Digital health and health informatics

This project aims to develop a rapid and accurate method to quantify femoral neck loads during ballistic exercise training.

There are two objectives: the first is to quantify the effects of different ballistic exercises on peak femoral loads; and the second is to quantify the accuracy of using wearable sensors and machine learning in the prediction of femoral loads.

Team members

• Dr Zainab Altai - Research Follow in the School of Sport, Rehabilitation and Exercise Sciences (SRES), University of Essex
• Dr Bernard Liew - Senior Lecturer in the School of Sport, Rehabilitation and Exercise Sciences (SRES), University of Essex
• Dr Jason Moran - Senior Lecturer in the School of Sport, Rehabilitation and Exercise Sciences (SRES), University of Essex
• Dr Xiaojun Zhai - Reader in the School of Computer Science and Electronic Engineering (CSEE), University of Essex
• Dr Andrew Phillips - Reader in the Department of Civil and Environmental Engineering, Imperial College London
• Department of Biomedical Engineering, University of Ghana

This project will evaluate and amplify the benefits to health and risk profile of customers who engage with the YuLife app using the latest modelling techniques and research from the public health and wellbeing domain.

Team members

• Dr Tasos Papastylianou - Postdoctoral Research Fellow at the Institute of Public Health and Wellbeing, University of Essex
• Dr Honor Bixby - Postdoctoral Research Fellow at the Institute of Public Health and Wellbeing, University of Essex
• Prof. Mariachiara Di Cesare - Director of the Institute of Public Health and Wellbeing, University of Essex
• Dr Abbas Salami - KTP Associate, Institute of Public Health and Wellbeing, University of Essex

In this project, researchers are working to reduce depressive symptoms among patients diagnosed with severe somatic conditions.

The NEVERMIND e-health system consists of a smart shirt and a mobile application with lifestyle behavioural advice, mindfulness-based therapy, and cognitive behavioural therapy.

Team members

• Dr Tasos Papastylianou - Postdoctoral Research Fellow at the Institute of Public Health and Wellbeing, University of Essex
• Prof. Luca Citi - Professor in the School of Computer Science and Electronic Engineering (CSEE), University of Essex
• Prof. Riccardo Poli - Professor in the School of Computer Science and Electronic Engineering (CSEE), University of Essex
• Eirini Christinaki - Researcher in the Eye Diseases Research Group, Ku Leuven

Related papers

• Carli, Vladimir and Petros, Nuhamin Gebrewold and Hadlaczky, Gergö and Vitcheva, Tereza and Berchialla, Paola and Bianchi, Silvia and Carletto, Sara and Christinaki, Eirini and Citi, Luca and Dinis, Sérgio and Gentili, Claudio and Geraldes, Vera and Giovinazzo, Lorena and Gonzalez-Martinez, Sergio and Meyer, Björn and Ostacoli, Luca and Ottaviano, Manuel and Ouakinin, Silvia and Papastylianou, Tasos and Paradiso, Rita and Poli, Riccardo and Rocha, Isabel and Settanta, Carmen and Scilingo, Enzo Pasquale and Valenza, Gaetano (2022) The NEVERMIND e-health system in the treatment of depressive symptoms among patients with severe somatic conditions: A multicentre, pragmatic randomised controlled trial. eClinicalMedicine, 48. p. 101423. DOI https://doi.org/10.1016/j.eclinm.2022.101423

By analysing a vast dataset of skin lesions, Dr Haider, in the School of Computer Science and Electronic Engineering, has developed an AI model with exceptional accuracy, surpassing traditional methods. This project has a transformative impact by advancing the field of skin cancer detection through AI innovation.

The new C4C risk score and the integration of image and metadata enhance diagnostic precision, reducing false positives and unnecessary biopsies. Commercializing this technology as a Class IIa medical device in the near future, and expanding its application to diverse skin tones, will improve early detection globally.

This partnership exemplifies cutting-edge research translating into practical, life-saving solutions in healthcare.

CoughDetect is a web application that provided cheap and readily available COVID-19 pre-screening, easily accessible on mobile phones, PCs and laptops.

Dr Andreu-Perez, from the School of Computer Science and Electronic Engineering, has developed using more than 8,000 clinically validated samples of coughs, an artificial intelligence model to recognise the sound of COVID-19-positive coughs.

In testing, CoughDetect was found to yield detection rates similar to alternative rapid testing, while being exceptionally cost-effective, with one pre-screening session costing less than 10 US cents in cloud computing power. The application has been deployed across a number of clinics in Mexico, with the team using CoughDetect data to map the prevalence of COVID-19 by location over time.

Dr Kampouridis, Dr Doctor, and Dr Yang from Schools of Mathematics, Statistics and Actuarial Science, and Computer Science and Electronic Engineering, in partnership with East Suffolk and North Essex NHS Foundation Trust are developing an AI driven decision support tool for consultants and operational managers responsible for scheduling patients’ surgeries.

The project is providing a baseline for improving patient throughput for selected elective pathways and helping to remove potential bottlenecks within selected patient elective pathways. The project focuses on the scheduling of Trauma and Orthopaedics surgeries with the potential to expand to other specialities.