用眼动数据预测复杂VR训练中的认知负荷，提升训练适应性。
Exploring Eye Tracking to Detect Cognitive Load in Complex Virtual Reality Training
- 通过眼动特征构建机器学习模型，预测用户认知负荷。
- 在22名参与者中，眼动数据与NASA-TLX评分相关性显著。
- 适用于高复杂度虚拟现实培训场景的实时反馈系统设计。
虚拟现实（VR）在先进制造等领域的培训中具有显著优势，但用户可能因设备使用或任务复杂性而产生较高认知负荷。已有研究表明眼动追踪具备检测认知负荷的潜力，但在涉及复杂时空任务（如装配与拆卸）的VR环境中仍研究不足。本文开展一项正在进行的研究，基于眼动追踪的机器学习方法检测用户认知负荷。我们开发了针对冷喷涂技术的VR培训系统，共招募22名参与者，获得19个有效眼动数据集和对应的NASA-TLX评分。采用多层感知机（MLP）与随机森林（RF）模型，对比瞳孔扩张和注视持续时间对认知负荷（即NASA-TLX）的预测精度。初步分析表明，在复杂时空类VR体验中，眼动追踪可有效用于认知负荷检测，为后续深入探索提供了依据。
原文摘要 · Abstract (English)
Virtual Reality (VR) has been a beneficial training tool in fields such as advanced manufacturing. However, users may experience a high cognitive load due to various factors, such as the use of VR hardware or tasks within the VR environment. Studies have shown that eye-tracking has the potential to detect cognitive load, but in the context of VR and complex spatiotemporal tasks (e.g., assembly and disassembly), it remains relatively unexplored. Here, we present an ongoing study to detect users' cognitive load using an eye-tracking-based machine learning approach. We developed a VR training system for cold spray and tested it with 22 participants, obtaining 19 valid eye-tracking datasets and NASA-TLX scores. We applied Multi-Layer Perceptron (MLP) and Random Forest (RF) models to compare the accuracy of predicting cognitive load (i.e., NASA-TLX) using pupil dilation and fixation duration. Our preliminary analysis demonstrates the feasibility of using eye tracking to detect cognitive load in complex spatiotemporal VR experiences and motivates further exploration.
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