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Electrooculogram (EOG), Fatigue, Stress, Visual cue, Entropy, Complexity parameter, Statistical analysis
The primary factors contributing to road accidents are drowsiness and fatigue. Additionally, it diminishes productivity within work environments and elevates the likelihood of accidents. The analysis of bio-signals is crucial in the examination of various physical conditions and the physiological state of an individual. Various biological signals were utilized to identify the presence of fatigue and drowsiness that is associated with fatigue. Various physiological signals were employed to identify driver or operator fatigue and drowsiness. Out of all these non-invasive signals, electrooculogram (EOG) exhibits well-accepted outcomes for detecting drowsiness and fatigue. By employing an EOG-based study, the real-time monitoring of the muscle and mental fatigue of the human subject can be done when they are engaging in their everyday activities. The present studies sought to employ a statistical analysis of electrooculograms (EOGs) to ascertain the stress levels of participants and provide insight into their state of fatigue and drowsiness. Two different experimental studies were performed with 120 and 80 healthy male and female research scholars of National Institute of Technology Durgapur, India. EOGs were recorded by the Biopac MP 45 data acquisition system at two and three different sessions of a day with huge cognitive tasks in between. Several entropies are evaluated from the time domain and frequency domain. The others complexity parameters are also incorporated to enrich the results of the experimental processes. An inferential statistical analysis based on the parametric t-test and non-parametric Wilcoxon test for study-I was considered to compare the stress levels between morning and evening sessions. Similarly, in study-II, the parametric ANOVA test and non-parametric Friedman test were carried out to monitor stress level in three different sessions of a day. The Tukey-Kramer post-hoc test is also undertaken to compare the outcomes among three different sessions and find the statistical differences based on a 5% significance level. Most complexity parameters show excellent results and clear differences in fatigue states for both the experiments and these analyses indicates the presence of onset fatigue among the subjects under consideration.
Ashis Kumar Das, Prashant Kumar, Suman Halder, Santanu Metia, "A Statistical Approach for Investigation and Comparison of Fatigue and Drowsiness based on Complexity Parameters of EOGs", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.15, No.5, pp. 39-59, 2023. DOI:10.5815/ijigsp.2023.05.04
Global status report on road safety 2018: summary. Geneva: World Health Organization; summary. Geneva: World Health Organization;
F.W. Wang, Q. Xu, R.R. Fu, “Study on the effect of man-machine response mode to relieve driving fatigue based on EEG and EOG.” Sensors (2019).
Y. Liu, A.G. Wheaton, D.P. Chapman, T.J, Cunningham, H. Lu, J.B. Croft, “Prevalence of Healthy Sleep Duration among Adults — United States, 2014,” MMWR Morb Mortal Wkly Rep, vol.65, pp. 137–141, 2016.
G. Yang, Y. Lin, and P. Bhattacharya, “A driver fatigue recognition model based on information fusion and dynamic Bayesian network. Inf. Sci. vol. 180, no. 10, pp. 1942–1954, 2010.
P. Thiffault and J. Bergeron, “Monotony of road environment and driver fatigue: A simulator study,” Accident Anal. Prevention, vol. 35, no. 3, pp. 381–391, 2003.
C. Varghese and U. Shankar, “Passenger vehicle occupant fatalities by day and night-a contrast,” Nat. Highway Trafﬁc Safety Admin., vol. 51, no. 4, p. 443, 2008.
S. Nordbakke, F. Sagberg, “Sleepy at the wheel: Knowledge, symptoms and behaviour among car drivers,” Transportation Research Part F: Traffic Psychology and Behaviour, vol. 10, no. 1, pp 1-10, 2007.
W. Gottlieb, L. Galley, R. Schleicher, N. Galley, and J. Churan, “EEG and ocular parameters while driving in a simulation study,” Tech. Rep., 2004.
R.N. Khushaba, S. Kodagoda, S. Lal, G. Dissanayake, “Uncorrelated fuzzy neighbourhood preserving analysis-based feature projection for driver drowsiness recognition,” Fuzzy Sets Syst, vol. 221, pp. 90–111, 2013.
W.Z. Kong, W.C. Lin, B. Fabio, S.Q. Hu, B. Gianluca, “Investigating driver fatigue versus alertness using the granger causality network,” Sensors vol. 15, no. 8, pp. 19181–19198, 2015.
