Nonlinear Dynamics

, Volume 88, Issue 4, pp 2873–2887 | Cite as

Characterization of the vibration transmitted in the human arm using selected recurrence quantification parameters

  • C. A. Kitio KwuimyEmail author
  • S. A. Adewusi
  • C. Nataraj
Original Paper


This paper deals with the analysis of the time series signals extracted from the vibration of a human arm. The data are extracted at the wrist, elbow and shoulder of three male subjects with different vibration pre-exposure levels. The method of recurrence analysis is used to extract features in order to characterize the nature of the vibration transmitted from the wrist to the elbow and the shoulder. A comparative analysis among the individual levels of pre-exposure is carried out. It is shown that some recurrence parameters, namely the recurrence rate, the entropy, the mean diagonal and the trapping time appear to be efficient in characterizing the nature of the vibration. Overall, the analysis performed in this paper shows that the method of recurrence analysis can effectively be used to quantitatively discriminate between the nature of the vibration transmitted in the human arm and illustrate the effects of the vibration pre-exposure.


Hand-arm vibration Recurrence quantification parameters Time series analysis State space reconstruction 



Part of this work was carried out during the Postdoctoral fellowship of C.A.K. Kwuimy at the Villanova Center for Analytics of Dynamic Systems (VCADS). He would like to thank the US Office of Naval Research for the financial support (Grant N00014-13-1-0485) and Capt. Lynn Petersen for his guidance.


