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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: 

Illustration of room and radar signals bouncing around from different angles

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.

adult hand and baby hand

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.

refugee camp

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

City skyline

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 currently working as an Assistant Professor at the 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. She is currently working on the research topics of Preterm Birth Prediction and Pregnancy Monitoring.

Dr Xu was awarded a PhD in Electronic Engineering from King's College London (KCL) in 2018. Her PhD thesis "Advanced Signal Processing Methods for Analysis of Cortico-Muscular Coherence" focuses on analysing electrophysiological signals (EEG and EMG) and developing novel algorithms to aid the classification and stratification of patients with movement disorders. Before working at Coventry University, she worked as a post-doctoral researcher at the Department of Basic & Clinical Neuroscience and Department of Informatics at KCL on a project to apply the techniques of signal processing, statistics and information theory to predict the epileptic seizures.

In addition to implementing mathematical theories into clinical neuroscience, her previous research has also focused on software development for healthcare applications and communication systems (e.g. Intermediate-Frequency Receiver for Missile Control, Deepwater Communication system).

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. 

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