Deep Learning-Based Drowsiness Detection System Using IoT

The primary focus of disaster-related literature historically has been on natural disasters due to their severity. There is less than sufficient focus in the academic world regarding man-made disasters despite their high magnitude. While the world is exposed to the hazards of natural disasters often, the frequency of man-made disasters is also very high. Seventy-five million people died in World War II due to deliberate genocides, massacres and bombings. Millions of people were displaced or made refugees from 1990 to 2003 during fifty-nine armed conflicts in forty-eight separate locations. The recent casualties in the ongoing wars between Russia and Ukraine and Israel and Palestine further strengthen the overall narrative of man-made disasters. Road traffic deaths is another critical area where lots of human lives are lost every year. The world loses 1.3 million people every year due to road traffic crashes. 1,54,732 people lost their lives in road accidents in India in the year 2019. Road traffic accidents fall in the category of man-made disasters. The present article examines road traffic accidents and deaths in Delhi. It also analyses the role of various proactive measures taken by the Delhi Traffic Police in curbing the increasing trend of fatalities on roads. These measures were implemented after proper analyses of the accident sites. These measures broadly fall in the domain of road engineering, law enforcement and education of road users, technically known as triple E (engineering, enforcement and education). Installation of over-speeding violation detection cameras and red-light violation detection cameras on affected roads has substantially reduced the number of accidents and has also acted as psychological deterrents for road users. The analysis tries to use the theoretical construct of triple T (targeting, testing and tracking) used by Prof. Sherman in the context of evidence-based policing for preventing crime.

Research Article| March 01, 2016 The Internet of Geophysical Things: Raspberry Pi Enhanced REF TEK (RaPiER) System Integration and Evaluation Frank Sepulveda; Frank Sepulveda aDepartment of Geology, Baylor University, One Bear Place #97354, Waco, Texas 76798 U.S.A.frank_sepulveda@baylor.edu Search for other works by this author on: GSW Google Scholar Jay Pulliam Jay Pulliam aDepartment of Geology, Baylor University, One Bear Place #97354, Waco, Texas 76798 U.S.A.frank_sepulveda@baylor.edu Search for other works by this author on: GSW Google Scholar Author and Article Information Frank Sepulveda aDepartment of Geology, Baylor University, One Bear Place #97354, Waco, Texas 76798 U.S.A.frank_sepulveda@baylor.edu Jay Pulliam aDepartment of Geology, Baylor University, One Bear Place #97354, Waco, Texas 76798 U.S.A.frank_sepulveda@baylor.edu Publisher: Seismological Society of America First Online: 14 Jul 2017 Online Issn: 1938-2057 Print Issn: 0895-0695 © 2016 by the Seismological Society of America Seismological Research Letters (2016) 87 (2A): 345–357. https://doi.org/10.1785/0220150234 Article history First Online: 14 Jul 2017 Cite View This Citation Add to Citation Manager Share Icon Share Facebook Twitter LinkedIn MailTo Tools Icon Tools Get Permissions Search Site Citation Frank Sepulveda, Jay Pulliam; The Internet of Geophysical Things: Raspberry Pi Enhanced REF TEK (RaPiER) System Integration and Evaluation. Seismological Research Letters 2016;; 87 (2A): 345–357. doi: https://doi.org/10.1785/0220150234 Download citation file: Ris (Zotero) Refmanager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentBy SocietySeismological Research Letters Search Advanced Search ABSTRACT The proliferation of commercial Internet of Things (IoT) devices is raising consumers’ awareness of the benefits of enhancing everyday objects with the ability to communicate, sense, and process information. Commercial‐off‐the‐shelf (COTS) versions of the embedded technologies responsible for the rise of the IoT are easy‐to‐use, inexpensive, and relatively powerful. IoT Makers, technological do‐it‐yourself enthusiasts, utilize these technologies and the IoT ecosystem to create IoT devices that range from casual hobbyist to entrepreneurial in nature.In an effort to develop a “no‐engineer‐needed” Internet of Geophysical Things (IoGT) device, we integrated a COTS embedded computer with a geoscience‐related COTS data acquisition system (DAS). Using skills common to geoscientists, we integrated the Raspberry Pi System‐On‐Chip with the REF TEK (a Trimble brand) 130‐01 DAS. This Raspberry Pi enhanced REF TEK (RaPiER) provides IoGT Makers a platform for the development of geoscientific sensor node or network enhancements. IoGT Makers can use the RaPiER to tinker with IoT capabilities while simultaneously acquiring research‐quality data. Geoscientific (i.e., seismic) application areas that may benefit from RaPiER, particularly RaPiER nodal processing, include earthquake and engineering seismology.In this article, we discuss the emerging technologies that allow IoGT Makers to build their own IoGT device. We review our selection of the Raspberry Pi and REF TEK 130‐01 for IoGT device integration. We provide a RaPiER system integration guide and REF TEK interface software compiled for the Raspberry Pi ( available in the electronic supplement to this article). We present methods to expand RaPiER capabilities using the Raspberry Pi’s ecosystem. Last, we discuss our evaluation of RaPiER’s performance and its suitability for realistic field deployments. You do not have access to this content, please speak to your institutional administrator if you feel you should have access.

