Exploring Dimensions of Expertise in AR-Guided Psychomotor Tasks

TL;DR

This research investigates how experts and novices differ in performing AR-guided psychomotor tasks, analyzing performance, cognitive load, and physiological responses to inform personalized AR training systems.

Contribution

It introduces a multidimensional expertise estimation approach based on multimodal data to enhance AR training personalization.

Findings

Experts outperform novices in decision-making speed and accuracy

Higher perceived performance correlates with increased physiological stress

Study provides foundational data for personalized AR training models

Abstract

This study aimed to explore how novices and experts differ in performing complex psychomotor tasks guided by augmented reality (AR), focusing on decision-making and technical proficiency. Participants were divided into novice and expert groups based on a pre-questionnaire assessing their technical skills and theoretical knowledge of precision inspection. Participants completed a post-study questionnaire that evaluated cognitive load (NASA-TLX), self-efficacy, and experience with the HoloLens 2 and AR app, along with general feedback. We used multimodal data from AR devices and wearables, including hand tracking, galvanic skin response, and gaze tracking, to measure key performance metrics. We found that experts significantly outperformed novices in decision-making speed, efficiency, accuracy, and dexterity in the execution of technical tasks. Novices exhibited a positive correlation between perceived performance in the NASA-TLX and the GSR amplitude, indicating that higher perceived performance is associated with increased physiological stress responses. This study provides a foundation for designing multidimensional expertise estimation models to enable personalized industrial AR training systems.