  Ergonomic assessment of human posture plays a vital role in understanding
work-related safety and health. Current posture estimation approaches face
occlusion challenges in teleoperation and physical human-robot interaction. We
investigate if the leader robot is an adequate sensor for posture estimation in
teleoperation and we introduce a new probabilistic approach that relies solely
on the trajectory of the leader robot for generating observations. We model the
human using a redundant, partially-observable dynamical system and we infer the
posture using a standard particle filter. We compare our approach with postures
from a commercial motion capture system and also two least-squares optimization
approaches for human inverse kinematics. The results reveal that the proposed
approach successfully estimates human postures and ergonomic risk scores
comparable to those estimates from gold-standard motion capture.
