IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v506y2018icp290-304.html
   My bibliography  Save this article

Humans are able to self-paced constant running accelerations until exhaustion

Author

Listed:
  • Billat, Véronique
  • Brunel, Nicolas J-B.
  • Carbillet, Thomas
  • Labbé, Stéphane
  • Samson, Adeline

Abstract

Although it has been experimentally reported that speed variations is the optimal way of optimizing his pace for achieving a given distance in a minimal time, we still do not know what the optimal speed variations (i.e. accelerations) are. At first, we have to check the hypothesis that human is able to accurately self-pacing its acceleration and this even in a state of fatigue during exhaustive self-pacing ramp runs. For that purpose, 3 males and 2 females middle-aged, recreational runners ran, in random order, three exhaustive acceleration trials. We instructed the five runners to perform three self-paced acceleration trials based on three acceleration intensity levels: ”soft”, ”medium” and ”hard”. We chose a descriptive modelling approach to analyse the behaviour of the runners. Once we knew that the runners were able to perceive three acceleration intensity levels, we proposed a mean-reverting process (Ornstein–Uhlenbeck) to describe those accelerations: dat=−θ(at−a)dt+σdWt where a is the mean acceleration, at is the measured acceleration at each time interval t, θ the ability of the runner to correct the variations around a mean acceleration and σ the human induced variations. The goodness-of-fit of the Ornstein–Uhlenbeck process highlights the fact that humans are able to maintain a constant acceleration and are able to precisely regulate their acceleration (regardless of its intensity) in a run leading to exhaustion in the range from 1 min 36 s to 20 min.

Suggested Citation

  • Billat, Véronique & Brunel, Nicolas J-B. & Carbillet, Thomas & Labbé, Stéphane & Samson, Adeline, 2018. "Humans are able to self-paced constant running accelerations until exhaustion," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 506(C), pages 290-304.
    • Handle: RePEc:eee:phsmap:v:506:y:2018:i:c:p:290-304
    DOI: 10.1016/j.physa.2018.04.058
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437118304941
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2018.04.058?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Rainer Schöbel & Jianwei Zhu, 1999. "Stochastic Volatility With an Ornstein–Uhlenbeck Process: An Extension," Review of Finance, European Finance Association, vol. 3(1), pages 23-46.
    2. Dennis M. Bramble & Daniel E. Lieberman, 2004. "Endurance running and the evolution of Homo," Nature, Nature, vol. 432(7015), pages 345-352, November.
    3. Billat, Véronique L. & Mille-Hamard, Laurence & Meyer, Yves & Wesfreid, Eva, 2009. "Detection of changes in the fractal scaling of heart rate and speed in a marathon race," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(18), pages 3798-3808.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Véronique Billat & Damien Vitiello & Florent Palacin & Matthieu Correa & Jean Renaud Pycke, 2020. "Race Analysis of the World’s Best Female and Male Marathon Runners," IJERPH, MDPI, vol. 17(4), pages 1-6, February.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Eduardo Abi Jaber, 2022. "The characteristic function of Gaussian stochastic volatility models: an analytic expression," Working Papers hal-02946146, HAL.
    2. Damir Filipovi'c & Martin Larsson, 2017. "Polynomial Jump-Diffusion Models," Papers 1711.08043, arXiv.org, revised Jul 2019.
    3. Nicolas Langrené & Geoffrey Lee & Zili Zhu, 2016. "Switching To Nonaffine Stochastic Volatility: A Closed-Form Expansion For The Inverse Gamma Model," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 19(05), pages 1-37, August.
    4. Rehez Ahlip & Laurence A. F. Park & Ante Prodan, 2017. "Pricing currency options in the Heston/CIR double exponential jump-diffusion model," International Journal of Financial Engineering (IJFE), World Scientific Publishing Co. Pte. Ltd., vol. 4(01), pages 1-30, March.
    5. Björn Lutz, 2010. "Pricing of Derivatives on Mean-Reverting Assets," Lecture Notes in Economics and Mathematical Systems, Springer, number 978-3-642-02909-7, July.
    6. Gerrard, Russell & Kyriakou, Ioannis & Nielsen, Jens Perch & Vodička, Peter, 2023. "On optimal constrained investment strategies for long-term savers in stochastic environments and probability hedging," European Journal of Operational Research, Elsevier, vol. 307(2), pages 948-962.
    7. Roger Lord, 2010. "Comment on: A Note on the Discontinuity Problem in Heston's Stochastic Volatility Model," Applied Mathematical Finance, Taylor & Francis Journals, vol. 17(4), pages 373-376.
    8. Benjamin Cheng & Christina Nikitopoulos-Sklibosios & Erik Schlogl, 2015. "Pricing of Long-dated Commodity Derivatives with Stochastic Volatility and Stochastic Interest Rates," Research Paper Series 366, Quantitative Finance Research Centre, University of Technology, Sydney.
    9. João Pedro Vidal Nunes & Tiago Ramalho Viegas Alcaria, 2016. "Valuation of forward start options under affine jump-diffusion models," Quantitative Finance, Taylor & Francis Journals, vol. 16(5), pages 727-747, May.
    10. Gallagher, Andrew, 2013. "Stature, body mass, and brain size: A two-million-year odyssey," Economics & Human Biology, Elsevier, vol. 11(4), pages 551-562.
    11. Kaustav Das & Nicolas Langren'e, 2020. "Explicit approximations of option prices via Malliavin calculus in a general stochastic volatility framework," Papers 2006.01542, arXiv.org, revised Jan 2024.
    12. Alexander Lipton, 2024. "Hydrodynamics of Markets:Hidden Links Between Physics and Finance," Papers 2403.09761, arXiv.org.
    13. Cui, Zhenyu & Kirkby, J. Lars & Nguyen, Duy, 2017. "Equity-linked annuity pricing with cliquet-style guarantees in regime-switching and stochastic volatility models with jumps," Insurance: Mathematics and Economics, Elsevier, vol. 74(C), pages 46-62.
    14. Damien Ackerer & Damir Filipović, 2020. "Option pricing with orthogonal polynomial expansions," Mathematical Finance, Wiley Blackwell, vol. 30(1), pages 47-84, January.
    15. Eduardo Abi Jaber, 2022. "The Laplace transform of the integrated Volterra Wishart process," Mathematical Finance, Wiley Blackwell, vol. 32(1), pages 309-348, January.
    16. Alexander Lipton, 2023. "Kelvin Waves, Klein-Kramers and Kolmogorov Equations, Path-Dependent Financial Instruments: Survey and New Results," Papers 2309.04547, arXiv.org.
    17. Robert Kurzban & Peter DeScioli, 2013. "Adaptationist punishment in humans," Journal of Bioeconomics, Springer, vol. 15(3), pages 269-279, October.
    18. Fathi Abid & Bilel Kaffel, 2018. "The extent of virgin olive-oil prices’ distribution revealing the behavior of market speculators," Review of Quantitative Finance and Accounting, Springer, vol. 50(2), pages 561-590, February.
    19. Yijuan Liang & Xiuchuan Xu, 2019. "Variance and Dimension Reduction Monte Carlo Method for Pricing European Multi-Asset Options with Stochastic Volatilities," Sustainability, MDPI, vol. 11(3), pages 1-21, February.
    20. Viktor Bezborodov & Luca Persio & Yuliya Mishura, 2019. "Option Pricing with Fractional Stochastic Volatility and Discontinuous Payoff Function of Polynomial Growth," Methodology and Computing in Applied Probability, Springer, vol. 21(1), pages 331-366, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:506:y:2018:i:c:p:290-304. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.