Publisher
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University of Exeter

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PhD Studentship: Digital Movement Biomarkers of Mood and Fatigue in Older Adults University of Exeter in United Kingdom

Degree Level

PhD

Field of study

Computer Science

Funding

Available

Deadline

Dec 8, 2026

Country flag

Country

United Kingdom

University

University of Exeter

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Keywords

Computer Science
Psychology
Biomedical Engineering
Artificial Intelligence
Computer Vision
Neuropsychology
Medical Science
Motion Analysis
Explainable Ai
Anxiety
Older Adults
Major Depressive Disorder
Statistics
Long-term Conditions
Pose Estimation
ML

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About this position

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

This PhD studentship at the University of Exeter focuses on developing digital movement biomarkers to detect depression, anxiety, and fatigue in older adults, including people living with and without multiple long-term conditions (MLTCs). The project sits at the intersection of computer vision, artificial intelligence, functional movement analysis, and health research, with the goal of creating scalable, objective tools for monitoring psychological wellbeing through movement.

Older adults with conditions such as stroke, Parkinson’s disease, frailty, arthritis, and mild cognitive impairment often experience mood-related symptoms that are difficult to detect accurately using questionnaires or infrequent clinical interviews. This project addresses that challenge by analysing existing video-based movement data from more than 1,000 functional movement assessments. The dataset includes adults aged 60+ and contains movement recordings alongside demographics, clinical characteristics, functional outcomes, mood measures, and fatigue scales.

The research will use state-of-the-art markerless pose estimation tools such as OpenPose, MediaPipe, and MoveNet to extract biomechanical and behavioural features from activities including posture, balance, transfers, walking, and activities of daily living. Features of interest include spatiotemporal characteristics, joint kinematics, movement smoothness, symmetry, postural alignment, and inter-limb coordination. The project will then combine interpretable statistical analysis with nonlinear movement metrics such as entropy, fractal dynamics, and recurrence-based measures to identify subtle signatures associated with mood and fatigue.

Building on these features, the PhD will develop an explainable machine learning framework for digital biomarker discovery, using methods such as Random Forests, XGBoost, and LightGBM. The work has clear translational potential for healthcare, community, and home settings, offering a low-cost and non-invasive approach to monitoring wellbeing in older populations.

The studentship provides fees plus a stipend of £21,805 per annum for 2026/7. The application deadline is 08 Dec 2026. This is a strong opportunity for candidates interested in AI for health, movement science, digital phenotyping, and applications of computer vision to clinically meaningful problems in ageing and long-term conditions.

Funding details

Available

What's required

Applicants should be prepared to work with computer vision, artificial intelligence, functional movement analysis and machine learning methods. The project is suitable for candidates interested in older adult health, digital biomarkers, biomechanics, and analysis of video-based movement data. No specific degree classification, GPA, or language-test requirement is stated in the advert.

How to apply

Apply via the University of Exeter funding application link provided in the advert. Review the project details carefully before submitting the application. Ensure your application is submitted by 08 Dec 2026.

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