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Development and motor control : from the first step on

par Chéron, Guy ;Cebolla Alvarez, Ana Maria ;Leurs, Françoise ;Bengoetxea, Ana ;Dan, Bernard
Référence Chapter in Progress in Motor Control : Motor control and learning over the livespan, Mark L. Latash and Francis Lestienne, Springer, USA, Vol. Motor control and learning, Ed. 4, Motor control and learning, page (127-139)
Publication Publié, 2005

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Résumé :

AbstractFor performing their very first unsupported steps, often considered as a ‘milestone’ event in locomotor development, toddlers must find a compromise between at least two requirements: (1) the postural stability of the erect posture integrating the direction of gravity and (2) the dynamic control of the body and limbs for forward progression these two aspects. In adults, a series of experimental studies have provided evidence for coordinative laws that lead to a reduction of kinematic degrees of freedom. When the elevation angles of the thigh, shank and foot are plotted one versus the others, they describe a regular gait loop which lies to a plane. The plane orientation and the loop shape reflect the phase relationship between the different segments and therefore the timing of intersegmental coordination. The general pattern of intersegmental coordination and the stabilization of the trunk with respect of vertical are immature at the onset of unsupported walking in toddlers, but they develop in parallel very rapidly in the first few weeks of walking experience. Adult-like cross-correlation function parameters were reached earlier for shank-foot pairs than for thigh-shank indicating disto-proximal maturation of the lower limb segments coordination. We also demonstrated that a dynamic recurrent neural network (DRNN) is able to reproduce lower limb kinematics in toddler locomotion by using multiple raw EMG data. In the context of motor learning the DRNN may be considered as a model of biological learning mechanisms underlying motor adaptation. Using this artificial learning during the very first steps we found that the attractor states reached through learning correspond to biologically interpretable solutions.