This research examines mental health and help-seeking among neurodivergent young people. Northern Ireland data reveal substantially higher rates of anxiety, depression, suicidal thoughts and self-harm, alongside greater online help-seeking. The research evaluates digital mental health support and works directly with young people to develop more accessible, neurodiversity-informed services and guidance.

This research investigates whether Alexa can support speech practice for people with Parkinson’s disease. After eight weeks of home use, nine of ten participants developed stronger voices. Although not a replacement for professional speech therapy, voice assistants could provide accessible, everyday opportunities to practise louder, clearer speech outside clinical settings.

This research challenges the assumption that screens are always harmful to sleep. Digital media may either displace rest or help people relax, depending on how it is used. A new research app combines sleep tracking, smartphone activity, and daily self-reports to investigate real-world relationships between media use and sleep.

This research explores whether altering bodily sensations can change emotional experiences. By manipulating perceived heartbeat, blinking, and muscle tension through wearable devices and virtual reality, it demonstrates that emotions can be reshaped without changing physiology, opening new possibilities for treating trauma, anxiety, eating disorders, and other mental health conditions

This research develops patient-specific digital twins of the heart to improve radiofrequency ablation for cardiac arrhythmias. By simulating heat transfer, tissue damage, and electrical activity, these computational models could improve treatment accuracy, reduce repeat procedures, accelerate medical device development, and advance the future of personalised cardiovascular medicine.

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.