Publications

Characterising Parkinson's Disease-like Walking Using Wrist-worn Accelerometers

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

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.

Small SR. et al, (2024), Med Sci Sports Exerc, 56, 1945 - 1953

Daily steps are a predictor of, but perhaps not a modifiable risk factor for Parkinson's Disease: findings from the UK Biobank.

Acquah A. et al, (2024)

Self-supervised learning for human activity recognition using 700,000 person-days of wearable data.

Yuan H. et al, (2024), NPJ Digit Med, 7

Development and Validation of a Machine Learning Wrist-worn Step Detection Algorithm with Deployment in the UK Biobank.

Small SR. et al, (2023)

Search results (6)

Characterising Parkinson's Disease-like Walking Using Wrist-worn Accelerometers

Comparing two wrist-worn accelerometers (Axivity AX3 and Matrix 003) for measuring movement behaviours in British and Chinese older adults

Self-Supervised Machine Learning to Characterize Step Counts from Wrist-Worn Accelerometers in the UK Biobank.

Daily steps are a predictor of, but perhaps not a modifiable risk factor for Parkinson's Disease: findings from the UK Biobank.

Self-supervised learning for human activity recognition using 700,000 person-days of wearable data.

Development and Validation of a Machine Learning Wrist-worn Step Detection Algorithm with Deployment in the UK Biobank.

Leading Human Health Forward

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Search results (6)

Characterising Parkinson's Disease-like Walking Using Wrist-worn Accelerometers

Comparing two wrist-worn accelerometers (Axivity AX3 and Matrix 003) for measuring movement behaviours in British and Chinese older adults

Self-Supervised Machine Learning to Characterize Step Counts from Wrist-Worn Accelerometers in the UK Biobank.

Daily steps are a predictor of, but perhaps not a modifiable risk factor for Parkinson's Disease: findings from the UK Biobank.

Self-supervised learning for human activity recognition using 700,000 person-days of wearable data.

Development and Validation of a Machine Learning Wrist-worn Step Detection Algorithm with Deployment in the UK Biobank.

Leading Human Health Forward