Z.K. Gao, X.M. Wang, Y.X. Yang, C.X. Mu, Q. Cai, W.D. Dang, S.Y. Zuo, “EEG-based spatio-temporal convolutional neural network for driver fatigue evaluation,” IEEE Trans. Neural Netw. Learning Syst. Vol. 30, no. 9, pp. 2755–2763, 2019.
C. Zhang, H. Wang and R. Fu, "Automated Detection of Driver Fatigue Based on Entropy and Complexity Measures," IEEE Transactions on Intelligent Transportation Systems, vol. 15, no. 1, pp. 168-177, 2014.
M. Lin, H. Liang, K. Lin and Y. Hwang, “Electromyographical assessment on muscular fatigue—an elaboration upon repetitive typing activity,” Journal of Electromyography and Kinesiology, vol. 14, no. 6, pp. 661-669, 2004.
S.Y. Hu, G.T. Zheng, “Driver drowsiness detection with eyelid related parameters by Support Vector Machine,” Expert Syst. Appl., vol. 36, no. 4, pp. 7651–7658, 2009.
A. K. Kokonozi, E. M. Michail, I. C. Chouvarda and N. M. Maglaveras, "A study of heart rate and brain system complexity and their interaction in sleep-deprived subjects," Computers in Cardiology, pp. 969-971, 2008.
S. J. Jung, H. S. Shin and W. Y. Chung, “Driver fatigue and drowsiness monitoring system with embedded electrocardiogram sensor on steering wheel,” IET Intelligent Transport Systems, vol. 8, no. 1, pp. 43–50, 2014.
S. Kar and A. Routray, "Effect of Sleep Deprivation on Functional Connectivity of EEG Channels," IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 43, no. 3, pp. 666-672, 2013.
J.N. Côté, D. Raymond, P.A. Mathieu, A.G. Feldman, M.F. Levin, “Differences in multijoint kinematic patterns of repetitive hammering in healthy, fatigued and shoulderinjured individuals,” Clin Biomech (Bristol, Avon), vol. 20, no. 6, pp. 581-90, 2005.
H. Iridiastadi, M.A. Nussbaum, “Muscle fatigue and endurance during repetitive intermittent static efforts: development of prediction models,” Ergonomics, vol. 49, vol. 4, pp. 344-60, 2006.
K. Bylykbashi, E. Qafzezi, M. Ikeda, K. Matsuo, L. Barolli, “Fuzzy-based Driver Monitoring System (FDMS): Implementation of two intelligent FDMSs and a testbed for safe driving in VANETs,” Future Generation Computer Systems, vol. 105, pp 665-674, 2020.
Y. Wang, R. Huang and L. Guo, “Eye gaze pattern analysis for fatigue detection based on GP-BCNN with ESM. Pattern Recognition Letters, vol. 123, pp. 61-74, 2019.
J. Li, H. Li, W. Umer, H. Wang, X. Xing, S. Zhao and J. Hou, “Identification and classification of construction equipment operators' mental fatigue using wearable eye-tracking technology,” Automation in Construction, vol. 109, 103000, 2020.
“World Medical Association,” World Medical Association Declaration of Helsinki: Ethical Principles for Medical Research Involving Human Subjects. JAMA. Vol. 310 no. 20, pp:2191–2194, 2013.
Ashis Kumar Das, Prashant Kumar, Suman Halder, Anwesha Banerjee, D.N. Tibarewala, “A Laboratory Based Experimental Evaluation of Ocular Parameters as Fatigue and Drowsiness Measures,” Procedia Computer Science, vol. 167, pp. 2051-2059, 2020.
S. Datta, A. Banerjee, M. Pal, A. Konar, D. N. Tibarewala and R. Janarthanan, "Blink recognition to detect the possibility of eye dystonia based on electrooculogram analysis," Proceedings of the 2014 International Conference on Control, Instrumentation, Energy and Communication (CIEC), Calcutta, pp. 186-190.
Anwesha Banerjee, Monalisa Pal, Shreyasi Datta, D.N. Tibarewala, Amit Konar, Eye movement sequence analysis using electrooculogram to assist autistic children, Biomedical Signal Processing and Control, vol 14, pp. 134-140, 2014.
C. E. Shannon, "A mathematical theory of communication," The Bell System Technical Journal, vol. 27, no. 3, pp. 379-423, 1948.
