Literature DB >> 16501988

Body-goal variability mapping in an aiming task.

Joseph P Cusumano1, Paola Cesari.   

Abstract

Given the number of joints and muscles in the human body, there are typically an infinite number of ways to perform the same action, a feature of directed movements known as equifinality (Bernstein, The coordination and regulation of movements, Oxford, Pergamon, 1967). Here we present a new type of performance analysis based on the idea of a body-goal variability mapping. We show how this mapping arises naturally from the idea of a goal function that theoretically defines a task and, in the presence of equifinality, determines the set of all possible task solution strategies, the goal equivalent manifold (GEM). The approach also yields estimates of the sensitivity of goal-level errors to body-level perturbations, and we derive a general formula expressing the relationship between the two. We apply these ideas to the analysis of redundant kinematic data from subjects performing an aiming task carried out with and without a laser pointer. It is shown that in order to characterize performance one must consider two factors in addition to the body variability: first, the degree of alignment between body variability and the GEM; and second, the sensitivity parameters that control the degree to which goal-relevant body variability is amplified at the target. Both of these factors can be computed using the estimated body-goal mapping. We show that the performance for three conditions involving two different nominal postures and two different sensory conditions (laser/no laser) can be classified by examining the clustering of data in an orientation- sensitivity parameter plane associated with the map.

Entities:  

Mesh:

Year:  2006        PMID: 16501988     DOI: 10.1007/s00422-006-0052-1

Source DB:  PubMed          Journal:  Biol Cybern        ISSN: 0340-1200            Impact factor:   2.086


  59 in total

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2.  Extracting synergies in gait: using EMG variability to evaluate control strategies.

Authors:  Rajiv Ranganathan; Chandramouli Krishnan
Journal:  J Neurophysiol       Date:  2012-06-20       Impact factor: 2.714

3.  Re-interpreting detrended fluctuation analyses of stride-to-stride variability in human walking.

Authors:  Jonathan B Dingwell; Joseph P Cusumano
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4.  Motor-equivalent covariation stabilizes step parameters and center of mass position during treadmill walking.

Authors:  Julius Verrel; Martin Lövdén; Ulman Lindenberger
Journal:  Exp Brain Res       Date:  2010-09-23       Impact factor: 1.972

Review 5.  Complex Adaptive Behavior and Dexterous Action.

Authors:  Steven J Harrison; Nicholas Stergiou
Journal:  Nonlinear Dynamics Psychol Life Sci       Date:  2015-10

6.  Joint-level kinetic redundancy is exploited to control limb-level forces during human hopping.

Authors:  Jasper T Yen; Arick G Auyang; Young-Hui Chang
Journal:  Exp Brain Res       Date:  2009-06-04       Impact factor: 1.972

7.  It's Not (Only) the Mean that Matters: Variability, Noise and Exploration in Skill Learning.

Authors:  Dagmar Sternad
Journal:  Curr Opin Behav Sci       Date:  2018-03-01

8.  Variability in motor learning: relocating, channeling and reducing noise.

Authors:  R G Cohen; D Sternad
Journal:  Exp Brain Res       Date:  2008-10-25       Impact factor: 1.972

Review 9.  Movement variability near goal equivalent manifolds: fluctuations, control, and model-based analysis.

Authors:  Joseph P Cusumano; Jonathan B Dingwell
Journal:  Hum Mov Sci       Date:  2013-11-07       Impact factor: 2.161

10.  Changes in muscle activity and kinematics of highly trained cyclists during fatigue.

Authors:  Jonathan B Dingwell; Jason E Joubert; Fernando Diefenthaeler; Joel D Trinity
Journal:  IEEE Trans Biomed Eng       Date:  2008-11       Impact factor: 4.538

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