Health and Medicine Research

The School of Computing and Mathematics carries out research in a variety of specialities within Health and Medicine. We collaborate with various partners in the delivery of world class research that produces innovative solutions to real world problems. Our multidisciplinary approach utlises knowledge and expertise in many fields of research, including Artificial Intelligence, Biomedical Engineering, Fluid Dynamics, and Software Engineering. 

Medical research makes a huge difference to people’s lives and within the school we focus on the delivery of cutting edge and high impact research. This is a showcase of some recent research that clearly demonstrates the excellence and innovation of research within Health and Medicine.


We research, design, evaluate and prototype systems digital health technologies together with clinical collaborators, physiotherapists and health researchers, engineers and enterprise partners.  Our projects include:

(i) evaluations of wearables for epilepsy seizure monitoring,

(ii) assessments of consumer-grade wearable health technologies including studies on heart rate reliability and device updates,

(ii) prototypes of wearable systems with clinical collaborators for 24-hour patient monitoring.

(iii) smart injection sensing systems with clinical collaborators.  Designing smartphone apps and wireless sensing systems that supports individuals practice injections with real-time autoinjector training feedback.

Evaluation of Wearable Electronics for Epilepsy, A Systematic Review, Rukasha, T., Woolley, S. I., Kyriacou, T. and Collins, T., 2020, Electronics 9 (6), 968

Investigation of Wearable Health Tracker Version Updates, Woolley, S., Collins, T., Mitchell, J. and Fredericks, D., 2019, BMJ Health & Care Informatics, 26.

Version Reporting and Assessment Approaches for New and Updated Activity and Heart Rate Monitors, Collins, T., Woolley, S.I., Oniani, S., Pires, I.M., Garcia, N.M., Ledger, S.J. and Pandyan, A., 2019. Sensors19(7), p.1705.

The Quantified Outpatient-Challenges and Opportunities in 24hr Patient Monitoring, Infante Sanchez, D., Woolley, S., Collins, T., Pemberton, P., Veenith, T., Hume, D., Laver, K. and Small, C., 2017. EFMI Medical Informatics Europe, Manchester, 2017 (best poster award).

Evaluation of AllergiSense Smartphone Tools for Adrenaline Injection Training, Hernandez-Munoz, L.U., Woolley, S.I., Luyt, D., Stiefel, G., Kirk, K., Makwana, N., Melchior, C., Dawson, T.C., Wong, G., Collins, T. and Diwakar, L., 2017.  IEEE Journal of Biomedical and Health Informatics, 21(1), pp.272-282.


Party balloons suffer localized bulging when inflated, but (fortunately) healthy arteries do not. If they do, we have aneurysms. What is then the design principle for bulge-resistant arteries, and under what pathological changes can aneurysms occur? Some of the questions that we have answered so far include: 'Why do party balloons bulge when inflated and at what pressure do they bulge?' 'Can we improve bulge resistance by increasing wall thickness?' and also 'Arteries are multi-layed and fibre-reinforced. How do fibre reinforcement and optimized fibre orientation help prevent bulge formation?'


Fast and accurate segmentation of musculoskeletal ultrasound images is an on-going challenge. Two principal factors make this task difficult: firstly, the presence of speckle noise arising from the interference that accompanies all coherent imaging approaches; secondly, the sometimes subtle interaction between musculoskeletal components that leads to non-uniformity of the image intensity. Our work presents investigates of the potential of Convolutional Neural Networks (CNNs) to address both of these problems. The impact of this research has shown that CNN performance, when using expert ground truth image, is better than in the case of using Canny ground truth image. Our technique is promising and has the potential to speed-up the image processing pipeline using appropriately trained CNNs.


Cell-based therapy (using autologous cartilage, bone and stem cells) is used mainly for the treatment, repair and regeneration of small areas of cartilage and bone damage resulting from accidental injury (e.g., to the knee joint) or tissue degeneration (e.g., in arthritis and osteo-arthritis). This therapy is being considered as a promising and viable alternative to artificial implants in large tissue regeneration such as the knee and hip.  It could potentially improve the quality of life for the millions of arthritis and osteoarthritis sufferers worldwide.  However, its benefit has not yet been clinically proven and is still only in the research trial phase. One of the main obstacles that clinicians face is that it is difficult and not feasible to continuously monitor the repair process following surgical insertion into the patient’s knee joint, for example. Hence, many details of the repair process are unknown. Our research focuses on developing mathematical models to enable better understanding of the repair process in humans. 

We developed a mathematical model of cartilage regeneration after cell therapy. This model has enabled much better understanding of the repair process and has addressed some fundamental questions that are very useful in guiding practitioners of this cell-based therapy. Subsequent research activity has focussed on including cell-to-cell interactions between the chondrocytes and cartilage cells into our model. We have shown that co-implantation of both chondrocytes and stem cells could be more beneficial to the repair process than implanting each individually