Journal articles on the topic 'Analysis of human drowsiness'
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Murthy, K. Sree Rama, Bhavana Siddineni, Vijay Kashyap Kompella, Kondaveeti Aashritha, Boddupalli Hemanth Sri Sai, and V. M. Manikandan. "An Efficient Drowsiness Detection Scheme using Video Analysis." International Journal of Computing and Digital Systems 11, no. 1 (January 20, 2022): 573–81. http://dx.doi.org/10.12785/ijcds/110146.
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Mashko, Alina. "SUBJECTIVE METHODS FOR ASSESSMENT OF DRIVER DROWSINESS." Acta Polytechnica CTU Proceedings 12 (December 15, 2017): 64. http://dx.doi.org/10.14311/app.2017.12.0064.
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The paper deals with the issue of fatigue and sleepiness behind the wheel, which for a long time has been of vital importance for the research in the area of driver-car interaction safety. Numerous experiments on car simulators with diverse measurements to observe human behavior have been performed at the laboratories of the faculty of the authors. The paper provides analysis and an overview and assessment of the subjective (self-rating and observer rating) methods for observation of driver behavior and the detection of critical behavior in sleep deprived drivers using the developed subjective rating scales.
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Ogino, Mikito, and Yasue Mitsukura. "Portable Drowsiness Detection through Use of a Prefrontal Single-Channel Electroencephalogram." Sensors 18, no. 12 (December 18, 2018): 4477. http://dx.doi.org/10.3390/s18124477.
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Drowsiness detection has been studied in the context of evaluating products, assessing driver alertness, and managing office environments. Drowsiness level can be readily detected through measurement of human brain activity. The electroencephalogram (EEG), a device whose application relies on adhering electrodes to the scalp, is the primary method used to monitor brain activity. The many electrodes and wires required to perform an EEG place considerable constraints on the movement of users, and the cost of the device limits its availability. For these reasons, conventional EEG devices are not used in practical studies and businesses. Many potential practical applications could benefit from the development of a wire-free, low-priced device; however, it remains to be elucidated whether portable EEG devices can be used to estimate human drowsiness levels and applied within practical research settings and businesses. In this study, we outline the development of a drowsiness detection system that makes use of a low-priced, prefrontal single-channel EEG device and evaluate its performance in an offline analysis and a practical experiment. Firstly, for the development of the system, we compared three feature extraction methods: power spectral density (PSD), autoregressive (AR) modeling, and multiscale entropy (MSE) for detecting characteristics of an EEG. In order to efficiently select a meaningful PSD, we utilized step-wise linear discriminant analysis (SWLDA). Time-averaging and robust-scaling were used to fit the data for pattern recognition. Pattern recognition was performed by a support vector machine (SVM) with a radial basis function (RBF) kernel. The optimal hyperparameters for the SVM were selected by the grind search method so as to increase drowsiness detection accuracy. To evaluate the performance of the detections, we calculated classification accuracy using the SVM through 10-fold cross-validation. Our model achieved a classification accuracy of 72.7% using the PSD with SWLDA and the SVM. Secondly, we conducted a practical study using the system and evaluated its performance in a practical situation. There was a significant difference (* p < 0.05) between the drowsiness-evoked task and concentration-needed task. Our results demonstrate the efficacy of our low-priced portable drowsiness detection system in quantifying drowsy states. We anticipate that our system will be useful to practical studies with aims as diverse as measurement of classroom mental engagement, evaluation of movies, and office environment evaluation.
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M, Charan. "Driver Drowsiness Detection System using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (June 14, 2021): 2270–72. http://dx.doi.org/10.22214/ijraset.2021.33888.
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We propose a Driver drowsiness detection system, the purposes of which are to prevent from dangerous cause and to avoid accidents. Since all the processes on image recognition performed on a smart phone, the system does not need to send images to a server and runs on an android smart phone in a real-time way. Automatic image-based recognition is a particularly challenging task. Traditional image analysis approaches have achieved low classification accuracy in the past, whereas deep learning approaches without human supervision real-time drowsiness detection. This model classifies whether the person’s eyes are opened or closed. To recognize the face, a user should have to adjust camera such a way that it covers his face first, and then the system starts recognition within the indicated bounding boxes. In addition, the system estimates the actions of the person. This recognition process is performed repeatedly about every second. We will implement this system as Web application effectively for real-time recognition.
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Markovics, Zigurds, Juris Lauznis, Matiss Erins, Olesja Minejeva, and Raivis Kivlenieks. "Testing and Analysis of the HRV Signals from Wearable Smart HRV Sensors." International Journal of Engineering & Technology 7, no. 4.36 (December 9, 2018): 1211. http://dx.doi.org/10.14419/ijet.v7i4.36.28191.
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The objective of the test procedure is to obtain bio signals from Photoplethysmograph and Electrocardiograph sensors on selected consumer devices and to statistically validate the data for use with a drowsiness estimation method.The method selected for validation uses LF/HF ratio calculated by a set of R-R interval data to estimate drowsiness state of a human. The value LF to HF ratio calculates balance between sympathetic and parasympathetic activity that can be measured from HRV (Heart rate variability) signals. The statistical data collected are processed by using Fast Fourier Transform and HRV frequency domain analysis on a set of test participants.There is a correlation between medical ECG equipment control output and Matlab tool’s HRVAS (Burg) output of data processed from ECG based wearable smart sensor when the LF/HF ratio is calculated in all observed volunteer data. The results for Photoplethysmograph sensors of this test correlate with other tested tools but level of the values is lower, and data from optical biosensor devices which are designed to measure HRV time-domain properties as pulse did not confirm with ECG equipment results for frequency-domain analysis required for use with selected drowsiness estimation method. The result affecting factors are sensor placement, motion artefacts and discrete vendor-specific signal pre-processing of wearable device output data.The following results confirm the use of consumer grade biosensor that produces discretely pre-processed R-R interval data for the frequency based HRV method and application validation against directly processed ECG data from certified medical equipment.
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Markovics, Zigurds, Juris Lauznis, Matiss Erins, Olesja Minejeva, and Raivis Kivlenieks. "Testing and Analysis of the HRV Signals from Wearable Smart HRV Sensors." International Journal of Engineering & Technology 7, no. 4.36 (December 9, 2018): 1211. http://dx.doi.org/10.14419/ijet.v7i4.36.28214.
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The objective of the test procedure is to obtain bio signals from Photoplethysmograph and Electrocardiograph sensors on selected consumer devices and to statistically validate the data for use with a drowsiness estimation method.The method selected for validation uses LF/HF ratio calculated by a set of R-R interval data to estimate drowsiness state of a human. The value LF to HF ratio calculates balance between sympathetic and parasympathetic activity that can be measured from HRV (Heart rate variability) signals. The statistical data collected are processed by using Fast Fourier Transform and HRV frequency domain analysis on a set of test participants.There is a correlation between medical ECG equipment control output and Matlab tool’s HRVAS (Burg) output of data processed from ECG based wearable smart sensor when the LF/HF ratio is calculated in all observed volunteer data. The results for Photoplethysmograph sensors of this test correlate with other tested tools but level of the values is lower, and data from optical biosensor devices which are designed to measure HRV time-domain properties as pulse did not confirm with ECG equipment results for frequency-domain analysis required for use with selected drowsiness estimation method. The result affecting factors are sensor placement, motion artefacts and discrete vendor-specific signal pre-processing of wearable device output data.The following results confirm the use of consumer grade biosensor that produces discretely pre-processed R-R interval data for the frequency based HRV method and application validation against directly processed ECG data from certified medical equipment.
