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A Functional Data Analysis of the Pinch Force of Human Fingers

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  • 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
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    Cited by:

    1. 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.
    2. Daniel R. Kowal & Antonio Canale, 2021. "Semiparametric Functional Factor Models with Bayesian Rank Selection," Papers 2108.02151, arXiv.org, revised May 2022.
    3. 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.

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