In a country like India, it is very challenging to provide medical facilities to such a large population. The lack of professionals and super-specialty hospitals has made this problem more acute. Due to excessive vehicles on the roads, the death rate in road accidents has increased a lot. Road accidents share a huge portion of casualties that happen every year across the world. VANET is a growing technology in the field of wireless communication that has enhanced the traffic system and helped in reducing road accidents. Wireless body sensor network provides a continuous health monitoring of a patient without hammering daily life activities and can transmit physical health information over wireless networks in real-time. Integration of WBSN and VANET can be used to develop a framework for remote health monitoring, smart accident detection, real-time alerts to the ambulance, and optimizing the traffic for the ambulance. In this paper, we have presented India’s road traffic safety and accident scenario. We have identified the major causes of road accidents in India. We have given an overview of VANET and WBSN related technologies, their requirements, and applications. Moreover, we proposed a framework of WBSN and VANET based healthcare monitoring systems to provide emergency treatment during traveling in Indian perspectives. We have critically analyzed the implementation issues of the proposed health monitoring system in India.

There's one death every 4 minutes due to road accidents in India. One of the main reasons for these road accidents to happen is poor road conditions. Lately, the roads are becoming harder to maintain due to the extreme weather conditions of India. It is a challenging task for the municipality to patrol across the city to check the condition of the roads. People complain about bad roads all the time but have no way to detect or report them at scale. To tackle this problem, we will be mounting a camera connected to a raspberry pi, onto a vehicle. This camera is going to capture the image of the road in front of the vehicle. An onboard object detection model will be run on the input images from the camera for detecting any road deformations like potholes, cracks, or ruts. When the model detects any kind of deformation on the road, the latitude and longitude of that location will be immediately logged into an onboard or online database. A GPS module can be used to detect the lat long coordinates. This information can then be passed on to the civic authorities with proper visualizations on a 2D map, which can be helpful for the construction workers to directly go to the location and do the necessary repair works, which means workers can spend less time finding potholes and more time fixing them.

Driver drowsiness detection is a system that uses various sensors and algorithms to detect when a driver is becoming fatigued or drowsy. When a driver is drowsy, they are not operating in full alert mode rather, they are only somewhat tired. The majority of traffic accidents throughout the world are caused by fatigued and sleepy drivers. This can be done through monitoring eye movements, facial expressions, and head position, as well as analyzing driving patterns such as sudden lane changes or prolonged periods of inactivity on the steering wheel. The system can then alert the driver to take a break or pull over to rest, potentially preventing accidents caused by drowsy driving. It can also be used in commercial vehicles for safety of drivers and other people on the road. Accidents in the present day are increasingly being caused by this consisting of ocular fatigue. These characteristics show that the driver's condition is not right. For the purpose of detecting sleepiness, the ratio of distances between the horizontal and vertical eye landmarks is calculated using the EAR (Eye Aspect Ratio). The proposed method calculates the landmark since the landmarks are identified precisely enough to do so. This study determines the eye aspect ratio (EAR), a single scalar quantity that characterizes the opening of the eyes in each frame, and extracts it. Finally, an SVM classifier developed by Haar Cascade recognizes eye blinks as a pattern of EAR values. However, both driver fatigue and distraction may result in slower response times, reduced driving efficiency, and a higher risk of being involved in an accident. The output is delivered to the detection system, and the alert will be activated, if the driver's degree of fatigue and the estimated amount of sleepiness are determined. The true number of accidents brought on by driver fatigue is difficult to ascertain because it is frequently under reported. The driver typically doesn't notice the small change from being tired to nodding off. This explains why it is critical to continue research in this area with the goal of reducing the frequency of driver drowsy accidents and motivating for a driver sleepiness detection system.

