Operator support in Industry 5.0

How can we measure and improve the (cognitive) ergonomics in human-robot collaboration during assembly work?

With this project we aim to develop:

• An ergonomic model (digital twin) of a human-cobot interaction for industrial (assembly) contexts​

• Adaptive self-learning cobot control based on this ergonomic model and fueled by interaction-data in different contexts:​

• Automatically generated/captured (video) data from different operators and contexts​

• Predictive control model focusing on HRI assembly tasks ​

• Remote training/simulation/control for operators and (future) assembly lines​

We focus on the use of our ExperienceTwin framework to determine the cognitive/psychological markers (i.e., exploring HMD eye tracking, hand tracking & object interactions to measure hesitation, doubt, load, fatigue & risk behavior)​. In addition, we aim to track these markers during human-robot collaborations to build models of cognitive interaction between robot and human​.