RESEARCH OF HUMAN FATIGUE AND MEASUREMENT PARAMETERS FOR WORKABILITY ASSESSMENT

Authors

  • Matīss Eriņš Scientific assistant in Riga Technical University, Faculty of Computer Sciences, Institute of Computer systems. (LV)
  • Oļesja Minejeva Scientific assistant in Riga Technical University, Faculty of Computer Sciences, Institute of Computer systems. (LV)
  • Zigurds Markovičs Riga Technical University, Professor (LV)
  • Juris Lauznis Riga Technical University, Researcher, Doctor of Engineering (Dr.sc.ing.) (LV)
  • Raivis Kivlenieks Scientific assistant in Riga Technical University, Faculty of Computer Sciences, Institute of Computer systems. (LV)

DOI:

https://doi.org/10.17770/etr2019vol2.4148

Keywords:

human fatigue, functional state, workability

Abstract

Human fatigue is reduced working capability for certain period of time as the result of unusual or prolonged workload. Fatigue arises when the body’s energy requirements exceed its supply. Fatigue first manifests as reduced concentration capability causing movement coordination and precision disruption leading to decreasing workability. Fatigue is an object of research in physiology, psychology, work ergonomics, medicine, and biotechnology where each domain has a focus on mental fatigue. The functional state in the context of professional activity is defined as a complex of characteristics of functions and qualities that determine the fulfilment of labour activity. Furthermore, a comprehensive estimation of subject functional state in combination with other factors like subject self-assessment and objective performance tests (cognitive load tests) is a necessary input for the evaluation of workability and efficiency on task. The heterogeneous nature of fatigue as a systemic manifestation requires analysis of multiple key parameters which are relevant to the specific type. The current feasibility study focuses on human biological signal from electrical activity of heart, brain, muscles and skin potentials as well as temperature, position, and respiration to obtain diagnostic parameters reflecting the state of cardiovascular, muscles, and central nervous systems for physiological monitoring of vital signs. The fatigue physiological parameter and feature formalization aim to support the development of a platform with complex passive multi-level fatigue monitoring system and workability evaluation system designed in order to provide an integrated service.

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References

Phillips, Ross. (2014). What is fatigue and how does it affect the safety performance of human transport operators?.

Hatice Ceylan Koydemir and Aydogan Ozcan, Wearable and Implantable Sensors for Biomedical Applications, Annual Review of Analytical Chemistry 2018 11:1, 127-146.

Cercarelli, L. R., & Ryan, G. A. (1996). Long Distance Driving Behaviour of Western Australian Drivers. Proceedings of the Second International Conference on Fatigue and Transportation: Engineering, Enforcement and Education Solutions, Canning Bridge, Promaco, 35-45.

Marcora, Samuele M.; Staiano, Walter; Manning, Victoria (January 2009). "Mental fatigue impairs physical performance in humans". Journal of Applied Physiology. 106 (3): 857–64. doi:10.1152/japplphysiol.91324.2008. PMID 19131473.

de Vries, J. M., Hagemans, M. L., Bussmann, J. B., van der Ploeg, A. T., & van Doorn, P. A. (2009). Fatigue in neuromuscular disorders: focus on Guillain-Barré syndrome and Pompe disease. Cellular and molecular life sciences : CMLS, 67(5), 701-13. doi: 10.1007/s00018-009-0184-2.

Bessonov V.A., Kipor G.V., Goncharov S.F., Ishkov A.V. Assessment of the degree of fatigability of drivers.

Bills, A. (1934). General experimental psychology. New York: Longmans, Green & Co.

Lim, Eric KS; Oster, Andrew JK; Rafferty, Andrew T (2014). Churchill's pocketbook of differential diagnosis (Fourth Edition ed.). Elsevier Health Sciences. ISBN 0702054046. Retrieved 1 March 2019.

David Ponka, Michael Kirlew Can Fam Physician, Top 10 differential diagnoses in family medicine: Fatigue. 2007 May; 53(5): 892.PMCID: PMC1949177.

Task force of the European society of cardiology and the North American society of pacing and electrophysiology, “Heart rate variability:standards of measurement, physiological interpretation, and clinical use.” Circulation vol. 93, no.5, 1043-1065, 1996.

Bonjyotsna, A., & Roy, S. (2014). Correlation of drowsiness with electrocardiogram: A review. International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, 3(5), 9538-9544.

Thayer JF, Hansen AL, Saus-Rose E, Johnsen BH.Ann Behav Med. Heart rate variability, prefrontal neural function, and cognitive performance: the neurovisceral integration perspective on self-regulation, adaptation, and health. 2009 Apr;37(2):141-53. doi: 10.1007/s12160-009-9101-z. Epub 2009 May 8.

Appelhans, B., & Luecken, L. (2006). Heart rate variability as an index of regulated emotional responding. Review Of General Psychology, 10(3), 229-240. doi: 10.1037/1089-2680.10.3.229.

Schmitt, L., Regnard, J., & Millet, G. P. (2015). Monitoring Fatigue Status with HRV Measures in Elite Athletes: An Avenue Beyond RMSSD?. Frontiers in physiology, 6, 343. doi:10.3389/fphys.2015.00343.

Melo, H. M., Nascimento, L. M., & Takase, E. (2017). Mental fatigue and heart rate variability (HRV): The time-on-task effect. Psychology & Neuroscience, 10(4), 428-436.

http://dx.doi.org/10.1037/pne0000110.

Chua, E. C., Tan, W. Q., Yeo, S. C., Lau, P., Lee, I., Mien, I. H., Puvanendran, K., … Gooley, J. J. (2012). Heart rate variability can be used to estimate sleepiness-related decrements in psychomotor vigilance during total sleep deprivation. Sleep, 35(3), 325-34. doi:10.5665/sleep.1688.

