A computational neural and biomechanical system for human bicycle pedaling is developed in order to study the interaction between the central nervous system and the biomechanical system. It consists of a genetic algorithm, artificial neural network, muscle system, and skeletal system. Our first finding is that the genetic algorithm is a robust tool to formulate human movement. We also find that our developed models are able to handle mechanical perturbation and neural noise. In addition, we observe variability and repeatability of pedaling motion with or without perturbation and noise. Movement phase dependent feedback nature is seen through computation too. This system shows many human movement qualities and is useful for further neural and motor control investigations. / text
| Identifer | oai:union.ndltd.org:UTEXAS/oai:repositories.lib.utexas.edu:2152/ETD-UT-2012-08-6123 |
| Date | 19 November 2012 |
| Creators | Jin, Hiroshi |
| Source Sets | University of Texas |
| Language | English |
| Detected Language | English |
| Type | thesis |
| Format | application/pdf |
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