Steve Pincus , "Approximate entropy (ApEn) as a complexity measure", Chaos 5, pp. 110-117, 1995.
Joanna Olbrys, Elzbieta Majewska, “Approximate entropy and sample entropy algorithms in financial time series analyses,” Procedia Computer Science, vol. 207, pp. 255-264, 2022.
J.M. Yentes, N. Hunt, K.K. Schmid et al., “The Appropriate Use of Approximate Entropy and Sample Entropy with Short Data Sets,” Ann Biomed Eng, vol.41, pp. 349–365, 2013.
J.S. Richman, D.E. Lake, J.R. Moorman, “Sample entropy,” Methods Enzymol, Vol. 384, pp:172-84, 2004.
C. Bandt, B. Pompe, “Permutation Entropy: A Natural Complexity Measure for Time Series,” Phys. Rev. Lett., vol. 88, 174102, 2002.
Jinyang Li, Pengjian Shang, Xuezheng Zhang, “Financial time series analysis based on fractional and multiscale permutation entropy,” Communications in Nonlinear Science and Numerical Simulation, vol. 78, 2019.
M. Costa, A.L. Goldberger, C. K. Peng, “Multiscale entropy analysis of complex physiologic time series,” Phys. Rev. Lett., vol. 89, 068102, 2002.
W. Chen, Z. Wang, H. Xie and W. Yu, "Characterization of Surface EMG Signal Based on Fuzzy Entropy," IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 15, no. 2, pp. 266-272, 2007.
M. Rostaghi and H. Azami, "Dispersion Entropy: A Measure for Time-Series Analysis," IEEE Signal Processing Letters, vol. 23, no. 5, pp. 610-614, 2016.
Tsallis, Constantino, “Generalized entropy-based criterion for consistent testing. Phys. Rev. E Stat. Phys. Plasmas Fluids Relat.Interdiscip. Top., vol. 58, pp. 1442–1445, 1998.
Mehran Azimbagirad, Luiz Otavio Murta Junior, “Tsallis generalized entropy for Gaussian mixture model parameter estimation on brain segmentation application,” Neuroscience Informatics, vol. 1, no. 1–2, 2021.
R.K. Pathria, Beale, Paul, “Shannon entropy,” Statistical Mechanics (Third ed.). Academic Press, p. 51, 2011.
A. Lempel and J. Ziv, "On the Complexity of Finite Sequences," IEEE Transactions on Information Theory, vol. 22, no. 1, pp. 75-81, 1976.
C.K. Peng, S.V. Buldyrev, S. Havlin, M. Simons, H.E. Stanley, A.L. Goldberger, “Mosaic organization of DNA nucleotides,” Phys Rev E Stat Phys Plasmas Fluids Relat Interdiscip Topics, vol. 49, no. 2, pp. 1685-9, 1994.
B. Qian and K.M. Rasheed, “Hurst Exponent And Financial Market Predictability,” 2005
N. Sriraam, “Correlation dimension based lossless compression of EEG signals,” Biomedical Signal Processing and Control, vol. 7, no. 4, pp. 379-388, 2012.
T. Higuchi, “Approach to an irregular time series on the basis of the fractal theory,” Physica D: Nonlinear Phenomena, vol. 31, no. 2, pp. 277-283, 1988.
J. Jeong, J.H. Chae, S.Y. Kim, S.H. Han, “Nonlinear dynamic analysis of the EEG in patients with Alzheimer's disease and vascular dementia,” J Clin Neurophysiol, vol. 18, no. 1, pp:58-67, 2001.
D.G. Altman, and J.M. Bland, “Measurement in Medicine: The Analysis of Method Comparison Studies,” The Statistician, vol. 32, pp. 307-317, 1983.
Rafdzah Zaki, Awang Bulgiba, Noor Azina Ismail, “Testing the agreement of medical instruments: Overestimation of bias in the Bland–Altman analysis,” Preventive Medicine, vol. 57, Supplement, pp. S80-S82, 2013.
D. Liljequist, B. Elfving and K.S. Roaldsen, “Intraclass correlation – A discussion and demonstration of basic features,” PLoS ONE, vol. 14, no. 7, 2019.
C. Langner, E. Svensson and S. Harvey, “Flexibility analysis of chemical processes considering dependencies between uncertain parameters,” Computer Aided Chemical Engineering, vol. 50, pp. 1105-1110, 2021.