Health Drive: Mobile Healthcare Onboard Vehicles to Promote Safe Driving

Hu, Xiping; Li, Xitong; Ngai, Edith C.-H.; Zhao, Jidi; Leung, Victor C. M.; Nasiopoulos, Panos

doi:10.1109/hicss.2015.371

Cited by 18 publications

(8 citation statements)

References 24 publications

(28 reference statements)

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“…Moreover, WSNs are appropriate for time-sensitive applications; for instance, the traffic management and vehicular safety. That it could collaborate with another cloud tier to alert drivers about road anomalies through their smartphones in real-time 6,7 .…”

Section: Wireless Sensor Networkmentioning

confidence: 99%

Data Mining and Fusion Techniques for WSNs as a Source of the Big Data

Fouad

Oweis

Gaber

et al. 2015

Procedia Computer Science

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Section: Wireless Sensor Networkmentioning

confidence: 99%

Data Mining and Fusion Techniques for WSNs as a Source of the Big Data

Fouad

Oweis

Gaber

et al. 2015

Procedia Computer Science

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“…In practice, those approaches mostly depended on the road-shape, the way of driving, and performance of the vehicle. The author utilized electroencephalograph (EEG), electrocardiogram (ECG), electrooculography (EOG), and surface electromyogram (sEMG) sensors to predict driver fatigue [ 131 , 132 , 133 ]. The authors detected wake and sleep conditions of drivers based on the sensors.…”

Section: Iot-based Architectures For Dfd Systemsmentioning

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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“…Based on mobile cloud platform, a depression diagnosis approach was presented in [21]. In a mobile cloud based health drive system, a vehicular social network was established to collect and process diverse sensing data [59], so that safety improvement service was delivered to drivers in a real-time manner. A non-contact Electrocardiogram (ECG) measurement system was established relying on mobile cloud infrastructure [44], through which health issues for elderly people could be reported in an efficient manner.…”

Section: Telemonitoring and Biosignal Processingmentioning

confidence: 99%

An Overview of Mobile Cloud Computing for Pervasive Healthcare

Wang

Jin

2019

IEEE Access

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Mobile devices, along with wearable sensors, allow patients to access healthcare services from anywhere at any time. The longstanding constraints of computational capability and storage space on mobile devices can be alleviated by outsourcing computation-or data-intensive tasks to remote cloud centers. Thus, mobile cloud computing (MCC) has been recognized as a promising approach to provide pervasive healthcare services to people in their daily life. As the development and adoption of MCC techniques in healthcare, new optimization strategies have been explored and studied to help mobile cloud healthcare services to be deployed in a more effective and efficient manner. In this survey, we demonstrate how MCC techniques have been extensively deployed in various healthcare applications and, specifically, describe the general architecture and design considerations one should take into account while designing an MCC for healthcare scenarios. Given a large number of factors that may affect the performance of the MCC and even result in catastrophic consequences in healthcare, this paper presents the state-of-the-art optimization methods on the MCC for meeting the diverse priorities and achieving the optimal tradeoff among multiple objectives. Finally, the security and privacy issues of the MCC in healthcare are also discussed. INDEX TERMS Mobile cloud computing, pervasive healthcare. optimization, offloading, security and privacy. NOMENCLATURE

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Health Drive: Mobile Healthcare Onboard Vehicles to Promote Safe Driving

Cited by 18 publications

References 24 publications

Data Mining and Fusion Techniques for WSNs as a Source of the Big Data

Data Mining and Fusion Techniques for WSNs as a Source of the Big Data

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

An Overview of Mobile Cloud Computing for Pervasive Healthcare

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