EEG and ECG-Based Multi-Sensor Fusion Computing for Real-Time Fatigue Driving Recognition Based on Feedback Mechanism

Wang, Ling; Song, Fangjie; Zhou, Tie Hua; Hao, Jiayu; Ryu, Keun Ho

doi:10.3390/s23208386

Cited by 5 publications

(1 citation statement)

References 27 publications

(46 reference statements)

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“…This method is convenient and intuitive, but the human subjective feeling can not accurately identify the real driving state. Passive detection methods can be divided into three methods, the first detection method is based on the driver's physiological signals for fatigue driving detection [5][6][7][8]. The method can achieve a high detection accuracy, but the collection of physiological signals need to be in direct contact with the driver's body to affect the driver's operation and the cost of wearing equipment is high.…”

Section: Introductionmentioning

confidence: 99%

Fusion of Lightweight Networks and DeepSort for Fatigue Driving Detection Tracking Algorithm

Xu,

Li,

Chen

et al. 2024

IEEE Access

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The fatigue driving detection process faces issues such as a large number of parameters, low accuracy and insufficient continuous detection. To address these, this paper proposes a method combining enhanced YOLOv5s and DeepSort for fatigue driving detection. First, the improved Mobilenet_ECA lightweight backbone is introduced to reconstruct the YOLOv5s backbone part. Then, the CSPDarknet53(C3) module in the neck network is integrated with the Triplet Attention Module (TAM) to enhance the fusion of contextual information and improve the network's facial feature extraction capability. In addition, FocalEIOU Loss is used to optimize the problem of large errors in Complete Intersection over Union (CIoU) Loss prediction box regression calculations. Next, the DeepSort facial feature tracking algorithm is combined for continuous classification tracking to optimize the problem of driver facial information loss. Finally, the facial feature detection results are combined with the number of consecutive eyeclosed frames, the number of consecutive yawning frames, and the Percentage of Eyelid Closure over the Pupil over Time (PERCLOS) score to build a fatigue driving detection model. The experimental results show that compared with the baseline model YOLOv5s, the algorithm proposed in this article has improved mAP@0.5 and P by 1% and 1.8%, Params decreased by 56.3%, FLOPs decreased by 63.2%, and the model size is only 6.4 MB. The final recognition rate of fatigue and nonfatigue driving was as high as 97.4%. It is verified that the algorithm in this paper can maintain high detection accuracy while being lightweight, and can effectively identify driver states, providing strong support for the deployment of vehicle edge devices.

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Section: Introductionmentioning

confidence: 99%

Fusion of Lightweight Networks and DeepSort for Fatigue Driving Detection Tracking Algorithm

Xu,

Li,

Chen

et al. 2024

IEEE Access

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P‐4.35: Application of Virtual Reality Technology in the Diagnosis and Treatment of Psychological Disorders: An Electroencephalography (EEG)‐Based Approach

Ma,

Li,

et al. 2024

Symp Digest of Tech Papers

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In the wake of the incessant evolution of virtual reality (VR) technology, its pervasive integration into an expanding array of sectors, notably the medical field, has become increasingly conspicuous. Amid these developments, post‐traumatic stress disorder (PTSD), a prevalent and debilitating mental health condition, has garnered heightened attention from the medical community. The contemporary therapeutic landscape predominantly revolves around psychological interventions, exemplified by exposure therapy and positive thinking therapy. However, a groundbreaking advancement, namely virtual reality exposure therapy (VRET), has emerged as a prominent achievement, effectively intertwining VR technology with medical applications, particularly in the nuanced treatment of PTSD. As the realms of human‐computer interaction and brain‐computer interface undergo continuous and dynamic expansion, electroencephalography (EEG) has surfaced as a pivotal convergence point across diverse disciplines. The confluence of VRET technology with EEG signals holds the promise of enhancing the precision and objectivity of patient diagnoses. Furthermore, leveraging EEG signals enables a more nuanced and individualized approach to treatment within the framework of VRET technology. Despite extant studies that offer comprehensive summaries of VRET technology, the integration with EEG signals has thus far been a relatively unexplored terrain. This research endeavors to direct attention toward recent advancements in VRET technology within the sphere of mental health treatment. A meticulous synthesis and exposition of existing techniques and applications are presented, providing a comprehensive overview of the current landscape. The exploration extends to the developmental trajectory and prospective implications arising from the amalgamation of VRET technology and EEG technology. Furthermore, a rigorous analysis of the tangible contributions and practical significance of EEG signals in the domain of mental health treatment is undertaken, underscoring the potential avenues for their future applications and the transformative impact on the trajectory of therapeutic interventions.

