# Real-time Robot-assisted Ergonomics

arXiv: https://arxiv.org/abs/1805.06270 · HTML: https://mlsanity.com/paper/1805.06270/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 May 2018.

## Built on by (most cited first, 7 of 7)

- [DULA: A Differentiable Ergonomics Model for Postural Optimization in Physical HRI](https://mlsanity.com/paper/2107.06875/) (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)
- [A vision-based framework for human behavior understanding in industrial assembly lines](https://mlsanity.com/paper/2409.17356/) (2024)
- [Computational ergonomics for task delegation in Human-Robot Collaboration: spatiotemporal adaptation of the robot to the human through contactless gesture recognition](https://mlsanity.com/paper/2203.11007/) (2022)
- [Online Action Recognition for Human Risk Prediction with Anticipated Haptic Alert via Wearables](https://mlsanity.com/paper/2401.05365/) (2023)
- [Intelligent Framework for Human-Robot Collaboration: Dynamic Ergonomics and Adaptive Decision-Making](https://mlsanity.com/paper/2503.07901/) (2025)

## Abstract (arXiv)

This paper describes a novel approach in human robot interaction driven by ergonomics. With a clear focus on optimising ergonomics, the approach proposed here continuously observes a human user's posture and by invoking appropriate cooperative robot movements, the user's posture is, whenever required, brought back to an ergonomic optimum. Effectively, the new protocol optimises the human-robot relative position and orientation as a function of human ergonomics. An RGB-D camera is used to calculate and monitor human joint angles in real-time and to determine the current ergonomics state. A total of 6 main causes of low ergonomic states are identified, leading to 6 universal robot responses to allow the human to return to an optimal ergonomics state. The algorithmic framework identifies these 6 causes and controls the cooperating robot to always adapt the environment (e.g. change the pose of the workpiece) in a way that is ergonomically most comfortable for the interacting user. Hence, human-robot interaction is continuously re-evaluated optimizing ergonomics states. The approach is validated through an experimental study, based on established ergonomic methods and their adaptation for real-time application. The study confirms improved ergonomics using the new approach.