From Kinematics To Dynamics: Estimating Center of Pressure and Base of Support from Video Frames of Human Motion
This work addresses the cross-modality mapping challenge in computer vision for applications in kinesiology, medicine, sports, and robotics, but it is incremental as it builds on existing deep learning techniques for a specific domain.
The paper tackled the problem of predicting foot pressure heatmaps from 2D human pose images, using deep learning models PressNet and PressNet-Simple, and validated the approach on a dataset of 813,050 synchronized pairs from Taiji movements, achieving reliable predictions and enabling computation of Center of Pressure and Base of Support for stability analysis.
To gain an understanding of the relation between a given human pose image and the corresponding physical foot pressure of the human subject, we propose and validate two end-to-end deep learning architectures, PressNet and PressNet-Simple, to regress foot pressure heatmaps (dynamics) from 2D human pose (kinematics) derived from a video frame. A unique video and foot pressure data set of 813,050 synchronized pairs, composed of 5-minute long choreographed Taiji movement sequences of 6 subjects, is collected and used for leaving-one-subject-out cross validation. Our initial experimental results demonstrate reliable and repeatable foot pressure prediction from a single image, setting the first baseline for such a complex cross modality mapping problem in computer vision. Furthermore, we compute and quantitatively validate the Center of Pressure (CoP) and Base of Support (BoS) from predicted foot pressure distribution, obtaining key components in pose stability analysis from images with potential applications in kinesiology, medicine, sports and robotics.