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Biomedical subjects

Fabrice Mégrot

Publications and source records attributed to Fabrice Mégrot.

2 recordsLinked to original sources

Changes in phase space during learning an unstable balance.

Six participants learned to maintain an unstable balance on a stabilometer, during 6 consecutive days of practice (total of 90 trials). Lateral and angular variations of body segments and body center of mass were analysed, and their evolution over the learning period was compared to the changes in dimensional variables capturing the structure of the movement itself (embedding and correlation dimension, largest Lyapunov exponent). Results indicated that (i) learning occurred, (ii) was accompanied by persistence in the dimension of the movement, and (iii) by a reduction in chaotic (or stochastic) components. Compared to other results in the learning literature, these results suggest that dimensional changes over learning are task-specific.

Analysis of Variance↗

Dimensionality and the dynamics of human unstable equilibrium.

Maintaining an unstable equilibrium requires that multiple joints be coordinated so that the center of mass is kept above the base of support. The authors' aim in the present study was to discover the underlying dynamics of local (foot, hip, or head) and global (center of mass) components involved in balance control and how those dynamics are affected by changes in the available information. Participants (N = 6) had to maintain their balance on an unstable platform. Using dimensional analyses (largest Lyapunov exponent and correlation dimension), the authors examined the active degrees of freedom involved in balance control. Results indicated a similarity in dimension between local (joints) and global (center of mass) components, between a fixed point and a limit cycle. The behavior of the center of mass was found to be more predictable than the behavior of its local constituents. In addition, the available visual information affected the predictability of the postural behavior, which suggests that vision is used in the stabilization of the low-dimensional dynamics underlying balance control.

Algorithms↗