Relevant bibliographies by topics / Analysis of human drowsiness

Academic literature on the topic 'Analysis of human drowsiness'

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Published: 30 May 2022
Last updated: 31 May 2022

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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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Dissertations / Theses on the topic "Analysis of human drowsiness"

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Padmanabhan, Sivasankar. "Drowsiness detection using HRV analysis." Thesis, California State University, Long Beach, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=1596988.

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The field of drowsiness detection is gaining more attention these days. An estimate by National Highway Traffic Safety Administration states that the total number of people falling asleep on the wheel is increasing day by day. If there is an effective way to monitor this condition and alert the drivers, many fatal accidents can be prevented. This thesis work elaborates on one such simple, yet effective drowsiness detection algorithm, the HRV - Heart Rate Variability analysis. Many psychological researchers have found out that when a person becomes drowsy, there is a variation in their heart signal. Monitoring this physiological variation would be more efficient than monitoring their facial movements such as blinking, eye brow contraction, and yawning, which are said to happen after much longer time when compared to the immediate changes in the heart rate. Hence, an algorithm that detects drowsiness based on HRV analysis is developed and implemented by analyzing heart signals. Simple hardware setups were used to collect the ECG data, and digital filters were used to remove noise and extract the desired information for further analysis. The developed algorithm was implemented successfully and the results obtained were more precise and satisfactory. This approach of monitoring drowsiness is more reliable and accurate and when implemented with its necessary features, it can monitor drowsiness more effectively and save hundreds of lives every day.

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Yonkers, Shonee L. Kenyon. "An analysis of ANAM Readiness Evaluation System (ARES) as a predictor of performance degradation induced by sleep deprication in Officer Indoctrination School (OIS) students." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2004. http://library.nps.navy.mil/uhtbin/hyperion/04Jun%5FYonkers.pdf.

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Altmüller, Tobias [Verfasser]. "Driver Monitoring and Drowsiness Detection by Steering Signal Analysis / Tobias Altmüller." Aachen : Shaker, 2007. http://d-nb.info/1164338684/34.

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Jin, Ning. "Human motion analysis." Thesis, University of Surrey, 2007. http://epubs.surrey.ac.uk/804406/.

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Sridharan, Kousik Sarathy. "Real-time acquisition and analysis ofElectro-oculography signals." Thesis, Linköpings universitet, Biomedicinsk instrumentteknik, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-76734.

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Electro-oculography signals are corneo-retinal potentials that carry informationpertaining to eye movements. This information can be used to estimate drowsinesslevel of the subject which could provide interesting insights into research of acci-dent prevention. Of all features present, blink duration has been proved to be aneffective measure of drowsiness. The aim of this thesis work is to build a portablesystem to acquire and analyze electro-oculographic (EOG) signals in real-time.The system contains two sub-systems; a hardware sub-system that consists of thefilters, amplifiers, data acquisition card and isolation and the software sub-systemthat contains the program to acquire and analyze the signal and present the resultsto the observer. The filters were designed starting with simulation, implementa-tion on the prototype board, culminating in the design of a printed circuit board(PCB) and packaging. The complete software was written in PythonTMusing sev-eral relevant libraries for data processing. A text-based user interface was createdto enable easy user interaction. The results are graphically displayed in real-time. Ex-situ tests were done with two volunteers while in-situ test was done onone subject. The data from the in-situ tests showed "good signal quality" in a"noisy" environment concurring with the design specifications. To motivate theimportance of calibration, two calibration paradigms were used during ex-situtests, where one paradigm records only normal blinks while the other records longblinks and the results showed differences in detection and error rates. The obser-vations made from performance tests at various levels gave "satisfactory results"and proved the usefulness of the system for experimental purposes in-situ.
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Taylor-Wiedeman, Jean. "Analysis of human cytomegalovirus in the healthy human carrier." Thesis, Open University, 1992. http://oro.open.ac.uk/57400/.

