PhD Studentship : AI-Powered Fatigue Solutions: Transforming Care for Neurological Conditions

About the GW4 BioMed2 Doctoral Training Partnership

The partnership brings together the Universities of Bath, Bristol, Cardiff (lead) and Exeter to develop the next generation of biomedical researchers. Students will have access to the combined research strengths, training expertise and resources of the four research-intensive universities, with opportunities to participate in interdisciplinary and 'team science'. The DTP already has over 90 studentships over 6 cohorts in its first phase, along with 58 students over 3 cohorts in its second phase.

Project Information

Research Theme: Neuroscience & Mental Health

Summary: Fatigue is a major issue for individuals with neurological conditions, often leading to lower quality of life. This project aims to empower these individuals through an AI-powered fatigue management tool using data fusion from wearable technologies that track physiological data, such as heart rate, sleep patterns, and physical activity, combined with an app to log symptoms. A machine learning algorithm will be developed to predict fatigue patterns, allowing users to plan activities and rest proactively. This tool will help organise tasks and energy management, send personalised reminders, and support better fatigue management while assisting healthcare professionals in making informed decisions.

Project Description: Fatigue is a significant issue for individuals with neurological conditions, often resulting in lower quality of life and social isolation. Currently, no system utilises data fusion to predict fatigue based on individual data, hindering effective symptom management. To address this, the project aims to empower these individuals by leveraging wearable technologies to monitor physiological data (such as heart rate, sleep patterns, and physical activity) and AI-driven apps to log symptoms and track fatigue levels. This approach provides tailored advice based on specific conditions and daily routines, fostering a sense of control and independence in managing health and monitoring fatigue in real time. The collected data will be used to develop AI algorithms capable of predicting periods of high fatigue, enabling proactive activity and rest planning. By analysing patterns, identifying triggers, and correlating factors that exacerbate fatigue, these algorithms will assist both individuals and healthcare providers in managing fatigue more effectively. The predictive model will be instrumental in foreseeing highfatigue periods, allowing for better activity planning and rest. As part of the PhD program, an AI-powered tool will be co-developed to assist individuals with neurological conditions in organising and prioritising tasks and energy management. This tool will focus on essential activities, predict fatigue risk, and send personalised reminders to take breaks, perform relaxation exercises, or engage in low-intensity activities based on personalised data. This proactive approach promotes better fatigue management throughout the day.

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