DIABET: Dual sensing AI for non-invasive diabetes management
Background
Diabetes is a global epidemic. Every year, around 5 million people die globally from diabetes, or related conditions, according to World Health Organisation (WHO). However, many of these complications are predictable and preventable by better self-management (SM) and control of the disease, which must be a core feature of any effective care plan for a person with diabetes.
A major challenge in the successful management of diabetes is the accurate self-monitoring of blood-glucose (SMBG). Unfortunately, the invasive nature (finger prick test) and cost of best-in-class current glucose monitoring solutions means SMBG remains low. Semi invasive solutions are available, but these are also costly and still require combination with daily blood testing using finger prick method.
Summary
DIABET addresses the need for a non-invasive continuous monitoring solution and enables better self-management of diabetes. The DIABET project has developed an innovative digital device for high accuracy non-invasive blood glucose monitoring.
This project has allowed the improvement of accuracy using dual sensing technique (Radiofrequency and Infrared). Machine learning and data fusion help overcome imperfections arising from each technique acting individually.
Benefits
The combination of advanced sensors, data processing, and artificial intelligence developed by the consortium partners enables the first truly non-invasive continuous monitoring solution for people with diabetes
Through the continuous monitoring of blood-glucose levels, the DIABET smart monitor can also be able to act as a decision support system (DSS) e.g. offering dietary and lifestyle advice. With patient consent, DIABET can also enable "clinical decision-making support" through sharing of this patient-specific data between health care professionals (e.g. General-Practitioners, endocrinologists and psychiatrists), social-care professionals and diabetes nurses.
Outcome
The research led to the development of a table top device, to be used at home, or in Point of Care settings, coupled to a bracelet for overnight blood glucose trend monitoring. The developed prototype system combines advanced sensors for multiwavelength electromagnetic transmission (Infrared and Radiofrequency), data processing, and Artificial Intelligence algorithms developed by the consortium partners to enable a truly non-invasive continuous monitoring solution for people with diabetes.
Project Partners
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Meet the Principal Investigator(s) for the project
Professor Tat-Hean Gan - Professional Qualifications CEng. IntPE (UK), Eur Ing BEng (Hons) Electrical and Electronics Engg (Uni of Nottingham) MSc in Advanced Mechanical Engineering (University of Warwick) MBA in International Business (University of Birmingham) PhD in Engineering (University of Warwick) Languages English, Malaysian, Mandarin, Cantonese Professional Bodies Fellow of the British Institute of NDT Fellow of the Institute of Engineering and Technology Tat-Hean Gan has 10 years of experience in Non-Destructive Testing (NDT), Structural Health Monitoring (SHM) and Condition Monitoring of rotating machineries in various industries namely nuclear, renewable energy (eg Wind, Wave ad Tidal), Oil and Gas, Petrochemical, Construction and Infrastructure, Aerospace and Automotive. He is the Director of BIC, leading activities varying from Research and development to commercialisation in the areas of novel technique development, sensor applications, signal and image processing, numerical modelling and electronics hardware. His experience is also in Collaborative funding (EC FP7 and UK TSB), project management and technology commercialisation.
Related Research Group(s)
Brunel Innovation Centre - A world-class research and technology centre that sits between the knowledge base and industry.
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Project last modified 12/10/2023