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Much circumstantial evidence has pointed to peripheral blood leukocytes as one site of persistence of Human Cytomegalovirus (HCMV) in healthy carriers. However, the exact population of peripheral blood cells that carry HCMV and to what extent they express HCMV gene products in not known. I have examined the sites of HCMV persistence in the peripheral blood of healthy carriers. Analysis of pure cell populations by the use of the polymerase chain reaction (PCR), sensitive to between 1 and 10 copies of the HCMV genome, showed that the predominant site of persistence was the monocyte. In addition, analysis of healthy seronegative subjects revealed that a significant number (30%) also harbored HCMV. Finally, study of granulocytes demonstrated no evidence of persistent HCMV. Expression of HCMV during persistence was also analyzed, by using reverse transcription PCR (RT-PCR) with a sensitivity of between 1 and 100 infected fibroblasts. RNA from monocytes showed no evidence of polyadenylated immediate early (IE) or late transcripts. In contrast, in vitro differentiated monocyte-derived macrophages (MDM) did show evidence of HCMV gene expression with the class of HCMV genes expressed dependent on the method of differentiation. MDM treated with hydrocortisone (HC) and phorbol 12-myristate 13-acetate, expressed only lEI, but not IE2, glycoprotein B (gB) or phosphoprotein 28 (pp28) transcripts. Whereas, MDM treated with granulocyte-macrophage colony stimulating factor and HC expressed lEI, IE2 and gB, but not pp28 transcripts. In both cases, cocultivation experiments did not show plaques. Therefore, in the healthy carrier, persistence of HCMV in monocytes is independent of HCMV lytic gene expression, but in vitro differentiation of monocytes to MDM induced endogenous HCMV transcription consistent with the known permissivity of in vivo differentiated macrophages to HCMV infection.
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Sekiguchi, Kazumasa. "Analysis of human mitochondrial DNA heteroplasmy for human identification." Kyoto University, 2004. http://hdl.handle.net/2433/148349.

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Kühnle, Tim. "Quantitative Analysis of Human Chronotypes." Diss., lmu, 2006. http://nbn-resolving.de/urn:nbn:de:bvb:19-51686.

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Denis, Emmanuelle. "Analysis of chimeric human hexosaminidases." Thesis, McGill University, 2000. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=30814.

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The major beta-hexosaminidase isozymes in humans are Hex A (deficient in Tay-Sachs disease, TSD), an alphabeta heterodimer and Hex B (deficient in Sandhoff disease) a betabeta homodimer. Hex S, the alphaalpha homodimer is physiologically unstable. Mature alpha and beta subunits share 60% sequence identity. The beta subunit active site hydrolyzes neutral substrates. The alpha subunit active site hydrolyzes neutral (4MUG) and charged substrates (4MUGS, GM2 ganglioside). Only Hex A hydrolyzes the natural substrate, GM2 ganglioside, in the presence of the GM2 activator protein (AP).
We investigated regions of the alpha and beta subunits involved in AP binding, subunit dimerization, and substrate specificity. We constructed four chimeric cDNAs: alpha1--259beta292--544 , alpha1--118beta152--544, beta 1--418alpha387--529, and beta1--151 alpha119--259beta292--544 (subscripts refer to amino acid residues). Chimeric cDNAs were expressed in a TSD neuroglial cell line, which produces no endogenous alpha subunits. The chimeric isozymes were chromatofocused and assayed for hydrolysis of (a) 4MUG, (b) 4MUGS and (c) GM2 ganglioside.
Transfection of the cDNA constructs lead to expression of homodimeric and heterodimeric chimeric proteins, albeit at lower yields than transfection of wild alpha-cDNA. All of the chimeric proteins hydrolyzed 4MUG but none were active towards 4MUGS or GM2 ganglioside. These results suggest that (a) all constructs contained sufficient information to form both heterodimeric and homodimeric chimeric proteins, (b) the chimeras lacked the alpha-subunit sequence necessary for the hydrolysis of charged substrates.
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Dankers, Lea Marie. "Physical analysis of human hair." Diss., Rolla, Mo. : University of Missouri-Rolla, 2007. http://scholarsmine.mst.edu/thesis/pdf/Dankers1_09007dcc8054f11e.pdf.

