3D posture estimation is important in analyzing and improving ergonomics in
physical human-robot interaction and reducing the risk of musculoskeletal
disorders. Vision-based posture estimation approaches are prone to sensor and
model errors, as well as occlusion, while posture estimation solely from the
interacting robot's trajectory suffers from ambiguous solutions. To benefit
from the advantages of both approaches and improve upon their drawbacks, we
introduce a low-cost, non-intrusive, and occlusion-robust multi-sensory 3D
postural estimation algorithm in physical human-robot interaction. We use 2D
postures from OpenPose over a single camera, and the trajectory of the
interacting robot while the human performs a task. We model the problem as a
partially-observable dynamical system and we infer the 3D posture via a
particle filter. We present our work in teleoperation, but it can be
generalized to other applications of physical human-robot interaction. We show
that our multi-sensory system resolves human kinematic redundancy better than
posture estimation solely using OpenPose or posture estimation solely using the
robot's trajectory. This will increase the accuracy of estimated postures
compared to the gold-standard motion capture postures. Moreover, our approach
also performs better than other single sensory methods when postural assessment
using RULA assessment tool.

提出了一种用于 3D 姿势估计的低成本、无侵入感的多传感器算法，基于 OpenPose 2D 姿势和机器人轨迹，使用粒子滤波器，技术可应用于 PHRI 领域，性能比使用 OpenPose 或机器人轨迹的单一传感器人体姿势估计更好，以及在使用 RULA 评估工具上的姿势评估表现更好。