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Sallie Lamb

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PhD Studentship: Computer vision to predict falls from everyday movement in older adults University of Exeter in United Kingdom

Degree Level

PhD

Field of study

Computer Science

Funding

Available

Deadline

Dec 8, 2026

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Country

United Kingdom

University

University of Exeter

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Keywords

Computer Science
Biomedical Engineering
Deep Learning
Computer Vision
Neuropsychology
Medical Science
Stability Analysis
Motion Analysis
Self-supervised Learning
Clinical Prediction
Older Adults
Fall Prevention
Video Analytics
Pose Estimation

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About this position

[Fees plus a stipend of £21,805 per annum for 2026/7.]

PhD Studentship at the University of Exeter: Computer vision to predict falls from everyday movement in older adults.

This project addresses an important clinical problem: falls are a major cause of injury-related hospital admission and death among older adults, yet current risk assessment methods are limited because they usually rely on brief one-off clinical tests. The project will use real-world-inspired movement data collected in the STEPPING OUT Home Scene, a simulated home environment at the University’s VSimulators facility, where older adults perform everyday actions such as standing up, sitting down, turning, moving around furniture, opening doors, changing surfaces, and responding to slips and trips. Multi-camera video, motion capture, and wearable sensors are used to capture movement, while participants’ falls and clinical histories are known.

The PhD will investigate whether a simple camera can detect stability and movement-quality markers that are visible to experienced clinicians. The research aims are to: derive measures from markerless pose estimation and validate them against motion capture; identify movement patterns that distinguish people who fall from those who do not, including near-falls and moments before loss of balance; test how well these measures predict falls and how early change can be detected; and adapt the methods for low-cost devices in homes, care homes, and hospitals, including on-device processing.

The student will work with computer vision, deep learning for movement sequences, self-supervised learning, and interpretable machine learning, with an emphasis on methods that clinicians can understand and act upon. Expected outputs include a validated analysis pipeline, open benchmarks for stability assessment from video, evidence on the predictive value of ordinary video for falls risk, and a tested low-cost prototype.

The studentship provides funding of fees plus a stipend of £21,805 per annum for 2026/7. The advertised deadline is 08 Dec 2026. The project offers training across computer vision, data science, biomechanics, and clinical falls research within a supportive supervisory environment.

Funding details

Available

What's required

Applicants should be interested in computer vision, data science, biomechanics, and clinical falls research. The project will use pose estimation, deep learning for movement sequences, self-supervised learning, and interpretable modelling, so relevant technical skills and an ability to work across engineering and health research are preferred. No explicit degree classification, GPA, or language-test requirement is stated in the provided text.

How to apply

Apply via the University of Exeter funding page using the application link provided. Review the project details and submit your application before the deadline. Ensure any required supporting documents are included with your submission.

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Professors

Professor at University of Exeter

University Name
Country flag

United Kingdom

Research Interests

Neuropsychology

Computer Science

Fall Prevention

Self-supervised Learning

Clinical Prediction