A wearable-glasses-based drowsiness-fatigue-detection system for improving road safety

Chen, Liangbi; Chang, Wan-Jung; Su, Jie; Ciou, Ji-Yi; Ciou, Yi-Jhan; Kuo, Cheng-Chin; Li, Katherine Shu-Min

doi:10.1109/gcce.2016.7800456

Cited by 18 publications

(9 citation statements)

References 5 publications

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“…In pharmaceutical sector IoT have been anticipated and offer product quality testing at various levels of manufacturing and for product optimization. There are several reports that show the application of IoT based wireless devices such as blood glucose sensors, real time glucose monitor, smart insulin pens, insulin pumps, and closed-loop systems etc for diabetes [43][44][45][46] . IoT based nasal airflow sensor used to monitor the airflow rate of a patient who is in a need of respiratory support.…”

Section: Role Of Internet Of Things (Iot)mentioning

confidence: 99%

Internet of Diagnostic Things: Emerging Horizon towards Precision and Digital Health Care

Verma¹,

Mishra

Bisht

et al. 2021

J Health Sci Res

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Digital revolution and advancement of information technologies, smart phone industry, multi-omic technologies, availability with 5G networks and emergence of big data era has greatly impacted on diagnostic, biomedical and e-healthcare sectors. Intelligent sensors, wearable, digital imaging, smart phone based diagnostic and connected networking provides smart approach and platform towards precision diagnostics. Recently, Internet of Things (IoT) have shown remarkable impact on diverge sectors such as digital imaging, smart sensing, digital diagnostic, digital biotech, telemedicine, precision farming, lab-on phone, smart home, smart city planning, smart retails and Research and Development (R&D) automation etc. Application of IoT technology in diagnostic and healthcare opens a way for personalization in the medical care depending on individual needs, additionally giving option for well-organized way for remote healthcare and management. This review focuses on theoretical, technological, advanced aspects of digital diagnostic utilizing IoT technology. Furthermore, we have also highlighted the opportunities and challenges for IoT in exploring the futuristic scope and innovation in diagnostic application. IoT based diagnostic, sensing and imaging devices provides unprecedented precision, automation in the diagnostic and thereby could significantly improve the biomedical and e-healthcare area.

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Section: Role Of Internet Of Things (Iot)mentioning

confidence: 99%

Internet of Diagnostic Things: Emerging Horizon towards Precision and Digital Health Care

Verma¹,

Mishra

Bisht

et al. 2021

J Health Sci Res

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show abstract

“…Furthermore, current systems implemented wearable smart glasses to detect drowsiness. In Chen et al [16] and Chang et al [17] introduced a drowsiness-fatiguedetection system using wearable smart glasses, a computer-based glasses integrated to an in-vehicle smart system, which is able to detect the drowsiness or fatigue state of the driver. Another widely used technique for feature detection is Haar featurebased classifiers, which was introduced by Viola and Jones [18], to perform rapid face and eyes detection in drowsiness detection systems.…”

Section: Drowsiness Detection Systemmentioning

confidence: 99%

“…To solve the driver drowsiness problem, recent researchers started to implement systems that employ smart glasses technology. Chen et al [16] introduced a drowsiness-fatigue-detection system using wearable smart glasses, a computer-based glasses integrated to an in-vehicle smart system, that is able to detect the drowsiness or fatigue state of the driver. Figure 2 illustrated the proposed prototype.…”

Section: Wearable-glasses-based Drowsiness Detectionmentioning

confidence: 99%

Computer Vision Based Driver Assistance Drowsiness Detection

Emashharawi

Khalifa

Malik

et al. 2021

Lecture Notes in Electrical Engineering

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Nowadays, drowsiness is a serious cause of traffic accidents, a problem of major concern to society. Driver fatigue or sleepiness decreases the driver's reaction time, reduces attention, and affects the quality of decision making which impairs the driving experience. Therefore, in this paper, a drowsiness detection system is designed based on computer vision, using a cascade of classifiers based on Haar-like features. The system is able to detect the face and eyes of the driver and determine the eyes closure or opening, which concludes the drowsiness of the driver. The paper presents the five primary steps involves which are: video acquirement, frame separation, face detection, eyes detection and drowsiness detection.

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“…The training results were obtained by application of various machine learning methods. In fact, the CNN deep learning neural networks [ 159 , 194 ] is applied to get data obtained by multimodal channels (acceleration and heart activity). Despite the tendency to learn from the training data, the loss is very high for most combinations of parameters, and the abrupt decrease of the loss for two of these combinations is just an illustration of over-training, but not the mark of the very reliable model.…”

Section: Architectural Comparisonsmentioning

confidence: 99%

Driver Fatigue Detection Systems Using Multi-Sensors, Smartphone, and Cloud-Based Computing Platforms: A Comparative Analysis

Abbas

Alsheddy

2020

Sensors

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Internet of things (IoT) cloud-based applications deliver advanced solutions for smart cities to decrease traffic accidents caused by driver fatigue while driving on the road. Environmental conditions or driver behavior can ultimately lead to serious roadside accidents. In recent years, the authors have developed many low-cost, computerized, driver fatigue detection systems (DFDs) to help drivers, by using multi-sensors, and mobile and cloud-based computing architecture. To promote safe driving, these are the most current emerging platforms that were introduced in the past. In this paper, we reviewed state-of-the-art approaches for predicting unsafe driving styles using three common IoT-based architectures. The novelty of this article is to show major differences among multi-sensors, smartphone-based, and cloud-based architectures in multimodal feature processing. We discussed all of the problems that machine learning techniques faced in recent years, particularly the deep learning (DL) model, to predict driver hypovigilance, especially in terms of these three IoT-based architectures. Moreover, we performed state-of-the-art comparisons by using driving simulators to incorporate multimodal features of the driver. We also mention online data sources in this article to test and train network architecture in the field of DFDs on public available multimodal datasets. These comparisons assist other authors to continue future research in this domain. To evaluate the performance, we mention the major problems in these three architectures to help researchers use the best IoT-based architecture for detecting DFDs in a real-time environment. Moreover, the important factors of Multi-Access Edge Computing (MEC) and 5th generation (5G) networks are analyzed in the context of deep learning architecture to improve the response time of DFD systems. Lastly, it is concluded that there is a research gap when it comes to implementing the DFD systems on MEC and 5G technologies by using multimodal features and DL architecture.

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A wearable-glasses-based drowsiness-fatigue-detection system for improving road safety

Cited by 18 publications

References 5 publications

Internet of Diagnostic Things: Emerging Horizon towards Precision and Digital Health Care

Internet of Diagnostic Things: Emerging Horizon towards Precision and Digital Health Care

Computer Vision Based Driver Assistance Drowsiness Detection

Driver Fatigue Detection Systems Using Multi-Sensors, Smartphone, and Cloud-Based Computing Platforms: A Comparative Analysis

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