Is The Leader Robot an Adequate Sensor for Posture Estimation and Ergonomic Assessment of A Human Teleoperator?
This addresses occlusion issues in posture estimation for teleoperators, though it is incremental as it adapts existing probabilistic methods to a new sensor modality.
The paper tackled the problem of estimating human posture for ergonomic assessment in teleoperation, where occlusions challenge existing methods, by proposing a probabilistic approach using only the leader robot's trajectory; the results showed successful posture and ergonomic risk score estimation comparable to gold-standard motion capture.
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.