Multi-Task Center-Of-Pressure Metrics Estimation From Skeleton Using Graph Convolutional Network

Center of pressure (COP) is an important measurement of postural and gait control in human biomechanical studies. A vision-based estimation of COP metrics offers a way to obtain these gold-standard metrics for the detection of balance and gait problems. In this paper, we propose an end-to-end framework to estimate the COP path length and the COP positions from the 3D skeleton, utilizing the spatial-temporal features learned by graph convolutional networks. We propose two single-task models for each metric and a multi-task approach jointly learning two metrics. To facilitate this line of research, we also release a novel 3D skeleton dataset containing a wide variety of action patterns with synchronized COP labels. The experiments on the dataset validate that our framework achieves state-of-the-art accuracies for both COP path length and COP position estimations, while the multi-task approach could yield more accurate and robust performance on COP path length estimation compared to the single-task model.