Driving activity classification using deep residual networks based on smart glasses sensors
Issued Date
2024-01-01
Resource Type
ISSN
10798587
eISSN
2326005X
Scopus ID
2-s2.0-85186905489
Journal Title
Intelligent Automation and Soft Computing
Volume
38
Issue
2
Start Page
139
End Page
151
Rights Holder(s)
SCOPUS
Bibliographic Citation
Intelligent Automation and Soft Computing Vol.38 No.2 (2024) , 139-151
Suggested Citation
Hnoohom N., Mekruksavanich S., Jitpattanakul A. Driving activity classification using deep residual networks based on smart glasses sensors. Intelligent Automation and Soft Computing Vol.38 No.2 (2024) , 139-151. 151. doi:10.32604/iasc.2023.033940 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/97611
Title
Driving activity classification using deep residual networks based on smart glasses sensors
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Author's Affiliation
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Abstract
Accidents are still an issue in an intelligent transportation system, despite developments in self-driving technology (ITS). Drivers who engage in risky behavior account for more than half of all road accidents. As a result, reckless driving behaviour can cause congestion and delays. Computer vision and multimodal sensors have been used to study driving behaviour categorization to lessen this problem. Previous research has also collected and analyzed a wide range of data, including electroencephalography (EEG), electrooculography (EOG), and photographs of the driver’s face. On the other hand, driving a car is a complicated action that requires a wide range of body movements. In this work, we proposed a ResNet-SE model, an efficient deep learning classifier for driving activity classification based on signal data obtained in real-world traffic conditions using smart glasses. End-to-end learning can be achieved by combining residual networks and channel attention approaches into a single learning model. Sensor data from 3-point EOG electrodes, tri-axial accelerometer, and tri-axial gyroscope from the Smart Glasses dataset was utilized in this study. We performed various experiments and compared the proposed model to baseline deep learning algorithms (CNNs and LSTMs) to demonstrate its performance. According to the research results, the proposed model outperforms the previous deep learning models in this domain with an accuracy of 99.17% and an F1-score of 98.96%.