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Interactive and Visualized Online Experimentation System for Engineering Education and Research

Interactive and Visualized Online Experimentation System for Engineering Education and Research

Published on: November 24, 2021

Area of Science:

Human Factors Engineering
Cognitive Science
Artificial Intelligence

Background:

Traditional ergonomics relies on human experiments, which have limitations in scale and population representation.
Real-time human-machine tasks face challenges with experiment-modeling-validation-application paths due to inflexible experiment updates.
Existing cognitive models struggle to adapt to dynamic online systems.

Purpose of the Study:

To propose generative artificial intelligence (GAI)-driven ergonomics as a novel approach to augment human factors engineering (HFE) research.
To introduce virtual-real hybrid experiments to enhance cognitive modeling and behavioral learning.
To improve the generality and robustness of human models in human-machine systems.

Main Methods:

Integration of generative artificial intelligence (GAI) techniques into ergonomic research.
Implementation of virtual-real hybrid experiments to supplement traditional human experiments.
Utilizing GAI to enhance input diversity for cognitive and behavioral modeling.

Main Results:

GAI-driven ergonomics effectively augments human experiment data.
Virtual-real hybrid experiments provide more heterogeneous samples for model training.
Enhanced human models demonstrate improved generality and robustness.

Conclusions:

Generative artificial intelligence offers a powerful paradigm to advance ergonomics and human factors engineering.
Hybrid experimental approaches are crucial for developing adaptive and robust human-machine systems.
GAI-driven ergonomics can overcome limitations of traditional experimental methods in HFE.