Skip to main content

Advertisement

Log in

Age-related alterations on the capacities to navigate on a bike: use of a simulator and entropy measures

  • Original Article
  • Published:
Medical & Biological Engineering & Computing Aims and scope Submit manuscript

Abstract

Studying the impact of age is important to understand the phenomenon of aging and the disorders that are associated with it. In this work, we analyze age-related alterations on the capacities to navigate on a bike. For this purpose, we use CycléoONE, a bike simulator, and entropy measures. We thus record navigation data (handlebar angle and speed) during the ride. They are processed with two cross-distribution entropy methods (time-shift multiscale cross-distribution entropy and multiscale cross-distribution entropy). We also analyze the time series with a detrended cross-correlation analysis to determine which method can best underline age-related alterations. Our results show that methods based on cross-distribution entropy may be efficient to stress the decrease in navigation capacities with age. The results are very encouraging for our future goal of adding medical benefits to a leisure equipment. They also show the value of using virtual reality to study the impact of age.

This study deals with the use of the signal processing methods (multiscale cross-entropy and multiscale cross-correlation) applied on naviagtion data, acquired with a bike simulator, to study the impact of age on two populations (young healthy subjects and older adults with loss of autonomy).

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2

Similar content being viewed by others

References

  1. Arlati S, Colombo V, Spoladore D, Greci L, Pedroli E, Serino S, Cipresso P, Goulene K, Stramba-Badiale M, Riva G, Gaggioli A, Ferrigno G, Sacco M (2019) A social virtual reality-based application for the physical and cognitive training of the elderly at home. Sensors 19(2)

  2. Beauchet O, Dubost V, Herrmann F, Rabilloud M, Gonthier R, Kressig RW (2005) Relationship between dual-task related gait changes and intrinsic risk factors for falls among transitional frail older adults. Aging Clin Exp Res 17(4):270–275

    Article  Google Scholar 

  3. Boyer KA, Johnson RT, Banks JJ, Jewell C, Hafer JF (2017) Systematic review and meta-analysis of gait mechanics in young and older adults. Exp Gerontol 95:63–70

    Article  Google Scholar 

  4. Castiglioni P, Parati G, Faini A (2019) Information-domain analysis of cardiovascular complexity: Night and day modulations of entropy and the effects of hypertension. Entropy 21(6):550

    Article  Google Scholar 

  5. Clanché F, Muhla F, Cosson A, Gauchard G (2017) The virtual reality scenarios to test the fall. In: AFIHM (ed) 29ème conférence francophone sur l’Interaction homme-machine. AFIHM, p 12

  6. Costa M, Goldberger AL, Peng CK (2002) Multiscale entropy analysis of complex physiologic time series. Phys Rev Lett 89(6):068,102

    Article  Google Scholar 

  7. Cushman LA, Stein K, Duffy CJ (2008) Detecting navigational deficits in cognitive aging and alzheimer disease using virtual reality. Neurology 71(12):888–895

    Article  Google Scholar 

  8. Duque G, Boersma D, Loza-Diaz G, Hassan S, Suarez H, Geisinger D, Suriyaarachchi P, Sharma A, Demontiero O (2013) Effects of balance training using a virtual-reality system in older fallers. Clin Interv Aging 8:257–263

    Article  Google Scholar 

  9. He J, Shang P, Xiong H (2018) Multidimensional scaling analysis of financial time series based on modified cross-sample entropy methods. Physica A Stat Mech Appl 500:210–221

    Article  Google Scholar 

  10. He J, Shang P, Zhang Y (2019) Pid: a pdf-induced distance based on permutation cross-distribution entropy. Nonlinear Dyn 97(2):1329–1342

    Article  Google Scholar 

  11. Higuchi T (1988) Approach to an irregular time series on the basis of the fractal theory. Physica D Nonlinear Phenomena 31(2):277–283

    Article  Google Scholar 

  12. Horvatic D, Stanley HE, Podobnik B (2011) Detrended cross-correlation analysis for non-stationary time series with periodic trends. EPL 94(1):18,007. https://doi.org/10.1209/0295-5075/94/18007

    Article  Google Scholar 

  13. Hu M, Liang H (2011) Uncovering perceptual awareness of visual stimulus with adaptive multiscale entropy. In: 2011 3rd International Conference on Awareness Science and Technology (iCAST), pp 485–490

  14. Humeau-Heurtier A (2015) The multiscale entropy algorithm and its variants: a review. Entropy 17(5)

  15. Jamin A, Duval G, Annweiler C, Abraham P, Humeau-Heurtier A (2019) A novel multiscale cross-entropy method applied to navigation data acquired with a bike simulator. In: 2019 41st annual international conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp 733–736

  16. Jamin A, Humeau-heurtier A (2019) (Multiscale) cross-entropy methods: a review. Entropy 22(1)

  17. Killane I, Fearon C, Newman L, McDonnell C, Waechter SM, Sons K, Lynch T, Reilly RB (2015) Dual motor-cognitive virtual reality training impacts dual-task performance in freezing of gait. IEEE J Biomed Health Inform 19(6):1855–1861

    Article  Google Scholar 

  18. Li P, Liu C, Li K, Zheng D, Liu C, Hou Y (2015) Assessing the complexity of short-term heartbeat interval series by distribution entropy. Med Biol Eng Comput 53(1):77–87

