Enhancing Mental Fatigue Detection through Physiological Signals and Machine Learning Using Contextual Insights and Efficient Modelling
DOI: 10.3390/jsan12060077
URL: https://doi.org/10.3390/jsan12060077
archive: indexed pipeline: cataloged
Abstract
This research presents a machine learning modeling process for detecting mental fatigue using three physiological signals: electrodermal activity, electrocardiogram, and respiration. It follows the conventional machine learning modeling pipeline, while emphasizing the significant contribution of the feature selection process, resulting in, not only a high-performance model, but also a relevant one. The employed feature selection process considers both statistical and contextual aspects of feature relevance.
Access
Route: open_access
Publisher: MDPI