The Impacts of Temporal Variation and Individual Differences in Driver Cognitive Workload on ECG-Based Detection

Yang, Shiyan; Kuo, Jonny; Lenné, Michael G.; Fitzharris, Michael; Horberry, Tim; Blay, Kyle; Wood, Darren; Mulvihill, Christine; Truche, Carine

doi:10.1177/0018720821990484

Cited by 13 publications

(6 citation statements)

References 41 publications

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“…A study conducted by Yang et al [29] explored the robustness of detecting driver cognitive workload using ECG, while considering changes over time and individual variations in cognitive workload. They conducted a driving simulation experiment to categorize driver cognitive workload across four experimental scenarios (baseline, N-back task, texting, and N-back task with texting distraction).…”

Section: Resultsmentioning

confidence: 99%

“…It also found that ECG are usually gathered in relatively short driving experiment, typically less than one hour for cognitive load detection. The reliability of cognitive load detection might be decreased in prolonged driving if the algorithms used are trained solely with early-stage driving data [29]. This is due to temporal variations in the cognitive load.…”

Section: Resultsmentioning

confidence: 99%

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A review of driver cognitive load detection using ECG signals

Tjolleng,

Dyota Pramudita

2024

SHS Web Conf.

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Detection of the driver’s cognitive load while driving is crucial to prevent the likelihood of traffic collisions and improve road safety. A physiological-based approach has gained significant attention due to its potential to provide reliable indicators for the driver’s state. The physiological signal of electrocardiography (ECG) is considered a promising biomarker for detecting the driver’s cognitive load. Despite the interest in cognitive load detection using ECG, an attempt has yet to be made to identify the relationship between ECG measures and driver cognitive load level. This paper seeks to investigate this gap in cognitive load literature. The finding demonstrates that further research is still needed on ECG-based driver’s cognitive load detection by examining and analyzing the limitations of research challenges and earlier studies. This study also addresses the performance and problems faced in the detection of a driver’s cognitive load considering ECG. With a better understanding of how cognitive load affects ECG measures, both researchers and companies can design more effective driver’s state detection systems.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

A review of driver cognitive load detection using ECG signals

Tjolleng,

Dyota Pramudita

2024

SHS Web Conf.

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“…Future studies could extend our study to include a more diverse population (e.g., including individuals with both genders and with diverse driving experience) in order to consider the potential impact of gender and driving experience on the experimental results. Finally, the measures used in this experiment represent average values over a certain period, without considering temporal changes in MWL [32]. Thus, future research could explore the temporal characteristics of MWL and examine how MWL would change over time.…”

Section: Limitationsmentioning

confidence: 99%

“…In simulated driving environments, gaze fixation duration decreases with an increase in task load [31]. Heart rate and heart rate variability are also commonly used and are effective ECG measures for assessing drivers' MWL [32]. Skin conductance level (SCL) and skin conductance response (SCR) are frequently used in electrodermal activity measurements to assess MWL.…”

Section: Introductionmentioning

confidence: 99%

Assessment of Drivers’ Mental Workload by Multimodal Measures during Auditory-Based Dual-Task Driving Scenarios

Huang,

Zhang,

Zhang

et al. 2024

Sensors

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Assessing drivers’ mental workload is crucial for reducing road accidents. This study examined drivers’ mental workload in a simulated auditory-based dual-task driving scenario, with driving tasks as the main task, and auditory-based N-back tasks as the secondary task. A total of three levels of mental workload (i.e., low, medium, high) were manipulated by varying the difficulty levels of the secondary task (i.e., no presence of secondary task, 1-back, 2-back). Multimodal measures, including a set of subjective measures, physiological measures, and behavioral performance measures, were collected during the experiment. The results showed that an increase in task difficulty led to increased subjective ratings of mental workload and a decrease in task performance for the secondary N-back tasks. Significant differences were observed across the different levels of mental workload in multimodal physiological measures, such as delta waves in EEG signals, fixation distance in eye movement signals, time- and frequency-domain measures in ECG signals, and skin conductance in EDA signals. In addition, four driving performance measures related to vehicle velocity and the deviation of pedal input and vehicle position also showed sensitivity to the changes in drivers’ mental workload. The findings from this study can contribute to a comprehensive understanding of effective measures for mental workload assessment in driving scenarios and to the development of smart driving systems for the accurate recognition of drivers’ mental states.

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“…pressure could be exploited with blood pressure cuff applied to the upper arm and connected to monitor [82,83]. And "posture" involving head, face, hand and body features is mainly recorded by camera [84].…”

Section: Plos Onementioning

confidence: 99%

Understanding the domain of driving distraction with knowledge graphs

Feng

2022

PLoS ONE

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This paper aims to provide insight into the driving distraction domain systematically on the basis of scientific knowledge graphs. For this purpose, 3,790 documents were taken into consideration after retrieving from Web of Science Core Collection and screening, and two types of knowledge graphs were constructed to demonstrate bibliometric information and domain-specific research content respectively. In terms of bibliometric analysis, the evolution of publication and citation numbers reveals the accelerated development of this domain, and trends of multidisciplinary and global participation could be identified according to knowledge graphs from Vosviewer. In terms of research content analysis, a new framework consisting of five dimensions was clarified, including “objective factors”, “human factors”, “research methods”, “data” and “data science”. The main entities of this domain were identified and relations between entities were extracted using Natural Language Processing methods with Python 3.9. In addition to the knowledge graph composed of all the keywords and relationships, entities and relations under each dimension were visualized, and relations between relevant dimensions were demonstrated in the form of heat maps. Furthermore, the trend and significance of driving distraction research were discussed, and special attention was given to future directions of this domain.

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The Impacts of Temporal Variation and Individual Differences in Driver Cognitive Workload on ECG-Based Detection

Cited by 13 publications

References 41 publications

A review of driver cognitive load detection using ECG signals

A review of driver cognitive load detection using ECG signals

Assessment of Drivers’ Mental Workload by Multimodal Measures during Auditory-Based Dual-Task Driving Scenarios

Understanding the domain of driving distraction with knowledge graphs

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