About the project
This PhD project explores the application of advanced machine learning on wearable biosignal data, such as heart rate and activity levels, to enhance personalized health monitoring. Focus areas include multimodal data integration, real-time processing, and privacy-preserving techniques for predictive health insights, offering impactful advancements in personalized digital healthcare solutions.
This project focuses on developing advanced machine learning methods to transform wearable biosignal data, like heart rate, activity levels, and sleep patterns, into actionable health insights. With wearable technology collecting vast amounts of health data, this project aims to create algorithms that can predict health events, deliver personalized recommendations, and ultimately guide users toward healthier lifestyles.
You will work on critical challenges, including data heterogeneity from various biosignal sources, real-time processing for immediate insights, and personalization to accommodate individual health variations. Methods will include multimodal machine learning for integrating diverse datasets, edge computing for efficient real-time analysis, and adaptive modelling to tailor insights to each user.
You will work within a multidisciplinary team at the forefront of digital healthcare research, contributing to the creation of impactful, user-centred health monitoring solutions. Research directions may include federated learning to ensure user privacy, interpretability methods to enhance trust in machine learning models, and collaborations with health and technology experts for broader industry impact.
This project is ideal if you are passionate about wearable health technology, machine learning, and personalized healthcare. Outcomes will contribute to better disease prediction, improved health management, and more responsive healthcare in a digital era.
You will complete essential training through the Doctoral College, covering research ethics, integrity, and data management. Legally required courses include equality and diversity, health and safety, and security awareness. Additionally, you might need to complete some training before engaging in any teaching activities.
