no code implementations • ECCV 2020 • Jesse Scott, Bharadwaj Ravichandran, Christopher Funk, Robert T. Collins, Yanxi Liu
We propose and validate two end-to-end deep learning architectures to learn foot pressure distribution maps (dynamics) from 2D or 3D human pose (kinematics).
no code implementations • 23 Jun 2022 • Jesse Scott, John Challis, Robert T. Collins, Yanxi Liu
Quantitative evaluation of human stability using foot pressure/force measurement hardware and motion capture (mocap) technology is expensive, time consuming, and restricted to the laboratory.
no code implementations • 2 Jan 2020 • Jesse Scott, Christopher Funk, Bharadwaj Ravichandran, John H. Challis, Robert T. Collins, Yanxi Liu
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.
no code implementations • 30 Nov 2018 • Christopher Funk, Savinay Nagendra, Jesse Scott, Bharadwaj Ravichandran, John H. Challis, Robert T. Collins, Yanxi Liu
In biomechanics, Center of Pressure (CoP) is used in studies of human postural control and gait.