Digital peer-supported and peer-led self-management of mental and sexual wellbeing for people with acquired brain injury (HOPE4ABI): a feasibility randomised controlled trial

ABI can cause a range of problems across multiple areas of health and wellbeing - including relationship and intimacy issues. 


Intelligent prediction of preterm birth using AI-empowered electrohysterography sensing

NIHR funded project aiming to develop an AI solution to predict preterm birth.


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.


Inkjet-printed respiratory rate wearable sensors for infants: Towards remote monitoring for low-setting villages and refugee camp

The monitoring of infants respiratory rate can contribute to the identification of several health problems and life threats. However, it is still the least documented and monitored parameter due to the shortage in advanced and user-friendly monitoring sensors/systems.


ifeed: User-centred development of an online and mobile phone intervention to support infant feeding choice and confidence to sustain breastfeeding and/or safe formula feeding

ifeed was launched in August 2018 to coincide with World Breastfeeding week. In the first week it had 800 views and was shared by organisations supporting mothers and babies across the UK and globally.


The MyWay Project

A randomised controlled feasibility trial of a tailored digital behaviour change intervention with e-referral system to increase attendance at NHS Stop Smoking Services


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

There is an increasing need for remote, low-cost, reliable and comfortable respiratory rate  that provide physicians with accurate newborn readings.


Religious Health Interventions in Behavioural Sciences (RHIBS)

Investigators aim to create a foundational, shared language for researchers and practitioners to rigorously develop and evaluate religiously integrated health interventions.


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

The primary aim of this project is to develop and evaluate a multistatic (multiple RADAR sensor nodes) RADAR sensing system to monitor the ADL [Activities of Daily Living] including but not limited to walking, sitting down, standing up, eating, lying on bed and picking up objects in multiple older adults simultaneously using machine learning algorithms. Specifically, the aim is to capture critical events such as falls and wandering behaviour.


A big-data-centric hearing impairment rehabilitation solution

A big-data-centric hearing impairment rehabilitation solution using a novel and affordable hearing aid tailored for tonal language speakers, personalised hearing screening, and online therapeutic calibration and motivation service


JiCSAV: Justice in Covid-19 for Sexual Abuse and Violence

Impacts of the Covid-19 pandemic on criminal justice journeys of adult and child survivors of sexual abuse, rape, and sexual assault


The ‘Taste & See’ cluster randomised controlled trial

Taste & See is a church based programme for developing a healthy relationship with food.


Develop in the Open (DITO)

Investigators seek to improve patient outcomes and reduce staff administration time when developing digital systems to enable their constant improvement and remove vendor lock-in.


Mental Health and Productivity Pilot

Coventry University is co-leading a group of health professionals, academics and business leaders who have been awarded £6.8m by Government to tackle poor mental health in the workplace with a focus on the East and West Midlands regions.


Compressive Population Health: Cost-Effective Profiling of Prevalence for Multi Non-Communicable Diseases via health Data Science

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.