Research Article
FatigueSet: A Multi-modal Dataset for Modeling Mental Fatigue and Fatigability
@INPROCEEDINGS{10.1007/978-3-030-99194-4_14, author={Manasa Kalanadhabhatta and Chulhong Min and Alessandro Montanari and Fahim Kawsar}, title={FatigueSet: A Multi-modal Dataset for Modeling Mental Fatigue and Fatigability}, proceedings={Pervasive Computing Technologies for Healthcare. 15th EAI International Conference, Pervasive Health 2021, Virtual Event, December 6-8, 2021, Proceedings}, proceedings_a={PERVASIVEHEALTH}, year={2022}, month={3}, keywords={Fatigue Multi-modal sensing Cognitive performance}, doi={10.1007/978-3-030-99194-4_14} }- Manasa Kalanadhabhatta
Chulhong Min
Alessandro Montanari
Fahim Kawsar
Year: 2022
FatigueSet: A Multi-modal Dataset for Modeling Mental Fatigue and Fatigability
PERVASIVEHEALTH
Springer
DOI: 10.1007/978-3-030-99194-4_14
Abstract
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.
