Abstract
The results of recent studies suggest that humans can form internal models that they use in a feedforward manner to compensate for both stable and unstable dynamics. To examine how internal models are formed, we performed adaptation experiments in novel dynamics, and measured the endpoint force, trajectory and EMG during learning. Analysis of reflex feedback and change of feedforward commands between consecutive trials suggested a unified model of motor learning, which can coherently unify the learning processes observed in stable and unstable dynamics and reproduce available data on motor learning. To our knowledge, this algorithm, based on the concurrent minimization of (reflex) feedback and muscle activation, is also the first nonlinear adaptive controller able to stabilize unstable dynamics.
| Original language | English |
|---|---|
| Pages (from-to) | 4491-4494 |
| Number of pages | 4 |
| Journal | Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings |
| Volume | 26 VI |
| Publication status | Published - 2004 |
| Externally published | Yes |
| Event | Conference Proceedings - 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2004 - San Francisco, CA, United States Duration: 2004 Sept 1 → 2004 Sept 5 |
Keywords
- Internal models
- Motor learning
- Nonlinear adaptive control
- Unstable dynamics
ASJC Scopus subject areas
- Signal Processing
- Biomedical Engineering
- Computer Vision and Pattern Recognition
- Health Informatics