# Occlusion-Robust Multi-Sensory Posture Estimation in Physical Human-Robot Interaction

arXiv: https://arxiv.org/abs/2208.06494 · HTML: https://mlsanity.com/paper/2208.06494/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 77 references that are in the map.
- First posted on arXiv 12 Aug 2022.

## Builds on (most cited first, 11 of 11)

- [OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1812.08008/) (2018)
- [Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1611.08050/) (2016)
- [RMPE: Regional Multi-person Pose Estimation](https://mlsanity.com/paper/1612.00137/) (2016)
- [Markerless tracking of user-defined features with deep learning](https://mlsanity.com/paper/1804.03142/) (2018)
- [Movement science needs different pose tracking algorithms](https://mlsanity.com/paper/1907.10226/) (2019)
- [Data-Driven Approach to Simulating Realistic Human Joint Constraints](https://mlsanity.com/paper/1709.08685/) (2017)
- [Planning for Muscular and Peripersonal-Space Comfort during Human-Robot Forceful Collaboration](https://mlsanity.com/paper/1807.11323/) (2018)
- [A Deep Learning Approach for Multi-View Engagement Estimation of Children in a Child-Robot Joint Attention task](https://mlsanity.com/paper/1812.00253/) (2018)
- [Toward Personalized Affect-Aware Socially Assistive Robot Tutors in Long-Term Interventions for Children with Autism](https://mlsanity.com/paper/2101.10580/) (2021)
- [Ergonomically Intelligent Physical Human-Robot Interaction: Postural Estimation, Assessment, and Optimization](https://mlsanity.com/paper/2108.05971/) (2021)
- [DULA and DEBA: Differentiable Ergonomic Risk Models for Postural Assessment and Optimization in Ergonomically Intelligent pHRI](https://mlsanity.com/paper/2205.03491/) (2022)

## Abstract (arXiv)

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.