  1. 1.
    Silipo, R., Vergassola, G.D.R., Bartsch, H.: Dynamics extraction in multivariate biomedical time series. Biol. Cybern. 79(1), 15–27 (1998)CrossRefzbMATHGoogle Scholar
  2. 2.
    Nataraj, C.: How engineers can help doctors save lives. (2015)
  3. 3.
    Schlenker, J., Nedelka, T., Riedlbauchova, L., Socha, V., Hana, K., Kutlek, P.: Recurrence quantification analysis: a promising method for data evaluation in medicine. Eur. J. Biomed. Inform. 10(1), 35–40 (2014)Google Scholar
  4. 4.
    Benavides, A.M., Pozo, R.F., Toledano, D.T., Murillo, J.L.B., Gonzalo, E.L., Gomez, L.H.: Analysis of voice features related to obstructive sleep apnoea and their application in diagnosis support. Comput. Speech Lang. 28(2), 434–452 (2014)CrossRefGoogle Scholar
  5. 5.
    de Ipina, K.L., Sole-Casals, J., Eguiraun, H., Alonso, J., Travieso, C., Ezeiza, A., Barroso, N., Ecay-Torres, M., Martinez-Lage, P., Beitia, B.: Feature selection for spontaneous speech analysis to aid in alzheimer’s disease diagnosis: A fractal dimension approach. Comput. Speech Lang. 30(1), 43–60 (2015)CrossRefGoogle Scholar
  6. 6.
    Loskutov, A., Mironyuk, O.: Time series analysis of ECG: a possibility of the initial diagnostics. Int. J. Bifurc. Chaos 17(10), 3709–3713 (2007)CrossRefzbMATHGoogle Scholar
  7. 7.
    Sole-Casals, J., Munteanu, C.: Martn, O.C., Barb, F., Queipo, C., Amilibia, J., Durn-Cantolla, J.: Detection of severe obstructive sleep apnea through voice analysis. Appl. Soft Comput. 23, 346–354 (2014)CrossRefGoogle Scholar
  8. 8.
    Ganz, R., Lenz, C.: A program for the user-independent computation of the correlation dimension and the largest lyapunov exponent of heart rate dynamics from small data sets. Comput. Methods Programs Biomed. 49(1), 61–68 (1996)CrossRefGoogle Scholar
  9. 9.
    Liu, X., Du, H., Wang, G., Zhou, S., Zhang, H.: Automatic diagnosis of premature ventricular contraction based on Lyapunov exponents and LVQ neural network. Comput. Methods Programs Biomed. 122(1), 47–55 (2015)CrossRefGoogle Scholar
  10. 10.
    Marwan, N., Romano, M.C., Thiel, M., Kurths, J.: Recurrence plots for the analysis of complex system. Phys. Rep. 438(5–6), 237–329 (2007)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Casdagli, M.C.: Recurrence plots revisited. Physica D 108, 12–4 (1997)MathSciNetCrossRefzbMATHGoogle Scholar
  12. 12.
    Fontaine, S., Dia, S., Renner, M.: Nonlinear friction dynamics on fibrous materials, application to the characterization of surface quality. Part I: global characterization of phase spaces. Nonlinear Dyn. 66(4), 625–646 (2011)CrossRefGoogle Scholar
  13. 13.
    Guhathakurta, K., Bhattacharya, B., Chowdhury, A.R.: Using recurrence plot analysis to distinguish between endogenous and exogenous stock market crashes. Physica A Stat. Mech. Appl. 389(9), 1874–1882 (2010)CrossRefGoogle Scholar
  14. 14.
    Iwaniec, J., Uhl, T., Staszewski, W.J., Klepka, A.: Detection of changes in cracked aluminium plate determinism by recurrence analysis. Nonlinear Dyn. 70(1), 125–140 (2012)CrossRefGoogle Scholar
  15. 15.
    Johnsson, F., Zijerveld, R., Schouten, J., van den Bleek, C., Lecknera, B.: Characterization of fluidization regimes by time-series analysis of pressure fluctuations. Int. J. Multiph. Flow 26(4), 663–715 (2000)CrossRefzbMATHGoogle Scholar
  16. 16.
    Kwuimy, C.A.K., Nataraj, C.: Recurrence analysis and synchronization dynamics of multi limit cycles energy harvesters. In: Belhag M. (eds.) Structural Nonlinear Dynamics & Control and Diagnosis, pp. 97–123 Springer, New York (2015)Google Scholar
  17. 17.
    Kwuimy, C.A.K., Samadani, M., Kappaganthu, K., Nataraj, C.: Sequential recurrence analysis of experimental time series of a rotor response with bearing outer race faults. In: International Conference on Vibration Engineering and Technology of Machinery (2014)Google Scholar
  18. 18.
    Kwuimy, C.K., Kadji, H.E.: Recurrence analysis and synchronization of oscillators with coexisting attractors. Phys. Lett. A (0), (2014)Google Scholar
  19. 19.
    Litak, G., Borowiec, M., Friswell, M.I., Adhikari, S.: Energy harvesting in a magnetopiezoelastic system driven by random excitations with uniform and gaussian distributions. J. Theor. Appl. Mech. 49, 757 (2011)Google Scholar
  20. 20.
