I'm Millen James Theophilus, a PhD researcher studying human movement. I investigate how we can measure it, understand its patterns, and use that knowledge to answer questions about health.
My work brings experimental science together with wearable sensing and computational methods. My interests span movement mechanics, behaviour in daily life, and new approaches to digital health and public health research.
Skills & expertise
My experience spans laboratory assessment, measurement in daily life, and computational analysis.
I use laboratory assessment to investigate movement mechanics, with experience in neuromuscular function, balance, and biomechanical simulation.
Methods & tools3D motion capture · Force plates · Surface EMG · Dynamometry · MATLAB / Simscape
I assess how well sensors capture movement in the lab or daily life, testing device performance alongside the algorithms used to interpret their signals.
Methods & toolsWearable sensors · Accelerometry · Signal processing · Device and algorithm validation · Metrology
I use computer simulation to explore movement mechanics. Statistical and predictive models help me investigate patterns in the data.
Methods & toolsR · Python · MATLAB · Feature extraction · Meta-regression · Reproducible workflows
PhD research (current)
My PhD examines how well wearable devices measure physical activity, sedentary behaviour, and sleep. I test devices, evaluate algorithms, and synthesise evidence to assess the validity of these estimates.
I also develop open tools that help researchers interpret and reuse movement data in digital health and public health research.
Explore my researchHow I work
My work combines experimental measurement with computational analysis, from laboratory testing of sensors to statistical models of movement data.
I develop reproducible software and behavioural data standards to help researchers examine methods, reuse data, and apply findings in new settings.
More about my researchFeatured work
Open resources for evaluating methods, exploring evidence, and making movement data more useful.
Evidence synthesis
An evidence explorer for comparing studies of wrist-accelerometer models that estimate sleep, sedentary behaviour, or physical activity.
Measurement science
Laboratory testing of wearable accelerometers at rest and in motion to characterise their measurement performance.
Modelling · MSc coursework
A forward-dynamics model exploring how joint torque and activation affect the simulated rise from a deep single-leg squat.
Opportunities & collaboration
I am interested in academic and R&D roles where I can apply my movement science background to new questions. Areas of interest include biomechanics, wearable technology, digital health, and public health.
I can contribute to experimental studies, method validation, or computational analysis, as well as the software needed to put research into practice.