Real-Time Foot-Ground Contact Detection for Inertial Motion Capture Based on an Adaptive Weighted Naive Bayes Model

Abstract

Detecting foot-ground contact is important for inertial motion capture systems because it can provide kinematic constraints to improve human motion capture accuracy. The popular contact detection methods based on zero-velocity detection are only applicable to applications with regular movement patterns, such as pedestrian dead reckoning and gait analysis. As for arbitrary locomotion in motion capture, reliable foot-ground contact detection for universal inertial motion capture is challenging in the presence of motion kinematics and dynamics estimation errors. This paper proposes a novel foot-ground contact detection method for inertial motion capture by the fusion of inaccurate estimations of body kinematics and dynamics with a Naive Bayes probabilistic contact model. Based on physical analysis of contact, a series of kinematic and dynamic motion features calculated from motion capture are considered as observations of contact status classification. The proposed framework consists of offline model training based on a constructed labeled multi-person foot contact dataset, real-time contact detection, and an online model adaptation mechanism for enhancing detection performance of different users and setups. Quantitative evaluations of the proposed contact detection method are presented, and the resulting average foot-ground contact detection accuracy among various locomotion is 95.8%.