The cases of road accidents in India are increasing day by day. Due to this a significant amount of death is recorded due to the gap between emergency responders and accident victims. In this context, a number of car making companies started developing accident prevention and notification system which are costly and not able to maintain by all the vehicle users. Thus to enhance the reach of such applications for road safety measures a new system is required. In this work we present, a new Location-based Vehicle Accident Safety Measure (LVASM) system developed using the android platform. The LVASM has zero cost and can help any vehicle users by notifying the emergency responders. The proposed LVASM application based notification system works in three modules to assist the user's, first initialization, second observation, and third is notification. The LVASM model is able to efficiently respond means detect the accident and start to send the notifications to the emergency responders. Additionally, the use of multiple responders makes it more valuable. The experimental analysis demonstrates the application accurately detects the accident and start notification automatically. The LVASM system helps to save a life during road accident scenarios. The comparison with the two similar accidental prevention systems shows the LVASM working model efficient in terms of the time of response or detection of accidental situation.

Pattern of Road Traffic Accidents in Bhubaneswar, Odisha

The proposed system aims to enhance transportation safety by reducing the number of accidents caused by driver fatigue and drowsiness, which have become increasingly common. The system analyzes facial and bodily gestures such as eye tiredness and yawning to identify signs of drowsiness and fatigue. Eye aspect ratio (EAR) is used to detect drowsiness by measuring the ratio of distances between horizontal and vertical eye landmarks. Yawn detection is performed by calculating a YAWN value based on the distance between the upper and lower lips and comparing it to a threshold value. The system incorporates an eSpeak module, a text-to-speech synthesizer, to provide appropriate voice alerts when drowsiness or yawning is detected. The ultimate goal of the proposed system is to reduce the number of fatalities resulting from road accidents and contribute to technology advancements. Keywords: Drowsiness, eSpeak module, Eye aspect ratio, Yawn Detection.

Fatigue-induced cognitive impairment is a leading contributor to road fatalities, with motorcyclists representing a particularly vulnerable demographic due to the lack of active safety features in two-wheelers. While Deep Learning models (such as CNNs) offer high detection accuracy, their computational demands render them unsuitable for battery-powered, wearable safety devices. This paper presents the design and implementation of a resource-efficient "Smart Helmet" system for real-time vigilance monitoring. Unlike computationally intensive neural networks, the proposed framework utilizes an optimized Histogram of Oriented Gradients (HOG) and Linear SVM pipeline to extract facial features on constrained edge hardware (Raspberry Pi). By leveraging the scalar Eye Aspect Ratio (EAR) metric, the system detects microsleeps with high-frequency inference (>15 FPS) without reliance on cloud connectivity. The system integrates visual monitoring with haptic feedback, validating its potential as a standalone, low-latency safety solution for riders.

In the logistics industry, fresh food transportation requires effective management of driver fatigue to maintain safety and service quality. With the increasing digital connectivity, Internet of Things (IoT) technology has become a promising solution to monitor driver fatigue in real-time. This research aims to design an adaptive IoT-based driver fatigue monitoring system specifically for fresh food transportation. Through a case study in the logistics industry, we identified specific challenges and needs in driver fatigue management. Based on the identification results, we developed a system that utilizes IoT sensors integrated in vehicles and drivers' smartphones. The system automatically monitors the driver's physical condition and behavior and provides real-time alerts if signs of fatigue are detected. With an adaptive approach, the system can customize recommendations and interventions according to the measured fatigue level. The results of the case study show that the designed system is able to reduce the risk of driver fatigue, increase transportation safety, and improve service quality in fresh food transportation. This research contributes to the practical understanding of the implementation of IoT technology in driver fatigue management, particularly in the context of the logistics and fresh food transportation industries.

