This research has developed a five-minute smartphone memory test that detects subtle cognitive changes associated with early Alzheimer's disease. The tool identified symptom-free individuals with underlying disease and predicted future cognitive decline, outperforming expensive brain scans while offering a simple, accessible, and affordable approach to early diagnosis.

This research develops an ultra-low-power, battery-free newborn monitoring system for under-resourced hospitals. Using on-device artificial intelligence and energy harvesting, it continuously detects signs of distress while protecting patient privacy. The technology aims to support overstretched nurses, enable earlier intervention, and reduce preventable newborn deaths worldwide.

This project developed AI Care, a voice-based caregiving system for people with early-stage Alzheimer's disease. Unlike conventional voice assistants, it uses caregiver-maintained medical records to provide personalised, safety-aware support. By adapting to users rather than requiring users to adapt to technology, AI Care aims to extend safe, independent living at home.

This research examines whether changes in walking patterns can predict frailty before serious health events occur. Using smart insoles, GPS tracking, and machine learning, mobility data from older adults is analyzed to identify early warning signs of decline. The goal is to enable proactive interventions and support healthier aging.

This research investigated whether AI-guided handheld ultrasound can help diagnose deep vein thrombosis (DVT) in primary care. Through a systematic review, a clinical study involving 565 patients, and stakeholder interviews, the research found promising results but highlighted challenges involving image quality, accountability, and integration into NHS healthcare systems.

This research develops soft, tissue-like implantable sensors capable of monitoring molecular signals inside the body in real time. By combining high-performance electronics with flexible, biocompatible materials, these devices could detect inflammation, stress, or organ damage before symptoms arise, enabling earlier diagnosis and more personalized healthcare.

This 3MT® presentation describes how artificial intelligence can help non-specialist clinicians diagnose deep vein thrombosis using AI-guided handheld ultrasound devices. By enabling faster point-of-care diagnosis in GP surgeries, the project aims to reduce hospital referrals, improve accessibility for vulnerable patients, and help healthcare systems manage increasing clinical demand more efficiently.

This research introduces iCares, a smart wound-monitoring bandage designed to detect infection and inflammation before visible symptoms appear. Using biosensors, fluid sampling, and machine learning, the system provides real-time wound analysis, enabling earlier intervention, personalized treatment, reduced complications, and improved healing outcomes for patients with chronic wounds.

This research redefines digital health literacy for an AI-driven world, emphasizing the alignment between users and technology. Using a Delphi method, it identifies three core components—knowledge, skills, and context. The resulting framework guides the design of digital health tools that better support behavior change by adapting to users’ real-world needs.

This research explores exergames that combine gaming and exercise to improve fitness. By integrating adaptive difficulty, full-body motion, and narrative storytelling, it aims to create experiences that are both engaging and physically effective. The goal is to motivate sustained exercise by making workouts enjoyable and personalized through game design.