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Updated: Feb 27, 2026

Usability Evaluation of Augmented Reality: A Neuro-Information-Systems Study

Usability Evaluation of Augmented Reality: A Neuro-Information-Systems Study

Published on: November 30, 2022

A New Perspective for the Training Assessment: Machine Learning-Based Neurometric for Augmented User's Evaluation.

Gianluca Borghini^1,2,3, Pietro Aricò^1,2,3, Gianluca Di Flumeri^2,3,4

¹Department of Molecular Medicine, Sapienza Università di RomaRome, Italy.

Frontiers in Neuroscience

|June 30, 2017

Summary

This summary is machine-generated.

This study introduces a novel neurometric using brain activity (Electroencephalogram) to assess cognitive load during training. This brain-based metric offers a more objective evaluation of user progress for high-risk professions.

Keywords:

EEG brain activity human factor human machine interaction machine learning training assessment

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Area of Science:

Neuroscience
Cognitive Science
Human Factors Engineering

Background:

Standard training assessments lack insight into cognitive resource allocation.
High-risk professions (pilots, surgeons) require objective measures of cognitive load for safety.
Current methods cannot evaluate preparedness for unexpected events.

Purpose of the Study:

To develop a brain activity-based metric (neurometric) for assessing cognitive load during training.
To provide instructors with objective data for evaluating user progress and tailoring training.
To enhance training assessment in high-stakes professions.

Main Methods:

Collected Electroencephalogram (EEG) and performance data from 10 participants over 3 weeks.
Estimated behavioral and EEG indexes to track training progress.
Developed a machine learning-based neurometric quantifying training level using task execution and cognitive stability.

Main Results:

The proposed methodology successfully quantified and tracked user training progress.
The neurometric provided objective insights into cognitive stability and task performance.
Demonstrated the potential for more precise and adaptive training program adjustments.

Conclusions:

The developed neurometric offers a valuable tool for objective training assessment.
This brain-based approach can improve training effectiveness and safety in critical professions.
Future work can refine the neurometric for broader application in skill-based training.