Funding for: UK Students, EU Students, International Students
Funding amount: UK or Overseas tuition fees, plus £15,609 p.a. (2021/22) stipend awarded for three years
Hours: Full time
Data collected from wearable devices and the Internet of Things system can be utilised to create intelligently expanded end user healthcare portfolios. Analytics are required to extract relevant information from the large amount of complex data, and to translate this information into useful insights to assist decision making regarding diet, lifestyle and physical exercise.
The project aims to promote healthy lifestyles and help improve the general state of health and wellbeing of the UK population. This project will collect health and wellbeing data from multiple sources, including wearable devices, interviews and self-reporting from Patient and Public Involvement (PPI) groups, social media platforms, and databases such as UK Data Service. These data will be analysed to monitor individuals’ history of illness, lifestyle parameters, mental and psychological parameters, socioeconomic parameters, gender parameters, contextual parameters (work, location, etc.) and cultural parameters. Applying machine learning and advanced data analytics to the collected data, we could create digital twins of individuals. The digital twins have several functionalities: 1) The digital twins produce wellbeing profiles for the individuals, associating indicators with well- and ill-being. The profiles enable identification, comparison and monitoring trends among interviewed people. 2) The digital twins identify factors leading to ill-being and their causal links. 3) The digital twins feed the collected data into the machine learning models to experiment intervention, and to test effectiveness of personalised healthy living advice, including healthy eating, work/life balance, physical activity plan, etc.
The outcome of this research will change the way we engage in eating, working and physical activity. It will also deliver personalised healthy eating, working and living advice to the public.
Applicants must meet English language requirements, and the project expects an IELTS of 6.5 in order to be accepted for the PhD programme. Read more here.
The successful candidate will be responsible for collecting data to create Digital Twins of real-world environments. The successful applicant will work closely with the research centres in Faculty of Business Law, including the Anglia Ruskin Innovation Centre with The Welding Institute, and Centre for Intelligent Supply Chain. The PhD candidate will be supervised by Prof. Ying Xie from Faculty of Business and Law, Anglia Ruskin University, and Prof. Barbara Pierscionek from Faculty of Healthcare, Education, Medicine and Social Care, Anglia Ruskin University. Ocado Technology will be the industrial partner of this project, to supervise the design, execution and analysis of digital twins’ models, in collaboration with academic supervisors. Ocado Technology will also advise the PhD student and academic supervisors on feasibility of digital twins in practice.
How to apply
Applications for the PhD Scholarship are made here. Please choose the course title “PhD with progression from MPhil School of Economics, Finance and Law”, Full Time, ARU Chelmsford Campus, R0177FCHE02D, 18/Jan/2022.
For further information, please contact Prof. Ying Xie via firstname.lastname@example.org
We will review all applications after the submission deadline of 24th October 2021. We will contact shortlisted applicants in the week commencing 1st November 2021. Interviews will be held in the week commencing 8th November 2021.
|Funding for:||UK Students, EU Students, International Students|
|Funding amount:||UK or Overseas tuition fees, plus £15,609 p.a. (2021/22) stipend awarded for three years|
|Placed On:||10th September 2021|
|Closes:||24th October 2021|
Type / Role: