Work

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

Year: 2020

Type: article

Abstract: 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.

Source: IEEE Access

DOI: 10.1109/access.2020.3009226

Authors: Naveen Senniappan Karuppusamy, Bo-Yeong Kang

Institutions: JCT College Of Engineering And Technology, Kyungpook National University

Language: English

Topic: Sleep and Work-Related Fatigue

Subfield: Experimental and Cognitive Psychology

Field: Psychology

Domain: Social Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $1,750