Study of human forearm posture maintenance with a physiologically based robotic arm and spinal level neural...

Area of Science:

• Robotics
• Biomechanics
• Neuroscience

Background:

• Understanding human neuro-musculo-skeletal motion control is crucial for developing advanced robotic systems.
• Existing robotic systems often lack the nuanced control and mechanical properties of the human arm.

Purpose of the Study:

• To apply human motion control principles to a biomechanically designed, neurally controlled robotic arm.
• To validate the robotic system's ability to replicate human arm responses.
• To use the robotic system for studying human motion control and predicting new phenomena.

Main Methods:

• Development of an anatomically accurate robotic elbow with McKibben pneumatic artificial muscles, force sensors, and mechanical muscle spindles.
• Implementation of a physiologically analogous artificial neural network controller emulating spinal segmental reflex circuitry (Ia and Ib afferent feedbacks).
• Conducting experiments on elbow posture maintenance and responses to torque perturbation, including blocking afferent pathways.

Main Results:

• The robotic system demonstrated comparable responses to the human arm in comparable experiments.
• Muscle co-contraction and Ia afference with gamma dynamic motoneuron excitation were identified as effective methods for increasing joint stiffness and damping.
• The study introduced a 'covariance diagram' and a linear model for analyzing system component roles.

Conclusions:

• The developed robotic arm system successfully mimics human neuro-musculo-skeletal motion control.
• Increased joint stiffness and damping, achieved through muscle co-contraction and specific neural pathways, enhance system stability and reduce sensitivity to perturbations.
• This research provides a valuable platform for further investigation into human motor control and the development of advanced prosthetics and robotics.