This project aims to examine the dynamical interplay between physical activity, sedentary behaviour, the natural environment and wellbeing in the elderly[1-7]. Movement and exertion, and in particular, the difference in the amount of physical activity and sedentary behaviour have consistently been found to be related to elderly wellbeing and a wide range of health outcomes. We will implement new
methodologies, incorporating data analysis, with state-of-art wearable devices, to offer novel insights into elderly activity and the relationship to their wellbeing with a particular focus of the impact of environmental factors. The study will also incorporate the development and refinement of a prototype method to look at intervention with real-time analysis and real-time feedback of physical inactivity and sedentary behaviour.
Methodology: The use of experience sampling and ecological momentary assessment; repeated-entry diary techniques; monitoring of physiological function in combination with or without physical behaviour using wearable sensors and ambient sensors; acquisition of ambient environmental parameters, statistics and data modelling.
Expected progress beyond the state-of-the-art: The project will implement new assessments methodologies based on the previous research [1-7], incorporating data analysis, with state-of-art wearable devices [2]. This would include the use of accelerometer, magnetometer and gyroscope intelligent multi-sensor based personalized risk assessment systems to monitor gait and stability as well as a wrist device for activity recognition and environmental monitoring.
Academic qualifications
A first degree (at least a 2.1) ideally in Computing, or Computing Engineering, or Electronics and electrical engineering, or Robotics, or Mathematics with a good fundamental knowledge of software engineering, programming, wearable sensors and data analysis.
English language requirement
IELTS score must be at least 6.5 (with not less than 6.0 in each of the four components). Other, equivalent qualifications will be accepted. Full details of the University’s policy are available online.
Essential attributes:
· Experience of fundamental sensor technology and data analysis
· Competent in statistics and data modelling
· Knowledge of applied statistics
· Good written and oral communication skills
· Strong motivation, with evidence of independent research skills relevant to the project
· Good time management
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