Aidan Acquah
Postdoctoral Researcher in Wearable Sensors
Aidan is a researcher in the OxWearables group, led by Prof. Aiden Doherty at the Big Data Institute, University of Oxford. His research focuses on advancing human activity recognition by using the latest machine learning techniques to derive objective measures of physical activity and exploring their associations with health outcomes.
This builds on the work during his DPhil, also completed at University of Oxford in 2025, which investigated the use of wrist-worn accelerometers to predict Parkinson’s disease.
Before joining Oxford, Aidan worked as a consultant analyst programmer, working on a variety of projects for oil and gas, consumer and healthcare industry clients. In his spare time, Aidan enjoys travelling, volunteering and playing Dungeons & Dragons.
Recent publications
Evaluating Open-Source Wrist-Worn Accelerometer Models for Sedentary Time Detection Against Thigh-Worn Accelerometer Data
Preprint
Acquah A. et al, (2026)
Characterising Parkinson's Disease-like Walking Using Wrist-worn Accelerometers
Conference paper
Acquah A. et al, (2025), Ubicomp Companion 2025 Companion of the 2025 ACM International Joint Conference on Pervasive and Ubiquitous Computing, 50 - 54
Comparing two wrist-worn accelerometers (Axivity AX3 and Matrix 003) for measuring movement behaviours in British and Chinese older adults
Journal article
Brocklebank LAURA., (2025), Journal for the Measurement of Physical Behaviour
Self-Supervised Machine Learning to Characterize Step Counts from Wrist-Worn Accelerometers in the UK Biobank.
Journal article
Small SR. et al, (2024), Medicine and science in sports and exercise, 56, 1945 - 1953
Self-supervised learning for human activity recognition using 700,000 person-days of wearable data.
Journal article
Yuan H. et al, (2024), NPJ digital medicine, 7