An Informational Algorithm as the Basis for Perception-Action Control of the Instantaneous Axes of the Knee.

Area of Science:

• Biomechanics
• Human-Environment Interaction
• Control Theory

Background:

• Traditional locomotion research prioritizes movement trajectories over sensory feedback.
• Existing models often overlook the dynamic interplay between the individual and their environment.
• A gap exists in understanding how sensory information directly influences motor control.

Purpose of the Study:

• To propose and test an information-based theory of locomotion control.
• To investigate the role of sensory perception in controlling human movement.
• To mathematically characterize the energy requirements for knee joint rotational twists.

Main Methods:

• Developed a mathematical framework to analyze the energy expenditure of knee joint rotational twists.
• Investigated the concept of 'co-perception' of muscle activation as a substitute for ground reaction force.
• Compared model predictions with established joint force data.

Main Results:

• Demonstrated that muscle activation perception can effectively substitute for ground reaction force perception.
• The proposed information-based control model accurately predicted known knee joint forces.
• The model shows promise for predicting knee behavior during rotational movements.

Conclusions:

• Locomotion is controlled, not commanded, through information derived from self-perception within the environment.
• Control is a distributed function within the human-environment system.
• This framework offers a novel approach to understanding and predicting joint dynamics in locomotion.