Evaluation of driver drowsiness using respiration analysis by thermal imaging on a driving simulator

Kiashari, Serajeddin Ebrahimian Hadi; Nahvi, Ali; Bakhoda, Hamidreza; Homayounfard, Amirhossein; Tashakori, Masoumeh

doi:10.1007/s11042-020-08696-x

Cited by 47 publications

(36 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Studies of thermal IR imaging in driving contexts stated that a rise in mental workload leads to an increase in the difference between nose and forehead temperature [108,110]. Moreover, thermal IR imaging was demonstrated to be a valuable indicator of the driver arousal level, from alertness to drowsiness [64,111]. A recent study employed it to detect human thermal discomfort in order to develop a fully automated climate control system in the vehicles [112].…”

Section: Thermal Ir Imaging-based Affective Computing In Intelligent mentioning

confidence: 99%

Thermal Infrared Imaging-Based Affective Computing and Its Application to Facilitate Human Robot Interaction: A Review

et al. 2020

View full text Add to dashboard Cite

Over recent years, robots are increasingly being employed in several aspects of modern society. Among others, social robots have the potential to benefit education, healthcare, and tourism. To achieve this purpose, robots should be able to engage humans, recognize users’ emotions, and to some extent properly react and "behave" in a natural interaction. Most robotics applications primarily use visual information for emotion recognition, which is often based on facial expressions. However, the display of emotional states through facial expression is inherently a voluntary controlled process that is typical of human–human interaction. In fact, humans have not yet learned to use this channel when communicating with a robotic technology. Hence, there is an urgent need to exploit emotion information channels not directly controlled by humans, such as those that can be ascribed to physiological modulations. Thermal infrared imaging-based affective computing has the potential to be the solution to such an issue. It is a validated technology that allows the non-obtrusive monitoring of physiological parameters and from which it might be possible to infer affective states. This review is aimed to outline the advantages and the current research challenges of thermal imaging-based affective computing for human–robot interaction.

show abstract

Section: Thermal Ir Imaging-based Affective Computing In Intelligent mentioning

confidence: 99%

Thermal Infrared Imaging-Based Affective Computing and Its Application to Facilitate Human Robot Interaction: A Review

et al. 2020

View full text Add to dashboard Cite

show abstract

“…The accuracy achieved by the proposed method is compared with some state-of-the-art techniques mentioned by the investigators to detect drowsiness. From Table 5 , it can be seen that [ 37 , 43 , 44 ] show better accuracy than the proposed method. However, [ 37 ] used an oximeter, and in [ 43 ], thermal imaging was used to acquire the respiration rate.…”

Section: Discussionmentioning

confidence: 99%

“…Facial thermal imaging respiration analysis was proposed in [ 43 ] to detect driver drowsiness. Respiration causes temperature changes under the nostrils which was detected by thermal imaging.…”

Section: Related Workmentioning

confidence: 99%

Non-Invasive Driver Drowsiness Detection System

Siddiqui

Saleem

Brown

et al. 2021

Sensors

View full text Add to dashboard Cite

Drowsiness when in command of a vehicle leads to a decline in cognitive performance that affects driver behavior, potentially causing accidents. Drowsiness-related road accidents lead to severe trauma, economic consequences, impact on others, physical injury and/or even death. Real-time and accurate driver drowsiness detection and warnings systems are necessary schemes to reduce tiredness-related driving accident rates. The research presented here aims at the classification of drowsy and non-drowsy driver states based on respiration rate detection by non-invasive, non-touch, impulsive radio ultra-wideband (IR-UWB) radar. Chest movements of 40 subjects were acquired for 5 m using a lab-placed IR-UWB radar system, and respiration per minute was extracted from the resulting signals. A structured dataset was obtained comprising respiration per minute, age and label (drowsy/non-drowsy). Different machine learning models, namely, Support Vector Machine, Decision Tree, Logistic regression, Gradient Boosting Machine, Extra Tree Classifier and Multilayer Perceptron were trained on the dataset, amongst which the Support Vector Machine shows the best accuracy of 87%. This research provides a ground truth for verification and assessment of UWB to be used effectively for driver drowsiness detection based on respiration.

show abstract

“…In a real-time environment, it is important to detect and monitor driver behavior to save human lives. To resolve this problem, there were many automatic driver fatigue detection systems [ 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 ] developed in past studies. Several computer vision-based applications were developed in the past to detect and predict driver fatigue.…”

Section: Study Backgroundmentioning

confidence: 99%

“…Primarily, they used linear discriminate analysis (LDA), Naive Bayes, k-Nearest Neighbors (k-NN), Decision Trees (DT), artificial neural networks (ANN), support vector machine (SVM), and particle swarm optimization (PSO) [ 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 ]. Furthermore, some authors developed a hybrid system to combine visual features and non-visual features [ 46 ] for classification of the driver’s drowsiness. In the subsequent paragraphs, we discussed both the advantages and disadvantages of those systems.…”

Section: Study Backgroundmentioning

confidence: 99%

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

Abbas

Alsheddy

2020

Sensors

View full text Add to dashboard Cite

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.

show abstract

Evaluation of driver drowsiness using respiration analysis by thermal imaging on a driving simulator

Cited by 47 publications

References 48 publications

Thermal Infrared Imaging-Based Affective Computing and Its Application to Facilitate Human Robot Interaction: A Review

Thermal Infrared Imaging-Based Affective Computing and Its Application to Facilitate Human Robot Interaction: A Review

Non-Invasive Driver Drowsiness Detection System

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

Contact Info

Product

Resources

About