    Litakt, G., Syta, A., Gajewski, J., Jonak, J.: Detecting and identifying non-stationary courses in the ripping head power consumption by recurrence plots. Meccanica 45(4), 603–608 (2010)CrossRefzbMATHGoogle Scholar
  21. 21.
    Ouyang, G., Li, X., Dang, C., Richards, D.A.: Using recurrence plot for determinism analysis of EEG recordings in genetic absence epilepsy rats. Clin. Neurophysiol. 119(8), 1747–1755 (2008)CrossRefGoogle Scholar
  22. 22.
    Seeck, A., Rademacher, W., Fischer, C., Haueisen, J., Surber, R., Voss, A.: Prediction of atrial fibrillation recurrence after cardioversioninteraction analysis of cardiac autonomic regulation. Med. Eng. Phys. 35(3), 376–382 (2013)CrossRefGoogle Scholar
  23. 23.
    Sun, R., Wang, Y.: Predicting termination of atrial fibrillation based on the structure and quantification of the recurrence plot. Med. Eng. Phys. 30(9), 1105–1111 (2008)CrossRefGoogle Scholar
  24. 24.
    Zbilut, J.P., Thomasson, N., Webber, C.L.: Recurrence quantification analysis as a tool for nonlinear exploration of nonstationary cardiac signals. Med. Eng. Phys. 24(1), 53–60 (2002)CrossRefGoogle Scholar
  25. 25.
    Carrubba, S., Frilot, C., Chesson, A.L., Marino, A.A.: Numerical analysis of recurrence plots to detect effect of environmental-strength magnetic fields on human brain electrical activity. Med. Eng. Phys. 32(8), 898–907 (2010)CrossRefGoogle Scholar
  26. 26.
    Mohebbi, M., Ghassemian, H.: Prediction of paroxysmal atrial fibrillation using recurrence plot-based features of the RR-interval signal. Physiol. Meas. 32(8), 1147 (2011)CrossRefGoogle Scholar
  27. 27.
    Priano, L., Mauro, F.S.A., Guiot, C.: Non-linear recurrence analysis of NREM human sleep microstructure discloses deterministic oscillation patterns related to sleep stage transitions and sleep maintenance. In: Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4934–4937. (2010)Google Scholar
  28. 28.
    Little, M., McSharry, P., Roberts, S., Costello, D., Moroz, I.: Exploiting nonlinear recurrence and fractal scaling properties for voice disorder detection. Biomed. Eng. Online 6(1), 23 (2007)CrossRefGoogle Scholar
  29. 29.
    Socha, V., Szabo, S., Socha, L., Nemec, V.: Evaluation of the variability of respiratory rate as a marker of stress changes. In: Proceedings of the International Conference on Transport Means, pp. 23–24. (2014)Google Scholar
  30. 30.
    Gonzalez, H., Infante, O., Perez-Grovas, H., Jose, M.V., Lerma, C.: Nonlinear dynamics of heart rate variability in response to orthostatism and hemodialysis in chronic renal failure patients: Recurrence analysis approach. Med. Eng. Phys. 35(2), 178–187 (2013)CrossRefGoogle Scholar
  31. 31.
    Rangaprakash, D., Pradhan, N.: Study of phase synchronization in multichannel seizure EEG using nonlinear recurrence measure. Biomed. Signal Process. Control 11, 114–122 (2014)CrossRefGoogle Scholar
  32. 32.
    Yan, J., Wang, Y., Ouyang, G., Yu, T., Li, X.: Using max entropy ratio of recurrence plot to measure electrocorticogram changes in epilepsy patients. Physica A Stat. Mech. Appl. 443, 109–116 (2016)MathSciNetCrossRefGoogle Scholar
  33. 33.
    Adewusi, S., Rakheja, S., Marcotte, P., Boileau, P.E.: On the discrepancies in the reported human handarm impedance at higher frequencies. Int. J. Ind. Ergon. 38(910), 703–714, : Special Issue: Workplace Vibration Exposure Characterization, assessment and ergonomic interventions. Special Issue, Workplace Vibration Exposure (2008)Google Scholar
  34. 34.
    Adewusi, S., Rakheja, S., Marcotte, P., Boutin, J.: Vibration transmissibility characteristics of the human handarm system under different postures, hand forces and excitation levels. J. Sound Vib. 329(14), 2953–2971 (2010)CrossRefGoogle Scholar
  35. 35.
    Cherian, T., Rakheja, S., Bhat, R.: An analytical investigation of an energy flow divider to attenuate hand-transmitted vibration. Int. J. Ind. Ergon. 17, 455–467 (1996)CrossRefGoogle Scholar
  36. 36.
    Kattel, B., Fernandez, J.: The effect of rivet gun on handarm vibration. Int. J. Ind. Ergon. 23, 595–608 (1999)CrossRefGoogle Scholar
  37. 37.
    Kihlberg, S.: Biodynamic response of the handarm system to vibration from an impact hammer and a grinder. Int. J. Ind. Ergon. 16, 1–8 (1995)CrossRefGoogle Scholar
