Using Mobile Platform to Detect and Alerts Driver Fatigue

Abulkhair, Maysoon F.; Salman, Hesham A.; Ibrahim, Lamiaa Fattouh

doi:10.5120/ijca2015905428

Cited by 5 publications

(4 citation statements)

References 23 publications

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“…Some studies have reported the use of external sensors in conjunction with embedded sensors in mobile devices to address traffic issues (8,12,13,15,17,18,22,24,29,(36)(37)(38)(39)(40)(41). Camera sensors, for example, collect data from various sources such as facial and eye expressions, road images, road potholes, and traffic signs (19,21,25,31,(42)(43)(44)(45). Accelerometer and magnetometer sensors receive sudden braking, fast rotation, and fast acceleration data (36,(46)(47)(48).…”

Section: Information Management Aspects Of Mobile Solutionsmentioning

confidence: 99%

“…-apps to monitor traffic patterns and driving behavior (12-17) -apps to reduce fuel consumption (18) -apps to detect drowsiness (19)(20)(21) -apps to detect car accidents (22)(23)(24) -apps to inform of real-time road conditions (25-27) -apps for safe driving (28)(29)(30)(31)(32) A comprehensive categorization of traffic and road safety applications was carried out in a prior study, employing the Haddon matrix as a framework to discern the features of the applications (33). To the best of our knowledge, a comprehensive review of the academic literature on mobile solutions for traffic and road safety issues has not been conducted.…”

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confidence: 99%

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Functionalities of Mobile Solutions for Preventing Road Traffic Health and Safety Issues in the Context of Information Management Cycle

Aghayari,

Kalankesh,

Sadeghi-Bazargani

et al. 2023

Transportation Research Record: Journal of the Transportation R

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In the present study, researchers thoroughly analyzed the peer-reviewed literature on mobile solutions for road traffic health and safety issues with regard to their functionalities in the context of information management. A comprehensive review of papers reporting mobile solutions for road traffic health and safety was conducted using relevant eligibility criteria. Electronic sources were searched using a combination of keywords. To analyze the mobile solutions reported in the studies, the researchers deployed different aspects of the information management cycle and functionality of apps for preventing road traffic health and safety issues. Overall, 68 studies met the inclusion criteria and were included in the final analysis. A total of 72.1% of the studies reported using only the in-built sensors of smartphones in their solutions for data collection. Several processing approaches for the data were found, which can be referred to as threshold value setting, image processing, fuzzy logic, and decision tree. According to the finding, the highest percentage of processing techniques utilized in the mobile solutions belonged to threshold value setting technique (23.5%) followed by image processing technique (10.3%). The applications of mobile solutions in studies commonly include recording and providing feedback on driving behavior, real-time alerts for road conditions, accident detection, and drowsiness management. Finally, fulfilling these targets could affect humans, the physical environment, and the vehicle factors toward preventing road traffic health and safety issues. The domain researchers and developers of mobile solutions can revisit and enhance the functionalities with attention to the full cycle of information management, including data collection, data analysis, interventions, and data application in their initiatives.

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Section: Information Management Aspects Of Mobile Solutionsmentioning

confidence: 99%

mentioning

confidence: 99%

Functionalities of Mobile Solutions for Preventing Road Traffic Health and Safety Issues in the Context of Information Management Cycle

Aghayari,

Kalankesh,

Sadeghi-Bazargani

et al. 2023

Transportation Research Record: Journal of the Transportation R

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“…The argument is that increased mileage should not be viewed as risk reducing, since a driver who decides to drive more miles has a fixed claims probability for their original driven distance and the additional distance can only increase that probability, although the marginal increase in probability of claims per mile may decrease with additional miles driven. Some studies have examined drowsiness detection using wearable headbands (Rohit et al 2017), or fatigue using computer vision techniques (Abulkhair et al 2015). There have been studies analyzing driver behavior using information acquired from a vehicle's CAN (controller area network) bus, detailing sequences of actions including braking and turning events, steering wheel angle, and vehicle speed, most often in an attempt to identify drivers based on the information obtained from the CAN bus.…”

Section: Telematics Datamentioning

confidence: 99%

Nightly Automobile Claims Prediction from Telematics-Derived Features: A Multilevel Approach

Williams

Jin²,

Duer³

et al. 2022

Risks

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In recent years it has become possible to collect GPS data from drivers and to incorporate these data into automobile insurance pricing for the driver. These data are continuously collected and processed nightly into metadata consisting of mileage and time summaries of each discrete trip taken, and a set of behavioral scores describing attributes of the trip (e.g, driver fatigue or driver distraction), so we examine whether it can be used to identify periods of increased risk by successfully classifying trips that occur immediately before a trip in which there was an incident leading to a claim for that driver. Identification of periods of increased risk for a driver is valuable because it creates an opportunity for intervention and, potentially, avoidance of a claim. We examine metadata for each trip a driver takes and train a classifier to predict whether the following trip is one in which a claim occurs for that driver. By achieving an area under the receiver–operator characteristic above 0.6, we show that it is possible to predict claims in advance. Additionally, we compare the predictive power, as measured by the area under the receiver–operator characteristic of XGBoost classifiers trained to predict whether a driver will have a claim using exposure features such as driven miles, and those trained using behavioral features such as a computed speed score.

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“…The usage of smart phone greatly reduces the complexity of video acquisition, store and transmission [14]- [16]. However, since driver's driving environment is highly dynamic, most of the smart phone based algorithms are very susceptible to the dynamics in driving environment because the mobile platform cannot afford the computation and storage requirement for very sophisticated algorithms.…”

Section: Introductionmentioning

confidence: 99%

Driver Drowsiness Detection Using Multi-Channel Second Order Blind Identifications

Zhang

Zheng

et al. 2019

IEEE Access

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It is well known that blink, yawn, and heart rate changes give clue about a human's mental state, such as drowsiness and fatigue. In this paper, image sequences, as the raw data, are captured from smart phones which serve as non-contact optical sensors. Video streams containing subject's facial region are analyzed to identify the physiological sources that are mixed in each image. We then propose a method to extract blood volume pulse and eye blink and yawn signals as multiple independent sources simultaneously by multi-channel second-order blind identification (SOBI) without any other sophisticated processing, such as eye and mouth localizations. An overall decision is made by analyzing the separated source signals in parallel to determine the driver's driving state. The robustness of the proposed method is tested under various illumination contexts and a variety of head motion modes. Experiments on 15 subjects show that the multi-channel SOBI presents a promising framework to accurately detect drowsiness by merging multi-physiological information in a less complex way. INDEX TERMS Yawn, blink, blood volume pulse (BVP), drowsiness detection, second-order blind identification (SOBI).

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Using Mobile Platform to Detect and Alerts Driver Fatigue

Cited by 5 publications

References 23 publications

Functionalities of Mobile Solutions for Preventing Road Traffic Health and Safety Issues in the Context of Information Management Cycle

Functionalities of Mobile Solutions for Preventing Road Traffic Health and Safety Issues in the Context of Information Management Cycle

Nightly Automobile Claims Prediction from Telematics-Derived Features: A Multilevel Approach

Driver Drowsiness Detection Using Multi-Channel Second Order Blind Identifications

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