MPD-DF: Multimodal Phenotyping Dataset of Driving Fatigue -- The Raw Dataset and Questionnaire Information

Driving fatigue analysis using multimodal physiological signals has gained significant attention in human factor engineering. We established a publicly available Multimodal Phenotyping Dataset of Driving Fatigue (MPD-DF) from 50 participants using a standardized 2-hour driving simulation. This meticulously curated dataset incorporated multidimensional subjective and objective metrics for fatigue assessment, including multimodal physiological recordings (32-channel electroencephalogram, single-lead electrocardiogram, dual-channel electrooculogram, and thoracic respiratory effort signals). The supplementary metadata encompassed fatigue-associated questionnaire assessment results along with fatigue level annotations by an expert physician. The validity of the dataset was rigorously verified through multiple dimensions: (1) efficacy of fatigue induction, (2) statistical analysis of questionnaire results, (3) evaluation of signal quality, and (4) correlation between physiological signals and fatigue levels. This systematically validated dataset supports fatigue-related algorithm development, cross-dataset model validation, and investigation of fatigue mechanisms, thereby providing essential resources for transportation safety research.

基于多模态生理信号的驾驶疲劳分析在人因工程领域已受到广泛关注。本研究依托标准化2小时驾驶模拟实验，面向50名受试者构建了可公开获取的驾驶疲劳多模态表型数据集（Multimodal Phenotyping Dataset of Driving Fatigue, MPD-DF）。该数据集经过精心整理，囊括了用于疲劳评估的多维度主客观指标，包含多模态生理记录：32通道脑电图（electroencephalogram, EEG）、单导联心电图（electrocardiogram, ECG）、双通道眼电图（electrooculogram, EOG）以及胸式呼吸努力信号。补充元数据涵盖疲劳相关问卷评估结果，以及专业医师标注的疲劳等级信息。本数据集的有效性通过多维度严格验证：1）疲劳诱导效果验证；2）问卷结果的统计分析；3）信号质量评估；4）生理信号与疲劳等级的相关性分析。本经过系统性验证的数据集可支撑疲劳相关算法开发、跨数据集模型验证与疲劳机制研究，为交通安全研究提供关键支撑资源。

搜集汇总

背景与挑战

背景概述

MPD-DF数据集是一个公开的多模态驾驶疲劳数据集，包含50名参与者的生理信号记录和问卷信息，适用于疲劳相关算法开发和交通安全研究。