Energy Spectral Density Analysis of Muscle Fatigue

  • Noor Aisyah Ab RahmanEmail author
  • Mahfuzah Mustafa
  • Rosdiyana Samad
  • Nor Rul Hasma Abdullah
  • Norizam Sulaiman
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 538)


Driver’s vigilance level is easily distracted when in a state of fatigue and drowsiness. Most drivers’ shows sign of visual fatigue and loss of vigilance during long and monotonous driving. Their ability to maintain adequate driving performance is affected by various factors. A Popular technique to estimate driver’s vigilance level is physiological measure that uses electromyogram (EMG) signal in estimating driver muscle fatigue while driving. In this project, the EMG signal will be obtained by attaching the electrodes to the biceps brachii of each 15 subjects during playing Need for Speed (NFS) game for two hours. Be-fore that, subjects will answer a set of questionnaires and the scores obtained will be calculated. From the questionnaires, driver condition can be determined whether the driver is non-fatigue or mild fatigue or fatigue. Then signal preprocessing is applied to remove artifact in EMG signal. Next, the EMG signal is analyzed by using frequency domain analysis and Energy Spectral Density (ESD) extracted from the analysis. Mean, variance and peak energy of ESD is obtained from all the samples. Based on results obtained, the normalized mean (non-fatigue: 0.0514–0.1255), (mild fatigue: 0.0554–0.0802) and (fatigue: 0.0069–0.0188). For the variance range (non-fatigue: 0.0050–0.0311), (mild fatigue: 0.0054–0.0802) and (fatigue: 0.0006–0.0047). While for the peak energy of ESD (non-fatigue: 28,480–2,943,000 J/Hz), (mild fatigue: 99,440–120,500 J/Hz) and (fatigue: 537.7–11,440 J/Hz).


Fatigue ESD EMG 



The research was supported by a Grant at the Universiti Malaysia Pahang (RDU160391).


  1. 1.
    Chen, L.L., Zhao, Y., Ye, P.F., Zhang, J., Zou, J.Z.: Detecting driving stress in physiological signals based on multimodal feature analysis and kernel classifiers. Expert Syst. Appl. 85, 279–291 (2017)CrossRefGoogle Scholar
  2. 2.
    Williamson, A., Friswell, R.: Investigating the relative effects of sleep deprivation and time of day on fatigue and performance. Accid. Anal. Prev. 43(3), 690–697 (2011)CrossRefGoogle Scholar
  3. 3.
    Horberry, T., Anderson, J., Regan, M.A., Triggs, T.J., Brown, J.: Driver distraction: the effects of concurrent in-vehicle tasks, road environment complexity and age on driving performance. Accid. Anal. Prev. 38(1), 185–191 (2006)CrossRefGoogle Scholar
  4. 4.
    Rahman, N.S.A. et al.: Initial experiment of muscle fatigue during driving game using electromyography. In: Proceedings of 7th IEEE International Conference on System Engineering Technology ICSET 2017, pp. 101–105. IEEE, Malaysia (2017)Google Scholar
  5. 5.
    Meiss, R.: Skeletal muscle and smooth muscle. In: Structure, pp. 152–176Google Scholar
  6. 6.
    Hostens, I., Ramon, H.: Assessment of muscle fatigue in low level monotonous task performance during car driving. J. Electromyogr. Kinesiol. 15(3), 266–274 (2005)CrossRefGoogle Scholar
  7. 7.
    Sahayadhas, A., Sundaraj, K., Murugappan, M.: Detecting driver drowsiness based on sensors: a review. Sensors 12(12), 16937–16953 (2012)CrossRefGoogle Scholar
  8. 8.
    Akin, M., Kurt, M.B., Sezgin, N., Bayram, M.: Estimating vigilance level by using EEG and EMG signals. Neural Comput. Appl. 17(3), 227–236 (2008)CrossRefGoogle Scholar
  9. 9.
    Sahayadhas, A., Sundaraj, K., Murugappan, M.: Drowsiness detection during different times of day using multiple features. Australas. Phys. Eng. Sci. Med. 36(2), 243–250 (2013)CrossRefGoogle Scholar
  10. 10.
    Abd Rahman, N.S.: Assessing muscle fatigue during driving game using EMG Signal. Bachelor Degree Thesis, Universiti Malaysia Pahang (UMP) (2016)Google Scholar
  11. 11.
    Mohd Azli, M.A.S.: Electromyograph (EMG) signal analysis to predict muscle fatigue. Bachelor Degree Thesis, Universiti Malaysia Pahang (UMP) (2017)Google Scholar
  12. 12.
    Phinyomark, A., Phukpattaranont, P., Limsakul, C.: Feature reduction and selection for EMG signal classification. Expert Syst. Appl. 39(8), 7420–7431 (2012)CrossRefGoogle Scholar
  13. 13.
    Kumar, A., Arya, N.: A study relation between energy spectral density and probability density function with impulse response first order control system. In: International Conference on Electronics, Communication and Aerospace Technology, pp. 152–154. IEEE, India (2017)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Noor Aisyah Ab Rahman
    • 1
    Email author
  • Mahfuzah Mustafa
    • 1
  • Rosdiyana Samad
    • 1
  • Nor Rul Hasma Abdullah
    • 1
  • Norizam Sulaiman
    • 1
  1. 1.Universiti Malaysia PahangPekanMalaysia

Personalised recommendations