Center of Pressure Estimation by Analyzing Walking Videos

Center of pressure (COP) serves as a widely utilized indicator for evaluating balance-related issues, e.g., gait quality of neurological disorders, fall risk of the elderly, and recovery of the injured. Existing methods for acquiring COP mostly rely on expensive force platforms or wearable force-sensing sensors that are relatively difficult to deploy. We propose a novel low-cost strategy to estimate COP with only visual information. Specifically, we first collect a database with walking videos. The plantar pressure of the subjects during walking is synchronously collected for calculating COP ground truth. Then, we propose an encoder-decoder model for estimating COPs using human pose and body shape as inputs. A perceptual codebook is used to tolerate the error in human pose estimation to improve the accuracy of the COP estimation. Experiments demonstrate the effectiveness of our proposed method. Our proposal achieves a correlation coefficient of 0.962, which is 0.8% better than the baseline model using Multi-layer Perceptron (MLP). The normalized RMSEs are improved by 13.9% and 19.0% in the anterior-posterior and medial-lateral directions, respectively. Compared to existing methods using wearable sensors or plantar pressure plates, our method is cheaper and easier to deploy.