Abstract
Driving drowsiness is a major cause of traffic accidents worldwide and has drawn the attention of researchers in recent decades. This paper presents an application for in-vehicle non-intrusive mobile-device-based automatic detection of driver sleep-onset in real time. The proposed application classifies the driving mental fatigue condition by analyzing the electroencephalogram (EEG) and respiration signals of a driver in the time and frequency domains. Our concept is heavily reliant on mobile technology, particularly remote physiological monitoring using Bluetooth. Respiratory events are gathered, and eight-channel EEG readings are captured from the frontal, central, and parietal (Fpz-Cz, Pz-Oz) regions. EEGs are preprocessed with a Butterworth bandpass filter, and features are subsequently extracted from the filtered EEG signals by employing the wavelet-packet-transform (WPT) method to categorize the signals into four frequency bands: α, β, θ, and δ. A mutual information (MI) technique selects the most descriptive features for further classification. The reduction in the number of prominent features improves the sleep-onset classification speed in the support vector machine (SVM) and results in a high sleep-onset recognition rate. Test results reveal that the combined use of the EEG and respiration signals results in 98.6% recognition accuracy. Our proposed application explores the possibility of processing long-term multi-channel signals.
Original language | English |
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Pages (from-to) | 17915-17936 |
Number of pages | 22 |
Journal | Sensors |
Volume | 14 |
Issue number | 10 |
DOIs | |
Publication status | Published - 26 Sept 2014 |
Externally published | Yes |
Keywords
- Adaptive threshold filter
- Electroencephalogram
- Mobile healthcare
- Mutual information
- Respiration
- Sleep onset
- Support vector machine
- Wavelet packet transform
ASJC Scopus subject areas
- Analytical Chemistry
- Biochemistry
- Atomic and Molecular Physics, and Optics
- Instrumentation
- Electrical and Electronic Engineering