  38. 38.
    Reynolds, D., Angevine, E.: Handarm vibration. Part II: vibration transmission characteristics of the hand and arm. J. Sound Vib. 51, 255–265 (1977)CrossRefGoogle Scholar
  39. 39.
    Xu, X., Welcome, D., Mcdowell, T., Warren, C., Dong, R.: An investigation on characteristics of the vibration transmitted to the wrist and elbow in the operation of impact wrenches. Int. J. Ind. Ergon. 39, 174–184 (2008)CrossRefGoogle Scholar
  40. 40.
    Bovenzi, M.: Exposure-response relationship in the handarm vibration syndrome: an overview of current epidemiology research. Int. Arch. Occup. Environ. Health 71, 509–519 (1998)CrossRefGoogle Scholar
  41. 41.
    Friden, J.: Vibration damage to the hand: clinical presentation, prognosis and length and severity of vibration requiered. J. Hand Surg. 26, 471–474 (2001)CrossRefGoogle Scholar
  42. 42.
    Brammer, A.J.: Dose-response relarelations for hand-transmitted vibration. Scand. J. Work Environ. Health 12, 284–288 (1986)CrossRefGoogle Scholar
  43. 43.
    Griffin, M.J.: Foundations of hand-transmitted vibration standards. Nagoya J. Med. Sci. 57, 147–164 (1994)Google Scholar
  44. 44.
    Nilsson, T., Burstrom, L., Hagberg, M.: Risk assessment of vibration exposure and white finger among platters. Int. Arch. Occup. Environ. Health 61, 473–481 (1989)CrossRefGoogle Scholar
  45. 45.
    Jahn, R., Hesse, M.: Applications of handarm models in the investigation of the interaction between man and machine. Scand. J. Work Environ. Health 12, 343–346 (1986)CrossRefGoogle Scholar
  46. 46.
    Gurram, R., Rakheja, S., Gouw, G.: Mechanical impedance of the human handarm system subject to sinusoidal and stochastic excitations. Int. J. Ind. Ergon. 16, 135–145 (1995)CrossRefGoogle Scholar
  47. 47.
    Marcotte, P., Aldien, Y., Boileau, P., Rakheja, S., Boutin, J.: Effect of handle size and handhandle contact force on the biodynamic response of the handarm system under zh-axis vibration. J. Sound Vib. 283, 1071–1091 (2005)CrossRefGoogle Scholar
  48. 48.
    ISO-5349-1: Mechanical vibration and shock-measurement and evaluation of human exposure to hand-transmitted vibration—part 1: general requirements (2001)Google Scholar
  49. 49.
    Bovenzi, M.: Epidemiological evidence for new frequency weightings of hand-transmitted vibration. Ind. Health 50, 377–387 (2012)Google Scholar
  50. 50.
    Griffin, M.: Frequency-dependence of psychophysical and physiological responses to hand-transmitted vibration. Ind. Health 50, 354–369 (2012)CrossRefGoogle Scholar
  51. 51.
    Taylor, W., Nagalingam, M., Pelmear, P.L., Leong, D., Fung, D.: Measurement of vibration of hand-held tools: weighted or unweighted. J. Occup. Med. 31, 902–908 (1998)Google Scholar
  52. 52.
    Abarbanel, H.D.I.: Analysis of Observed Chaotic Data. Springer, New York (1996)CrossRefzbMATHGoogle Scholar
  53. 53.
    Galka, A.: Topics in Nonlinear Time Series Analysis. Springer, New York (2000)CrossRefzbMATHGoogle Scholar
  54. 54.
    Kantz, H., Schreiber, T.: Nonlinear Time Series Analysis. Cambridge University Press, Cambridge (2004)zbMATHGoogle Scholar
  55. 55.
    Takens, F.: Detecting strange attractors in turbulence. In: Rand, D.A., Young, L.S. (eds.) Dynamical Systems and Turbulence. Lecture Notes in Mathematics, pp. 366–381. Springer, New York (1981)Google Scholar
  56. 56.
    Kwuimy, C., Samadani, M., Nataraj, C.: Bifurcation analysis of a nonlinear pendulum using recurrence and statistical methods: applications to fault diagnostics. Nonlinear Dyn. 76(4), 1963–1975 (2014)MathSciNetCrossRefzbMATHGoogle Scholar
  57. 57.
    Gao, J., Hu, J.: Fast monitoring of epileptic seizures using recurrence time statistics of electroencephalography. Front. Comput. Neurosci. 7, 7–15 (2013)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2017

Authors and Affiliations

  • C. A. Kitio Kwuimy
    • 1
    Email author
  • S. A. Adewusi
    • 2
  • C. Nataraj
    • 3
  1. 1.Department of Engineering Education College of Engineering and Applied ScienceUniversity of CincinnatiCincinnatiUSA
  2. 2.Department of Mechanical EngineeringJubail University CollegeJubail Industrial CityKingdom of Saudi Arabia
  3. 3.Villanova Center for Analytics of Dynamic SystemsVillanova UniversityVillanovaUSA

Personalised recommendations