Adaptive Training on Basic AR Interactions: Bi-Variate Metrics and Neuroergonomic Evaluation Paradigms

Abstract

Augmented Reality (AR) training is a cost-effective and safe alternative to traditional instructional methods. However, training novices in basic mid-air AR interactions remains challenging. To address this, we aimed to: (a) develop a robust metric to evaluate user performance across different AR interaction techniques and develop adaptation models to predict additional training requirements; (b) evaluate the adaptation models using a neuroergonomics approach. We conduct a two-phase study during which, novice participants perform simple AR interactions: poking and raycasting. In Phase-I, twenty-seven participants’ data is used to identify a bi-variate performance metric based on median completion ime and consistency. Unsupervised models are trained using this metric to classify participants as low/high performers. In Phase-II, we evaluate the models on twenty-one new participants and analyze the differences in performance, neural activity and heart-rate variability between low/high performers. Our study showcases the effectiveness of our models and further discusses the potential of integrating neuroergonomics for advanced AR-based training applications.

Keywords:

• Augmented reality
• adaptive training
• neurophysiological analysis

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This project was funded by the National Science Foundation awards [#2033592 and #2013122].

Notes on contributors

Shantanu Vyas

Shantanu Vyas is a PhD student in the J. Mike Walker ‘66 Department of Mechanical Engineering at Texas A&M University. His research interests include human-computer interaction, augmented and virtual reality, applied AI and generative design.

Shivangi Dwivedi

Shivangi Dwivedi is a master’s student at Texas A&M University in the Industrial & Systems Engineering Department. Her research interests are machine learning, signal processing, human computer interactions, wearables, usability, and accessibility.

Lindsey J. Brenner

Lindsey J. Brenner is a Project Manager for the NeuroErgonomics Laboratory at Texas A&M University. Her research interests include human factors and the integration of emerging technologies in the workforce.

Isabella Pedron

Isabella Pedron is a Chemical Engineer alumnus from Texas A&M University. Her research interests include addressing healthcare issues through implementation of human factors and human augmentation technologies.

Joseph L. Gabbard

Joseph L. Gabbard is a Professor of Human Factors at Virginia Tech’s Grado Department of Industrial & Systems Engineering. His research focuses on the connections between user interface design and human performance, and specifically the development of techniques to design and evaluate AR and VR user interfaces.

Vinayak R. Krishnamurthy

Vinayak R. Krishnamurthy is an Associate Professor in the J. Mike Walker ’66 Department of Mechanical Engineering and Computer Science and Engineering by Affiliation at Texas A&M University. His research is at the intersection of geometric & topological modeling, human-computer interaction, and product & engineering design.

Ranjana K. Mehta

Ranjana K. Mehta is a Professor in the Department of Industrial and Systems Engineering at the University of Wisconsin Madison. Her research focuses on understanding, monitoring, and augmenting mind-motor-machine interactions using brain-behavior approaches in high-risk environments).