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Wilson, Nicholas, Bijay Guragain, Ajay Verma, Lewis Archer, and Kouhyar Tavakolian. "Blending Human and Machine: Feasibility of Measuring Fatigue Through the Aviation Headset." Human Factors: The Journal of the Human Factors and Ergonomics Society 62, no. 4 (June 10, 2019): 553–64. http://dx.doi.org/10.1177/0018720819849783.
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Objective To determine viability of drowsiness detection, researchers study the feasibility of photoplethysmogram (PPG) data collection from the geography of the aviation headset, correlating to electrocardiogram (ECG) reference. Background Fatigue has been a probable cause, contributing factor, or a finding in 20% of transportation incidents and accidents studied between January 2001 and December 2012. This operational hazard is particularly troublesome within aviation and airline operations. Method PPG and ECG data were collected synchronously from Federal Aviation Administration (FAA) commercially rated pilots during flight simulation in the window of circadian low (WOCL). Valid PPG and ECG data from 14 participants were analyzed, which yielded approximately 2 hr of data per participant for fatigue-related analysis. Results The results of the study demonstrate clear trends toward decreased heart rate for both ECG and PPG and suggest progression of drowsiness between four separate periods (T1, T2, T3, and T4) selected during the study; however, the mean heart rate change from T1 to T4 was statistically significant. Conclusion The results suggest that ECG and PPG data can be an important tool to observe conditions where drowsiness or fatigue may add risk to the operation. In addition, the data show high correlation between ECG and PPG data, further suggesting that a simpler PPG sensor, mounted within the geography of the aviation headset, may streamline the operationalization of important physiological data. Application Incorporation of PPG sensors and associated signal processing methods into facilitating equipment, such as the aviation headset, may add a layer to operational safety.
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Vincze, Janos. "The Sleep Modeling in the Human Organism." Clinical Research and Clinical Trials 3, no. 4 (May 28, 2021): 01–04. http://dx.doi.org/10.31579/2693-4779/039.
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There are three alternating states of vigilance throughout our lives: wakefulness, NREM, and REM sleep. We usually yawn before falling asleep. Yawning is an ancient reaction, an instinctive action, manifested in a person by drowsiness or boredom. Yawning is often associated with the need for stretching. Yawning is a less strong territorial reflex. During deep sleep muscular tone is sharply reduced. Relaxation of the muscles and the lowering of their tone, howeever, are not constant and necessary components of sleep. Analysis of EEG recordings soon revealed that sleep is by no means a uniform process, but can be divided into at least two sharply separated states: one is characterized by slow waves in the EEG that are completely separate from the activity of wakefulness: this so-called slow wave sleep; the other is the so-called paradoxical sleep. Hypnopedia, as a discipline, deals with the input of fixed information introduced during the period of natural sleep, also known as sleep learning. Our hypnopedia researches was a pleasant surprise, because they were able to reproduce texts they did not know with an efficiency of approx. 25%.
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R, Aannd, Anil G N, Rishika Sankaran, Anushruti Adhikari, and Kruthika Ravishankar. "Machine Learning Approach to Detect Drowsiness on Behavioral Parameters." YMER Digital 21, no. 01 (January 3, 2022): 1–15. http://dx.doi.org/10.37896/ymer21.01/01.
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Object detection has received a lot of research attention in recent years because of its tight association with video analysis and picture interpretation. Face detection, vehicle detection, pedestrian counting, web photos, security systems, and self-driving automobiles are all examples of object detection. With little conscious thought, the human visual system can accomplish complicated tasks such as distinguishing multiple objects and detecting impediments. Thanks to the availability of large amounts of data, faster GPUs, and improved algorithms, we can now quickly train computers to detect and classify many elements inside a picture with high accuracy. Our project is focused on building a single-access platform for various object detection tasks. A user-interface where the user is asked for the relevant inputs and an output based on this is generated automatically by the system. Also, accuracy and precision measures are also displayed so that the user is wary of their liability extent on the generated results.
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Merlhiot, Gaëtan, and Mercedes Bueno. "How drowsiness and distraction can interfere with take-over performance: A systematic and meta-analysis review." Accident Analysis & Prevention 170 (June 2022): 106536. http://dx.doi.org/10.1016/j.aap.2021.106536.
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Zhao, X., D. Hou, Y. Lin, and W. Xu. "The effect of stroboscopic effect on human health indicators." Lighting Research & Technology 52, no. 3 (September 24, 2019): 389–406. http://dx.doi.org/10.1177/1477153519871688.
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The stroboscopic effect from LED light sources can become considerable in working environments. Therefore, this study aims to explore the short-term health effect of a temporal light artefact. The experiment was carried out featuring 10 university students. Three frequencies and three modulation depths were assessed. Psychological reaction was evaluated through subjective scales, while physiological parameters were also collected for mutual validation and analysis. It was found that when the conditions are in the high-risk zone defined by IEEE Standard 1789-2015, subjects considered these conditions to be unacceptable and reported discrete spatial movement and higher visual fatigue levels. Supported by psychological and physiological evidence, it is suggested that such fatigue is caused by a higher chance of flicker. Invisible flicker also significantly affected alpha and beta wave power density, suggesting that a strobe of low frequency could potentially decrease drowsiness and increase cortical arousal. Some limitations to the work performance of this study are also discussed.
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Adriansyah, Andi, and Ahmad Ghozali Amrullah. "Design of Health Monitoring Framework Model using oneM2M Standard." International Journal of Electrical, Energy and Power System Engineering 4, no. 1 (February 28, 2021): 107–12. http://dx.doi.org/10.31258/ijeepse.4.1.107-112.
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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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Sworna Kokila, M. L., and V. Gomathi. "Inattentive Drowsiness Behavior Profile Detection with Heavy Eyed Interval Approach for Biomedical Applications in Health Monitoring System." Journal of Medical Imaging and Health Informatics 11, no. 10 (October 1, 2021): 2584–97. http://dx.doi.org/10.1166/jmihi.2021.3846.
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The efficient tracking of vehicle drivers can be used to prevent collisions through visual human behaviour analysis. Many different methods have not been satisfactory enough such as iris-sklera research, driver’s approximation of gaze, and Hough transforming technological performance. Since these methods make it more difficult to spot drivers’ sleepiness and carelessness. This paper therefore suggested that it be careful to estimate the profile after finding the left eye, right eye, mouth and nose Absence of each of these traits marks a non-frontal approach. The Rectangular Face Classificatión control system monitors frontal faces by moving a rectangular filter on the image for testing the dullness of the face area. Once the facial regions are tracked, the Hybrid Balanced Networks separates the eye area from it depending on the greater axis and the smaller axis. Heavy Eyed Approach is often used to spot drowsiness and twitch of the brow. The intensity of the horizontal plot is measured and successive frames in the eye twitch are not counted as a closed eye for three seconds. The result of the proposed work therefore effectively improves accuracy efficiency.
