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 research investigates whether 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.

This research uses wearable data and AI to detect disease earlier by analyzing continuous health signals rather than isolated clinical snapshots. By personalizing models to individual baselines, the system identifies subtle changes linked to conditions like infections, heart issues, and mental health crises, enabling earlier intervention and potentially saving lives.

This research applies the concept of hormesis—where low doses are beneficial but high doses harmful—to pornography use. Since excessive porn use is associated with mental-health problems, the project seeks to identify the “healthy limit” of use. Participants will complete daily smartphone surveys over a month, allowing the researcher to model how porn consumption affects well-being and how moral beliefs modify these effects. The goal is to build a personalised app that guides individuals toward safe levels of use and reduces polarisation in debates about pornography.

Digital health expanded during COVID-19, but many services exclude people seeking support for alcohol and drug use. This research uses inclusive design, interviews, and workshops with people with lived experience to identify barriers, reduce stigma, improve usability, and guide industry toward creating accessible, equitable digital care for all.