Open AccessReal‐time drowsiness detection using wearable, lightweight brain sensing headbands
Author(s) -
Rohit Fnu,
Kulathumani Vinod,
Kavi Rahul,
Elwarfalli Ibrahim,
Kecojevic Vlad,
Nimbarte Ashish
Publication year - 2017
Publication title -
iet intelligent transport systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.579
H-Index - 45
eISSN - 1751-9578
pISSN - 1751-956X
DOI - 10.1049/iet-its.2016.0183
Subject(s) - support vector machine , wearable computer , electroencephalography , artificial intelligence , computer science , pattern recognition (psychology) , kernel (algebra) , cross validation , computer vision , speech recognition , mathematics , psychology , embedded system , combinatorics , psychiatry
The feasibility of real‐time drowsiness detection using commercially available, off‐the‐shelf, lightweight, wearable electroencephalogram (EEG) sensors is explored. While EEG signals are known to be reliable indicators of fatigue and drowsiness, they have not been used widely due to their size and form factor. However, the use of lightweight wearable EEGs alleviates this concern. Spectral analysis of EEG signals from these sensors using support vector machines (SVMs) is shown to classify drowsy states with high accuracy. The system is validated using data collected on 23 subjects in fresh and drowsy states. An accuracy of 81% is obtained at a per‐subject level and 74% in cross‐subject validation using SVM with radial basis kernel. Using a temporal aggregation strategy, the cross‐subject validation accuracy is shown to improve to 87%. The EEG signals are also used to characterise the blink duration and frequency of subjects. However, classification of drowsy states using blink analysis is shown to have lower accuracy than that using spectral analysis.