IDEAS home Printed from https://ideas.repec.org/a/bla/jorssc/v44y1995i1p17-30.html
   My bibliography  Save this article

A Functional Data Analysis of the Pinch Force of Human Fingers

Author

Listed:
  • J. O. Ramsay
  • X. Wang
  • R. Flanagan

Abstract

The ability of the human thumb and forefinger to adapt the pinch force to the static and dynamic characteristics of the object being grasped is one of the marvels of human physiology. We analyse a sample of records of the force applied during a brief squeeze by functional data analysis techniques in which familiar statistical concepts are adapted to observations that are functional in character. Except for scale, a graph of these force impulses closely resembles a log‐normal density function, and this has a plausible physiological rationale. Specially adapted smoothing spline approximations along with a functional version of principal components analysis reveal that the residual variation is essentially one dimensional in structure, and that the force functions can be described by a simple linear differential equation incorporating the effects of drag or viscosity in the joints and muscles involved.

Suggested Citation

  • J. O. Ramsay & X. Wang & R. Flanagan, 1995. "A Functional Data Analysis of the Pinch Force of Human Fingers," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 44(1), pages 17-30, March.
    • Handle: RePEc:bla:jorssc:v:44:y:1995:i:1:p:17-30
    DOI: 10.2307/2986192
    as

    Download full text from publisher

    File URL: https://doi.org/10.2307/2986192
    Download Restriction: no
    File URL: https://libkey.io/10.2307/2986192?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
    ---><---

    Citations

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


    Cited by:

    1. Michio Yamamoto, 2012. "Clustering of functional data in a low-dimensional subspace," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 6(3), pages 219-247, October.
    2. Ocaña, F. A. & Aguilera, A. M. & Valderrama, M. J., 1999. "Functional Principal Components Analysis by Choice of Norm," Journal of Multivariate Analysis, Elsevier, vol. 71(2), pages 262-276, November.
    3. Daniel R. Kowal & Antonio Canale, 2021. "Semiparametric Functional Factor Models with Bayesian Rank Selection," Papers 2108.02151, arXiv.org, revised May 2022.

    More about this item

    Statistics

    Access and download statistics

    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:bla:jorssc:v:44:y:1995:i:1:p:17-30. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Wiley Content Delivery (email available below). General contact details of provider: https://edirc.repec.org/data/rssssea.html .

    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.