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Thesis (M.S.)--University of Missouri--Rolla, 2007.
Vita. The entire thesis text is included in file. Title from title screen of thesis/dissertation PDF file (viewed August 5, 2008) Includes bibliographical references (p. 83-85).
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Books on the topic "Analysis of human drowsiness"

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Ahad, Md Atiqur Rahman, Upal Mahbub, and Tauhidur Rahman, eds. Contactless Human Activity Analysis. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-68590-4.

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1946-, Morrison Craig S., ed. Qualitative analysis of human movement. Champaign, IL: Human Kinetics, 1997.

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Analysis of human genetic linkage. Baltimore: Johns Hopkins University Press, 1985.

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Risk analysis and human behaviour. New York: Earthscan, 2012.

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Analysis of human genetic linkage. Baltimore: Johns Hopkins University Press, 1991.

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Analysis of human genetic linkage. 3rd ed. Baltimore: Johns Hopkins University Press, 1999.

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Theo, Gevers, and SpringerLink (Online service), eds. Computer Analysis of Human Behavior. London: Springer-Verlag London Limited, 2011.

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Chèze, Laurence. Kinematic Analysis of Human Movement. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781119058144.

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Rawal, Leena, and Sher Ali, eds. Genome Analysis and Human Health. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-4298-0.

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Salah, Albert Ali, and Theo Gevers, eds. Computer Analysis of Human Behavior. London: Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-994-9.

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Book chapters on the topic "Analysis of human drowsiness"

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Anitha, C. "Detection and Analysis of Drowsiness in Human Beings Using Multimodal Signals." In Digital Business, 157–74. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-93940-7_7.

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Selvathi, D. "FPGA Based Human Fatigue and Drowsiness Detection System Using Deep Neural Network for Vehicle Drivers in Road Accident Avoidance System." In Human Behaviour Analysis Using Intelligent Systems, 69–91. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-35139-7_4.

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Yin, Yabo, Yunkai Zhu, Shi Xiong, and Jiacai Zhang. "Drowsiness Detection from EEG Spectrum Analysis." In Informatics in Control, Automation and Robotics, 753–59. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-25992-0_101.

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Wang, Shu, Zhao Zhang, Zheng Wu, Jie Liu, and Chunmei Mo. "Driver Drowsiness Analysis Based on Eyelid Feature." In Lecture Notes in Electrical Engineering, 575–84. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-7945-5_40.

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Zou, K., M. Fard, J. L. Davy, and S. R. Robinson. "Effects of Vibration on Seated Human Drowsiness/Alertness." In Vibration Engineering for a Sustainable Future, 381–87. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-46466-0_51.

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Siddiqi, Abra Shafiq, Md Afshar Alam, Sherin Zafar, Samia Khan, and Nida Iftekhar. "Cognition of Driver Drowsiness to Inculcate Predictive Analysis." In Lecture Notes on Data Engineering and Communications Technologies, 445–57. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-3961-6_37.

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Siddiqi, Abra Shafiq, Sherin Zafar, M. Afshar Alam, Samia Khan, Nida Iftekhar, and Siddhartha Sankar Biswas. "Applying Predictive Analysis Methods for Detection of Driver Drowsiness." In Proceedings of International Conference on Recent Trends in Computing, 9–17. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-7118-0_2.

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Saurav, Sumeet, Shubhad Mathur, Ishan Sang, Shyam Sunder Prasad, and Sanjay Singh. "Yawn Detection for Driver’s Drowsiness Prediction Using Bi-Directional LSTM with CNN Features." In Intelligent Human Computer Interaction, 189–200. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-44689-5_17.