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Dyah Sintarani, Cokorda Istri, Desak Ketut Indrasari Utami, and Anak Agung Ayu Putri Laksmidewi. "Deficiency Serum Vitamin 25 (OH) D Levels Increase Risk Factors of Poor Quality Sleep in Resident at Sanglah Hospital." International Journal of Research and Review 8, no. 10 (October 8, 2021): 8–13. http://dx.doi.org/10.52403/ijrr.20211002.
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Introduction/aim: Serum vitamin D level, estimated as 25(OH) D, is related to an increase in number of physiological mechanisms of sleep. The circadian period of the sleep can be deferred with sun exposure and vitamin D supplementation. The mechanism of lower vitamin D level causing sleep disturbances is unknown, several possible mechanisms have been proposed. Receptors of vitamin D are commonly found in most human tissues, including central nervous system. Low level of vitamin D is related with an increased risk of sleep problems, such as low sleep quality, short sleep length and excessive drowsiness. This study aims to Methods: This study is an analytical study with a case-control design in resident st Sanglah Hospital from July-September 2021 Result: A total of 54 subjects were divided into case and control groups, age 25-35 years. From the statistical analysis using SPSS program on bivariate analysis with Chi-square obtained OR 12.6 (95% Confidence Interval=3.4-46.0; p=<0,001). Multivariate analysis showed that cortisol serum had P-value <0,001 Conclusion: Deficiency serum vitamin 25(OH) D levels increase risk factors of poor quality sleep thirteen times in resident at sanglah hospital Keywords: poor sleep quality, resident, vitamin 25 (OH) D, young people.
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Weemaes, Matthias, Martin Hiele, and Pieter Vermeersch. "High anion gap metabolic acidosis caused by D-lactate." Biochemia medica 30, no. 1 (February 15, 2020): 153–57. http://dx.doi.org/10.11613/bm.2020.011001.
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Introduction: D-lactic acidosis is an uncommon cause of high anion gap acidosis. Materials and methods: A 35-year old woman was admitted to the emergency room with somnolence, drowsiness, dizziness, incoherent speech and drunk appearance. Her past medical history included a Roux-en-Y bypass. Point-of-care venous blood analysis revealed a high anion gap acidosis. Based on the clinical presentation, routine laboratory results and negative toxicology screening, D-lactate and 5-oxoprolinuria were considered as the most likely causes of the high anion gap acidosis. Urine organic acid analysis revealed increased lactate, but no 5-oxoproline. Plasma D-lactate was < 1.0 mmol/L and could not confirm D-lactic acidosis. What happened: Further investigation revealed that the blood sample for D-lactate was drawn 12 hours after admission, which might explain the false-negative result. Data regarding the half-life of D-lactate are, however, scarce. During a second admission, one month later, D-lactic acidosis could be confirmed with an anion gap of 40.7 mmol/L and a D-lactate of 21.0 mmol/L measured in a sample collected at the time of admission. Main lesson: The time of blood collection is of utmost importance to establish the diagnosis of D-lactic acidosis due to the fast clearance of D-lactate in the human body
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Wang, He, Xinshan Zhu, Peiyin Chen, Yuxuan Yang, Chao Ma, and Zhongke Gao. "A gradient-based automatic optimization CNN framework for EEG state recognition." Journal of Neural Engineering 19, no. 1 (January 24, 2022): 016009. http://dx.doi.org/10.1088/1741-2552/ac41ac.
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Abstract Objective. The electroencephalogram (EEG) signal, as a data carrier that can contain a large amount of information about the human brain in different states, is one of the most widely used metrics for assessing human psychophysiological states. Among a variety of analysis methods, deep learning, especially convolutional neural network (CNN), has achieved remarkable results in recent years as a method to effectively extract features from EEG signals. Although deep learning has the advantages of automatic feature extraction and effective classification, it also faces difficulties in network structure design and requires an army of prior knowledge. Automating the design of these hyperparameters can therefore save experts’ time and manpower. Neural architecture search techniques have thus emerged. Approach. In this paper, based on an existing gradient-based neural architecture search (NAS) algorithm, partially-connected differentiable architecture search (PC-DARTS), with targeted improvements and optimizations for the characteristics of EEG signals. Specifically, we establish the model architecture step by step based on the manually designed deep learning models for EEG discrimination by retaining the framework of the search algorithm and performing targeted optimization of the model search space. Corresponding features are extracted separately according to the frequency domain, time domain characteristics of the EEG signal and the spatial position of the EEG electrode. The architecture was applied to EEG-based emotion recognition and driver drowsiness assessment tasks. Main results. The results illustrate that compared with the existing methods, the model architecture obtained in this paper can achieve competitive overall accuracy and better standard deviation in both tasks. Significance. Therefore, this approach is an effective migration of NAS technology into the field of EEG analysis and has great potential to provide high-performance results for other types of classification and prediction tasks. This can effectively reduce the time cost for researchers and facilitate the application of CNN in more areas.
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Wang, Jian. "Music Education to Rescue Psychological Stress in Social Crisis Based on Fuzzy Prediction Algorithm." Scientific Programming 2021 (October 25, 2021): 1–6. http://dx.doi.org/10.1155/2021/2039235.
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In order to alleviate human drowsiness in social crisis, it is necessary to predict the psychological rescue function of music education in social crisis and improve the accurate monitoring and analysis ability of the psychological rescue function of music education in social crisis; this paper puts forward a prediction model of psychological rescue function of music education in social crisis based on quantitative statistical analysis. The prediction and control model of the psychological rescue function of music education in social crisis is constructed, the fuzzy prediction algorithm is combined, the psychological rescue function characteristics of music education in social crisis are analyzed, and the descriptive statistical analysis model of the psychological rescue function of music education in social crisis is established. Through the fuzzy feature extraction method, the big data feature detection of the psychological rescue function of music education in social crisis is carried out, the statistical analysis model of the psychological rescue function of music education in social crisis is established, the dynamic analysis and prediction of the psychological rescue function of music education in social crisis are carried out by combining fuzzy information mining and adaptive learning methods, and the dynamic feature mining of the psychological rescue function of music education in social crisis is carried out by adopting the quantitative statistical feature analysis method. Statistical characteristics of the psychological rescue function of music education in social crisis are established, dynamic monitoring and feature prediction according to the analysis of the psychological rescue function of music education in social crisis are carried out, the ambiguity prediction and feature optimization judgment ability of the psychological rescue function of music education in social crisis are improved, and accurate prediction of the psychological rescue function of music education in social crisis based on the optimization statistical analysis results is carried out. The simulation results show that the statistical analysis ability and fuzzy judgment ability of using this method to predict the psychological rescue function of music education in social crisis are better, which improves the pertinence and effectiveness of music education.
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Loh, Hui Wen, Chui Ping Ooi, Jahmunah Vicnesh, Shu Lih Oh, Oliver Faust, Arkadiusz Gertych, and U. Rajendra Acharya. "Automated Detection of Sleep Stages Using Deep Learning Techniques: A Systematic Review of the Last Decade (2010–2020)." Applied Sciences 10, no. 24 (December 15, 2020): 8963. http://dx.doi.org/10.3390/app10248963.
