Motion based markerless gait analysis using standard events of gait and ensemble Kalman filtering

We present a novel approach to gait analysis using ensemble Kalman filtering which permits markerless determination of segmental movement. We use image flow analysis to reliably compute temporal and kinematic measures including the translational velocity of the torso and rotational velocities of the lower leg segments. Detecting the instances where velocity changes direction also determines the standard events of a gait cycle (double-support, toe-off, mid-swing and heel-strike). In order to determine the kinematics of lower limbs, we model the synergies between the lower limb motions (thigh-shank, shank-foot) by building a nonlinear dynamical system using CMUs 3D motion capture database. This information is fed into the ensemble Kalman Filter framework to estimate the unobserved limb (upper leg and foot) motion from the measured lower leg rotational velocity. Our approach does not require calibrated cameras or special markers to capture movement. We have tested our method on different gait sequences collected from the sagttal plane and presented the estimated kinematics overlaid on the original image frames. We have also validated our approach by manually labeling the videos and comparing our results against them.