FatigueSet: A Multi-modal Dataset for Modeling Mental Fatigue and Fatigability

• Published in: Pervasive Computing Technologies for Healthcare pp. 204 - 217
• Main Authors: Kalanadhabhatta, Manasa, Min, Chulhong, Montanari, Alessandro, Kawsar, Fahim
• Format: Book Chapter
• Language: English
• Published: Cham Springer International Publishing
• Series: Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
• Subjects: Cognitive performance; Fatigue; Multi-modal sensing
• ISBN: 3030991938; 9783030991937
• ISSN: 1867-8211; 1867-822X
• DOI: 10.1007/978-3-030-99194-4_14

A comprehensive understanding of fatigue and its impact on performance is a prerequisite for fatigue management systems in the real world. However, fatigue is a multidimensional construct that is often poorly defined, and most prior work does not take into consideration how different types of fatigue collectively influence performance. The physiological markers associated with different types of fatigue are also underexplored, hindering the development of fatigue management technologies that can leverage mobile and wearable sensors to predict fatigue. In this work, we present FatigueSet, a multi-modal dataset including sensor data from four wearable devices that are collected while participants are engaged in physically and mentally demanding tasks. We describe the study design that enables us to investigate the effect of physical activity on mental fatigue under various situations. FatigueSet facilitates further research towards a deeper understanding of fatigue and the development of diverse fatigue-aware applications.