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Sleep is vital for one’s general well-being, but it is often neglected, which has led to an increase in sleep disorders worldwide. Indicators of sleep disorders, such as sleep interruptions, extreme daytime drowsiness, or snoring, can be detected with sleep analysis. However, sleep analysis relies on visuals conducted by experts, and is susceptible to inter- and intra-observer variabilities. One way to overcome these limitations is to support experts with a programmed diagnostic tool (PDT) based on artificial intelligence for timely detection of sleep disturbances. Artificial intelligence technology, such as deep learning (DL), ensures that data are fully utilized with low to no information loss during training. This paper provides a comprehensive review of 36 studies, published between March 2013 and August 2020, which employed DL models to analyze overnight polysomnogram (PSG) recordings for the classification of sleep stages. Our analysis shows that more than half of the studies employed convolutional neural networks (CNNs) on electroencephalography (EEG) recordings for sleep stage classification and achieved high performance. Our study also underscores that CNN models, particularly one-dimensional CNN models, are advantageous in yielding higher accuracies for classification. More importantly, we noticed that EEG alone is not sufficient to achieve robust classification results. Future automated detection systems should consider other PSG recordings, such as electroencephalogram (EEG), electrooculogram (EOG), and electromyogram (EMG) signals, along with input from human experts, to achieve the required sleep stage classification robustness. Hence, for DL methods to be fully realized as a practical PDT for sleep stage scoring in clinical applications, inclusion of other PSG recordings, besides EEG recordings, is necessary. In this respect, our report includes methods published in the last decade, underscoring the use of DL models with other PSG recordings, for scoring of sleep stages.
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Dalievska, D. Ya, and O. S. Pokotylo. "Physico-chemical indicators of kefir with biologically active iodine in the process of fermentation." Scientific Messenger of LNU of Veterinary Medicine and Biotechnologies 23, no. 95 (April 9, 2021): 72–77. http://dx.doi.org/10.32718/nvlvet-f9512.
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Iodine is a natural trace element that is necessary for the human body. The function of iodine in the human body is the synthesis of thyroid hormones. Iodine deficiency has many negative consequences for the human body. Iodine deficiency is especially dangerous for pregnant women and children. Insufficient iodine in pregnant women increases the risk of miscarriage and birth of a child with cognitive impairment. Iodine deficiency in children has the following consequences: delayed physical development, delayed intellectual development, decreased mental activity, drowsiness, lethargy. Expanding food with iodine is a necessary step to overcome the problem of iodine deficiency.The quality of dairy products in Ukraine is very high. Due to the mandatory implementation of the HACCP system, manufacturers are improving equipment and production technologies. Accordingly, the requirements for the quality of raw materials increase in proportion to the competitiveness of the enterprise. That is why it is important to develop dairy products that will be in demand among consumers.Kefir with a biologically active additive “Iodis-concentrate” is a source of the required amount of iodine for the body. Jodis-concentrate is a certified biologically active additive that is widely used in the food industry. It has already found application in water production and the meat industry. The article presents a comparative analysis of changes in titrated acidity and active acidity (pH) in control and experimental samples of kefir during fermentation with the addition of biologically active iodine. The source of iodine was a biologically active additive "Iodis-concentrate". It is shown that the titrated acidity in both samples of kefir – control and experimental – had the same dynamics before growth during fermentation, which indicates no effect of the addition of biologically active iodine on the dynamics of titrated acidity. The same tendency to decrease the active acidity (pH) in control and experimental samples of kefir during fermentation with a difference within the significant error, indicating no effect of adding biologically active iodine to kefir on active acidity (pH).
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Fridayanti, Virlia Dian, and Dwi Prasetyanto. "Model Hubungan antara Angka Korban Kecelakaan Lalu Lintas dan Faktor Penyebab Kecelakaan pada Jalan Tol Purbaleunyi. (Hal. 124-132)." RekaRacana: Jurnal Teknil Sipil 5, no. 2 (September 25, 2019): 124. http://dx.doi.org/10.26760/rekaracana.v5i2.123.
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ABSTRAKKecelakaan lalu lintas merupakan hasil dari kombinasi faktor-faktor penyebab yang yang terdiri dari faktor manusia, kendaraan, jalan, dan lingkungan. Penelitian ini bertujuan untuk mengetahui variabel dominan dari beberapa faktor penyebab kecelakaan dengan memodelkan hubungan antara angka korban kecelakaan lalu lintas dengan variabel faktor penyebab kecelakaan di Jalan Tol Purbaleunyi pada tahun 2015–2017. Data yang digunakan pada penelitian ini berupa data sekunder yang terdiri dari data jumlah korban dan jumlah kecelakaan yang diakibatkan oleh faktor-faktor penyebab kecelakaan. Metode penelitian yang digunakan dalam penelitian ini adalah metode analisis regresi linear berganda dengan melakukan uji linearitas dan uji korelasi terlebih dahulu. Uji linearitas digunakan untuk memastikan apakah data yang akan dianalisis dapat menggunakan analisis regresi linear atau tidak, sedangkan uji korelasi digunakan untuk menentukan hubungan antara variabel baik antara sesama variabel bebas maupun antara variabel peubah bebas dengan variabel peubah tidak bebas. Berdasarkan hasil penelitian yang dilakukan pada tahun 2015–2017, variabel utama faktor kecelakaan diakibatkan oleh faktor manusia dan faktor kendaraan yaitu variabel mengantuk ( ) dan rem blong ( ).Kata kunci: Kecelakaan lalu lintas, faktor penyebab kecelakaan lalu lintas, regresi linear berganda. ABSTRACTTraffic accidents are the result of a combination of factors causes which consists of the human factor, vehicle, road, and environment. This study aims to determine the majority of the accidents variable of several factors that cause accidents by modeling the relationship between the numbers of traffic accident victims with variable factors causing the accident on Highway Purbaleunyi in 2015–2017. The data used in this study of secondary data consists of data on the number of victims and the number of accidents caused by factors that cause accidents. The method used in this research is multiple linear regression analysis to test the linearity and correlation test beforehand. Linearity test used to determine whether the data will be analyzed using linear regression analysis or not, whereas the correlation test was used to determine the relationship between both variables among the independent variables and the independent variables with the variable variable variable is not free. Based on the results of research conducted in 2015–2017, the main variable of the accident factor is caused by human factors and vehicle factors, which are variable drowsiness ( ) and brake failure ( ).Keywords: Traffic accidents, the causes of traffic accidents, multiple linear regression.
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Yan, Xiao, and Ashardi Abas. "Preliminary on Human Driver Behavior: A Review." International Journal of Artificial Intelligence 7, no. 2 (December 7, 2020): 29–34. http://dx.doi.org/10.36079/lamintang.ijai-0702.146.
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Drowsiness is one of the main factors causing traffic accidents. Research on drowsiness can effectively reduce the traffic accident rate. According to the existing literature, this paper divides the current measurement techniques into subjective and objective ones. Among them, invasive detection and non-invasive detection based on vehicles or drivers are the main objective detection methods.Then, this paper studies the characteristics of drowsiness, and analyzes the advantages and disadvantages of each detection method in practical application. Finally, the development of detection technology is prospected, and provides ideas for the follow-up development of fatigue driving detection technology.
