Advancing NASA-TLX: Automatic User Interaction Analysis for Workload Evaluation in XR Scenarios

TL;DR

This paper enhances the NASA-TLX workload assessment method by adapting it for XR environments and introduces an autonomous system for collecting user performance metrics, reducing reliance on subjective feedback.

Contribution

It proposes an improved NASA-TLX methodology tailored for XR scenarios and develops an automatic system for user performance data collection.

Findings

Enhanced NASA-TLX for XR environments

Automated user performance data collection system

Reduced dependence on subjective questionnaires

Abstract

Calculating the effort required to complete a task has always been somewhat difficult, as it depends on each person and becomes very subjective. For this reason, different methodologies were developed to try to standardize these procedures. This article addresses some of the problems that arise when applying NASA-Task Load Index (NASA-TLX), a methodology to calculate the mental workload of tasks performed in industrial environments. In addition, an improvement of this methodology is proposed to adapt it to the new times and to emerging Extended Reality (XR) technologies. Finally, a system is proposed for automatic collection of user performance metrics, providing an autonomous method that collects this information and does not depend on the users' willingness to fill in a feedback questionnaire.