Measuring Driver Perception: Combining Eye-Tracking and Automated Road Scene Perception

Stapel, Jork; Hassnaoui, Mounir el; Happee, Riender

doi:10.1177/0018720820959958

Cited by 22 publications

(18 citation statements)

References 50 publications

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“…Head movement features have been used in a few studies assessing whether a patient is under stress-they are usually more frequent, faster, and more significant [10]. The application of electrooculography (EOG) in the design of braincomputer interfaces and activity recognition has been described in several studies [12][13][14][15][16]. Electrooculography (EOG) is a technique for measuring the resting electrical potential between the cornea and retina of the human eye by registering the electrical signal via electrodes placed around the eyes [12,17].…”

Section: Introductionmentioning

confidence: 99%

The Relationship between Stress Levels Measured by a Questionnaire and the Data Obtained by Smart Glasses and Finger Pulse Oximeters among Polish Dental Students

et al. 2021

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Stress is a physical, mental, or emotional response to a change and is a significant problem in modern society. In addition to questionnaires, levels of stress may be assessed by monitoring physiological signals, such as via photoplethysmogram (PPG), electroencephalogram (EEG), electrocardiogram (ECG), electrodermal activity (EDA), facial expressions, and head and body movements. In our study, we attempted to find the relationship between the perceived stress level and physiological signals, such as heart rate (HR), head movements, and electrooculographic (EOG) signals. The perceived stress level was acquired by self-assessment questionnaires in which the participants marked their stress level before, during, and after performing a task. The heart rate was acquired with a finger pulse oximeter and the head movements (linear acceleration and angular velocity) and electrooculographic signals were recorded with JINS MEME ES_R smart glasses (JINS Holdings, Inc., Tokyo, Japan). We observed significant differences between the perceived stress level, heart rate, the power of linear acceleration, angular velocity, and EOG signals before performing the task and during the task. However, except for HR, these signals were poorly correlated with the perceived stress level acquired during the task.

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Section: Introductionmentioning

confidence: 99%

The Relationship between Stress Levels Measured by a Questionnaire and the Data Obtained by Smart Glasses and Finger Pulse Oximeters among Polish Dental Students

et al. 2021

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“…The studies conducted by Niwa et al [9] and Doniec et al [17] are the only known study on drivers' behavior which used JINS MEME smart glasses. Another recent study on recognizing the gaze on the left turn was conducted by Stapel et al [18].…”

Section: Introductionmentioning

confidence: 99%

Recognition of Drivers’ Activity Based on 1D Convolutional Neural Network

et al. 2020

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Background and objective: Driving a car is a complex activity which involves movements of the whole body. Many studies on drivers’ behavior are conducted to improve road traffic safety. Such studies involve the registration and processing of multiple signals, such as electroencephalography (EEG), electrooculography (EOG) and the images of the driver’s face. In our research, we attempt to develop a classifier of scenarios related to learning to drive based on the data obtained in real road traffic conditions via smart glasses. In our approach, we try to minimize the number of signals which can be used to recognize the activities performed while driving a car. Material and methods: We attempt to evaluate the drivers’ activities using both electrooculography (EOG) and a deep learning approach. To acquire data we used JINS MEME smart glasses furnished with 3-point EOG electrodes, 3-axial accelerometer and 3-axial gyroscope. Sensor data were acquired on 20 drivers (ten experienced and ten learner drivers) on the same 28.7 km route under real road conditions in southern Poland. The drivers performed several tasks while wearing the smart glasses and the tasks were linked to the signal during the drive. For the recognition of four activities (parking, driving through a roundabout, city traffic and driving through an intersection), we used one-dimensional convolutional neural network (1D CNN). Results: The maximum accuracy was 95.6% on validation set and 99.8% on training set. The results prove that the model based on 1D CNN can classify the actions performed by drivers accurately. Conclusions: We have proved the feasibility of recognizing drivers’ activity based solely on EOG data, regardless of the driving experience and style. Our findings may be useful in the objective assessment of driving skills and thus, improving driving safety.

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“…Over time, these paradigms have been extended to tasks such as scene perception. Recarte and Nunes (2000) and Stapel et al (2020), for instance, have used the boundary method to analyze eye movements during driving tasks and thereby assess the effect of driver awareness on gaze behavior. In particular, Recarte and Nunes (2000) have observed that distracted drivers had higher fixation durations, higher pupil dilations, and lower fixation counts in the mirror/speedometer AOIs compared to control subjects.…”

Section: Psychologymentioning

confidence: 99%

Eye Movement and Pupil Measures: A Review

Mahanama

Jayawardana

Rengarajan

et al. 2022

Front. Comput. Sci.

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Our subjective visual experiences involve complex interaction between our eyes, our brain, and the surrounding world. It gives us the sense of sight, color, stereopsis, distance, pattern recognition, motor coordination, and more. The increasing ubiquity of gaze-aware technology brings with it the ability to track gaze and pupil measures with varying degrees of fidelity. With this in mind, a review that considers the various gaze measures becomes increasingly relevant, especially considering our ability to make sense of these signals given different spatio-temporal sampling capacities. In this paper, we selectively review prior work on eye movements and pupil measures. We first describe the main oculomotor events studied in the literature, and their characteristics exploited by different measures. Next, we review various eye movement and pupil measures from prior literature. Finally, we discuss our observations based on applications of these measures, the benefits and practical challenges involving these measures, and our recommendations on future eye-tracking research directions.

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Measuring Driver Perception: Combining Eye-Tracking and Automated Road Scene Perception

Cited by 22 publications

References 50 publications

The Relationship between Stress Levels Measured by a Questionnaire and the Data Obtained by Smart Glasses and Finger Pulse Oximeters among Polish Dental Students

The Relationship between Stress Levels Measured by a Questionnaire and the Data Obtained by Smart Glasses and Finger Pulse Oximeters among Polish Dental Students

Recognition of Drivers’ Activity Based on 1D Convolutional Neural Network

Eye Movement and Pupil Measures: A Review

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