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Montanini, Laura, Ennio Gambi, and Susanna Spinsante. "An OpenCV Based Android Application for Drowsiness Detection on Mobile Devices." In Mobile Networks for Biometric Data Analysis, 145–58. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-39700-9_12.

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Alioua, Nawal, Aouatif Amine, Mohammed Rziza, and Driss Aboutajdine. "Driver’s Fatigue and Drowsiness Detection to Reduce Traffic Accidents on Road." In Computer Analysis of Images and Patterns, 397–404. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23678-5_47.

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Conference papers on the topic "Analysis of human drowsiness"

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MURATA, Atsuo, Kohei FUKUDA, and Taiga KORIYAMA. "An Attempt to Predict Point in Time with High Risk of Accident by Trend Analysis-Method for Detecting Significant Trend of Change of Behavioral Measures-." In Applied Human Factors and Ergonomics Conference. AHFE International, 2019. http://dx.doi.org/10.54941/ahfe100153.

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From the practical viewpoint, only behavioral measures (in this study, eight behavioral measures) were used for drowsiness prediction. A variety of baseline of drowsiness (arousal state) was used in this study. More concretely, each behavioral measure was used as the base line of drowsiness (arousal state) as well as the self-reported evaluation of drowsiness, and thus we made an attempt to predict the participant’s drowsiness for each base line. Trend analysis of each evaluation measure was carried out by using a single regression model where time and base line of drowsiness (one of evaluation measures) corresponded to an independent variable and a dependent variable, respectively. Using the result of trend analysis, we proposed a new approach to predict the point in time (we call this the point in time of virtual accident) when the participant would have encountered a crucial accident if he was driving a car. On the basis of results of all participants, the proposed approach could identify the point in time of virtual accident, and was promising for identifying and predicting the time zone with potentially high risk (probability) of inducing an accident due to drowsy driving in advance, and for warning drivers of such a state.
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Vasudevan, Kashyap, Anjana P. Das, Sandhya B, and Subith P. "Driver drowsiness monitoring by learning vehicle telemetry data." In 2017 10th International Conference on Human System Interactions (HSI). IEEE, 2017. http://dx.doi.org/10.1109/hsi.2017.8005044.

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Hachisuka, Satori. "Human and Vehicle-Driver Drowsiness Detection by Facial Expression." In 2013 International Conference on Biometrics and Kansei Engineering (ICBAKE). IEEE, 2013. http://dx.doi.org/10.1109/icbake.2013.89.

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Revelo, Adriana, Robin Alvarez, and Felipe Grijalva. "Human Drowsiness Detection In Real Time, Using Computer Vision." In 2019 IEEE Fourth Ecuador Technical Chapters Meeting (ETCM). IEEE, 2019. http://dx.doi.org/10.1109/etcm48019.2019.9014884.

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Binti Amirudin, Nurul Aisyah, Naufal Saad, Syed Saad Azhar Ali, and Syed Hasan Adil. "Detection and Analysis of Driver Drowsiness." In 2018 3rd International Conference on Emerging Trends in Engineering, Sciences and Technology (ICEEST). IEEE, 2018. http://dx.doi.org/10.1109/iceest.2018.8643326.

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MURATA, Atsuo, Akira NAKATSUKA, and Makoto MORIWAKA. "Effectiveness of Back and Foot Pressures for Assessing Drowsiness of Drivers." In Applied Human Factors and Ergonomics Conference. AHFE International, 2019. http://dx.doi.org/10.54941/ahfe100154.

