This dissertation demonstrates a new method for inferring a representation of the motor command, generated by the central nervous system for interactive point-to-point movements. This new tool, the input inference neural network or IINN, allows estimation of the complexity of the motor command. The IINN was applied to experimental data gathered from 7 volunteer subjects who performed point-to-point tasks while interacting with a specially constructed haptic robot. The motor plan inference demonstrates that, for the point-to-point movement tasks executed during experiments, the motor command can be projected onto a low-dimensional manifold. This dimension is estimated to be 4 or 5 and far less than the degrees of freedom available in the arm. It is hypothesized that subjects simplify the problem of adapting
to changing environments by projecting the motor control problem onto a motor manifold of low dimension. Reducing the dimension of the movement optimization problem through the
development of a motor manifold can explain rapid adaptation to new motor tasks.
| Identifer | oai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:BVAU.2429/59 |
| Date | 05 1900 |
| Creators | Atsma, Willem Jentje |
| Source Sets | Library and Archives Canada ETDs Repository / Centre d'archives des thèses électroniques de Bibliothèque et Archives Canada |
| Language | English |
| Detected Language | English |
| Format | 1542111 bytes, application/pdf |
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