Deep learning-based estimation of whole-body kinematics from multi-view
  images
Kien X. Nguyen Liying Zheng Ashley L. Hawke Robert E. Carey Scott P. Breloff Kang Li Xi Peng
It is necessary to analyze the whole-body kinematics (including joint
locations and joint angles) to assess risks of fatal and musculoskeletal
injuries in occupational tasks. Human pose estimation has gotten more attention
in recent years as a method to minimize the errors in determining joint
locations. However, the joint angles are not often estimated, nor is the
quality of joint angle estimation assessed. In this paper, we presented an
end-to-end approach on direct joint angle estimation from multi-view images.
Our method leveraged the volumetric pose representation and mapped the rotation
representation to a continuous space where each rotation was uniquely
represented. We also presented a new kinematic dataset in the domain of
residential roofing with a data processing pipeline to generate necessary
annotations for the supervised training procedure on direct joint angle
estimation. We achieved a mean angle error of $7.19^\circ$ on the new Roofing
dataset and $8.41^\circ$ on the Human3.6M dataset, paving the way for
employment of on-site kinematic analysis using multi-view images.