Open Access

ARTICLE

Driving Activity Classification Using Deep Residual Networks Based on Smart Glasses Sensors

Narit Hnoohom¹, Sakorn Mekruksavanich², Anuchit Jitpattanakul^3,4,*

1 Department of Computer Engineering, Image Information and Intelligence Laboratory, Faculty of Engineering, Mahidol University, Nakhon Pathom, 73170, Thailand
2 Department of Computer Engineering, School of Information and Communication Technology, University of Phayao, Phayao, Thailand
3 Department of Mathematics, Faculty of Applied Science, King Mongkut’s University of Technology North Bangkok, Bangkok, 10800, Thailand
4 Intelligent and Nonlinear Dynamic Innovations Research Center, Science and Technology Research Institute, King Mongkut’s University of Technology North Bangkok, Bangkok, 10800, Thailand

* Corresponding Author: Anuchit Jitpattanakul. Email:

Intelligent Automation & Soft Computing 2023, 38(2), 139-151. https://doi.org/10.32604/iasc.2023.033940

Received 01 July 2022; Accepted 19 August 2022; Issue published 05 February 2024

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%.

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Keywords

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