Movement observation
Video, markerless approaches, and task-specific kinematic descriptions.
Measurement and modeling
What can an observation actually tell us?
Movement measurement is an inference problem. Sensors produce signals; methods turn signals into quantities; models connect quantities to possible mechanisms. Each step adds assumptions and uncertainty.
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Methodological position
Accessible measurement can support ambitious science.
Scientific sophistication does not require making the most expensive instrument the center of the laboratory.
Portable video, wearable sensing, physiological signals, imaging, and computational models can be combined when the question justifies them and their common timeline, uncertainty, and validity are explicit.
Wearable sensing
Distributed inertial and physiological measurements in laboratory and field contexts.
Sensor integration
Synchronization, calibration, uncertainty, and common-time representations.
Mechanistic models
Dynamical and neuromusculoskeletal representations that generate testable behavior.
Foundational question
How much of the scientifically important structure of movement can we recover from strategically chosen measurements?
This question connects clinical feasibility to observability, dimensionality, state estimation, sensor fusion, system identification, movement variability, and model identifiability.