Abstract

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/.

Highlights

  • The ability of humans to perform activities in an effective and sustainable way is crucial in situations where tasks are not fully automatic

  • We summarize the main contributions of the WAUC dataset:

  • We presented for each physical workload level the total number of sessions rated as high for low/high mental workload (MW) sessions

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Summary

Introduction

The ability of humans to perform activities in an effective and sustainable way is crucial in situations where tasks are not fully automatic. Human performance might be safety-critical for human lives, such as in the case of tasks performed by aircraft pilots, firefighters, and first responders In these cases, monitoring and quantifying the current capability of a subject. WAUC: Database for Mental Workload Assessment to correctly perform a task may be critical to prevent accidents and, save lives. In this context, the Operator Functional State (OFS) (Hockey, 2003a) research framework can be used to breakdown the relationship between human performance and the level of difficulty of the respective task (Ting et al, 2009). The capability of reliably monitoring OFS is key to constraining work shifts and adapting task demand levels, ensuring that operators are safely and optimally performing the designated tasks (Wilson and Russell, 2003a)

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