The application of health protocols is a regulation that is applied to prevent the spread of Covid-19. Public awareness of the implementation of health protocols is still lacking. This study aims to determine the performance of the detection system using the Blimp Drone as an effort to mitigate the spread of Covid-19 based on the Internet of Things. The method used in system development consists of literature review, needs analysis, design, manufacture, and testing. This system uses the Blimp Drone as a vehicle to carry out flight missions. Raspberry pi camera as a component for distance detection in crowds and mask detection, and Flir Lepton 2.5 as a component for temperature detection. In addition, thealso used raspberry pi 4 is as a component for image processing of the distance detection system for crowd, mask, and temperature detection. The results of the tests carried out are: (1) the results of the analysis of the lifting power of the Blimp Drone using pure helium and a diameter of 0.91m capable of lifting a load of 0.45kg (2) the system can detect the distance of each person in the crowd (3) the system can detect everyone who using a mask and not wearing a mask (4) the system can detect the temperature of each person using image processing (5) an audio warning sounds when a person who is not wearing a mask is detected and the distance between 2 people in a crowd of less than 1 meter is detected (6) the advantages of this tool ie can work autonomous in flight missions to detect the distance the crowd, masks, and temperature, as well as integrated with system the Internet of Things so that data the results capturing can be accessed in real time for further action by the authorities.

Internet of Things (IoTs) is a technology that links objects and mechanisms to the Internet. IoT devices are deployed in a number of areas within the transportation sector, notably in security and surveillance. The ability to track driving behaviour on the road using IoT can improve driver safety and reduce traffic accidents. In this paper, a prototype system to detect a driver’s drowsiness is proposed. In particular, a facial landmark is used to extract the eye region, the eye aspect ratio, and closure patterns. This system will audibly alert the driver and invoke third party notification if drowsiness of the driver is detected. Through this system, traffic accidents caused by drowsy drivers can be avoided and driving behaviour can be evaluated by relevant personnel or employers.

The widespread adoption of additive manufacturing (AM), also known as 3D printing, highlights the importance of maintaining stringent quality standards in 3D-printed parts. This work introduces an innovative approach integrating Industry 4.0 with AM, focusing on harnessing the Internet of Things (IoT) for feedback control to swiftly detect defects. Operating within the framework of AM as a service (AMaaS), the system efficiently manages multiple 3D printers to meet client demands. Utilizing Raspberry Pi (RPi) and advanced Python socket modules, a user-friendly graphical interface (GUI) enriched with live video streams enhances user interaction. Clients can securely upload STL files over the Internet, accessing the system via robust authentication mechanisms. Live video feeds from RPi high-quality (HQ) cameras offer real-time insights into the printing process, while a deep learning (DL)-based convolutional neural network (CNN) model accurately detects warpage defects. Trained on images captured by RPi cameras, the CNN model’s performance metrics are systematically explored. This research integrates Industry 4.0 principles with 3D printing, advancing AMaaS with proactive quality control and live monitoring, showcasing innovative DL-based defect detection. The framework contributes to smart AM practices, promising widespread applicability across industries.

In recent studies, drowsiness of drivers while driving is seen as one of the most important causes of deaths due to traffic accidents. Therefore, in this study, an algorithm that determines driver fatigue by examining the driver's eye conditions in real-time is proposed. Python versions of OpenCV (Open Source Computer Vision Library) and machine learning libraries were used to create this proposed algorithm. In the study, the face and eyes were detected with the Cascade classifier using Haar features. Detected eyes were classified as “open” “closed” or “half open” with the model trained by the SVM (Support Vector Machine) method according to the HOG (Histogram of Oriented Gradient) feature descriptor. Driver fatigue was decided according to the PERCLOS (Percentage of Eyelid Closure) method, which examines the percentage of eyelid closure over time. In the proposed system, fatigue was detected according to the time the driver's eyes were in the “half-open” or “closed” state, and a related user interface was designed and audio and visual warnings were given to the driver.

Overspeeding on highways is a leading cause of fatal road accidents in India. Traditional traffic enforcement methods are often labor-intensive, prone to error, and inefficient for high-volume traffic. This paper proposes an automated system for detecting overspeeding vehicles and recognizing their license plates using deep learning (YOLOv8, OCR) models and computer vision (image-based analysis). The system leverages the YOLOv8 object detection model to identify vehicles in real-time from surveillance footage. A Region of Interest is defined to calculate vehicle speed. If a vehicle exceeds the predefined speed limit, its image is captured. Subsequently, the number plate is detected, and the characters are extracted using EasyOCR. The recognized plate number can then be used to identify the vehicle's owner and send an automated alert. This approach aims to overcome the limitations of conventional systems by providing a scalable, accurate, and efficient solution for traffic law enforcement, ultimately enhancing road safety.