首页|Report Summarizes Machine Learning Study Findings from University of Murcia (Studying Drowsiness Detection Performance While Driving Through Scalable Machine Learning Models Using Electroencephalography)
Report Summarizes Machine Learning Study Findings from University of Murcia (Studying Drowsiness Detection Performance While Driving Through Scalable Machine Learning Models Using Electroencephalography)
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NETL
NSTL
Springer Nature
Investigators publish new report on Machine Learning. According to news reporting out of Murcia, Spain, by NewsRx editors, research stated, “Driver drowsiness is a significant concern and one of the leading causes of traffic accidents. Advances in cognitive neuroscience and computer science have enabled the detection of drivers’ drowsiness using Brain-Computer Interfaces (BCIs) and Machine Learning (ML).” Financial support for this research came from Fundacin Sneca. Our news journalists obtained a quote from the research from the University of Murcia, “However, the literature lacks a comprehensive evaluation of drowsiness detection performance using a heterogeneous set of ML algorithms, being also necessary to study the performance of scalable ML models suitable for groups of subjects. To address these limitations, this work presents an intelligent framework employing BCIs and features based on electroencephalography for detecting drowsiness in driving scenarios. The SEED-VIG dataset is used to evaluate the best-performing models for individual subjects and groups. Random Forest (RF) outperformed other models used in the literature, such as Support Vector Machine (SVM), with a 78% f1-score for individual models. Regarding scalable models, RF reached a 79% f1-score, demonstrating the effectiveness of these approaches. This publication highlights the relevance of exploring a diverse set of ML algorithms and scalable approaches suitable for groups of subjects to improve drowsiness detection systems and ultimately reduce the number of accidents caused by driver fatigue. The lessons learned from this study show that not only SVM but also other models not sufficiently explored in the literature are relevant for drowsiness detection. Additionally, scalable approaches are effective in detecting drowsiness, even when new subjects are evaluated.”
MurciaSpainEuropeCyborgsEmerging TechnologiesMachine LearningUniversity of Murcia
NETL
NSTL
Springer Nature
