip or knee replacement, up to 30% of patients experience long-term pain after surgery [4]. With changing expectations of surgical outcome and demographic trends [19], conventional assessments of health outcomes must evolve to keep up with these changing trends
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