Drivers Drowsiness Detection with Speed Limiter Integrated Fatigue Anlyzer (SLIFA) on Fuel Tank Truck

Pranoto, Hadi; Leman, Abdul Mutalib; Sukmajati, Danto; Hanum, Bethriza; Baba, Ishak

doi:10.30880/ijie.2018.10.02.013

Cited by 5 publications

(5 citation statements)

References 8 publications

(5 reference statements)

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“…Another function of the HSCU is to carry out calculations related to the input of each sensor in the vehicle. For example, evaluating the monitoring of physical safety through heart rate sensors and then setting the rules that have been provided to then change the vehicle access control [12][13] as an actuator. With the existence of HSCU in E-Health Monitoring, it supports input from sensors in real-time in responding to decisions or preventive actions.…”

Section: E In-vehicle E-health Monitoring (Iv-ehm) Frameworkmentioning

confidence: 99%

“…These data are then integrated with the report and monitoring process [6][7]. Several research results prove that monitoring the health and fitness condition of drivers based on IoT can progress the efficiency of the transportation business, cut the cost of driver health care and reduce the risk of road accidents [5] [8][9][10][11][12][13]. Health services that are integrated into vehicles are certainly a challenge for developers as well as users [14].…”

Section: Introductionmentioning

confidence: 99%

“…Health services that are integrated into vehicles are certainly a challenge for developers as well as users [14]. In addition, the system must also ensure the reliability of the connectivity of sensor devices in vehicles [15] with the application of health care centres on the internet, as well as produce responses in emergencies [12] [16]. Generally, the IoT framework is built based on stakeholders' interests for specific business needs based on the chosen architecture and standardization models [17].…”

Section: Introductionmentioning

confidence: 99%

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Design of Health Monitoring Framework Model using oneM2M Standard

Adriansyah

Amrullah

2021

IJEEPSE

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The causes of traffic accidents are affected by human factors. Driver’s illness, such as exhaustion, drowsiness, and other chronic diseases, are the critical reasons for this. The creation of the Internet of Things (IoT) technology has tried to resolve these issues. The emphasis is on tracking and regulating driving safety and conditions. Unfortunately, there is no uniform IoT standard for this device. This study aims to provide a model for monitoring and handling the situation of drivers by combining the E-Health Tracking (EHM) and the Automotive Health and Safety (AHS) frameworks. The results of the system design are referred to as In-vehicle E-Health Monitoring (IV-EHM). The IV-EHM framework model analysis based on the oneM2M standard has been carried out. Based on the study, it can be said that the system has the specified requirements.

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Section: E In-vehicle E-health Monitoring (Iv-ehm) Frameworkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Design of Health Monitoring Framework Model using oneM2M Standard

Adriansyah

Amrullah

2021

IJEEPSE

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“…The mechanism to integrate both of them physically using the Universal Serial Bus (USB) cable, and programmatically using serial programming. The SLIFA device connected as client device, while the PC or Computer installed with SLIFA application act as a server [16]. The architecture to integrated these two devices describe as shown in Figure 11.…”

Section: Integrating the Devices And The Applicationmentioning

confidence: 99%

Speed limiter integrated fatigue analyzer (SLIFA): engineering design and concept

et al. 2019

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Main issue in urban transportation system is traffic accident which caused by several factors where there are two factors that has a significant contribution in accident are overspeed and fatigue. Therefore, main objective of this research is to develop Speed Limiter Integrated Analyzer (SLIFA). This device will be controlling the speed by cutting off the fuel supply to the engine when driver's fatigue or speeds are beyond limitation. This research was consists of four steps which are Step 1 is study literature on types of vehicle. Step 2, detail engineering design that is focused in this research. Step 3 is describing the target of SLIFA installation. Step 4 is fabricating and testing of SLIFA on truck and bus in range of July 2017 to January 2018. The result shows the appropriate voltage for speed at 70 km/h is 10.7 volt for limiting the speed. The traffic accident has succesfully decreased up to 47% after SLIFA installed on truck and bus. It can be concluded that the SLIFA application on transportation especially on truck and bus was much recommended to reduce traffic accident and play an appropriate government regulation.

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“…Li and his co-authors [5] used Fp1 and O1 EEG channels to discriminate between alert and drowsy. Pranoto et al [6] detected drowsiness in truck drivers using a speed limiter integrated fatigue analyser. The system is based on Arduino UNO, sensing the driver's body temperature and heart rate.…”

Section: Introductionmentioning

confidence: 99%

Drowsiness Detection using Band Power and log Energy Entropy Features Based on EEG Signals

Krishnan¹,

Yaacob²

2019

IJITEE

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Sleeping on the wheels due to drowsiness is one of the significant causes of death tolls all over the world. The primary reason for the sleepiness is due to lack of sleep and irregular sleep patterns. Several methods such as unobtrusive sensors, vehicle dynamics and obtrusive physiology sensors are used to diagnose drowsiness in drivers. However, the unobtrusive sensors detect drowsiness in the later stage. Whereas the physiological methods use obtrusive sensors such as electro-ocular, electro-myo and electro-encephalograms produce high accuracy in the early detection of drowsiness, which makes them a preferable solution. The objective of this research article is to classify drowsiness with alertness based on the electroencephalographic (EEG) signals using band power and log energy entropy features. A publicly available ULg DROZY database used in this research. The raw multimodal signal is processed to extract the five EEG channels. A passband filter with the cut off frequencies of 0.1 Hz and 50 Hz attenuates the high-frequency components. Another bandpass filter bank is designed to slice the raw signals into eight sub-bands, namely delta, theta, low alpha, high alpha, low beta, mid-beta, high beta and gamma. The preprocessed signals are segmented into an equal number of frames with a frame duration of 2 seconds using a rectangular time windowing approach with an overlap of 50%. Frequency domain features such as log energy entropy and band power were extracted. The extracted feature sets were further normalised between 0 and 1 and labelled as drowsy and alert and then combined to form the final dataset. The K-fold cross-validation method is used to divide the dataset into training and testing sets. The processed dataset is then trained using Discriminant analysis, k-nearest neighbour network, Binary decision tree, ensemble, Naive Bayes and support vector machine classifiers and the results are compared with the literature. The kNN classifier produces 95% classification accuracy. The developed model can provide a tool for drowsiness detection in drivers

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Drivers Drowsiness Detection with Speed Limiter Integrated Fatigue Anlyzer (SLIFA) on Fuel Tank Truck

Cited by 5 publications

References 8 publications

Design of Health Monitoring Framework Model using oneM2M Standard

Design of Health Monitoring Framework Model using oneM2M Standard

Speed limiter integrated fatigue analyzer (SLIFA): engineering design and concept

Drowsiness Detection using Band Power and log Energy Entropy Features Based on EEG Signals

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