This project is responding to the urgent clinical need to develop a reliable preterm prediction technique which can improve diagnosis and achieve satisfactory accuracy for routine clinical use.
Healthcare Technology and Innovation
Focus of our research
Our research is focused on next generation of digital healthcare ecosystem with transformational wearable sensors to address unmet healthcare needs.
This theme aims to research and develop innovative healthcare technologies, devices and solutions with scientific and socioeconomic impacts to improve the health and wellbeing of individuals and communities. This will be through conducting globally significant and interdisciplinary research with different partners and stakeholders along the pathway of technology development and commercialization. We bring together industry, clinicians, policy makers, academics, and innovators to stimulate creative and transformational approaches, and aim to become a centre of international excellence.
- Innovative wearable technologies and devices with physiological measurements and/or imaging, advanced bio-signal processing, computer modelling and big data analysis;
- Research and development of novel biosensors, systems and methods to provide sensitive biomarkers for the early and accessible detection of chronic disease;
- Innovative and interdisciplinary data science solutions for analysing/interpreting healthcare data using machine learning and artificial intelligence;
- Smart mobile health system and mobile phone delivered interventions for chronic disease management, decision support systems, behavioural change management, as well as the use of latest 5G technology to transform how healthcare is delivered;
- Co-creation, prototyping and user-centred product evaluation services to entrepreneurial individuals and companies developing new healthcare technologies.
The theme is led by Professor Dingchang Zheng and showcases a multi-disciplinary team of researchers and product design experts to create innovative and user-friendly healthcare technology.
Prof Zheng is a leading research expert in innovative healthcare technology development through the pathway of physiological measurement, bio-signal processing, and technology evaluation. He has published over 150 peer-reviewed scientific papers and earned his international reputation in research and development of novel cardiovascular technologies and devices to address unmet clinical needs, particularly in novel blood pressure and arterial stiffness measurement techniques, and recent developments of wearable devices and IoT monitoring system using unique waveform characteristics derived from physiological signals for monitoring respiration rate, predicting pre-term labour and pregnancy induced hypertension, and developing big-data-centric hearing impairment rehabilitation solution. He has also been well recognized for his enthusiasm in promoting knowledge and technology exchanges, building up educational and research collaborations, and facilitating technology adoption in developing countries, including China, India, Jordan and Nigeria.
John holds the Chair in Biosensors and Bioinstrumentation at the Centre for Intelligent Healthcare at Coventry University. He is a leading researcher in vascular optics and novel sensing methods for the assessment of microvascular tissue perfusion, structure, composition, and viability. He is also an authority on the optical pulse technique known as photoplethysmography (PPG), with sensing and signal processing applications focusing on accessible detection of peripheral arterial disease, autonomic and endothelial (dys)function, and cardiovascular ageing. Professor Allen has wide ranging clinical applications knowledge linked to establishing and leading unique clinical microvascular measurement facilities in the UK and project leadership skills for innovation work in medical device development. He collaborates widely - many of his publications relate to the study of patients with Raynaud’s and autoimmune connective tissue disease, wound healing and peripheral vascular disease. He is ever more fascinated to explore the power of light in medicine for the benefit of society.
Professor Ala Szczepura has over thirty years’ experience in interdisciplinary health services research with specific interests in health technology assessment, re-design and assessment of services, and the needs of multi-ethnic populations. She has published over 180 peer-reviewed scientific articles in areas such as new diagnostics, digital health, online self-management, cancer, kidney transplants and dialysis, the ageing population and unobtrusive smart environments for independent living and, most recently, unconscious gender bias in medicine. Research grants won have totalled over £10m including projects funded by NIHR, Wellcome, MRC, ESRC, Innovate UK, the Deputy Prime Minister’s Office, Skills for Care, Joseph Rowntree Foundation and Industry.
Jiangtao is an Associate Professor with strong international reputation for his research on Ubiquitous Computing, Mobile Crowdsensing/Crowdsourcing, and Health Data Science. He collaborates closely with clinicians, environment engineers, industrial partners, and government policy makers to move ubiquitous computing and AI technology into the pathways of health care, in terms of both individual and population level. With several novel research perspectives, Jiangtao has achieved a number of top conference and journal publications. Jiangtao also serves for the journal editorial board such as Personal and Ubiquitous Computing, Frontiers in Sustainable Cities, etc, and PC members of multiple top international conferences.
Syed Aziz Shah is an Associate Professor of Mobile Health at the Centre for Intelligent Healthcare. Dr Aziz’s research work spans across multiple disciplines including wireless sensing, radar technology, software defined radios, machine learning, cyber security and intelligent healthcare technologies. Syed has (co)authored more than 50 technical articles in top-rank peer-reviewed multi-disciplinary journals (3 transactions) focusing on intelligent healthcare. His research interests include, but not limited to, mobile health, prototype design, radar sensing for healthcare technologies, non-invasive fall detection, physiological measurements, remote patient monitoring wireless sensing, machine learning and cyber security for intelligent healthcare.
Louise's research is focused on the development of products, interventions and services to benefit health and wellbeing. With a background in Psychology and Human Factors she employs a range of research methods as well as art-based approaches to ensure that new technology is functional as well as being desirable and acceptable to end-users and stakeholders. In particular she has applied design thinking and human factors to the development of assistive technology, solutions to support the management of long term health conditions as well as approaches to communication with parents following a positive newborn screening result. Louise is based primarily in the Centre for Arts, Memory and Communities where she leads the Well-being and the Arts theme. She also affiliated to the Centre for Intelligent Healthcare, working closely with the team on collaborative projects and PhD student supervision.
Our research aims to research and develop innovative healthcare technologies, devices and solutions with scientific and socioeconomic impacts to improve the health and wellbeing of individuals and communities.
Find out more about some of our projects:
The objective of project is to develop a cost effective, accurate, and industry friendly wearable sensor using nanotechnologies, to be integrated with remote monitoring of newborns’ respiratory rate for low-setting villages and refugee camps.
This project is aiming to improve patient outcomes and reduce staff administration time when developing digital systems to enable their constant improvement and remove vendor lock-in.
This project proposes a novel paradigm, called compressive population health (CPH for short), to reduce the data collection cost during the profiling of prevalence to the maximum extent.