LAUSR

Center of pressure (CoP) metrics, including its path length, sway area, and position, are important measurements of postural and balance control in biomechanical studies. A computer-vision-based CoP metrics estimation system offers a portable solution to obtain these gold-standard metrics with 3D multi-joint coordination underlying body movements for real-time evaluation of balance control. In this paper, we propose an end-to-end framework for video-level estimation of CoP path length and sway area, as well as the frame-level estimation of CoP position, utilizing the spatial-temporal features and adaptive graph structure learned by graph convolution network. This work is the first step toward demonstrating that these gold-standard metrics can be obtained with a more comprehensive tool than current force plate technologies. We propose two single-task models for video-level and frame-level estimation, respectively, and a multi-task learning approach that jointly learns the two-temporal-level features. To facilitate this line of research, we release a novel computer-vision-based 3D body landmark dataset containing a wide variety of action patterns with synchronized CoP labels using pose estimation. We also adapt our framework on an existing kinematic dataset collected by wearable markers. The experiments on both datasets validate that our framework achieves state-of-the-art accuracies for all metric estimations, while the proposed multi-task approach yields the most accurate and robust performance on video-level estimation.1