HEALTH AND WELL-BEING

From personal activity trackers to remote monitoring of long-term health conditions, IoT devices have become ubiquitous in the health, fitness and wellbeing space. Our researchers are working across the IoT spectrum, from sensor design through to data processing and machine learning, to develop new ways to harness the power of smart, connected devices for good health and patient-centred care.

Professor Michael Boniface Director of the IT Innovation Centre

We work in collaboration with clinicians from University Hospital Southampton NHS Foundation Trust and numerous other partners in the health sector, and with researchers from the University’s Centre for Health Technologies12

The use of IoT technology is blurring healthcare boundaries and enabling innovations such as virtual wards. Our researchers were instrumental in establishing COVID-19 virtual wards; their work on the home monitoring of patients’ oxygen levels and other vital signs fed into national policy during 2020.

Our researchers have also shared their expertise to inform the design of hospital infrastructure to take into account a combination of digital, virtual and physical environments, so that the hospitals of the future are equipped to take advantage of new digital approaches.

There is huge potential for data gathered using IoT monitoring to enhance public health datasets and therefore inform health research, funding and policy. In current datasets the links between medical data and the social determinants of health, such as lifestyle factors and activity levels, are minimal. A more complete understanding of public health requires the routine capture of these social determinants, and digital health and IoT systems will have a fundamental role to play in this. At Southampton researchers are looking at datasets on multimorbidities – where people have several health conditions at once – and how these datasets can be augmented from other sources to understand the contributory factors to ill-health. 12 https://www.cht.ecs.soton.ac.uk

Being able to gather data about patients’ health as they go about their daily lives can be enormously useful for clinicians. As well as providing more robust data to inform clinical decisions, it can facilitate a more personalised and efficient approach to care, enabling doctors to call people in for appointments only when their health data flags a need.

Our researchers are working with health professionals across numerous specialties to design such systems. For example, they are liaising with surgeons who carry out knee, ankle and shoulder operations to explore remote monitoring that will give them a full picture of their patients’ condition before and after operations, rather than relying on physical checks and self-reporting by patients at appointments. Similar work is being carried out with diabetes doctors on remote blood glucose monitoring that provides data about their patients’ day-to-day health.

Stroke rehabilitation is another area where remote monitoring can be useful. One common result of a stroke is loss of limb strength or function, for which a physiotherapist may prescribe remedial exercises for the patient to do at home. However, often patients don’t do the exercises, or don’t do them for the recommended length of time or in a way that is most effective.

To give physiotherapists a fuller picture, our researchers have developed a home-based monitoring system, using motion sensors rather than cameras to preserve patients’ privacy, to generate data about how often patients are doing exercises and the quality of the movements. This can be fed back to the therapist in real time so they can understand whether adherence to an exercise regime is a factor affecting patients’ recovery.

As well as designing remote monitoring systems, our researchers are developing new techniques to analyse and apply machine learning to the data these systems create, with the aim of predicting and preventing potential health issues. This strand of research uses existing health datasets to identify patterns against which real-time data can be analysed.

For example, in collaboration with cardiology consultants at Southampton General Hospital, researchers have developed a remote heart monitoring system which analyses electrocardiogram (ECG) data to help predict and prevent heart attacks. ECG data is gathered and transmitted via sensors worn on the patient’s body, and automated software analyses the data in real time using machine learning. This gives the doctor greater insights into the patient’s condition and enables them to make informed treatment decisions. Previously, the doctor might only get a snapshot of the patient’s heart health each time they visited the hospital.

Similarly, research has been undertaken in partnership with neurologists looking at the remote monitoring and processing of brain signal data. This work has been applied to the prediction of epileptic seizures, which in future could enable patients to take preventative action to avoid a seizure occurring, and to predict long-term neurodevelopmental conditions in young children.

To continue this work, a further study is planned which will involve recruiting children who may be at higher risk of developing a neurological disorder, for example due to complications at birth. The plan is to monitor their brain activity during early infancy until school age, during which time any disorders may be detected and diagnosed. The data can then be analysed

“From a medical consultant’s point of view, they don’t see a patient again until four or six week after an operation. They ask how it’s going, and the patient says ‘fine’ – but what does that mean? Remote monitoring would provide the data to tell clinicians them how far a patient is walking each day, or whether they have been following instructions to rest or do rehab exercises, to help inform their treatment.”

Professor Neil White Professor in Smart Electronic Materials and Systems

retrospectively to identify factors and trends, to underpin models for future predictive remote monitoring of children with similar vulnerabilities.

Our researchers are working on the development of novel sensors to detect vital signs and movement. One example is a sensor to measure respiratory rate. In a hospital ward, typically a patient’s breathing rate is monitored every few hours by a nurse counting how many times they breathe in and out in a minute. While these spot checks are useful, they don’t provide a picture of trends in real time. This can lead to the early signs of rapid-onset conditions being missed – one example is sepsis, where an increase in respiratory rate is an early indicator. Continuous monitoring, whether at home or in hospital, enables earlier, potentially lifesaving intervention.

Our researchers have developed a screen-printed motion sensor that can be attached to a garment to continually monitor a person’s respiratory rate – either in hospital or at home – transmitting data to clinicians and enabling them to respond to any significant changes. The sensor has been successfully tested on patients at the hospital.

In the field of physical fitness and performance, researchers have developed an adhering smart fabric that can be attached to the skin that monitors the body’s movement. The idea is to help athletes and their coaches to better understand and evaluate the execution of sports moves to improve technique and avoid injury. The sensor integrates piezoresistive graphene nanosheet thin films with kinesiology tape, which mimics the elasticity of skin, to capture detailed information about muscle contraction and joint angles in rapid motion. The fabric is extremely thin, cheap to produce, and has the potential for use in remote monitoring for sports training and other fields, such as rehabilitation after surgery or stroke.

While IoT-enabled systems have obvious benefits in the field of health and wellbeing, the ubiquity of personal fitness, sleep, diet and other wellbeing apps is raising new questions among researchers in the field of human and systems interaction. They are investigating how apps and smart devices can help people build the knowledge, skills and practice they need to take control of their own health, rather than encouraging them to outsource their wellbeing to external services on an ongoing basis.

“In terms of IoT devices, there has been great work so far on the ‘we’ll take care of you’ side of things, but there is lots more to be done to help people take care of themselves and build up their resilience. We are exploring design principles and testing technologies that move from helping people to build habits, which can be useful but are inherently brittle, to heuristics, which offer a template for making choices that can be adapted to any context.”

Professor m.c. schraefel Professor of Computer Science and Human Performance