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Quantitative examinations of internal representations for arm trajectory planning: minimum commanded torque change
1ATR Human Information Processing Research Laboratories, Kyoto 619-0288, Japan.
Journal of Neurophysiology
|May 13, 1999
Summary
The brain plans arm movements using an intrinsic-dynamic-neural model, minimizing changes in motor commands. This minimum commanded torque change model accurately predicts hand trajectories and learning in arm movement planning.
Area of Science:
- Neuroscience
- Biomechanics
- Robotics
Background:
- Human arm movements exhibit invariant features like straight paths and bell-shaped speed profiles.
- Trajectory curvature depends on movement location and direction in intrinsic body coordinates.
Purpose of the Study:
- To investigate internal representations for arm trajectory planning.
- To compare four computational models for predicting arm movement trajectories.
Main Methods:
- Collected extensive arm trajectory data in horizontal and sagittal planes.
- Evaluated models including minimum hand jerk, minimum angle jerk, minimum torque change, and minimum commanded torque change.
- Assessed model accuracy for curvature, position, velocity, acceleration, and torque.
Main Results:
- The minimum commanded torque change model best reproduced actual arm trajectories.
- Model predictions regarding trajectory curvature and movement duration were confirmed.
- Learned arm movements converged to model predictions with training.
Conclusions:
- The brain likely plans arm trajectories in intrinsic coordinates, considering dynamics.
- Motor planning involves representations of motor commands controlling muscle tensions.
- The minimum commanded torque change model offers a computable framework for motor command optimization.