Next-Generation Neurotechnologies Inspired by Motor Primitive Model for Restoring Human Natural Movement

Area of Science:

• Neuroscience
• Rehabilitation Engineering
• Artificial Intelligence

Background:

• Neuroengineering and AI are advancing motor rehabilitation technologies like robotic systems and brain-computer interfaces.
• A key challenge is modeling the central nervous system's complex control of naturalistic movements.
• Existing neurotechnologies struggle to replicate the natural complexity of human motor control.

Purpose of the Study:

• To critically examine limitations in current neurotechnology target settings for biomimetic control.
• To introduce the motor primitive (MP) model as a framework for understanding and replicating natural human movement.
• To present the potential of MP-inspired paradigms for next-generation neurorehabilitation.

Main Methods:

• This narrative review examines conventional target settings in neurotechnology.
• It introduces the motor primitive (MP) model, detailing its machine learning and physiological underpinnings.
• The review discusses the application of the MP model in kinematic, muscular, and neural domains.

Main Results:

• Conventional target settings have limitations in guiding biomimetic control.
• The MP model provides a framework for deconstructing and reproducing motor tasks using low-dimensional modules.
• MP models can digitally shape human-machine interaction for more naturalistic control.

Conclusions:

• The MP model offers a promising approach to bridge the gap in modeling neuromotor control complexity.
• MP-inspired paradigms can lead to intelligent, patient-specific, and naturalistic motor restoration.
• This framework has the potential to revolutionize rehabilitation for neurological and traumatic diseases.