Evaluation of mental workload during automobile driving using one-class support vector machine with eye movement data

Chihara, Takanori; Kobayashi, Fuminori; Sakamoto, Jiro

doi:10.1016/j.apergo.2020.103201

Cited by 27 publications

(10 citation statements)

References 26 publications

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“…Therefore, it is difficult to determine the effectiveness of the proposed method by simply comparing it with the classification accuracy of previous studies. However, a similar trend was demonstrated in previous studies [18], [19]. Therefore, the proposed method can accurately detect attentional resource exhaustion and is expected to prevent accidents caused by inattention.…”

Section: Methodsupporting

confidence: 86%

Real-time Driver Cognitive Strain Evaluation System Based on Multivariate Biosignal Analysis

Shimizu

Shima

Mukaeda

et al. 2022

Preprint

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Traffic accidents are often attributed to reduced attentional capacity, as vehicle operations involve complex tasks that depend on the driver's mental workload (MWL). Cognitive strain in MWL affects focus on other vehicles and pedestrians, which can result in serious accidents. Therefore, a method for effective cognitive-strain evaluation is required. In this study, the driver cognitive strain was determined using the a posteriori probability method. The posteriori probability of the driver strain state is computed using multidimensional biological signals mapped into a probabilistic space based on a Gaussian mixture model. Ultimately, the driver’s cognitive strain was estimated using a regression function based on the posteriori probability. In the experiments, tasks tracked the preceding vehicle, and N-back evaluation helped elucidate the cognitive strain. Other experiments have shown an increase in the evaluation value during the satnav operation. Accordingly, the proposed method is considered suitable for real-time driver cognitive strain evaluation.

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Section: Methodsupporting

confidence: 86%

Real-time Driver Cognitive Strain Evaluation System Based on Multivariate Biosignal Analysis

Shimizu

Shima

Mukaeda

et al. 2022

Preprint

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show abstract

“…Interestingly, MWL is correlated to task demand and performance, since it is usually considered that high, as well as low, levels of MWL may have a negative impact on task performance and increase the incidence of errors [ 5 , 6 , 7 ] during the execution of a task. Therefore, the assessment and quantification of MWL represent one of the main interests in ergonomics [ 8 ] with relevant potential impact in different fields such as aeronautics [ 9 ], automotive [ 10 ], education and training [ 11 ], clinical practice, and rehabilitation [ 12 , 13 ].…”

Section: Introductionmentioning

confidence: 99%

Reliability of Mental Workload Index Assessed by EEG with Different Electrode Configurations and Signal Pre-Processing Pipelines

Mastropietro

Pirovano

Marciano

et al. 2023

Sensors

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Background and Objective: Mental workload (MWL) is a relevant construct involved in all cognitively demanding activities, and its assessment is an important goal in many research fields. This paper aims at evaluating the reproducibility and sensitivity of MWL assessment from EEG signals considering the effects of different electrode configurations and pre-processing pipelines (PPPs). Methods: Thirteen young healthy adults were enrolled and were asked to perform 45 min of Simon’s task to elicit a cognitive demand. EEG data were collected using a 32-channel system with different electrode configurations (fronto-parietal; Fz and Pz; Cz) and analyzed using different PPPs, from the simplest bandpass filtering to the combination of filtering, Artifact Subspace Reconstruction (ASR) and Independent Component Analysis (ICA). The reproducibility of MWL indexes estimation and the sensitivity of their changes were assessed using Intraclass Correlation Coefficient and statistical analysis. Results: MWL assessed with different PPPs showed reliability ranging from good to very good in most of the electrode configurations (average consistency > 0.87 and average absolute agreement > 0.92). Larger fronto-parietal electrode configurations, albeit being more affected by the choice of PPPs, provide better sensitivity in the detection of MWL changes if compared to a single-electrode configuration (18 vs. 10 statistically significant differences detected, respectively). Conclusions: The most complex PPPs have been proven to ensure good reliability (>0.90) and sensitivity in all experimental conditions. In conclusion, we propose to use at least a two-electrode configuration (Fz and Pz) and complex PPPs including at least the ICA algorithm (even better including ASR) to mitigate artifacts and obtain reliable and sensitive MWL assessment during cognitive tasks.

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“…Indeed, traffic accidents take place when the drivers' performance is below the required levels for the traffic environment [24]. In this regard, a recent study confirmed that safer driving requires an assessment of driver mental states [25]. Furthermore, the authors have explained that the capacity of humans' mental resources that could be used to process information received (i.e., mental workload) is limited and the use of mobile phones claims further cognitive resources.…”

Section: Introductionmentioning

confidence: 99%

Effects of Mobile Phone Use on Driving Performance: An Experimental Study of Workload and Traffic Violations

Ortega

Saldaña

Boulagouas

et al. 2021

IJERPH

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The use of communication technologies, e.g., mobile phones, has increased dramatically in recent years, and their use among drivers has become a great risk to traffic safety. The present study assessed the workload and road ordinary violations, utilizing driving data collected from 39 young participants who underwent a dual-task while driving a simulator, i.e., respond to a call, text on WhatsApp, and check Instagram. Findings confirmed that there are significant differences in the driving performance of young drivers in terms of vehicle control (i.e., lateral distance and hard shoulder line violations) between distracted and non-distracted drivers. Furthermore, the overall workload score of young drivers increases with the use of their mobile phones while driving. The obtained results contribute to a better understanding of the driving performance of distracted young drivers and thus they could be useful for further improvements to traffic safety strategies.

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Evaluation of mental workload during automobile driving using one-class support vector machine with eye movement data

Cited by 27 publications

References 26 publications

Real-time Driver Cognitive Strain Evaluation System Based on Multivariate Biosignal Analysis

Real-time Driver Cognitive Strain Evaluation System Based on Multivariate Biosignal Analysis

Reliability of Mental Workload Index Assessed by EEG with Different Electrode Configurations and Signal Pre-Processing Pipelines

Effects of Mobile Phone Use on Driving Performance: An Experimental Study of Workload and Traffic Violations

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