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

Robot-enhanced motor learning: accelerating internal model formation during locomotion by transient dynamic

Jeremy L Emken1, David J Reinkensmeyer

  • 1Biomedical Engineering Department, University of California-Irvine, Irvine, CA 92697, USA. jemken@uci.edu

IEEE Transactions on Neural Systems and Rehabilitation Engineering : a Publication of the IEEE Engineering in Medicine and Biology Society
|April 9, 2005
PubMed
Summary

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

Robotic Technologies for Hand Recovery After Stroke: A Reflection on Research Paths in Honor of Dr. Steven Wolf.

Neurorehabilitation and neural repair·2026
Same author

Forecasting Dropout in Home-Based Movement Rehabilitation After Stroke With Sensors and Machine Learning.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society·2026
Same author

FDA-Listed Interactive Devices for Home Movement Rehabilitation After Stroke: A Mixed-Methods Study of Availability, User Needs, Information Gaps, and an Accompanying Dataset.

Bioengineering (Basel, Switzerland)·2026
Same author

Signal-Dependent Planning Noise Reduces Task Interference by Assisting in the Formation of Stable Motor Primitives in a Neural Network Model of Muscle Coordination Learning.

Neural computation·2026
Same author

Dynamic thumb localization and its adaptation: quantification with a novel robotic task.

Experimental brain research·2026
Same author

Distribution Analysis for Diagnostics and Therapeutics of Motor Actions.

IEEE journal of biomedical and health informatics·2025

Transiently amplifying environmental dynamics accelerated motor learning by 26% during treadmill stepping. This approach leverages internal model formation but may be limited by adaptive response nonlinearities.

Area of Science:

  • Neuroscience
  • Robotics
  • Motor Control

Background:

  • The nervous system forms internal models of environmental dynamics to anticipate forces during adaptation.
  • Motor learning is typically driven by reducing movement errors.

Purpose of the Study:

  • To test if transiently amplifying environmental dynamics accelerates motor learning.
  • To investigate the role of error-based learning in internal model formation.

Main Methods:

  • A robotic device applied a perpendicular viscous force field during treadmill stepping.
  • Environmental dynamics were amplified based on a per-subject computational learning model.

Main Results:

  • Subjects reduced the time to predict the applied force field by approximately 26% with amplification.

Related Experiment Videos

  • Observed acceleration was less than model predictions, possibly due to learning parameter nonstationarities.

    • Conclusions:

    • Motor learning in novel dynamic environments can be accelerated by amplifying dynamics.
    • Nonlinearities in adaptive responses may limit the extent of feasible acceleration.
    • Results support movement training devices that amplify, rather than reduce, movement errors.