Bio-signals Collecting System for Fatigue Level Classification

• Published in: 2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) Vol. 2023; pp. 1 - 5
• Main Authors: Lee, Younggun, Lee, Yongkyun, Kim, Dongsoo
• Format: Conference Proceeding Journal Article
• Language: English
• Published: United States IEEE 01.01.2023
• Subjects: Data acquisition; Deep learning; Fatigue; Fatigue - diagnosis; Humans; Machine Learning; Physiology; Quality of Life; Training; Training data; Visualization
• ISSN: 2694-0604
• DOI: 10.1109/EMBC40787.2023.10340350

Fatigue is a risk factor that reduces quality of life and work efficiency, and threatens safety in a high-risk environment. However, fatigue is not yet precisely defined and is not a quantified concept as it relies on subjective evaluation. The purpose of this study is to manage risks, improve mission efficiency, and prevent accidents through the development of machine learning and deep learning based fatigue level classifier. Acquiring true fatigue levels to train machine learning and deep learning fatigue classifier may play a fundamental role. Aims of this study are to develop a bio-signal collecting device and to establish a protocol for capturing and purifying data for extracting the true fatigue levels accurately. The bio-signal collection system gathered visual, thermal, and vocal signals at the same time for one minute. The true fatigue level of the subjects is classified through the Daily Multidimensional Fatigue Inventory and physiological indicators related to fatigue for screening the subjective factors out. The generated dataset is constructed as a DB along with the true fatigue levels and is provided to the research institutions. In conclusion, this study proposes a research method that collects bio-signals and extracts the true fatigue levels for training machine learning and deep learning based fatigue level classifier to evaluate the fatigue of healthy subjects in multi-levels.