Auditive beta stimulation as a countermeasure against driver fatigue

Moessinger, Michèle; Stürmer, Ralf; Mühlensiep, Markus

doi:10.1371/journal.pone.0245251

Cited by 3 publications

(2 citation statements)

References 36 publications

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“…The advancement in driver fatigue detection is challenged by the complexity of driving environments and the diversity of driver behaviors, necessitating robust data preprocessing methods [ 17 – 19 ]. Traditional facial behavior recognition techniques often falter with complex, blurred, or dynamically changing expressions, influenced by varying lighting and camera angles.…”

Section: Our Approachmentioning

confidence: 99%

Advancing driver fatigue detection in diverse lighting conditions for assisted driving vehicles with enhanced facial recognition technologies

Lin,

Zuo

2024

PLoS ONE

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Against the backdrop of increasingly mature intelligent driving assistance systems, effective monitoring of driver alertness during long-distance driving becomes especially crucial. This study introduces a novel method for driver fatigue detection aimed at enhancing the safety and reliability of intelligent driving assistance systems. The core of this method lies in the integration of advanced facial recognition technology using deep convolutional neural networks (CNN), particularly suited for varying lighting conditions in real-world scenarios, significantly improving the robustness of fatigue detection. Innovatively, the method incorporates emotion state analysis, providing a multi-dimensional perspective for assessing driver fatigue. It adeptly identifies subtle signs of fatigue in rapidly changing lighting and other complex environmental conditions, thereby strengthening traditional facial recognition techniques. Validation on two independent experimental datasets, specifically the Yawn and YawDDR datasets, reveals that our proposed method achieves a higher detection accuracy, with an impressive 95.3% on the YawDDR dataset, compared to 90.1% without the implementation of Algorithm 2. Additionally, our analysis highlights the method’s adaptability to varying brightness levels, improving detection accuracy by up to 0.05% in optimal lighting conditions. Such results underscore the effectiveness of our advanced data preprocessing and dynamic brightness adaptation techniques in enhancing the accuracy and computational efficiency of fatigue detection systems. These achievements not only showcase the potential application of advanced facial recognition technology combined with emotional analysis in autonomous driving systems but also pave new avenues for enhancing road safety and driver welfare.

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Section: Our Approachmentioning

confidence: 99%

Advancing driver fatigue detection in diverse lighting conditions for assisted driving vehicles with enhanced facial recognition technologies

Lin,

Zuo

2024

PLoS ONE

View full text Add to dashboard Cite

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“…Few of the clinical psychological test studies are adopted to solve real world problems. For example, Moessinger et al (2021) proposed a multi-modal based method, which included EEG and TAP attention performance test (Zimmermann and Fimm, 2002) to measure the driver's alertness in the night time and day time driving. Main goal of the study is to continuous monitoring of subject alertness level during long hours of drivings in the nighttime that affects human performance leads to dangerous situations and cause many accidents.…”

Section: Reaction Time For D Interfacesmentioning

confidence: 99%

Human reaction time in a mixed reality environment

et al. 2022

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Over the last few years applications based on the use of immersive environments, where physical and digital objects coexist and interact, have gained widespread attention. Thanks to the development of new visualization devices, even at low cost, and increasingly effective rendering and processing techniques, these applications are reaching a growing number of users. While the adoption of digital information makes it possible to provide immersive experiences in a number of different applications, there are still many unexplored aspects. In this work, a preliminary step to understand the impact of the scene content on human perception of the virtual 3D elements in a mixed reality has been performed. To this aim, a subjective test was designed and implemented to collect the reaction time of a set of users in a mixed reality application. In this test each user was asked to wear an augmented reality headset and to catch a virtual objects randomly appearing in the subject's field of view. We first estimated the detection accuracy through omitted, anticipated, and completed responses; then we related stimulus location, scene content and estimated accuracy. For this purpose, the area of stimulus presentation was divided into upper, lower, right, left, inner, and outer, to understand in which area responses were omitted and anticipated with respect to the central point of view. Experimental results show that, in addition to the saliency of the real scene, natural body gesture technology and limited field of view influenced human reaction time.

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The mitigation of the executive vigilance decrement via HD-tDCS over the right posterior parietal cortex and its association with neural oscillations

et al. 2023

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Vigilance—maintaining a prolonged state of preparation to detect and respond to specific yet unpredictable environmental changes—usually decreases across prolonged tasks, causing potentially severe real-life consequences, which could be mitigated through transcranial direct current stimulation (tDCS). The present study aimed at replicating previous mitigatory effects observed with anodal high-definition tDCS (HD-tDCS) over the right posterior parietal cortex (rPPC) while extending the analyses on electrophysiological measures associated with vigilance. In sum, 60 participants completed the ANTI-Vea task while receiving anodal (1.5 mA, n = 30) or sham (0 mA, n = 30) HD-tDCS over the rPPC for ~ 28 min. EEG recordings were completed before and after stimulation. Anodal HD-tDCS specifically mitigated executive vigilance (EV) and reduced the alpha power increment across time-on-task while increasing the gamma power increment. To further account for the observed behavioral and physiological outcomes, a new index of Alphaparietal/Gammafrontal is proposed. Interestingly, the increment of this Alphaparietal/Gammafrontal Index with time-on-task is associated with a steeper EV decrement in the sham group, which was mitigated by anodal HD-tDCS. We highlight the relevance of replicating mitigatory effects of tDCS and the need to integrate conventional and novel physiological measures to account for how anodal HD-tDCS can be used to modulate cognitive performance.

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Auditive beta stimulation as a countermeasure against driver fatigue

Cited by 3 publications

References 36 publications

Advancing driver fatigue detection in diverse lighting conditions for assisted driving vehicles with enhanced facial recognition technologies

Advancing driver fatigue detection in diverse lighting conditions for assisted driving vehicles with enhanced facial recognition technologies

Human reaction time in a mixed reality environment

The mitigation of the executive vigilance decrement via HD-tDCS over the right posterior parietal cortex and its association with neural oscillations

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