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Nakakita, Y., N. Tsuchimoto, Y. Takata, and T. Nakamura. "Effect of dietary heat-killed Lactobacillus brevis SBC8803 (SBL88™) on sleep: a non-randomised, double blind, placebo-controlled, and crossover pilot study." Beneficial Microbes 7, no. 4 (September 1, 2016): 501–9. http://dx.doi.org/10.3920/bm2015.0118.
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We previously reported that dietary heat-killed Lactobacillus brevis SBC8803 affects sleep rhythms in mice. The present study evaluated the effect of consumption of heat-killed SBC8803 on sleep architecture in humans. A non-randomised, placebo-controlled, double blind, and crossover pilot study was conducted using volunteers who scored at a slightly high level (i.e. ≥6) on the Athens Insomnia Scale (AIS). Male subjects (n=17; age 41-69 y) consumed placebo or SBC8803 capsules (25 mg/day of heat-killed SBC8803) for 10 days. Electroencephalograms (EEG) were recorded using a mobile, one-channel system, providing objective data on sleep. Subjects’ sleep journals and administration of the AIS provided subjective data on sleep. Three subjects were excluded from the statistical analysis. Analysis of the remaining 14 volunteers revealed no significant differences between placebo and SBC8803 consumption in either the AIS or the sleep EEG. The sleep journals revealed an improvement in ‘waking’ for the SBC8803 consumption periods (P=0.047), and there was a marginally significant effect on ‘drowsiness during the following day’ (P=0.067). Effects on the EEG delta power value (μV2/min) were revealed by a stratified analysis based on age, AIS, and the Beck Depression Inventory (BDI). Specifically, effects were found among subjects in their 40s who consumed the SBC8803 capsules (P=0.049) and among subjects with a BDI score less than the all-subjects average (13.3) (P=0.045). A marginally significant effect was found among subjects with an AIS score less than the all-subjects average (11.6) (P=0.065). The delta power value of 5 subjects with both BDI and AIS scores less than the average increased significantly (P=0.017). While the number of subjects was limited, a beneficial effect on sleep due to consumption of heat-killed L. brevis SBC8803 was found in subjects with slightly challenged sleep.
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MAXIMETS, S. M. "DETERMINATION OF PERSONAL AND SITUATION DETERMINANTS LAZINESS." Herald of Kiev Institute of Business and Technology 42, no. 4 (December 23, 2019): 115–19. http://dx.doi.org/10.37203/kibit.2019.42.18.
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This article contains the result of the theoretical and empirical study of personality and situational factors of laziness among young people and the presence of gender differences in the concept of laziness.
Laziness is synonymous with a lack of motivation, unwillingness to do anything. Situational laziness is more of a temporary nature of the lack of desire to do something. Personal laziness is a set of determined human characteristics that attend it throughout life.
Analysis of the results of self-assessment of laziness found that only 20% of respondents consider themselves to be a lazy person, while the other 80% consider themselves as hardworking.
The most important personal determinant of laziness (in 83% of persons) were features of physical and psychological state. Namely: tiredness, desire to rest, drowsiness, no mood, the poor state of feeling, boredom, etc. Other determinants such as lack of opportunities, lack of interest, external pressure are less important and cause personal laziness in single cases.
The personal and situational determinants of the laziness that have been investigated have different nature and manifestations. We believe that the features of the body condition can not be included in the group of personal determinants of laziness.
Opportunity deficits (lack of perseverance, lack of skills and time, difficult task, other distracting problems) can be attributed to the personal determinants of laziness.
Areas of maximum demonstration of laziness are (in descending order): professional (educational) activities, household chores, cases under pressure, and lack cases.
Based on the analysis of the results of the study of self-regulation of laziness, we can speak about differences in the concept of laziness between men and women."Male laziness" is perceived as a feeling of unwillingness, apathy, inaction, and focused on the inner world and feelings. "Female laziness" is conditioned by the situation. More demonstrated as outward orientation, responsibility, feelings of guilt.
Based on the results, it is planned to develop a program of correctional work based on a differentiated approach to people, taking into account factors that influence the manifestation of laziness.
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K Firke, Omprakash, and Dr Manish Jain. "Analysis of Driver Drowsiness Detection using EEG and EOG." International Journal of Engineering & Technology 7, no. 2.17 (April 15, 2018): 46. http://dx.doi.org/10.14419/ijet.v7i2.17.11557.
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This paper propose here a resolutely oriented approach to the study of the driver in order to detect the driver drowsiness starting from physiological information (related to the brain activity) and video (related to the ocular activity). The goal of this work is to develop a system for automatic detection of driver drowsiness in the driver from electroencephalographic (EEG) (describing brain activity) and video of the driver. This approach is motivated by the fact that driver drowsiness physicians mainly work from brain and visual data to detect driver drowsiness. In addition, the complementarity of brain and ocular activities seems to indicate that the contribution of cerebral information would improve the reliability of the camera-based approaches (thus using only the visual cues) used for the automatic detection of the decline of vigilance.
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., G. M. Bhandari. "YAWNING ANALYSIS FOR DRIVER DROWSINESS DETECTION." International Journal of Research in Engineering and Technology 03, no. 02 (February 25, 2014): 502–5. http://dx.doi.org/10.15623/ijret.2014.0302087.
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Forsman, Pia M., Bryan J. Vila, Robert A. Short, Christopher G. Mott, and Hans P. A. Van Dongen. "Efficient driver drowsiness detection at moderate levels of drowsiness." Accident Analysis & Prevention 50 (January 2013): 341–50. http://dx.doi.org/10.1016/j.aap.2012.05.005.
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AZIZAN, Amzar, Mohammad FARD, Michael F. AZARI, Bryndís BENEDIKTSDÓTTIR, Erna Sif ARNARDÓTTIR, Reza JAZAR, and Setsuo MAEDA. "The influence of vibration on seated human drowsiness." Industrial Health 54, no. 4 (2016): 296–307. http://dx.doi.org/10.2486/indhealth.2015-0095.
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Dabbu, Suman, M. Malini, B. Ram Reddy, and Yashwanth Sai Reddy Vyza. "ANN based Joint Time and frequency analysis of EEG for detection of driver drowsiness." Defence Life Science Journal 2, no. 4 (November 10, 2017): 406. http://dx.doi.org/10.14429/dlsj.2.10370.
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<p>Drowsiness detection plays a vital role in accidents avoidance systems, thereby saving many precious lives. According to the World Health Organization, Drowsiness has been the radical contributor of road fatalities. Electroencephalogram (EEG) is a physiological signal which relays the functioning of Brain and widely used in the diagnosis of Neurological Disorders. This study extrapolates the EEG signal analysis to examine several cognitive tasks. In this report, the EEG signal is processed to detect the behavioural patterns of the brain and drowsiness state of the drivers while performing monotonous driving for long distances. An eight-channel EEG data acquisition system is used to acquire the EEG data from 20 male volunteers. The EEG signal is pre-processed and decomposed into various rhythms by applying Digital filter in MATLAB 2007b (Mathworks, Inc., USA). Time-Frequency Domain analysis has been done to extract certain features PSG and PRMSD which are statistically significant (ρ < 0.05) in the detection of drowsiness. The driving profile is classified into Active and Drowsy by a threshold, and linear regression analysis has been performed on the features extracted. A Drowsiness index is proposed stating a positive correlation (0.8-0.9) between the Total mean and the drowsy mean of the subject.</p>
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Chandrasekaran, Anish. "Drowsy Face Detection using Deep Learning Algorithms." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (June 20, 2021): 2174–79. http://dx.doi.org/10.22214/ijraset.2021.35526.
