改进NASA-TLX：面向XR场景下工作负荷评估的自动用户交互分析
Advancing NASA-TLX: Automatic User Interaction Analysis for Workload Evaluation in XR Scenarios
AI总结：
针对XR场景下任务工作负荷评估主观性强、依赖用户问卷的问题，本文改进了NASA-TLX方法并提出自动收集用户表现指标的系统，提升了评估的自主性与适配性。
摘要：
计算完成任务所需的付出一直颇具难度，因为这因人而异且主观性很强。为此，人们开发了多种方法以试图规范这类评估流程。本文探讨了应用NASA-Task Load Index（NASA-TLX，一种用于计算工业环境中执行任务的脑力工作负荷的方法）时出现的部分问题。此外，本文提出对该方法进行改进，使其适配新时代及新兴的扩展现实（Extended Reality，XR）技术。最后，本文提出了一套自动收集用户表现指标的系统，提供了一种可自主收集该类信息、不依赖用户填写反馈问卷的意愿的方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
