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

• Published in: International journal of human-computer interaction 2024-10, Vol.40 (20), p.6252-6267
• Main Authors: Vyas, Shantanu, Dwivedi, Shivangi, Brenner, Lindsey J., Pedron, Isabella, Gabbard, Joseph L., Krishnamurthy, Vinayak R., Mehta, Ranjana K.
• Format: Article
• Language: English
• Subjects: Adaptation; adaptive training; Augmented reality; Effectiveness; Heart rate; neurophysiological analysis; Performance evaluation; Teaching methods
• ISSN: 1044-7318; 1532-7590; 1044-7318
• DOI: 10.1080/10447318.2023.2250578

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.