Multimodal System to Detect Driver Fatigue Using EEG, Gyroscope, and Image Processing

• Main Authors: Naveen Senniappan Karuppusamy, Bo-Yeong Kang
• Format: Article
• Language: English
• Published: IEEE 2020-01-01
• Series: IEEE Access
• Subjects: Deep neural networks; driver fatigue detection; electroencephalography module; gyroscope module; multimodal system; tensorflow
• Online Access: https://ieeexplore.ieee.org/document/9139955/
• ISSN: 2169-3536

Sleepiness detection system that evaluates driver's sleepiness level has always been the primary interest of researchers. There are a number of systems like electroencephalography-based sleepiness detection system (ESDS), vehicle based sleepiness estimator system, image acquisition technology and bio-mathematical models to detect drowsiness of drivers. However there has been less research on hybrid of these systems that detect sleepiness of drivers. In order to overcome the above limitation we propose a neural network based hybrid multimodal system that detects driver fatigue using electroencephalography(EEG) data, gyroscope data and image processing data. It was found that the proposed hybrid system performed well with a detection accuracy of 93.91% in identifying the drowsiness state of the driver.