A Computational Internal Model to Quantify the Effect of Sensorimotor Augmentation on Motor Output

Area of Science:

• Neuroscience
• Biomedical Engineering
• Rehabilitation Technology

Background:

• Aging and neurological disorders impair sensorimotor functions, leading to reduced motor output.
• Sensorimotor augmentation strategies, like exoskeletons and functional electrical stimulation, aim to compensate for these deficiencies.
• The closed-loop interaction between sensory feedback and motor output complicates predicting augmentation effects.

Purpose of the Study:

• To present a complete computational internal model of the sensorimotor loop.
• To enable anticipation of sensorimotor augmentation effects on motor outcomes.
• To provide a tool for designing therapeutic interventions using sensorimotor augmentation.

Main Methods:

• Development of a computational internal model representing the sensorimotor loop.
• Inclusion of numerical values to quantify sensorimotor augmentation effects.
• Indirect evaluation of the model's performance using existing experimental data.

Main Results:

• The computational model simulates the sensorimotor augmentation effects.
• Increased sensory gain was found to inversely affect motor output.
• Changes in motor gain showed minimal or no significant effect on motor output.

Conclusions:

• The developed computational model offers a novel approach to predict sensorimotor augmentation outcomes.
• The model highlights the differential impact of sensory versus motor gain adjustments.
• This tool can assist clinicians in optimizing sensorimotor augmentation therapies for improved patient outcomes.