Available Projects

Changes in behaviour can provide early indicators of declining physical, cognitive, or mental health. This project will develop intelligent methods to detect and track behavioural changes using data from wearables, smartphones, and connected environments. By combining AI, data analytics, and digital health technologies, the research aims to enable personalised, proactive monitoring that supports earlier intervention, improved wellbeing, and independent living.

Primary supervisor: Christopher James - c.james@warwick.ac.uk

Project detail:
Human behaviour is a powerful indicator of health and wellbeing. Changes in mobility, physical activity, sleep patterns, social interaction, communication, and daily routines can provide early warning signs of physical illness, cognitive decline, mental health challenges, or deteriorating wellbeing. With the widespread adoption of wearable devices, smartphones, smart home technologies, and connected healthcare systems, there is now an unprecedented opportunity to continuously monitor behavioural patterns and develop intelligent systems capable of detecting meaningful changes before significant adverse outcomes occur.

This PhD project will investigate novel approaches for detecting, quantifying, and tracking behavioural change using data from multiple digital sources. The research will explore how advanced data analytics, machine learning, and artificial intelligence can be applied to identify subtle deviations from an individual's normal behaviour and distinguish between expected variation and clinically significant change. Particular emphasis will be placed on developing personalised models that recognise the unique behavioural signatures of individuals, rather than relying solely on population-level trends.

The project will address several key challenges in digital health monitoring. These include the integration of heterogeneous data streams, dealing with incomplete and noisy real-world data, identifying meaningful behavioural markers, and generating interpretable insights that can support decision-making by healthcare professionals, carers, and individuals themselves. The successful candidate will have the opportunity to develop innovative methodologies that combine signal processing, statistical modelling, explainable AI, and human-centred design to create practical tools for health and wellbeing monitoring.

Potential application areas include supporting independent living in older adults, identifying early indicators of neurodegenerative diseases, monitoring recovery following illness or injury, detecting changes associated with stress, anxiety or depression, and enabling proactive intervention for vulnerable populations. The project may also explore how behavioural monitoring technologies can be implemented ethically and responsibly, ensuring privacy, transparency, and user trust remain central to future digital healthcare solutions.

This highly interdisciplinary project sits at the interface of biomedical engineering, data science, behavioural science, and healthcare technology. The successful student will gain expertise in advanced analytics, machine learning, digital health technologies, and translational research, while working alongside academic researchers, healthcare professionals, and industry partners. Opportunities may also exist to engage with real-world datasets and contribute to the development of next-generation digital health monitoring systems.
Ultimately, this research aims to transform the way behavioural information is used to support health and wellbeing, moving healthcare from reactive treatment towards proactive, personalised intervention. By developing robust methods for detecting and tracking behavioural change, the project seeks to enable earlier identification of health concerns, improve quality of life, and support more effective and sustainable healthcare delivery in an increasingly connected world.

How to apply for admission: www.warwick.ac.uk/pgrengineering