Remote patient monitoring using wearable devices and AI University of Sheffield in United Kingdom

Artificial intelligence (AI) has permeated various aspects of modern life, significantly influencing the healthcare sector. Fuelled by this momentum, remote patient monitoring facilitated by mobile sensing technology is on the way to changing how patients are monitored and treated. By analysing extensive data from wearable devices and smartphones, you will build AI-based models that can lead to early detection of potential health issues and personalised interventions. The integration of AI and remote patient monitoring holds immense promise for improving healthcare outcomes. In this PhD project, you will conduct a comprehensive analysis of extensive datasets, such as RADAR-CNS and/or Mobilise-D, collected from large cohorts of individuals monitored remotely for over two years. These datasets encompass a diverse range of physiological and behavioural parameters, including heart rate, sleep patterns, physical activity, GPS location, and phone/app use. They represent the most extensive observational studies utilising wearable devices to date.Your primary tasks will include: 1) to extract meaningful features from these sensor data and apply machine learning algorithms to predict health outcomes; 2) to explore advanced deep learning methodologies to further exploit the information embedded within the data, with the goal of improving prediction accuracy. The targeted medical conditions for analysis may include depression, multiple sclerosis, and Parkinson’ s disease.For informal discussion about the project, please contact Dr. Shaoxiong Sun, [email protected]. Please add quote [PHD-AI4Health] in the email subject line.Supervisor BioDr. Shaoxiong Sun is a Lecturer in Pervasive Data Science at the Department of Computer Science, the University of Sheffield. Previously, he was a Senior Research Associate in Data Science in Mobile Health at King’s College London. Dr. Sun's research interests primarily revolve around physiological and behavioural monitoring, leveraging advanced signal processing and machine learning methodologies. Dr. Sun has over 30 peer-reviewed publications and 2 patents granted through the PCT system. About the Department & Research Group The role will be based at the Department of Computer Science, the University of Sheffield. 99 percent of our research is rated in the highest two categories in the REF 2021, meaning it is classed as world-leading or internationally excellent. We are rated as 8th nationally for the quality of our research environment, showing that the Department of Computer Science is a vibrant and progressive place to undertake research. The successful candidate will join the Pervasive Computing research group. Its research focuses on the utilisation of mobile or pervasive sensing techniques to support faster and safer preventive care, improved patient-centred practice, and enhanced sustainability. Candidate RequirementsMinimum 2.1 Bachelor’s degree or Master’s degree in a relevant discipline (e.g., Computer Science, Electrical Engineering, Biomedical Engineering, and Bioinformatics), or its international equivalent.Self-motivated and passionate about conducting research in artificial intelligence (AI) and its applications in healthcare innovation.Proficient in data analytics, machine learning and signal processing (time series analysis), with hands-on experience in applying these techniques to real-world datasets.Strong programming skills in Python and/or MATLAB.Demonstrated experience in preparing scientific manuscripts for journals or conferences.How to ApplyTo apply for a PhD studentship, applications must be made directly to the University of Sheffield using the Postgraduate Online Application Form. Make sure you name Dr. Shaoxiong Sun as your proposed supervisor.Information on what documents are required and a link to the application form can be found here -https://www.sheffield.ac.uk/postgraduate/phd/apply/applyingThe form has comprehensive instructions for you to follow, and pop-up help is available.