Representation of Human Gait Trajectory through Temporospatial Image Modelling

Marker-based 2D temporospatial image modelling is a common strategy in characterizing human gait where Channel filtering, Threshold imaging, and Line feel algorithm are normally used in foreground segmentation targeting human lower limbs of a particular image frame. This paper presents Temporospatial Image Modelling approach in presenting segmented objects with spatiotemporal view by reflecting various poses of lower limbs for forward walking. Lower limbs joint movement characteristics and angle variations are also presented in this paper where pre-assigned marker-points are modelled in tracking the motion trajectories. Results show various patterns of motion trajectories and angle variations for Hip, Knee, Ankle, Heel, and Toe of lower limbs through observing the variations of times, locations, and spatiotemporal representations. The results also characterize that the Swing and Stance phases of a gait cycle are about 40% and 60% of a gait cycle respectively.