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Abstract:
A lot of studies on prediction or evaluation of drowsy driving are conducted using physiological measures in order to prevent traffic accidents due to drowsy driving. Although such physiological measures are to some extent useful for assessing drowsiness, there are some practical limitations such as necessity of attachment of measurement instruments to drivers. Therefore, in order to make drowsiness prediction system more practically usable, it is necessary to find behavioral measures which can be more easily measured than physiological measures. This study paid attention to the behavioral measures (the pressures on the backrest of the driving seat (back pressure) and the pressure on the soles of the foot (foot pressure)) while driving, and explored the effectiveness of these behavioral measures for predicting or evaluating drivers’ drowsiness. The behavioral measures (the back and the foot pressure) during the driving are promising and can be used effectively to evaluate and predict the drivers’ drowsiness.
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Ursulescu, Oana, Betina Ilie, and Georgiana Simion. "Driver Drowsiness Detection Based on Eye Analysis." In 2018 International Symposium on Electronics and Telecommunications (ISETC). IEEE, 2018. http://dx.doi.org/10.1109/isetc.2018.8583852.

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Tabrizi, Pooneh R., and Reza A. Zoroofi. "Open/Closed Eye Analysis for Drowsiness Detection." In 2008 First Workshops on Image Processing Theory, Tools and Applications (IPTA). IEEE, 2008. http://dx.doi.org/10.1109/ipta.2008.4743785.

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"Drowsiness Detection based on Video Analysis Approach." In International Conference on Computer Vision Theory and Applications. SciTePress - Science and and Technology Publications, 2013. http://dx.doi.org/10.5220/0004210004130416.

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Mardanov, R. R., A. L. Ovchinnikov, T. F. Shcherbakova, and S. S. Sedov. "Driver Drowsiness Warning Based on Electrocardiosignals Analysis." In 2021 Intelligent Technologies and Electronic Devices in Vehicle and Road Transport Complex (TIRVED). IEEE, 2021. http://dx.doi.org/10.1109/tirved53476.2021.9639208.

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Reports on the topic "Analysis of human drowsiness"

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Miller, D. P., and J. A. Forester. Aviation Safety Human Reliability Analysis Method. Office of Scientific and Technical Information (OSTI), December 2000. http://dx.doi.org/10.2172/773843.

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Boring, Ronald, and David Gertman. Human reliability analysis for NMSS P-406. Office of Scientific and Technical Information (OSTI), May 2011. http://dx.doi.org/10.2172/1467674.

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Zhong, Yi. Functional Analysis of Human NF1 in Drosophila. Fort Belvoir, VA: Defense Technical Information Center, January 2009. http://dx.doi.org/10.21236/ada532314.

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Cooper, S. E., A. M. Ramey-Smith, J. Wreathall, and G. W. Parry. A technique for human error analysis (ATHEANA). Office of Scientific and Technical Information (OSTI), May 1996. http://dx.doi.org/10.2172/249298.

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Rooks, Drew, and Trelanah McCalla. Human Dipping and Inserting Manipulation Motion Analysis. RPAL, December 2018. http://dx.doi.org/10.32555/2018.ir.001.

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Greitzer, F. L., R. V. Badalamente, and T. S. Stewart. Collaborative human-machine nuclear non-proliferation analysis. Office of Scientific and Technical Information (OSTI), October 1993. http://dx.doi.org/10.2172/10110684.

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Zhong, Yi. Functional Analysis of Human NF1 in Drosophila. Fort Belvoir, VA: Defense Technical Information Center, December 2008. http://dx.doi.org/10.21236/ada488787.

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Willages, Robert C. Human Factors Experimental Design and Analysis Reference. Fort Belvoir, VA: Defense Technical Information Center, July 2007. http://dx.doi.org/10.21236/ada470297.

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Zhong, Yi. Functional Analysis of Human NF1 in Drosophila. Fort Belvoir, VA: Defense Technical Information Center, January 2007. http://dx.doi.org/10.21236/ada465210.

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Wang, Xianshu. Analysis of Human AXIN2 and Other Wnt Signal Pathway Genes in Human Prostate Cancer. Fort Belvoir, VA: Defense Technical Information Center, March 2003. http://dx.doi.org/10.21236/ada415833.

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