Meng, Junqing & Zhao, Bi & Ma, Yechao & Yiyu, Ji & Nie, Baisheng. (2014). Effects of fatigue on the physiological parameters of labor employees. Natural Hazards. 74. 10.1007/s11069-014-1235-z.

Mizuhara, Wang, Kobayashi, & Yamaguchi (2004). A long-range cortical network emerging with theta oscillation in a mental task. Neuroreport, 15(8), 1233.

Niedermeyer & Lopes da Silva (2012, 6th edition). Electroencephalography: Basic Principles, Clinical Applications, and Related Fields. Philadelphia, PA: Lippincott Williams & Wilkins.

SpydelJ.D., FordM.R., Sheer D.E. Task dependent cerebral lateralization of the 40 biz EEG rhythm. - Psychophysiology, 1979?16, pp. 347-350.

Pulvermuller, F., Preissl, H., Lutzenberger, W., Birbaumer, N. Spectral responses in the gamma-band: physiological signs of higher cognitive processes? - NeuroReport, 1995, v. 6, pp. 2057-2064.

Jap, B.T., Lal, S., Fischer, P., & Bekiaris, E. (2009) Using EEG spectral components to assess algorithms for detecting fatigue. Expert Systems with Applications, 36, 2352-2359.

Klimesch, W. (1999). EEG alpha and theta oscillations reflect cognitive and memory performance: A review and analysis. Brain Research Reviews, 29(2-3), 169-195. http://dx.doi.org/10.1016/S0165-0173(98)00056-3.

Critchley, H. D. (2002). Electrodermal responses: what happens in the brain. The Neuroscientist : A Review Journal Bringing Neurobiology, Neurology and Psychiatry, 8(2), 132–142.

Karlen, Walter. (2009). Adaptive wake and sleep detection for wearable systems. 10.5075/epfl-thesis-4391.

Charbek, E. (2015 08). Medscape Normal Vital Vigns. [Online] Available at: http:// emedicine.medscape.com/article/2172054-overview, [Accessed 8 June 2017].

Katie L.StoneSoniaAncoli-Israel, Principles and Practice of Sleep Medicine (Fifth Edition) 2011, Pages 1668-1675 https://doi.org/10.1016/B978-1-4160-6645-3.00147-X.

Fibion Inc., An Energy Expenditure Estimation Method based on Tri-Axial Accelerometry and Advanced Activity Type Classification, 2018, whitepaper, rerieved on March 3, 2019

Saima Naz, Sheikh Ziauddin*, Ahmad R. Shahid, “Driver Fatigue Detection using Mean Intensity, SVM,and SIFT”, 2017

Czeisler, C. A., Zimmerman, J. C., Ronda, J. M., Moore-Ede, M. C., and Weitzman, E. D. (1980). Timing of rem sleep is coupled to the circadian rhythm of body temperature in man. Sleep, 2(3):329–46.

Kräuchi, K., Cajochen, C., and Wirz-Justice, A. (2004). Waking up properly: is there a role of thermoregulation in sleep inertia? Journal of Sleep Research, 13(2):121–127.

Shahid, Azmeh; Wilkinson, Kate; Marcu, Shai; Shapiro, Colin M. (2011-01-01). Shahid, Azmeh; Wilkinson, Kate; Marcu, Shai; Shapiro, Colin M., eds. STOP, THAT and One Hundred Other Sleep Scales. Springer New York. pp. 369–370. doi:10.1007/978-1-4419-9893-4_91. ISBN 9781441998927.

Miley, A. Å., Kecklund, G., & Åkerstedt, T. (2016). Comparing two versions of the Karolinska Sleepiness Scale (KSS). Sleep and biological rhythms, 14(3), 257-260.

Stein, K. D., Jacobsen, P. B., Blanchard, C. M., Thors, C. T. (2004). Further validation of the Multidimensional Fatigue Symptom Inventory-Short Form (MFSI-SF). Journal of Pain and Symptom Management, 27, 14-23.

B. Hudnall Stamm, 2009. Professional Quality of Life: Compassion Satisfaction and Fatigue Version 5 (ProQOL).

Anthony N. Nicholson The Neurosciences and the Practice of Aviation Medicine, page 221, CRC Press, 2011.

Zhang, Chong & Yu, Xiaolin. (2010). Estimating mental fatigue Based on electroencephalogram and heart rate variability. Pol J Med Phys Eng PL ISSN. 16. 67-84. 10.2478/v10013-010-0007-7.

Kirchner, W. K. (1958). "Age differences in short-term retention of rapidly changing information". Journal of Experimental Psychology. 55 (4): 352–358. doi:10.1037/h0043688.

Kaufman, Alan S.; Lichtenberger, Elizabeth (2006). Assessing Adolescent and Adult Intelligence (3rd ed.). Hoboken (NJ): Wiley. p. 3. ISBN 978-0-471-73553-3. Lay summary (22 August 2010).

Lee IS; Bardwell WA; Ancoli-Israel S; Dimsdale JE. Number of lapses during the psychomotor vigilance task as an objective measure of fatigue. J Clin Sleep Med 2010;6(2): 163-168.

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Published

2019-06-20

How to Cite

[1]
M. Eriņš, O. Minejeva, Z. Markovičs, J. Lauznis, and R. Kivlenieks, “RESEARCH OF HUMAN FATIGUE AND MEASUREMENT PARAMETERS FOR WORKABILITY ASSESSMENT”, ETR, vol. 2, pp. 38–43, Jun. 2019, doi: 10.17770/etr2019vol2.4148.