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A Structured Rehabilitation Protocol for Improved Multifunctional Prosthetic Control: A Case Study

A Structured Rehabilitation Protocol for Improved Multifunctional Prosthetic Control: A Case Study

Published on: November 6, 2015

Two-phase strategy of controlling motor coordination determined by task performance optimality.

Yury P Shimansky¹, Miya K Rand

¹School of Biological and Health Systems Engineering, Arizona State University, Tempe, AZ 85287-9709, USA. yury.shimansky@asu.edu

Biological Cybernetics

|December 4, 2012

Summary

This study generalizes a model of reach-to-grasp movements, revealing a two-phase motor control strategy. This strategy optimizes neural computation costs and precision demands for efficient goal-directed actions.

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Area of Science:

Neuroscience
Motor Control
Computational Neuroscience

Background:

Previous models quantified optimal coordination in reach-to-grasp movements.
The utility of these models for experimental data analysis was previously demonstrated.

Purpose of the Study:

To generalize a quantitative model of optimal coordination for diverse goal-directed movements.
To analyze motor coordination variability and its phase-dependent characteristics.
To investigate the role of neural computation costs and precision demands in optimal control.

Main Methods:

Generalization of a quantitative model for reach-type movements.
Analysis of motor coordination variability across movement phases.
Inference of optimality criteria including neural computation costs and precision demands.

Main Results:

A generalized optimal coordination model was developed for various goal-directed movements.
Execution noise was found to be low and not significantly impacting motor coordination.
A two-phase optimal control strategy was identified, balancing computation costs and precision demands.

Conclusions:

The generalized model and two-phase strategy apply to a broad range of goal-directed movements.
The initial phase prioritizes reduced neural computation costs, allowing for speed-accuracy tradeoff violations.
The final phase enhances precision in neural computations and motor coordination for accurate target contact.