A Wearable Device for Traffic Safety - A Study on Estimating Drowsiness with Eyewear, JINS MEME

Niwa, Shinji; Mori, Yuichi; Noro, Tetsushi; Shioya, Shunsuke; Inoue, Katsuro

doi:10.4271/2016-01-0118

Cited by 6 publications

(5 citation statements)

References 16 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 drawbacks of using a camera to track eye movements are the trade-off between processing time and detection accuracy and the susceptibility to lighting conditions, skin color and sunglasses [8]. Moreover, cameras mounted in vehicles cannot be used to detect the state of a driver outside the vehicle [9]. An alternative method is electrooculography (EOG), a technique for measuring the resting electrical potential between the cornea and retina of the human eye [10].…”

Section: Introductionmentioning

confidence: 99%

“…The reason for choosing EOG to detect eyeball movements was the availability of JINS MEME ES_R smart glasses which can register electrooculograms in a non-invasive way (without attaching the electrodes to the body) while performing various activities (including driving a car) [9,14] and the fact that the eyeball movements contain the most information related to activities related to driving a car [2,4,15]. Another reason for using only electrooculography was to design the system for recognizing drivers' activities regardless of their style of driving and experience and to find the minimal number of attributes required to recognize such activities [16].…”

Section: Introductionmentioning

confidence: 99%

“…To the best of our knowledge, the problem of extracting and selecting appropriate features from electrooculograms in recognition of drivers' behavior has not been thoroughly investigated due to the cumbersome placement of electrodes and the breadth of the topic, which indicates a clear need for further research. 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%

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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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“…Reprinted, with permission, from Nakamura et al ( 2018 ). 12 Republished with permission of SAE International, from Niwa et al ( 2016 ); permission conveyed through Copyright Clearance Center, Inc. 13 Reprinted with permission of Fuji Technology Press Ltd., from (Wang et al, 2018 ). 14 © (2016) IEEE.…”

Section: Resultsmentioning

confidence: 99%

Fatigue Monitoring Through Wearables: A State-of-the-Art Review

et al. 2021

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The objective measurement of fatigue is of critical relevance in areas such as occupational health and safety as fatigue impairs cognitive and motor performance, thus reducing productivity and increasing the risk of injury. Wearable systems represent highly promising solutions for fatigue monitoring as they enable continuous, long-term monitoring of biomedical signals in unattended settings, with the required comfort and non-intrusiveness. This is a p rerequisite for the development of accurate models for fatigue monitoring in real-time. However, monitoring fatigue through wearable devices imposes unique challenges. To provide an overview of the current state-of-the-art in monitoring variables associated with fatigue via wearables and to detect potential gaps and pitfalls in current knowledge, a systematic review was performed. The Scopus and PubMed databases were searched for articles published in English since 2015, having the terms “fatigue,” “drowsiness,” “vigilance,” or “alertness” in the title, and proposing wearable device-based systems for non-invasive fatigue quantification. Of the 612 retrieved articles, 60 satisfied the inclusion criteria. Included studies were mainly of short duration and conducted in laboratory settings. In general, researchers developed fatigue models based on motion (MOT), electroencephalogram (EEG), photoplethysmogram (PPG), electrocardiogram (ECG), galvanic skin response (GSR), electromyogram (EMG), skin temperature (Tsk), eye movement (EYE), and respiratory (RES) data acquired by wearable devices available in the market. Supervised machine learning models, and more specifically, binary classification models, are predominant among the proposed fatigue quantification approaches. These models were considered to perform very well in detecting fatigue, however, little effort was made to ensure the use of high-quality data during model development. Together, the findings of this review reveal that methodological limitations have hindered the generalizability and real-world applicability of most of the proposed fatigue models. Considerably more work is needed to fully explore the potential of wearables for fatigue quantification as well as to better understand the relationship between fatigue and changes in physiological variables.

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A Wearable Device for Traffic Safety - A Study on Estimating Drowsiness with Eyewear, JINS MEME

Cited by 6 publications

References 16 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

Fatigue Monitoring Through Wearables: A State-of-the-Art Review

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