| 研究実績の概要 |
The main objective of this project is to identify factors that can be exploited to increase the speed of learning a motor task. I previously developed a computational model that makes predictions about the speed of learning in isometric and dynamic arm reaching tasks. I have now extended the computational model to include muscle-actuated joints. The model predicts that the levels of muscle co-contraction during an arm-reaching task influence the speed of learning. This offers an alternative explanation for existing experimental observations.
Additionally, I have started to explore applications for the proposed computational framework. In stroke rehabilitation protocols based on myo-electric training, our framework shows, theoretically, that muscle pairs for training can be optimally selected to maximize the speed of rehabilitation, or learning. These pairs differ from the muscle pairs usually chosen in practice. Therefore, our model could be used to improve these protocols.
Finally, I have also developed a theoretical framework to model tasks involving the EMG space similarity feedback, which we have shown experimentally to allow subjects to learn expert-like muscle activation patterns. This framework will allow me to decrease the experimental workload for improving the EMG feedback.
|
| 現在までの達成度 (区分) |
現在までの達成度 (区分)
3: やや遅れている
理由
My research has mostly progressed according to the timeframe that I initially proposed. However, the publication of the results is slightly delayed due to delays in the writing of results and reasons out of my control, such as the internal operation of journals.
|