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An important aspect of machine vision and image processing could be drowsiness detection system due to its high significance. In recent years there have been many research projects reported in the literature in this field.In this paper unlike the conventional drowsiness detection methods using machine learning we used deep learning techniques.Driver drowsiness results in many car crashes and fatalities worldwide.Whereas drowsiness in online attendees results in less attention span and decrease in the learning capabilities, such as meetings, lectures, webinars held. The advancement in computing technology has provided the means for building intelligent face detection systems.Faces contain information that can be used to interpret levels of drowsiness.Here we employ deep learning to determine actual human behavior during drowsiness episodes targeting the facial features.
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Jeong, Ji-Hoon, Baek-Woon Yu, Dae-Hyeok Lee, and Seong-Whan Lee. "Classification of Drowsiness Levels Based on a Deep Spatio-Temporal Convolutional Bidirectional LSTM Network Using Electroencephalography Signals." Brain Sciences 9, no. 12 (November 29, 2019): 348. http://dx.doi.org/10.3390/brainsci9120348.
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Non-invasive brain-computer interfaces (BCI) have been developed for recognizing human mental states with high accuracy and for decoding various types of mental conditions. In particular, accurately decoding a pilot’s mental state is a critical issue as more than 70% of aviation accidents are caused by human factors, such as fatigue or drowsiness. In this study, we report the classification of not only two mental states (i.e., alert and drowsy states) but also five drowsiness levels from electroencephalogram (EEG) signals. To the best of our knowledge, this approach is the first to classify drowsiness levels in detail using only EEG signals. We acquired EEG data from ten pilots in a simulated night flight environment. For accurate detection, we proposed a deep spatio-temporal convolutional bidirectional long short-term memory network (DSTCLN) model. We evaluated the classification performance using Karolinska sleepiness scale (KSS) values for two mental states and five drowsiness levels. The grand-averaged classification accuracies were 0.87 (±0.01) and 0.69 (±0.02), respectively. Hence, we demonstrated the feasibility of classifying five drowsiness levels with high accuracy using deep learning.
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Paliwal, Sachin, and Birinderjit Singh Kalyan. "Driver’s Activity Detection System using Human antenna." Indian Journal of Energy and Energy Resources 1, no. 3 (May 30, 2022): 4–6. http://dx.doi.org/10.54105/ijeer.c1007.051322.
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Now a days we can see that there are many cases which are occurring due to the drowsiness of drivers and that has become a main problem of the automotive industry. To overcome this in automotive industry the introduction of new technologies that is by introducing of new sensor which can detect the different activities. Detection of activities by sensors is for biological measuring such as heartbeat, oxygen level, respiration activity, etc. By applying such widespread variety of sensor usage in the system has a very high implementation cost and also very complexity which is a bit challenging design. In this paper, we are going study that how humantenna effect is used to detect and test the drive drowsiness by using simple and budget sensors in automotive industry.
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Ahirwar, Makhan. "Real time Drowsy Driver Detection using Polynomial Kernel based Support Vector Machine." International Journal for Research in Applied Science and Engineering Technology 9, no. 9 (September 30, 2021): 1389–96. http://dx.doi.org/10.22214/ijraset.2021.38106.
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Abstract: Casualty increases from road accidents day by day. There are so many reasons that accident causes and mostly due to human errors. Driver drowsiness is one of them. A small drowsiness may turn it into a big accident that resulted heavy casualties. If any of the system automatically detects the driver’s drowsiness and alert at real time may secure many lives. Drowsiness can be recognized by different situations such as by opening full mouth, by closing both the eyes and a combination of both. This may advised not to drive at drowsy state. There are various techniques through which drowsiness can be detected at real time but accuracy matters. OpenCV is a highly utilized open source computer vision library through which facial features can be recognized effectively. Polynomial kernel based support vector machine (SVM) is an advanced classification technique through which drowsiness can be classified from face. SVM is advanced machine learning approach through which linear and non-linear data can be classified with higher level of accuracy. System pertained 96.17 % of accuracy. Polynomial kernel is useful for non-linear data separation. Here system classifies the expressional features of face and result accordingly for drowsiness detection. Keywords: Support Vector Machine (SVM), OpenCV, Machine Learning, Non-Linear SVM Model, Drowsiness Detection, Face Detection, Computer Vision.
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Stancin, Igor, Nikolina Frid, Mario Cifrek, and Alan Jovic. "EEG Signal Multichannel Frequency-Domain Ratio Indices for Drowsiness Detection Based on Multicriteria Optimization." Sensors 21, no. 20 (October 19, 2021): 6932. http://dx.doi.org/10.3390/s21206932.
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Drowsiness is a risk to human lives in many occupations and activities where full awareness is essential for the safe operation of systems and vehicles, such as driving a car or flying an airplane. Although it is one of the main causes of many road accidents, there is still no reliable definition of drowsiness or a system to reliably detect it. Many researchers have observed correlations between frequency-domain features of the EEG signal and drowsiness, such as an increase in the spectral power of the theta band or a decrease in the spectral power of the beta band. In addition, features calculated as ratio indices between these frequency-domain features show further improvements in detecting drowsiness compared to frequency-domain features alone. This work aims to develop novel multichannel ratio indices that take advantage of the diversity of frequency-domain features from different brain regions. In contrast to the state-of-the-art, we use an evolutionary metaheuristic algorithm to find the nearly optimal set of features and channels from which the indices are calculated. Our results show that drowsiness is best described by the powers in delta and alpha bands. Compared to seven existing single-channel ratio indices, our two novel six-channel indices show improvements in (1) statistically significant differences observed between wakefulness and drowsiness segments, (2) precision of drowsiness detection and classification accuracy of the XGBoost algorithm and (3) model performance by saving time and memory during classification. Our work suggests that a more precise definition of drowsiness is needed, and that accurate early detection of drowsiness should be based on multichannel frequency-domain features.
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Guede-Fernandez, Federico, Mireya Fernandez-Chimeno, Juan Ramos-Castro, and Miguel A. Garcia-Gonzalez. "Driver Drowsiness Detection Based on Respiratory Signal Analysis." IEEE Access 7 (2019): 81826–38. http://dx.doi.org/10.1109/access.2019.2924481.
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Priya, V. Vijay, and M. Uma. "EEG based Drowsiness Prediction Using Machine Learning Approach." Webology 18, no. 2 (December 23, 2021): 740–55. http://dx.doi.org/10.14704/web/v18i2/web18351.
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Drowsiness is the main cause of road accidents and it leads to severe physical injury, death, and significant economic losses. To monitor driver drowsiness various methods like Behaviour measures, Vehicle measures, Physiological measures and Hybrid measures have been used in previous research. This paper mainly focuses on physiological methods to predict the driver’s drowsiness. Several physiological methods are used to predict drowsiness. Among those methods, Electroencephalography is one of the non-invasive physiological methods to measure the brain activity of the subject. EEG brain signal extracted from the human scalp is analysed with various features and used for various health application like predicting drowsiness, fatigue etc. The main objective of the proposed system is to early predict the driver drowsiness with high accuracy so that we have divided our work into two steps. The first step is to collect the publicly available dataset of EEG based Eye state as (Eye open and Eye closed) where the signal acquisition process was done from Emotiv EEG Neuroheadset (14 electrodes) and analysed various feature engineering techniques and statistical techniques. The second step was applied with the machine learning classification model as K-NN and performance-based predicting models are used. In the Existing System, they used various machine learning classification models like K-NN and SVM for EEG Eye state classification and produced results around 80% -97%. Compared to the Existing system our proposed method produced better classification models for predicting driver drowsiness using different Feature engineering process and classification models as K-NN produced 98% of accuracy.
