A Survey on Mobile Sensing Based Mood-Fatigue Detection for Drivers

Tu, Wei; Wei, Lei; Hu, Wenyan; Sheng, Zhengguo; Nicanfar, Hasen; Hu, Xiping; Ngai, Edith C.-H.; Leung, Victor C. M.

doi:10.1007/978-3-319-33681-7_1

Cited by 6 publications

(4 citation statements)

References 30 publications

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“…In the more recent references, one sees a trend, growing with time, in the use of mobile devices, and in particular of smartphones [ 5 , 9 , 14 , 15 , 24 , 25 , 34 , 37 , 40 , 55 ]. A smartphone is relatively low-cost, and one can easily link it to a DMS.…”

Section: Survey Of Literature On Driver Monitoringmentioning

confidence: 99%

Survey and Synthesis of State of the Art in Driver Monitoring

Halin

Verly

Droogenbroeck

2021

Sensors

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Road vehicle accidents are mostly due to human errors, and many such accidents could be avoided by continuously monitoring the driver. Driver monitoring (DM) is a topic of growing interest in the automotive industry, and it will remain relevant for all vehicles that are not fully autonomous, and thus for decades for the average vehicle owner. The present paper focuses on the first step of DM, which consists of characterizing the state of the driver. Since DM will be increasingly linked to driving automation (DA), this paper presents a clear view of the role of DM at each of the six SAE levels of DA. This paper surveys the state of the art of DM, and then synthesizes it, providing a unique, structured, polychotomous view of the many characterization techniques of DM. Informed by the survey, the paper characterizes the driver state along the five main dimensions—called here “(sub)states”—of drowsiness, mental workload, distraction, emotions, and under the influence. The polychotomous view of DM is presented through a pair of interlocked tables that relate these states to their indicators (e.g., the eye-blink rate) and the sensors that can access each of these indicators (e.g., a camera). The tables factor in not only the effects linked directly to the driver, but also those linked to the (driven) vehicle and the (driving) environment. They show, at a glance, to concerned researchers, equipment providers, and vehicle manufacturers (1) most of the options they have to implement various forms of advanced DM systems, and (2) fruitful areas for further research and innovation.

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Section: Survey Of Literature On Driver Monitoringmentioning

confidence: 99%

Survey and Synthesis of State of the Art in Driver Monitoring

Halin

Verly

Droogenbroeck

2021

Sensors

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“…Minimal training was required as instructional information was provided at each step within the smartphone application. Previous work had also verified the usability and visual design of the application [13], [14], [16]. Once the application was deployed to each participant's personal device, the final step was for participants to turn on notifications for the application so they could receive reminders to take part in each test session.…”

Section: ) Application Deploymentmentioning

confidence: 99%

Towards Mobile Cognitive Fatigue Assessment as Indicated by Physical, Social, Environmental, and Emotional Factors

Price

Galway

et al. 2019

IEEE Access

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This research sought to establish which in-situ measures of cognitive fatigue, physical activity, social interaction, location, emotional state and facial landmarks, made using a smartphone application, could be used to indicate episodes of cognitive fatigue. This assessment was realised using cognitive tests (assessing memory, attention, reaction time, information processing speed and executive function), self-assessment, contextual factors and facial feature analysis. This study also investigated the use of an ensemble algorithm for the classification of cognitive fatigue utilising facial features and a Rotation Forest approach. Selfassessment of cognitive fatigue was shown to directly correlate with reaction time through a Psychomotor Vigilance Task (r = .643, p = .001), and self-reported increases in the level of social activity (r = .377, p = .001). Facial feature analysis revealed dominant emotions of sadness and anger when participants were cognitively fatigued. It also revealed underlying facial cues that indicated higher levels of cognitive fatigue including expressions of negative valence, and Facial Action Coding System units of increased brow furrow, eyelid tightening and lip suck. In addition, a Principle Component Analysis based Rotation Forest ensemble with a ternary output demonstrated a cognitive fatigue classification accuracy of 82.17%. The findings presented indicate that the inclusion of data relating to surrounding cognitive, social, physical and emotional factors can improve the accuracy of mobile in-situ cognitive fatigue assessment using our previously validated smartphone-based cognitive fatigue assessment approach. The findings further suggest gross-level fatigue status may be potentially classified to a reasonable degree of accuracy using facial features, which may give rise to personalised in-situ fatigue detection. INDEX TERMS Affective computing, cognitive fatigue, cognitive tests, context, facial analysis, human computer interaction, machine learning, mobile applications, neuropsychology, smart healthcare. The associate editor coordinating the review of this article and approving it for publication was Kaigui Bian. through questionnaires [6], [7], which primarily takes place under clinical supervision. A limitation of administering such assessments within a clinical setting is the need for a clinician to supervise, which is costly and time consuming. It also does not allow assessment in-situ, within the daily locations and routines of the patient, which is where problematic instances of cognitive fatigue occur. Development of easier to administer single-item scales has been proven as a way of assessing cognitive fatigue more efficiently than traditional clinic-based approaches [8]-[11]. Studies using state-of-theart approaches have the possibility of utilising advances in technology, namely smartphone technology, facial feature analysis, and cognitive testing to provide a more easily accessible means of assessment [12]-[15].

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“…Therefore, IoT-based systems utilize sensor-based smartphones [ 4 ] and cloud-based architecture to develop smart cities. In practice, IoT-based [ 5 , 6 ] applications provide novel solutions to decrease traffic accidents as a result of fatigue. Due to an increasing population, driving on highways [ 7 ] is becoming more complex and challenging, even for expert drivers.…”

Section: Introductionmentioning

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 Survey on Mobile Sensing Based Mood-Fatigue Detection for Drivers

Cited by 6 publications

References 30 publications

Survey and Synthesis of State of the Art in Driver Monitoring

Survey and Synthesis of State of the Art in Driver Monitoring

Towards Mobile Cognitive Fatigue Assessment as Indicated by Physical, Social, Environmental, and Emotional Factors

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

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