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Ergonomic assessment of human posture plays a vital role in understandingwork-related safety and health. Current posture estimation approaches faceocclusion challenges in teleoperation and physical human-robot interaction. Weinvestigate if the leader robot is an adequate sensor for posture estimation inteleoperation and we introduce a new probabilistic approach that relies solelyon the trajectory of the leader robot for generating observations. We model thehuman using a redundant, partially-observable dynamical system and we infer theposture using a standard particle filter. We compare our approach with posturesfrom a commercial motion capture system and also two least-squares optimizationapproaches for human inverse kinematics. The results reveal that the proposedapproach successfully estimates human postures and ergonomic risk scorescomparable to those estimates from gold-standard motion capture.

Is The Leader Robot an Adequate Sensor for Posture Estimation and Ergonomic Assessment of A Human Teleoperator?