Design of Smart Steering Wheel for Unobtrusive Health and Drowsiness Monitoring

Babušiak, Branko; Hajdučík, Adrián; Medvecky, Stefan; Lukac, Michal; Klarák, Jaromír

doi:10.3390/s21165285

Cited by 10 publications

(3 citation statements)

References 58 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Where fatigue detection is done by multi-feature weighted sum for fatigue state recognition in which different weights were assigned to different factors. In [25], a threshold-based system is proposed that monitors heart rate and blood oxygenation, as measured by an electrocardiograph and oximeter integrated into the steering wheel. An inertial unit was also used to study the driving pattern of a driver.…”

Section: A Thresholding-based Techniquesmentioning

confidence: 99%

IoT-Based Non-Intrusive Automated Driver Drowsiness Monitoring Framework for Logistics and Public Transport Applications to Enhance Road Safety

et al. 2023

View full text Add to dashboard Cite

The exponential growth in road accidents has led to a need for continuous driver monitoring to enhance road safety. Existing techniques rely on vehicle sensor-based and behavior analysis-based approaches, where the behavior analysis-based approaches are generally considered more desirable as they enable reliable detection of a more elaborate set of driver behaviors. They are categorized as intrusive and non-intrusive approaches. Unlike intrusive approaches that generally rely on constant direct human contact with sensors (physiological signals) and are sensitive to artifacts, non-intrusive approaches offer a more effective behavior monitoring using computer vision-based techniques. This paper proposes an end-to-end non-intrusive IoT-based automated framework to monitor driver behaviors, designed specifically for logistic and public transport applications. It consists of an embedded system, edge computing and cloud computing modules, and a mobile phone application, in an attempt to provide a holistic unified solution for drowsiness detection, monitoring, as well as evaluation of drivers. Drowsiness detection is based on detecting sleeping, yawning, and distraction behaviors using an image processing-based technique. To minimize the effects of latency, throughput, and packet losses, edge computing is performed using commercial off-the-shelf embedded boards. Moreover, a cloud-hosted real-time database for remote monitoring on interactive Android mobile application has been set up, where admin can add multiple drivers to get drowsiness notifications along with other useful related information for driver evaluation. An extensive experimental testing has been performed, obtaining encouraging results. An overall accuracy of 96% is achieved along with an enhanced robustness, portability, and usability of the proposed framework.INDEX TERMS ADAS, driver drowsiness, road safety, driver monitoring, IoT, edge computing, mobile application, road accidents, cloud computing.

show abstract

Section: A Thresholding-based Techniquesmentioning

confidence: 99%

IoT-Based Non-Intrusive Automated Driver Drowsiness Monitoring Framework for Logistics and Public Transport Applications to Enhance Road Safety

et al. 2023

View full text Add to dashboard Cite

show abstract

“…These systems detect the opening and closing of the eyelids or looking aside and notify the driver with a buzzer, but this does not affect the vehicle control. Babusiak et al developed a smart steering wheel for detecting drowsiness [ 57 ]. This steering detects heart rate, heart rate variability, and blood oxygenation.…”

Section: Drowsiness Detection and Estimation Based On Graphic Information Of A Drivermentioning

confidence: 99%

Trends and Future Prospects of the Drowsiness Detection and Estimation Technology

Arakawa

2021

Sensors

View full text Add to dashboard Cite

Drowsiness is among the important factors that cause traffic accidents; therefore, a monitoring system is necessary to detect the state of a driver’s drowsiness. Driver monitoring systems usually detect three types of information: biometric information, vehicle behavior, and driver’s graphic information. This review summarizes the research and development trends of drowsiness detection systems based on various methods. Drowsiness detection methods based on the three types of information are discussed. A prospect for arousal level detection and estimation technology for autonomous driving is also presented. In the case of autonomous driving levels 4 and 5, where the driver is not the primary driving agent, the technology will not be used to detect and estimate wakefulness for accident prevention; rather, it can be used to ensure that the driver has enough sleep to arrive comfortably at the destination.

show abstract

“…In addition, Lee B. et al propose employing remote physiological monitoring using Bluetooth to measure main parameters of consciousness state, such as electroencephalogram (EEG) and respiration signals of a driver in the time and frequency domains [10]. In order to reduce the stress of connected sensors or measurement techniques that might disturb the driver from his main tasks, Barbusiak et al [11] propose employing a smart steering wheel to record and analyze the movement patterns performed by the driver while driving. The developed intelligent steering wheel is usable in applications designed for monitoring the driver's state of health, drowsiness, or driving style.…”

mentioning

confidence: 99%

Advanced e-Call Support Based on Non-Intrusive Driver Condition Monitoring for Connected and Autonomous Vehicles

Minea

Dumitrescu

Costea

2021

Sensors

View full text Add to dashboard Cite

Background: The growth of the number of vehicles in traffic has led to an exponential increase in the number of road accidents with many negative consequences, such as loss of lives and pollution. Methods: This article focuses on using a new technology in automotive electronics by equipping a semi-autonomous vehicle with a complex sensor structure that is able to provide centralized information regarding the physiological signals (Electro encephalogram—EEG, electrocardiogram—ECG) of the driver/passengers and their location along with indoor temperature changes, employing the Internet of Things (IoT) technology. Thus, transforming the vehicle into a mobile sensor connected to the internet will help highlight and create a new perspective on the cognitive and physiological conditions of passengers, which is useful for specific applications, such as health management and a more effective intervention in case of road accidents. These sensor structures mounted in vehicles will allow for a higher detection rate of potential dangers in real time. The approach uses detection, recording, and transmission of relevant health information in the event of an incident as support for e-Call or other emergency services, including telemedicine. Results: The novelty of the research is based on the design of specialized non-invasive sensors for the acquisition of EEG and ECG signals installed in the headrest and backrest of car seats, on the algorithms used for data analysis and fusion, but also on the implementation of an IoT temperature measurement system in several points that simultaneously uses sensors based on MEMS technology. The solution can also be integrated with an e-Call system for telemedicine emergency assistance. Conclusion: The research presents both positive and negative results of field experiments, with possible further developments. In this context, the solution has been developed based on state-of-the-art technical devices, methods, and technologies for monitoring vital functions of the driver/passengers (degree of fatigue, cognitive state, heart rate, blood pressure). The purpose is to reduce the risk of accidents for semi-autonomous vehicles and to also monitor the condition of passengers in the case of autonomous vehicles for providing first aid in a timely manner. Reported abnormal values of vital parameters (critical situations) will allow interveneing in a timely manner, saving the patient’s life, with the support of the e-Call system.

show abstract

Design of Smart Steering Wheel for Unobtrusive Health and Drowsiness Monitoring

Cited by 10 publications

References 58 publications

IoT-Based Non-Intrusive Automated Driver Drowsiness Monitoring Framework for Logistics and Public Transport Applications to Enhance Road Safety

IoT-Based Non-Intrusive Automated Driver Drowsiness Monitoring Framework for Logistics and Public Transport Applications to Enhance Road Safety

Trends and Future Prospects of the Drowsiness Detection and Estimation Technology

Advanced e-Call Support Based on Non-Intrusive Driver Condition Monitoring for Connected and Autonomous Vehicles

Contact Info

Product

Resources

About