Human Leg Tracking by Fusion of Laser Range and Insole Force Sensing With Gaussian Mixture Model-Based Occlusion Compensation

Simultaneous measurement of spatial, temporal, and kinetic gait parameters has become important for monitoring patients with neurological disorders in a hospital. As spatial sensing technologies, laser range sensors (LRSs), which obtain highly accurate two-dimensional distance data over a wide range, have been employed to detect/track both legs during walking. However, when walking on curved trajectories, a continuous occlusion occurs over several sampling steps and produces false tracking. To address this issue in combination with temporal and kinetic sensing, this paper presents a fusion system of LRSs and force-sensing insoles and an occlusion compensation based on probabilistic motion models. First, the system pre-tracks a target person during walking along a straight line and curved paths under different curvatures/directions in a situation without occlusion. Gait cycles of each walking type are divided by foot-grounding obtained from the insoles. Relationships between leg trajectories and traveling directions during the gait cycle are then learned using user-specific Gaussian mixture models (GMMs). When an occlusion occurs in post-tracking, a maximum likelihood (ML) GMM is identified using a joint probability of both legs' trajectories, in accordance with biomechanics that both legs move in a coordinated manner. The ML GMM then compensates for the traveling direction and position of the occluded leg, and the system interpolates/re-tracks its positions to correct the states during occlusion. Experimental results demonstrated that the proposed method (fusion with the insoles, occlusion compensation, and interpolation/re-tracking) significantly enhanced tracking performance during occlusion and estimated leg positions with valid accuracy (errors of under 60 mm).