Traditional marker-based motion capture is accurate but constrained to the laboratory, requiring specialist equipment, calibration and lengthy setup that limit its use in ecologically valid, field-based, or large-scale settings. This research theme explores markerless approaches - using video and pose-estimation/deep learning methods - to capture human movement without reflective markers or wearable sensors.