[EN]
Our research paper, ‘Enhancing Mental Fatigue Detection through Physiological Signals and Machine Learning using Contextual Insights and Efficient Modeling,’ was recently published in the JSAN Journal. This paper is the result of the diligent work carried out by Carole-Anne Cos, a fourth year engineering student at ECE, during her internship at the LyRIDS research laboratory as part of the interdisciplinary research program (PI-ECE). The authors of this paper include Alexandre Lambert (Ph.D. student) and associate professors Dr. G. B. B. Aakash Soni, Dr. Hayfa Jeridi, Dr. Coralie Thieulin, and Dr. Amine Jaouadi. This accomplishment reflects the expertise and successful collaboration of our interdisciplinary research team.
The paper subject is described below:
Fatigue is a cognitive state resulting from inadequate rest or excessive cognitive demands and has significant consequences, including impaired decision-making and an increased risk of accidents. Monitoring physiological signals offers valuable insights into the body’s internal state and related cognitive state and enables early fatigue detection. In this context, the current paper investigates methods for utilizing physiological signals and designing techniques to efficiently model cognitive fatigue detection using machine learning algorithms. It highlights the importance of the feature selection process, which incorporates insights from the experimental design and feature characteristics, to create a relevant and high-performing machine learning model.
[FR]
Our research paper, entitled ‘Enhancing Mental Fatigue Detection through Physiological Signals and Machine Learning using Contextual Insights and Efficient Modeling,’ was recently published in the JSAN Journal. This article is the fruit of hard work by Carole-Anne Cos, a fourth-year engineering student at ECE, during her internship at the LyRIDS research laboratory as part of the interdisciplinary research program (PI-ECE). The authors of this article include Alexandre Lambert (doctoral student) and associate professors Dr. Aakash Soni, Dr. Hayfa Jeridi, Dr. Coralie Thieulin and Dr. Amine Jaouadi. This achievement testifies to the expertise and successful collaboration within our interdisciplinary research team.
The subject of the article is described below:
Fatigue is a cognitive state resulting from insufficient rest or excessive cognitive demands, and has significant consequences, including impaired decision-making and increased risk of accidents. Monitoring physiological signals provides valuable information on the internal state of the body and the associated cognitive state, enabling early detection of fatigue. In this context, this paper explores methods for using physiological signals and designing techniques to efficiently model cognitive fatigue detection using machine learning algorithms. It shows the importance of the feature selection process, which integrates information from experimental design and signal characteristics to create a relevant, high-performance machine learning model.
Updated 2 January 2024