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Assessment of age-related features of the condition of heavy-duty truck drivers based on spectral analysis of EEG-ECG and stress tests during mass occupational examinations

Fleishman,

Yauk,

Petrovskiy

et al. 2024

Med. tr. prom. èkol.

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The authors studied the physiological characteristics of drivers of heavy-duty coal mine vehicles based on spectral analysis of EEG-ECG and stress tests, which have prognostic value in professional activity. The study aims to assess the functional condition of heavy-duty truck drivers of different age groups based on a comprehensive spectral analysis of EEG-ECG under occupational examinations. During the survey, the authors have studied the spectral parameters of the EEG‒ECG in 300 male drivers aged 25 to 66 years. Specialists have performed EEG registration when placing electrodes in the 10–20 system. The EEG analysis was performed with a preliminary visual analysis followed by the removal of artifacts. The scientists assessed the level of representation of the main alpha rhythm and its spatial distribution. The ECG registration was in the first lead. The duration of EEG-ECG registration is about 15 minutes in a typical sequence of stress tests: baseline, rhythmic photostimulation, hyperventilation with recovery periods. The spectral analysis of the EEG‒ECG was performed automatically on a Neurotravel device (Italy). The authors found lower alpha and delta activity in the initial EEG spectra in older drivers aged 45–66 years, which contributed to the cumulative effect of stress load. Light stress stimulation significantly reduced alpha and delta activity and increased beta EEG activity in both groups, while in the older group the high-frequency range significantly exceeded the low-frequency range. Simultaneously with the changes in the EEG, stress photostimulation increased the energy of the ECG spectra in all frequency ranges. The authors established the appearance of additional stress indicators during light stimulation in older drivers based on the correlation of EEG‒ECG spectra (alpha/delta) and the absence of such changes in the group of young drivers aged 25–35 years. Moreover, the EEG–ECG correlation coefficients and their critical values increased in groups in increments of 6 years — from 35 to 65 years. Elderly drivers, based on neuro-cardiodynamic indicators, were in a state of chronic stress and the resulting risks to health and professional activity, and the identified physiological mechanisms of stress may be at the heart of ways to prevent it. Limitations. The study is limited to the assessment of EEG and ECG indicators in 300 heavy truck drivers. Ethics. The study was performed using non-invasive methods and approved by the Bioethical Committee of the Research Institute of Complex Problems of Hygiene and Occupational Diseases, Protocol No. 5, § 1 dated 12/26/2018, in accordance with the Helsinki Declaration of the World Medical Association "Ethical Principles of conducting medical research with human participation as a subject" as amended in 2013 and the "Rules of Good Clinical Practice" approved by by Order of the Ministry of Health of the Russian Federation No. 200n dated April 1, 2016. All the examined patients signed a voluntary informed consent to participate in the study.

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EEG and ECG-Based Multi-Sensor Fusion Computing for Real-Time Fatigue Driving Recognition Based on Feedback Mechanism

Cited by 5 publications

References 27 publications

Fusion of Lightweight Networks and DeepSort for Fatigue Driving Detection Tracking Algorithm

Fusion of Lightweight Networks and DeepSort for Fatigue Driving Detection Tracking Algorithm

P‐4.35: Application of Virtual Reality Technology in the Diagnosis and Treatment of Psychological Disorders: An Electroencephalography (EEG)‐Based Approach

Assessment of age-related features of the condition of heavy-duty truck drivers based on spectral analysis of EEG-ECG and stress tests during mass occupational examinations

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