Derdiyok Seyma, Akbulut Fatma Patlar, Catal Cagatay
Department of Computer Engineering, Yıldız Technical University, Istanbul, Turkey.
Department of Software Engineering, Istanbul Kültür University, Istanbul, Turkey.
Data Brief. 2023 Dec 9;52:109896. doi: 10.1016/j.dib.2023.109896. eCollection 2024 Feb.
The prevalence of mental fatigue is a noteworthy phenomenon that can affect individuals across diverse professions and working routines. This paper provides a comprehensive dataset of physiological signals obtained from 23 participants during their professional work and questionnaires to analyze mental fatigue. The questionnaires included demographic information and Chalder Fatigue Scale scores indicating mental and physical fatigue. Both physiological signal measurements and the Chalder Fatigue Scale were performed in two sessions, morning and evening. The present dataset encompasses diverse physiological signals, including electroencephalogram (EEG), blood volume pulse (BVP), electrodermal activity (EDA), heart rate (HR), skin temperature (TEMP), and 3-axis accelerometer (ACC) data. The NeuroSky MindWave EEG device was used for brain signals, and the Empatica E4 smart wristband was used for other signals. Measurements were carried out on individuals from four different occupational groups, such as academicians, technicians, computer engineers, and kitchen workers. The provision of comprehensive metadata supplements the dataset, thereby promoting inquiries about the neurophysiological concomitants of mental fatigue, autonomic activity patterns, and the repercussions of a cognitive burden on human proficiency in actual workplace settings. The accessibility of the aforementioned dataset serves to facilitate progress in the field of mental fatigue research while also laying the groundwork for the creation of customized fatigue evaluation techniques and interventions in diverse professional domains.
精神疲劳的患病率是一个值得关注的现象,它会影响各行各业和不同工作日常的个体。本文提供了一个全面的数据集,该数据集包含23名参与者在其职业工作期间获取的生理信号以及用于分析精神疲劳的问卷。问卷包括人口统计学信息和表示精神与身体疲劳的查尔德疲劳量表得分。生理信号测量和查尔德疲劳量表均在上午和晚上两个时段进行。本数据集涵盖多种生理信号,包括脑电图(EEG)、血容量脉搏(BVP)、皮肤电活动(EDA)、心率(HR)、皮肤温度(TEMP)以及三轴加速度计(ACC)数据。使用NeuroSky MindWave脑电图设备采集脑信号,使用Empatica E4智能腕带采集其他信号。对来自四个不同职业群体的个体进行了测量,如院士、技术人员、计算机工程师和厨房工作人员。提供全面的元数据补充了数据集,从而促进了对精神疲劳的神经生理伴随现象、自主活动模式以及认知负担对实际工作场所人类能力的影响的探究。上述数据集的可获取性有助于推动精神疲劳研究领域的进展,同时也为在不同专业领域创建定制化的疲劳评估技术和干预措施奠定基础。