A wearable sensor system will learn each patient’s normal breathing pattern and alert them when their chronic lung disease is about to worsen, before they need hospital care. Chronic obstructive pulmonary disease (COPD) affects over 210 million people globally and costs the NHS more than £800 million each year, with over half spent on hospital admissions. The problem is that existing wearable monitors are uncomfortable, produce unreliable vital-sign readings, have short battery lives, and do not help patients manage their own condition. Delays in spotting physiological changes make outcomes worse and drive up costs. The ASPIRE programme will embed machine-learning algorithms directly into lightweight, long-lasting sensors worn at home. These algorithms will combine sensor data with information from GP visits and hospital records to learn what is normal for each individual patient, accounting for other conditions they may have. The system will then give personalised recommendations for self-management. If successful, this could shift COPD care from expensive hospital treatment to proactive home monitoring, reducing NHS costs and improving patients’ quality of life. The same approach could later be adapted for other long-term conditions such as heart failure or diabetes.
View original technical description
There is an urgent, unmet need for reliable, intelligent systems that can monitor patient condition in the home, and which can help patients manage long-term conditions. Delays in recognition of the changes in physiological state worsen outcomes and increase healthcare costs. The ASPIRE programme uses chronic obstructive pulmonary disorder (COPD) as an exemplar, which affects over 210 million people globally. This condition costs the National Health Service over £800 million each year, over half of which is spent treating patients in hospital, rather than caring for them in their homes. Intelligent monitoring systems are required to address the needs of patients with long-term conditions in their homes. However, no wearable systems have penetrated into clinical practice at scale, due to: (i) poor tolerance of existing wearable devices for monitoring; (ii) a lack of robustness in the estimates of the vital signs that wearable sensors produce; (iii) very limited battery life that requires batteries to be re-charged at a rate that prevents their use on a large scale; and (iv) limited subsequent use of the data for helping the patient understand and manage their condition. We propose to develop an "intelligent" home-based system, with smart algorithms embedded within lightweight healthcare sensors, to overcome these limitations. Our novel work will incorporate next-generation machine learning algorithms to combine information from healthcare sensors with information from GP and hospital visits. This will enable the system to learn "normal" health condition for individual patients, with knowledge of other conditions from which they may be suffering, and which can then make recommendations to the patient concerning self-management of their condition. This work will include close working with world-leading clinicians to ensure that the recommendations provided by the system are correct for the individual patient.
Plain English summaries and category classifications on this site are generated by AI and may not perfectly reflect the original research.
Is something wrong? Let us know