Albuquerque Isabela, Tiwari Abhishek, Parent Mark, Cassani Raymundo, Gagnon Jean-François, Lafond Daniel, Tremblay Sébastien, Falk Tiago H
Institut National de la Recherche Scientifique - Énergie, Matériaux et Télécommunications, Université du Québec, Montréal, QC, Canada.
Thales Digital Solutions Inc., Québec City, QC, Canada.
Front Neurosci. 2020 Dec 1;14:549524. doi: 10.3389/fnins.2020.549524. eCollection 2020.
Assessment of mental workload is crucial for applications that require sustained attention and where conditions such as mental fatigue and drowsiness must be avoided. Previous work that attempted to devise objective methods to model mental workload were mainly based on neurological or physiological data collected when the participants performed tasks that did not involve physical activity. While such models may be useful for scenarios that involve static operators, they may not apply in real-world situations where operators are performing tasks under varying levels of physical activity, such as those faced by first responders, firefighters, and police officers. Here, we describe WAUC, a multimodal database of mental Workload Assessment Under physical aCtivity. The study involved 48 participants who performed the NASA Revised Multi-Attribute Task Battery II under three different activity level conditions. Physical activity was manipulated by changing the speed of a stationary bike or a treadmill. During data collection, six neural and physiological modalities were recorded, namely: electroencephalography, electrocardiography, breathing rate, skin temperature, galvanic skin response, and blood volume pulse, in addition to 3-axis accelerometry. Moreover, participants were asked to answer the NASA Task Load Index questionnaire after each experimental section, as well as rate their physical fatigue level on the Borg fatigue scale. In order to bring our experimental setup closer to real-world situations, all signals were monitored using wearable, off-the-shelf devices. In this paper, we describe the adopted experimental protocol, as well as validate the subjective, neural, and physiological data collected. The WAUC database, including the raw data and features, subjective ratings, and scripts to reproduce the experiments reported herein will be made available at: http://musaelab.ca/resources/.
评估心理负荷对于需要持续注意力且必须避免心理疲劳和困倦等情况的应用至关重要。先前试图设计客观方法来模拟心理负荷的工作主要基于参与者执行不涉及体力活动的任务时收集的神经学或生理学数据。虽然此类模型可能适用于涉及静态操作员的场景,但它们可能不适用于现实世界中操作员在不同体力活动水平下执行任务的情况,例如急救人员、消防员和警察所面临的情况。在此,我们描述了WAUC,一个在体力活动下进行心理负荷评估的多模态数据库。该研究涉及48名参与者,他们在三种不同的活动水平条件下执行了美国国家航空航天局修订的多属性任务电池II。通过改变固定自行车或跑步机的速度来控制体力活动。在数据收集过程中,除了3轴加速度计外,还记录了六种神经和生理模态,即:脑电图、心电图、呼吸频率、皮肤温度、皮肤电反应和血容量脉搏。此外,要求参与者在每个实验部分后回答美国国家航空航天局任务负荷指数问卷,并在博格疲劳量表上对他们的身体疲劳水平进行评分。为了使我们的实验设置更接近现实世界的情况,所有信号都使用可穿戴的现成设备进行监测。在本文中,我们描述了所采用的实验方案,并验证了所收集的主观、神经和生理数据。WAUC数据库,包括原始数据和特征、主观评分以及重现本文所报告实验的脚本,将在以下网址提供:http://musaelab.ca/resources/ 。