Multimodal Physiological Monitoring During Virtual Reality Piloting Tasks

This dataset includes multimodal physiologic, flight performance, and user interaction data streams, collected as participants performed virtual flight tasks of varying difficulty. In virtual reality, individuals flew an "Instrument Landing System" (ILS) protocol, in which they had to land an aircraft mostly relying on the cockpit instrument readings. Participants were presented with four levels of difficulty, which were generated by varying wind speed, turbulence, and visibility. Each of the participants performed 12 runs, split into 3 blocks of four consecutive runs, one run at each difficulty, in a single experimental session. The sequence of difficulty levels was presented in a counterbalanced manner across blocks. Flight performance was quantified as a function of horizontal and vertical deviation from an ideal path towards the runway as well as deviation from the prescribed ideal speed of 115 knots. Multimodal physiological signals were aggregated and synchronized using Lab Streaming Layer. Descriptions of data quality are provided to assess each data stream. The starter code provides examples of loading and plotting the time synchronized data streams, extracting sample features from the eye tracking data, and building models to predict pilot performance from the physiology data streams.

本数据集涵盖了多模态生理学、飞行性能以及用户交互数据流，这些数据是在参与者执行不同难度的虚拟飞行任务时收集的。在虚拟现实环境中，参与者需遵循“仪表着陆系统”（ILS）协议进行飞行，其中他们主要依靠驾驶舱仪表读数来降落飞机。参与者面临四种不同难度的飞行，这些难度通过调整风速、湍流和可见度来实现。每位参与者进行了12次飞行，分为三个连续的四次飞行组块，每个难度水平一次飞行，在一个实验会话中完成。难度水平的呈现顺序在各个组块间进行了平衡设计。飞行性能通过计算相对于理想跑道路径的横向和纵向偏差，以及相对于规定的理想速度115节的速度偏差来量化。使用Lab Streaming Layer对多模态生理信号进行聚合和同步。同时提供了数据质量描述，以评估每个数据流。启动代码提供了加载和绘制时间同步数据流、从眼动数据中提取样本特征以及从生理数据流中构建预测飞行员性能的模型的示例。