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Satti, Afraiz Tariq, Jiyoun Kim, Eunsurk Yi, Hwi-young Cho, and Sungbo Cho. "Microneedle Array Electrode-Based Wearable EMG System for Detection of Driver Drowsiness through Steering Wheel Grip." Sensors 21, no. 15 (July 27, 2021): 5091. http://dx.doi.org/10.3390/s21155091.
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Driver drowsiness is a major cause of fatal accidents throughout the world. Recently, some studies have investigated steering wheel grip force-based alternative methods for detecting driver drowsiness. In this study, a driver drowsiness detection system was developed by investigating the electromyography (EMG) signal of the muscles involved in steering wheel grip during driving. The EMG signal was measured from the forearm position of the driver during a one-hour interactive driving task. Additionally, the participant’s drowsiness level was also measured to investigate the relationship between muscle activity and driver’s drowsiness level. Frequency domain analysis was performed using the short-time Fourier transform (STFT) and spectrogram to assess the frequency response of the resultant signal. An EMG signal magnitude-based driver drowsiness detection and alertness algorithm is also proposed. The algorithm detects weak muscle activity by detecting the fall in EMG signal magnitude due to an increase in driver drowsiness. The previously presented microneedle electrode (MNE) was used to acquire the EMG signal and compared with the signal obtained using silver-silver chloride (Ag/AgCl) wet electrodes. The results indicated that during the driving task, participants’ drowsiness level increased while the activity of the muscles involved in steering wheel grip decreased concurrently over time. Frequency domain analysis showed that the frequency components shifted from the high to low-frequency spectrum during the one-hour driving task. The proposed algorithm showed good performance for the detection of low muscle activity in real time. MNE showed highly comparable results with dry Ag/AgCl electrodes, which confirm its use for EMG signal monitoring. The overall results indicate that the presented method has good potential to be used as a driver’s drowsiness detection and alertness system.
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Jasim, Sarah S., Alia K. Abdul Hassan, and Scott Turner. "Driver Drowsiness Detection Using Gray Wolf Optimizer Based on Face and Eye Tracking." ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY 10, no. 1 (May 5, 2022): 49–56. http://dx.doi.org/10.14500/aro.10928.
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It is critical today to provide safe and collision-free transport. As a result, identifying the driver’s drowsiness before their capacity to drive is jeopardized. An automated hybrid drowsiness classification method that incorporates the artificial neural network (ANN) and the gray wolf optimizer (GWO) is presented to discriminate human drowsiness and fatigue for this aim. The proposed method is evaluated in alert and sleep-deprived settings on the driver drowsiness detection of video dataset from the National Tsing Hua University Computer Vision Lab. The video was subjected to various video and image processing techniques to detect the drivers’ eye condition. Four features of the eye were extracted to determine the condition of drowsiness, the percentage of eyelid closure (PERCLOS), blink frequency, maximum closure duration of the eyes, and eye aspect ratio (ARE). These parameters were then integrated into an ANN and combined with the proposed method (gray wolf optimizer with ANN [GWOANN]) for drowsiness classification. The accuracy of these models was calculated, and the results demonstrate that the proposed method is the best. An Adadelta optimizer with 3 and 4 hidden layer networks of (13, 9, 7, and 5) and (200, 150, 100, 50, and 25) neurons was utilized. The GWOANN technique had 91.18% and 97.06% accuracy, whereas the ANN model had 82.35% and 86.76%.
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Ghole, Uzair, Pravin Chavan, Siddharth Gandhi, Rohit Gawde, and Kausar Fakir. "DROWSINESS DETECTION AND MONITORING SYSTEM." ITM Web of Conferences 32 (2020): 03045. http://dx.doi.org/10.1051/itmconf/20203203045.
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Wakefulness of a driver is an extremely important factor that needs to be continuously monitored.. A drowsy driver can be a cause of several mishaps and accidents on highways which could lead to loss of money, physical injuries, and the most important, loss of human life. Drowsiness detection system is a car safety technology that helps to prevent and thus reduce accidents caused by the driver getting drowsy. The system is designed for four-wheeler vehicles (or more) wherein the driver’s fatigue or drowsiness is detected and alerts are generated. The proposed method will use a USB camera that captures the driver’s face and eyes and processes the images to detect the driver’s fatigue. On the detection of drowsiness, the programmed system cautions the driver through an alarm to ensure vigilance. The proposed method consists of various stages to determine the wakefulness of the driver.
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Corbett, Mark A., and David G. Newman. "Student Drowsiness During Simulated Solo Flight." Aerospace Medicine and Human Performance 93, no. 4 (April 1, 2022): 354–61. http://dx.doi.org/10.3357/amhp.5275.2022.
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INTRODUCTION: Pilot fatigue is a significant concern in aviation, where efforts are directed at improving rosters, developing models, and improving countermeasures. Little attention has been given to in-flight detection of fatigue/drowsiness. The aims of this research were to determine whether drowsiness is an issue and explore whether infrared reflectance oculography could prove useful for continuous inflight monitoring.METHODS: Nine university-based pilot trainees wore activity monitors and completed diaries, prior to a simulated navigational exercise of approximately 4 h, during the secondary window of circadian low. During the flight they wore a head-mounted device. Oculographic data were collected and converted into a single number, using the Johns Drowsiness Scale (JDS), with increasing values indicating greater drowsiness (range 0.0 to 10.0).RESULTS: Peak JDS values reached 6.5. Values declined from shortly before top of descent, continuing until landing. Two of the nine participants (22.2%), reached drowsiness levels at or above a cautionary warning level, below which is considered safe for driving a motor vehicle.DISCUSSION: The results of this study revealed the timeline and levels of fatigue that might be experienced by student pilots; showing that drowsiness is a potential issue for student pilots operating in flying conditions similar to those in the simulation. Analysis indicated that pilots are likely to experience levels of drowsiness above a cautionary warning level when modeling predicted effectiveness below 90%, indicating a potential drowsiness issue for pilots. It was concluded that oculography is worthy of further investigation for use as an objective fatigue detection tool in aviation.Corbett MA, Newman DG. Student drowsiness during simulated solo flight. Aerosp Med Hum Perform. 2022; 93(4):354–361.
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Jang, Yun-Seok, Seul-Lee Lee, and Soo-Ah Ryu. "Characteristics of Frequency Band on EEG Signal Causing Human Drowsiness." Journal of the Korea institute of electronic communication sciences 8, no. 6 (June 30, 2013): 949–54. http://dx.doi.org/10.13067/jkiecs.2013.8.6.949.
