arXiv subjects

John H. Challis

Publications and source records attributed to John H. Challis.

3 recordsLinked to original sources

arXiv2020

From Kinematics To Dynamics: Estimating Center of Pressure and Base of Support from Video Frames of Human Motion

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.

cs.CV↗

arXiv2019

Learning Dynamics from Kinematics: Estimating 2D Foot Pressure Maps from Video Frames

Pose stability analysis is the key to understanding locomotion and control of body equilibrium, with applications in numerous fields such as kinesiology, medicine, and robotics. In biomechanics, Center of Pressure (CoP) is used in studies of human postural control and gait. We propose and validate a novel approach to learn CoP from pose of a human body to aid stability analysis. More specifically, we propose an end-to-end deep learning architecture to regress foot pressure heatmaps, and hence the CoP locations, from 2D human pose derived from video. We have collected a set of long (5min +) choreographed Taiji (Tai Chi) sequences of multiple subjects with synchronized foot pressure and video data. The derived human pose data and corresponding foot pressure maps are used jointly in training a convolutional neural network with residual architecture, named PressNET. Cross-subject validation results show promising performance of PressNET, significantly outperforming the baseline method of K-Nearest Neighbors. Furthermore, we demonstrate that our computation of center of pressure (CoP) from PressNET is not only significantly more accurate than those obtained from the baseline approach but also meets the expectations of corresponding lab-based measurements of stability studies in kinesiology.

cs.CV↗

arXiv2009

Theory of preparation and relaxation of a p-orbital atomic Mott insulator

We develop a theoretical framework to understand the preparation and relaxation of a metastable Mott insulator state within the first excited band of a 1D optical lattice. The state is loaded by "lifting" atoms from the ground to the first excited band by means of a stimulated Raman transition. We determine the effect of pulse duration on the accuracy of the state preparation for the case of a Gaussian pulse shape. Relaxation of the prepared state occurs in two major stages: double-occupied sites occurring due to quantum fluctuations initially lead to interband transitions followed by a spreading of particles in the trap and thermalization. We find the characteristic relaxation times at the earliest stage and at asymptotically long times approaching equilibrium. Our theory is applicable to recent experiments performed with 1D optical lattices [T. Müller, S. Fölling, A. Widera, and I. Bloch, Phys. Rev. Lett. \textbf{99}, 200405 (2007)].

cond-mat.other↗