Wearable validation
Benchmarking accelerometer devices and prediction models for physical activity, sedentary behaviour, sleep, and energy expenditure.
Digital health researcher
Wearable-sensor methods for physical behaviour and digital health research.
I develop, evaluate, and translate methods for measuring human movement behaviours using wearable devices, with a focus on accelerometry, validation, metrology, FAIR data systems, and open research tools.
4Core routes: metrology, validation, evidence synthesis, and semantics
24 hBehavioural focus spanning sleep, sedentary behaviour, and physical activity
OpenReusable tools, archived releases, and transparent research workflows
AppliedMethods relevant to academic, public health, and industry settings
Research areas
Areas of work
This site provides a concise route into my research portfolio, tools, publications, and collaboration interests.
Sensor metrology
Characterising signal quality, calibration, drift, frequency response, and inter-device comparability.
Evidence synthesis
Developing reviews, reporting tools, and model-selection resources for raw wrist accelerometry.
Semantic interoperability
Representing behavioural data with FAIR, privacy-aware, and machine-actionable standards.
Featured work
Selected projects and tools
Entry points into the main workstreams.
Evidence synthesis
SCOPE-MOVE
Static evidence explorer for accelerometer-based prediction models in 24-hour movement behaviour analysis.
Measurement science
Accelerometer metrology
Experimental work on the static and dynamic performance of research-grade wearable accelerometers.
Interoperability
COEL v2.0
A proof-of-concept framework for interoperable, privacy-aware behavioural event data.
Collaboration
Open to research collaborations, postdoctoral opportunities, and applied methods discussions.
I am especially interested in projects involving wearable sensor validation, accelerometer data processing, physical behaviour measurement, FAIR data systems, and reproducible digital health tools.