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Rajkiran, S., R. Ragul, and M. R. Ebenezar Jebarani. "Detecting the Drowsiness Using EGG Based Power Spectrum Analysis." Biosciences Biotechnology Research Asia 12, no. 2 (August 30, 2015): 1623–27. http://dx.doi.org/10.13005/bbra/1824.
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Varun Chand, H., and J. Karthikeyan. "CNN Based Driver Drowsiness Detection System Using Emotion Analysis." Intelligent Automation & Soft Computing 31, no. 2 (2022): 717–28. http://dx.doi.org/10.32604/iasc.2022.020008.
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Jeyabharathi, D., K. Jeevanantham, M. Kavinmukhil, and Quanith hasan J. B. Mohamed. "Intelligent Analysis for Drowsiness Alert using Conventional Neural Networks." Journal of Physics: Conference Series 1916, no. 1 (May 1, 2021): 012131. http://dx.doi.org/10.1088/1742-6596/1916/1/012131.
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Chellappa, Akalya, Mandi Sushmanth Reddy, R. Ezhilarasie, S. Kanimozhi Suguna, and A. Umamakeswari. "Fatigue Detection Using Raspberry Pi 3." International Journal of Engineering & Technology 7, no. 2.24 (April 25, 2018): 29. http://dx.doi.org/10.14419/ijet.v7i2.24.11993.
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Driver drowsiness is a primary cause of several highway calamities leads to severe physical injuries, loss of money, and loss of human life. The implementation of driver drowsiness detection in real-time will aid in avoiding major accidents. The system is designed for four-wheelers wherein the driver’s fatigue or drowsiness is detected and alerts the person. The proposed method will use 5-megapixel Raspbian camera that captures driver’s face and eyes and processes the images to detect driver’s fatigue. On the detection of drowsiness, the programmed system cautions the driver through an alarm to ensure vigilance. The proposed method constitutes of various stages to determine wakefulness of the driver. According to this output, the warning message is generated. Haar Cascade Classifiers is used to detect the blink duration of the driver and Eye Aspect Ratio (EAR) is calculated. Finally, the alert message along with car plate number is sent to the concerned person mobile with help of Ubidots cloud service and Twilio API. For this Raspberry Pi 3 with Raspbian (Linux Based) Operating System is used.
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Fahrurrasyid, Gita Indah Hapsari, Lisda Meisaroh, and Giva Andriana Mutiara. "Smart Helmet GPS-Based for Heartbeat Drowsiness Detection and Location Tracking." Journal of Biomimetics, Biomaterials and Biomedical Engineering 55 (March 28, 2022): 226–35. http://dx.doi.org/10.4028/p-wk322k.
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In Indonesia, motorcycle traffic accidents have increased rapidly. Traffic accidents result in high mortality. One of the causes is influenced by human psychological factors or human error. However, to improve the behavior of the riders and due reducing traffic accidents, the purpose of this research is developed a Smart Helmet that can detect drowsiness by measuring the heartbeats psychological riders. Besides that, this system equipped with an SOS button. Its function is to detect and help the riders if there were any emergency incidents on the roads. This proposed system designed using a heartbeat pulse sensor, GPS module, GSM module, Arduino Nano, push-button, and buzzer. Smart Helmet examined in several scenarios to test the performance of the drowsiness and the SOS button. The resulting test on 10 respondents defined that the drowsiness can be detected and give a buzzer alert when the heartbeat is below 60 bpm. The information can be seen without delay. The incident location can be tracked down by utilizing the google maps application. The shift position as the error distance of the GPS incident location only happens in the range of 21.96-42.63 meters. The conclusion is the helmet can detect drowsiness based on heartrate and give an alarm. The SOS button is functionally properly as long as the helmet is used in the outdoor area.
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Joshi, Adinath, Atharva Kamble, Akanksha Parate, Siddhesh Parkar, Digambar Puri, and Chandrakant Gaikwad. "Drowsiness Detection using EEG signals and Machine Learning Algorithms." ITM Web of Conferences 44 (2022): 03030. http://dx.doi.org/10.1051/itmconf/20224403030.
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Drowsiness is described as a state of reduced consciousness and vigilance accompanied by a desire or want to sleep. Driver tiredness is frequently detected using wearable sensors that track vehicle movement and camera-based systems that track driver behavior. Many alternative EEG-based drowsiness detection systems are developed due to the potential of electroencephalogram (EEG) signals to observe human mood and the ease with which they may be obtained. This paper applies Deep learning architecture like Convolutional Neural networks (CNN) and algorithms for the classification of EEG data for Drowsiness Detection. The key measures of video-based approaches include the detection of physical features; nevertheless, problems such as brightness limitations and practical challenges such as driver attention limits its usefulness. The main measure of video-based methods is the degree of closure of the eyelids; however, its success is limited by constraints like as brightness restrictions and practical challenges such as driver distraction. We have extracted statistical features and trained using various classifiers like Logistic Regression, Naïve Bayes, SVM, and K Nearest Neighbours and compared the accuracy using a deep learning CNN model. Results demonstrate that CNN achieved an accuracy of 94.75% by delegating feature extraction on itself. Upon comparing existing state–of–the–art drowsiness detection systems, the testing results reveal a higher detection capability. The results show that the the suggested method can be used to develop a reliable EEG-based driving drowsiness detection system.
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Rayineedu, Navya Chowdary, Afrin Bano, Mounika Uppalapati, Bhargavi Vemula, and Chandra Mohan Chetla. "Real-Time Driver Drowsiness Identification Based on Eye-State Analysis." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (May 31, 2022): 2387–90. http://dx.doi.org/10.22214/ijraset.2022.42853.
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Abstract: Driving while being drowsy has become one of the major reasons of causing road accidents. Drivers who drive at night or for a long distance without resting are more prone to get involved in an accident. Large amount of fatal injuries and deaths occur because of this reason. Hence, it has become an active area of research. Various systems exist for this purpose which makes use of behavioural patterns. Behavioural patterns considered here are visual behaviours of drive like eye blinking, eye closing, yawning, head bending etc. Most of these methods are time consuming and expensive. Recently, in research and development, machine learning methods have been used to predict a driver's conditions. Those conditions can be used as information that will improve road safety. Machine Learning has brought progression in video processing which enables images to be analysed with accuracy. Keywords: Drowsiness detection, OpenCV, Dlib, Driver Drowsiness, Eye-state analysis.
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Bhagat, Sapna, and V. K. "Analysis of Driver Drowsiness Detection System by using Soft Computing." International Journal of Computer Applications 165, no. 8 (May 17, 2017): 35–37. http://dx.doi.org/10.5120/ijca2017913983.
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Mourits Rumagit, Arthur, Izzat Aulia Akbar, and Tomohiko Igasaki. "Gazing Time Analysis for Drowsiness Assessment Using Eye Gaze Tracker." TELKOMNIKA (Telecommunication Computing Electronics and Control) 15, no. 2 (March 1, 2017): 919. http://dx.doi.org/10.12928/telkomnika.v15i1.6145.
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Mourits Rumagit, Arthur, Izzat Aulia Akbar, and Tomohiko Igasaki. "Gazing Time Analysis for Drowsiness Assessment Using Eye Gaze Tracker." TELKOMNIKA (Telecommunication Computing Electronics and Control) 15, no. 2 (March 1, 2017): 919. http://dx.doi.org/10.12928/telkomnika.v15i2.6145.
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