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

作     者：Narit Hnoohom Sakorn Mekruksavanich Anuchit Jitpattanakul 

作者机构：Department of Computer EngineeringImage Information and Intelligence LaboratoryFaculty of EngineeringMahidol UniversityNakhon Pathom73170Thailand Department of Computer EngineeringSchool of Information and Communication TechnologyUniversity of PhayaoPhayaoThailand Department of MathematicsFaculty of Applied ScienceKing Mongkut’s University of Technology North BangkokBangkok10800Thailand Intelligent and Nonlinear Dynamic Innovations Research CenterScience and Technology Research InstituteKing Mongkut’s University of Technology North BangkokBangkok10800Thailand 

出 版 物：《Intelligent Automation & Soft Computing》 (智能自动化与软计算（英文）)

年 卷 期：2023年第38卷第11期

页      面：139-151页

学科分类：080202[工学-机械电子工程] 08[工学] 0802[工学-机械工程] 

基　　金：support provided by Thammasat University Research fund under the TSRI,Contract Nos.TUFF19/2564 and TUFF24/2565 for the project of“AI Ready City Networking in RUN”,based on the RUN Digital Cluster collaboration scheme.This research project was also supported by the Thailand Science Research and Innovation fund,the University of Phayao(Grant No.FF65-RIM041) supported by National Science,Research and Innovation(NSRF),and King Mongkut’s University of Technology North Bangkok,Contract No.KMUTNB-FF-66-07 

主　　题：Smart glasses human activity recognition deep learning wearable sensors driving activity 

摘      要：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 *** a result,reckless driving behaviour can cause congestion and *** vision and multimodal sensors have been used to study driving behaviour categorization to lessen this *** research has also collected and analyzed a wide range of data,including electroencephalography(EEG),electrooculography(EOG),and photographs of the driver’s *** the other hand,driving a car is a complicated action that requires a wide range of body *** this work,we proposed a ResNet-SE model,an efficient deep learning classifier for driving activity clas-sification based on signal data obtained in real-world traffic conditions using smart ***-to-end learning can be achieved by combining residual networks and channel attention approaches into a single learning *** data from 3-point EOG electrodes,tri-axial accelerometer,and tri-axial gyroscope from the Smart Glasses dataset was utilized in this *** performed various experiments and compared the proposed model to base-line deep learning algorithms(CNNs and LSTMs)to demonstrate its *** 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%.