Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

A model for learning human reaching movements

A Karniel1, G F Inbar

  • 1Department of Electrical Engineering, Technion-IIT, Haifa, Israel. karniel@tx.technion.ac.il

Biological Cybernetics
|November 14, 1997
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Toward Perceiving Robots as Humans: Three Handshake Models Face the Turing-Like Handshake Test.

IEEE transactions on haptics·2016
Same author

Perception and Action in Teleoperated Needle Insertion.

IEEE transactions on haptics·2015
Same author

Optimization of pulse duration and pulse charge during transcutaneous electrical nerve stimulation.

The Australian journal of physiotherapy·2014
Same author

Rhythmic movements are larger and faster but with the same frequency on removal of visual feedback.

Journal of neurophysiology·2011
Same author

Early switching between movement types: indication of predictive control?

Brain research bulletin·2010
Same author

Automated measurement of proprioception following stroke.

Disability and rehabilitation·2008
Same journal

Harmonic memory in phasor neural networks.

Biological cybernetics·2026
Same journal

Correction: Decreased spinal inhibition leads to undiversified locomotor patterns.

Biological cybernetics·2026
Same journal

Foundational issues of network models in biology.

Biological cybernetics·2026
Same journal

Dynamical mechanisms for coordinating long-term working memory based on the precision of spike-timing in cortical neurons.

Biological cybernetics·2026
Same journal

Distinct dopaminergic spike-timing-dependent plasticity rules are suited to different functional roles.

Biological cybernetics·2026
Same journal

Fluctuation-response relations for a two-stage population of spiking neurons stimulated by common noise.

Biological cybernetics·2026
See all related articles

This study presents a mathematical model for human arm ballistic reaching movements, demonstrating the central nervous system

Area of Science:

  • * Biomechanics and Motor Control
  • * Computational Neuroscience
  • * Robotics

Background:

  • * Reaching movements are characterized by straight paths and bell-shaped speed profiles.
  • * Existing models often require complex control signals for accurate movement.
  • * Understanding the central nervous system's (CNS) control strategy is crucial for developing effective prosthetic and robotic systems.

Purpose of the Study:

  • * To present a mathematical model for the control of human arm ballistic reaching movements.
  • * To investigate the role of muscle non-linearity and a learning scheme in motor control.
  • * To demonstrate the CNS's ability to generate reaching movements using a simplified feedforward controller.

Main Methods:

  • * A 2-DOF planar manipulator model with a non-linear Hill-type muscle model was used.

Related Experiment Videos

  • * The nervous system was modeled as an adjustable pattern generator using an artificial neural network.
  • * A sensitivity model was employed to train the neural network based on endpoint error and knowledge of results.

    • Main Results:

    • * The model successfully generated typical reaching movements with a feedforward controller adjusting pulse timing and amplitude.
    • * The learning scheme effectively mapped targets to control parameters.
    • * Non-linear muscle properties were found to be essential for simplified control.

    Conclusions:

    • * The CNS can generate reaching movements using a simple feedforward controller with adjustable parameters.
    • * Reducing control dimensionality through parameter adjustment is a key motor control strategy.
    • * Motor control emerges from the interaction between the nervous system and musculoskeletal dynamics.