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Physical Activity Recognition and Intensity System (PARIS): automatically identifying the activity that the wearer of a tri-axial accelerometer is engaged in

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  • Full or part time
    Dr M Hillsdon
  • Application Deadline
    No more applications being accepted
  • Funded PhD Project (European/UK Students Only)
    Funded PhD Project (European/UK Students Only)

Project Description

The University of Exeter’s College of Life and Environmental Sciences, in partnership with Activinsights, are inviting applications for a fully funded PhD studentship to commence early in 2016 For eligible students the studentship will cover UK/EU tuition fees plus an annual tax-free stipend of £14,057 for three years. Students who pay international tuition fees are eligible to apply, but should note that the award will only provide payment for part of the international tuition fee and no stipend. Studentships will be awarded on the basis of academic merit and are awarded for three years of full-time study (part-time pro-rata). The student would be based in Sport and Health Sciences in the College of Life and Environmental Sciences at the St Luke’s Campus in Exeter.


Associate Professor Melvyn Hillsdon & Professor Richard Everson, University of Exeter; Joss Langford, Activinsights

Project Description

The overall aim of this project is to use a combination of mathematics, statistical analysis and software development to automatically identify the activity (sitting, walking, running, etc) that the wearer of a tri-axial accelerometer is engaged in. Features that characterise the activity will be derived both theoretically (using knowledge about the position of the accelerometer) and by machine learning of "sparse features" from the large amounts of data available. With the features on hand, we plan to use models such as hidden Markov models, switching state-space models and conditional random fields to map the temporal patterns of features into activities. You will have the opportunity to use and develop several state-of-the-art machine learning techniques applied to "big data”, whilst also using a variety of programming languages (such as R, MATLAB and Python). The main output from the project will be an open source software program to automatically segment accelerometer data into clinically meaningful physical activity classifications according to type, frequency, duration and intensity that can be used by researchers and clinicians.

Activinsights specialises in wrist-worn, raw data accelerometers for academics and clinicians. The GENEActiv accelerometer is deployed in a wide variety of global research initiatives, including studies on daily activity and behaviours, lifestyle, sleep, tremor, sedentary behaviours, fracture rehabilitation and physical activity classification. This studentship offers a fantastic opportunity to work with both a wide-range of academics (both from sports science and mathematics) and industry leaders to produce a novel and unique system for automatically translating raw accelerometer data into behavioural metrics that can advance the science of behavioural change with the aim of benefiting people’s overall health.

Entry requirements

Applicants for this studentship must have obtained, or be about to obtain, a First or Upper Second Class UK Honours degree, or the equivalent qualifications gained outside the UK, in an appropriate area of science or technology. An MSc (or near completion) in an appropriate area would be an advantage. The successful candidate will have a strong interest in machine learning, pattern recognition, signal processing or a related area. Strong analytical and programming skills are essential.

How good is research at University of Exeter in Sport and Exercise Sciences, Leisure and Tourism?

FTE Category A staff submitted: 23.30

Research output data provided by the Research Excellence Framework (REF)

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