Addressing increasing needs for self-management of infections, including new viruses, non-communicable long-term conditions, and disability, for an ageing population facing increasing multimorbidity, frailty, and polypharmacy. It builds on our close links with Psychology and international reputation for developing and evaluating digital interventions.
ECS at Southampton is leading in the following areas
ECS is a trusted and internationally recognised pioneer in the future of healthcare through leadership and collaboration in digital health systems research and transformation. We are improving patient outcomes, clinical decision making and cost reductions by increasing digital and data literacy in our future digital health workforce through education programmes designed to create a workforce with the digital skills for a health system in 2030, whilst also increasing student experience and employability, and providing continuous professional development education programmes for current health and social care.
We have strong industrial partnerships across all areas of the ecosystem, including medtech and pharma, as well as deep connections with the local NHS Trust. We have deep relationships with regional Trusts including NHS University Health Southampton (UHS), Hampshire Hospitals NHS Foundation Trust (HHFT) (where ECS staff contribute with visitor status as Data Champions), Portsmouth Hospitals NHS Trust, Solent and Southern, and beyond nationally.
The newly established Social Data Foundation, uniting Southampton City Council, UHS and the University to collaborate on the transformation of health and social care, aims to bring together data across institutional remits for better research.
Can audio sensing be used for social good? The answer is “yes”. I am Jagmohan Chauhan, a lecturer in the Cyber Physical Systems Group, and my research interest is in the design of mobile systems for healthcare which span areas as diverse as sensing, artificial intelligence, systems, signal processing and data science.
At the beginning of the pandemic, we started exploring the idea of using crowdsourced audio data captured through smartphone microphones to check the feasibility of digital testing of COVID-19. This approach could help authorities to identify COVID-19 cases efficiently, affordably and at scale as everyone owns a smartphone. To date, a large number of audio samples have been amassed from varied demographic populations across the world.
By analysing the collected data using state-of-the-art deep learning techniques, we have created a suite of tools that demonstrates reasonable accuracy and shows the potential of using audio for timely detection of COVID-19. This work has generated a great deal of interest around the globe and has been covered by many news outlets. Other than being an automated screening tool, this can also provide complementary information to clinicians, and be used as a personalised tool to remotely monitor progression of the disease.