Harnessing Electrocardiography Signals for Driver State Classification Using Multi-layered Neural Networks

IF 1.5 4区工程技术 Q3 ENGINEERING, MECHANICAL

International Journal of Automotive Technology Pub Date : 2024-06-18 DOI:10.1007/s12239-024-00109-4

Amir Tjolleng, Kihyo Jung

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引用次数: 0

Abstract

Driving under conditions of cognitive overload or drowsiness poses serious safety risks and is recognized as a major cause of vehicle collisions. Thus, timely detection of the driver’s state is crucial for preventing accidents. This study proposed the utilization of electrocardiography (ECG) data in conjunction with multi-layered neural network (MNN) models to determine the driver’s state. ECG signals were obtained from 67 participants during simulated driving scenarios that induced either cognitive load or drowsiness. The study considered five driver states: drowsiness, fighting-off drowsiness, normal, medium cognitive load, and high cognitive load. Statistical analysis revealed significant changes in ECG measurements as the driver’s attentiveness levels varied from low (drowsiness) to high (cognitive overload). Multiple MNN models were developed to address individual variations in heart response and achieved classification accuracies exceeding 95%. These findings demonstrated the potential of ECG signal utilization for driver’s state detection to prevent vehicle accidents.

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利用多层神经网络对心电图信号进行驾驶员状态分类

在认知负荷过重或昏昏欲睡的情况下驾驶会带来严重的安全风险，被认为是车辆碰撞的主要原因。因此，及时发现驾驶员的状态对于预防事故至关重要。本研究提出利用心电图（ECG）数据结合多层神经网络（MNN）模型来确定驾驶员的状态。67 名参与者在模拟驾驶场景中获得了心电图信号，这些场景诱发了认知负荷或嗜睡。研究考虑了五种驾驶员状态：嗜睡、打瞌睡、正常、中等认知负荷和高认知负荷。统计分析显示，当驾驶员的注意力水平从低（嗜睡）到高（认知负荷过重）变化时，心电图测量结果也会发生明显变化。针对心脏反应的个体差异开发了多个 MNN 模型，分类准确率超过 95%。这些研究结果证明了利用心电图信号检测驾驶员状态以预防车辆事故的潜力。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

International Journal of Automotive Technology 工程技术-工程：机械

CiteScore

3.10

自引率

12.50%

发文量

129

审稿时长

6 months

期刊介绍： The International Journal of Automotive Technology has as its objective the publication and dissemination of original research in all fields of AUTOMOTIVE TECHNOLOGY, SCIENCE and ENGINEERING. It fosters thus the exchange of ideas among researchers in different parts of the world and also among researchers who emphasize different aspects of the foundations and applications of the field. Standing as it does at the cross-roads of Physics, Chemistry, Mechanics, Engineering Design and Materials Sciences, AUTOMOTIVE TECHNOLOGY is experiencing considerable growth as a result of recent technological advances. The Journal, by providing an international medium of communication, is encouraging this growth and is encompassing all aspects of the field from thermal engineering, flow analysis, structural analysis, modal analysis, control, vehicular electronics, mechatronis, electro-mechanical engineering, optimum design methods, ITS, and recycling. Interest extends from the basic science to technology applications with analytical, experimental and numerical studies. The emphasis is placed on contributions that appear to be of permanent interest to research workers and engineers in the field. If furthering knowledge in the area of principal concern of the Journal, papers of primary interest to the innovative disciplines of AUTOMOTIVE TECHNOLOGY, SCIENCE and ENGINEERING may be published. Papers that are merely illustrations of established principles and procedures, even though possibly containing new numerical or experimental data, will generally not be published. When outstanding advances are made in existing areas or when new areas have been developed to a definitive stage, special review articles will be considered by the editors. No length limitations for contributions are set, but only concisely written papers are published. Brief articles are considered on the basis of technical merit.