Human motor control: learning to control a time-varying, nonlinear, many-to-one system
| Autor: | Gideon F. Inbar, Amir Karniel |
|---|---|
| Rok vydání: | 2000 |
| Předmět: |
Adaptive control
business.industry Computer science Estimation theory Motor control Control engineering Machine learning computer.software_genre Computer Science Applications Human-Computer Interaction Nonlinear system Models of neural computation Control and Systems Engineering Many to one Redundancy (engineering) Artificial intelligence Electrical and Electronic Engineering business Intelligent control computer Software Information Systems |
| Zdroj: | IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews). 30:1-11 |
| ISSN: | 1094-6977 |
| DOI: | 10.1109/5326.827449 |
| Popis: | Human motor control has always presented a great challenge to both scientists and engineers. It has presented most of the problems they have found difficult to handle and manipulate, which is a consequence of it being a distributed, nonlinear, time-varying system with multiple degrees of freedom that include redundancy on many levels. In recent years, the fast development of computers and the emergence of the new scientific field of neural computation have enabled consideration of complex, adaptive, parallel architectures in the modeling of human motor-control performance. In this paper, some of the models that have been used in the study of motor control are reviewed, and some open questions are formalized and discussed. The main topics are adaptive and artificial neural-network control, parameter estimation, nonlinear properties of the muscles, and parallelism and redundancy. |
| Databáze: | OpenAIRE |
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