    Article  Google Scholar 

  19. Mirelman A, Rochester L, Maidan I, Del Din S, Alcock L, Nieuwhof F, Rikkert MO, Bloem BR, Pelosin E, Avanzino L, Abbruzzese G, Dockx K, Bekkers E, Giladi N, Nieuwboer A, Hausdorff JM (2016) Addition of a non-immersive virtual reality component to treadmill training to reduce fall risk in older adults (V-TIME): a randomised controlled trial. Lancet (London,England) 388(10050):1170–1182

    Article  Google Scholar 

  20. Montero-Odasso M, Casas A, Hansen KT, Bilski P, Gutmanis I, Wells JL, Borrie MJ (2009) Quantitative gait analysis under dual-task in older people with mild cognitive impairment: a reliability study. J NeuroEng Rehab 6(1):35

    Article  Google Scholar 

  21. Murman DL (2015) The impact of age on cognition. Semin Hear 36(3):111–121

    Article  Google Scholar 

  22. Pincus SM (1991) Approximate entropy as a measure of system complexity. Proc Natl Acad Sci USA 88(6):2297–2301

    Article  CAS  Google Scholar 

  23. Pincus SM, Singer BH (1996) Randomness and degrees of irregularity. Proc Natl Acad Sci 93(5):2083–2088

    Article  CAS  Google Scholar 

  24. Rana M, Varan AQ, Davoudi A, Cohen RA, Sitaram R, Ebner NC (2016) Real-time fmri in neuroscience research and its use in studying the aging brain. Front Aging Neurosci 8:239

    Article  Google Scholar 

  25. Rendon AA, Lohman EB, Thorpe D, Johnson EG, Medina E, Bradley B (2012) The effect of virtual reality gaming on dynamic balance in older adults. Age Ageing 41(4):549–552

    Article  Google Scholar 

  26. Richman JS, Moorman JR (2000) Physiological time-series analysis using approximate entropy and sample entropy. Am J Physiol Heart Circ Physiol 278(6):H2039

    Article  CAS  Google Scholar 

  27. Velazquez A, Campos-Francisco W, García-Vázquez JP, López-Nava H, Rodríguez MD, Pérez-San Pablo AI, Martínez-Rebollar A, Estrada-Esquivel H, Martinez-García A, Muṅoz-Meléndez A, Favela J (2014) Exergames as tools used on interventions to cope with the effects of ageing: a systematic review. In: Ambient assisted living and daily activities. Springer, Cham, pp 402–405

  28. Wang Y, Shang P (2018) Analysis of financial stock markets through the multiscale cross-distribution entropy based on the Tsallis entropy. Nonlinear Dyn 94(2):1361–1376

    Article  Google Scholar 

  29. Wei HC, Xiao MX, Ta N, Wu HT, Sun CK (2019) Assessment of diabetic autonomic nervous dysfunction with a novel percussion entropy approach. Complexity 2019:11

    Google Scholar 

  30. World Health Organization (2015) World report on ageing and health. World Health Organization

  31. Wu HT, Lee CY, Liu CC, Liu AB (2013) Multiscale cross-Approximate entropy analysis as a measurement of complexity between ECG r-R interval and PPG pulse amplitude series among the normal and diabetic subjects. Comput Math Methods Med 2013:7

    Google Scholar 

  32. Wu SD, Wu CW, Lin SG, Lee KY, Peng CK (2014) Analysis of complex time series using refined composite multiscale entropy. Phys Lett A 378(20):1369–1374. https://doi.org/10.1016/j.physleta.2014.03.034

    Article  CAS  Google Scholar 

  33. Wu SD, Wu CW, Lin SG, Wang CC, Lee KY (2013) Time series analysis using composite multiscale entropy. Entropy 15(3):1069–1084

    Article  Google Scholar 

  34. Wu Y, Shang P, Li Y (2018) Multiscale sample entropy and cross-sample entropy based on symbolic representation and similarity of stock markets. Commun Nonlinear Sci Numer Simul 56:49–61

    Article  Google Scholar 

  35. Xie HB, Zheng YP, Guo JY, Chen X (2010) Cross-fuzzy entropy: a new method to test pattern synchrony of bivariate time series. Inf Sci 180(9):1715–1724

    Article  Google Scholar 

  36. Yan R, Yang Z, Zhang T (2009) Multiscale cross entropy: a novel algorithm for analyzing two time series. In: 2009 Fifth international conference on natural computation, vol 1, pp 411–413

  37. Yin Y, Shang P (2015) Asymmetric asynchrony of financial time series based on asymmetric multiscale cross-sample entropy. Chaos 25(3):032,101

    Article  Google Scholar 

  38. Yin Y, Shang P, Feng G (2016) Modified multiscale cross-sample entropy for complex time series. Appl Math Comput 289:98–110

    Google Scholar 

Download references

Acknowledgments

The authors would like to thank Lucie Vigreux who participated in the development of the DCCA code.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Antoine Jamin.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

A CIFRE grant No 2017/1165 was awarded by ANRT to the company COTTOS Médical to support the work of graduate student A. Jamin.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jamin, A., Duval, G., Annweiler, C. et al. Age-related alterations on the capacities to navigate on a bike: use of a simulator and entropy measures. Med Biol Eng Comput 59, 13–22 (2021). https://doi.org/10.1007/s11517-020-02257-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11517-020-02257-y

Keywords

Navigation