Healthcare Sensing Technology

Healthcare Sensing Technology is a research cluster within the Centre for Intelligent Healthcare.

About the cluster

This cluster 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.

Core research areas

• Innovative wearable technologies and devices with physiological measurements and/or imaging, advanced bio-signal processing, computer modelling and big data analysis;
• Advanced radio frequency (RF) sensing and signal processing for physiological measurements.  
• 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 healthcare technology evaluation services to entrepreneurial individuals and companies developing new healthcare technologies.

Project list (externally funded)

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: 

RADAR Sensing for Human Activity Monitoring of Daily Living Simultaneously in Multiple Subjects

The primary aim of this project is to develop and evaluate a multistatic RADAR sensing system to monitor the human activities of daily living (ADL) in multiple older adults simultaneously using machine learning algorithms. Spefically, the aim is to detect, track and identify critical events such as falls and wandering behaviours.

Intelligent prediction of preterm birth using AI-empowered electrohysterography sensing

Preterm birth increases not only the risks of neonatal mortality, but numerous health risks for surviving babies. Pregnant women have been defined by the NIHR as an under-served group whose inclusion needs to be improved in clinical research.

Inkjet-printed Respiratory Rate Wearable Sensors for Infants: Towards Remote Monitoring Solutions for Low-setting Villages and Refugee Camps

Inkjet-printed Respiratory Rate Wearable Sensors for Infants: Towards Remote Monitoring Solutions for Low-setting Villages and Refugee Camps

Development of a uterine electrohysterogram system to predict preterm labor

Preterm labor which occurs in ~10% of pregnant women is a leading cause of neonatal mortality and morbidity. However, unsatisfactory and inaccurate diagnosis of preterm labor is an immense clinical challenge to the obstetricians.

Cluster lead / Co-lead

The cluster is led by Professor Dingchang Zheng and Dr Syed Aziz Shah and showcases a multi-disciplinary team of researchers to create innovative and user-friendly healthcare technologies.

Professor Dingchang Zheng (Cluster Lead & Director, CIH)

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 200 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.

Dr Syed Aziz Shah (Cluster Co-Lead and PGR Lead, CIH)

Dr. Syed Aziz Shah was appointed as an Associate Professor in the Research Centre for Intelligent Healthcare (CIH) at Coventry University (CU) in 2020. He is an interdisciplinary researcher focusing on advanced radio frequency (RF) sensor design and signal processing using RF and THz sensing, specifically for physiological measurements. He is highly motivated to utilize and develop advanced technologies that address the unmet healthcare challenges of unobtrusive monitoring of older adult daily activities for health and wellbeing purposes. He is recipient of UK's prestigious Engineering and Physical Sciences Research Council (EPSRC) New Investigator Award. Dr. Aziz also received the endorsement of UK exceptional talent candidate (‘Emerging Leader’) in year 2018, for the pioneering work in the field of wireless sensing for remote patient monitoring. These prestigious awards are given to early-career, world-leading innovators and scientists from the Royal Academy of Engineering.

Dr Yuhang Xu (Assistant Professor)

Dr Xu is an Assistant Professor at Research Centre for Intelligent Healthcare, in the cluster of Healthcare Sensing Technology. Her research has spanned a wide range of areas within Biomedical Engineering and Electronic Engineering, especially developing advanced signal processing approaches for healthcare applications.

Dr Xu is currently working on the research topics of Preterm Birth Prediction and Pregnancy Monitoring. Her research vision is to reduce health inequalities, particularly to reduce stillbirth, maternal and neonatal mortality, improve the awareness of the group of pregnant women and enhance the engagement of pregnant women with healthcare interventions. She has successfully led the projects funded by, e.g., NIHR, UKRI, as the project lead, collaborating with clinicians and midwives at UHCW NHS Trust and Birmingham City University.

Her previous research has also focused on implementing mathematical theories into clinical neuroscience, where EEG and EMG were used to develop novel algorithms to aid the classification and stratification of patients with movement disorders.

Dr Haipeng Liu (Research Fellow)

Dr Haipeng Liu is a research fellow from the Healthcare Sensing Technology Cluster within the Centre for Intelligent Healthcare. His research interests include computational simulation of cardiovascular system at macro- and microvascular levels, photoplethysmography and biosignal processing, AI-empowered medical data analysis, and wearable sensor development, with results published on reputed journals including Stroke and Journal of Neurology. He has published 65 scientific papers (index: 17), delivered over 10 invited talks/keynote lectures, and peer-reviewed over 50 papers for more than 20 scientific journals. Dr Liu has the memberships of World Stroke Organization and Chinese Stroke Association. He is currently a supervisor of three PhD students on healthcare sensing and novel medical technologies and keeps exploring new pathways of collaborations on local and international levels.