基于生理信号和深度学习方法的真实世界驾驶员压力识别模型的模糊性能评估

3区 计算机科学 Q1 Computer Science Journal of Ambient Intelligence and Humanized Computing Pub Date : 2024-08-03 DOI:10.1007/s12652-024-04834-7
Muhammad Amin, Khalil Ullah, Muhammad Asif, Habib Shah, Abdul Waheed, Irfanud Din
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引用次数: 0

摘要

众所周知,驾驶员的精神压力是造成交通事故的首要因素。这些车祸的破坏性往往会造成人员、车辆和基础设施的损失。同样,持续的精神压力也会导致精神、心血管和腹部疾病。该领域的前期研究主要集中在特征工程和传统的机器学习(ML)方法上。这些方法基于从各种模式(包括生理、物理和上下文数据)中提取的手工特征来识别不同的压力水平。使用特征工程从这些模态中获取高质量的特征往往是一项艰巨的工作。深度学习(DL)算法的最新发展,通过自动提取和学习有弹性的特征,缓解了特征工程的难度。然而,传统的深度学习模型由于参数过多而经常出现过度拟合。因此,大型网络面临梯度消失问题,导致学习失败和泛化错误增加。此外,从零开始训练深度学习模型通常很难获得大型数据集。为了克服驾驶员压力识别领域的这些问题,本文提出了基于 Xception 预训练神经网络的快速、计算高效的深度迁移学习模型。这些模型通过心电图(ECG)、心率(HR)、皮肤电反应(GSR)、肌电图(EMG)和呼吸(RESP)信号对驾驶员的低、中、高压力水平进行分类。连续小波变换(CWT)分别获取心电图、心率、GSR、肌电图和 RESP 信号的扫描图。然后根据这些扫描图训练单模态 Xception 模型,对三种压力等级进行分类。根据心电图、RESP、心率、GSR 和 EMG 信号,所提出的 Xception 模型的平均验证准确率分别达到了 97.2%、86.4%、82.7%、71.9% 和 68.9%。模糊 EDAS(基于与平均解的距离的评估)方法还根据准确度、召回率、精确度、F-分数和特异性评估了所提模型的性能。对于驾驶员的三种压力水平,模糊 EDAS 性能评估表明,基于心电图、RESP 和心率的 Xception 模型分别获得了第一、第二和第三名。
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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Fuzzy performance estimation of real-world driver’s stress recognition models based on physiological signals and deep learning approach

Driver’s mental stress is known as a prime factor in road crashes. The devastation of these crashes often results in losses of humans, vehicles, and infrastructure. Likewise, persistent mental stress could develop mental, cardiovascular, and abdominal disorders. Preceding research in this domain mostly focuses on feature engineering and conventional machine learning (ML) approaches. These approaches recognize different stress levels based on handcrafted features extracted from various modalities including physiological, physical, and contextual data. Acquiring the good quality features from these modalities using feature engineering is often a difficult job. The recent developments in the form of deep learning (DL) algorithms have relieved feature engineering by automatically extracting and learning resilient features. Conventional DL models, however, frequently over-fit due to large number of parameters. Thus, large networks face gradient vanishing issues causing an increase in learning failure and generalization errors. Furthermore, it is often hard to acquire a large dataset for training a deep learning model from scratch. To overcome these problems for driver’s stress recognition domain, this paper proposes fast and computationally efficient deep transfer learning models based on Xception pre-trained neural networks. These models classify the driver’s Low, Medium, and High stress levels through electrocardiogram (ECG), heart rate (HR), galvanic skin response (GSR), electromyogram (EMG), and respiration (RESP) signals. Continuous Wavelet Transform (CWT) acquires the scalograms for ECG, HR, GSR, EMG, and RESP signals separately. Then unimodal Xception models are trained based on these scalograms to classify the three stress levels. The proposed Xception models have achieved 97.2%, 86.4%, 82.7%, 71.9%, and 68.9% average validation accuracies based on ECG, RESP, HR, GSR, and EMG signals, respectively. The fuzzy EDAS (evaluation based on distance from average solution) approach also evaluates the performance of proposed models based on accuracy, recall, precision, F-score, and specificity. For the driver’s three stress levels, fuzzy EDAS performance estimation shows that the proposed ECG, RESP, and HR based Xception models achieved 1st, 2nd, and 3rd positions, respectively.

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来源期刊
Journal of Ambient Intelligence and Humanized Computing
Journal of Ambient Intelligence and Humanized Computing COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCEC-COMPUTER SCIENCE, INFORMATION SYSTEMS
CiteScore
9.60
自引率
0.00%
发文量
854
期刊介绍: The purpose of JAIHC is to provide a high profile, leading edge forum for academics, industrial professionals, educators and policy makers involved in the field to contribute, to disseminate the most innovative researches and developments of all aspects of ambient intelligence and humanized computing, such as intelligent/smart objects, environments/spaces, and systems. The journal discusses various technical, safety, personal, social, physical, political, artistic and economic issues. The research topics covered by the journal are (but not limited to): Pervasive/Ubiquitous Computing and Applications Cognitive wireless sensor network Embedded Systems and Software Mobile Computing and Wireless Communications Next Generation Multimedia Systems Security, Privacy and Trust Service and Semantic Computing Advanced Networking Architectures Dependable, Reliable and Autonomic Computing Embedded Smart Agents Context awareness, social sensing and inference Multi modal interaction design Ergonomics and product prototyping Intelligent and self-organizing transportation networks & services Healthcare Systems Virtual Humans & Virtual Worlds Wearables sensors and actuators
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