Jove
Visualize
Contact Us

Related Concept Videos

Reinforcement01:23

Reinforcement

186
Positive and negative reinforcement are key concepts in operant conditioning, a learning process where the consequences of a behavior affect the likelihood of that behavior being repeated.
Positive reinforcement occurs when a behavior is followed by the presentation of a rewarding stimulus, increasing the frequency of that behavior. For example:
186

You might also read

Related Articles

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

Sort by
Same author

From sequential to simultaneous prosthetic control: Decoding simultaneous finger movements from individual ground truth EMG patterns.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same author

Novel Wearable Device for Mindful Sensorimotor Training: Integrating Motor Decoding and Somatosensory Stimulation for Neurorehabilitation.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society·2024
Same author

Intuitive control of additional prosthetic joints via electro-neuromuscular constructs improves functional and disability outcomes during home use-a case study.

Journal of neural engineering·2024
Same author

Deep Learning for Enhanced Prosthetic Control: Real-Time Motor Intent Decoding for Simultaneous Control of Artificial Limbs.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society·2024
Same author

Cutting Edge Bionics in Highly Impaired Individuals: A Case of Challenges and Opportunities.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society·2024
Same author

A highly integrated bionic hand with neural control and feedback for use in daily life.

Science robotics·2023
Same journal

Enhancing Volumetric Imaging in Linear-Array Photoacoustic Tomography: multiview fusion with deep learning.

IEEE transactions on bio-medical engineering·2026
Same journal

Robust Rule-based Heuristic Assistance Strategy for a Semi-Active Shoulder Exoskeleton Used in Overhead Work.

IEEE transactions on bio-medical engineering·2026
Same journal

Highly Accelerated 1-mm Isotropic 3D Chemical Exchange Saturation Transfer MRI Using Wave-Co-CAIPI at 5 Tesla.

IEEE transactions on bio-medical engineering·2026
Same journal

Systematic Evaluation of Hip Exoskeleton Assistance Parameters for Enhancing Gait Stability During Ground Slip Perturbations.

IEEE transactions on bio-medical engineering·2026
Same journal

SleepConFormer: A Single-Channel EEG Framework for Sleep Staging and Consciousness Assessment in Patients with Disorders of Consciousness.

IEEE transactions on bio-medical engineering·2026
Same journal

Modeling Partial and Total Support of Left Ventricular Assist Device for Discrete Hemodynamic Control Framework.

IEEE transactions on bio-medical engineering·2026
See all related articles
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 Video

Updated: Jun 13, 2025

WheelCon: A Wheel Control-Based Gaming Platform for Studying Human Sensorimotor Control
08:18

WheelCon: A Wheel Control-Based Gaming Platform for Studying Human Sensorimotor Control

Published on: August 15, 2020

4.9K

Fine-Tuning Myoelectric Control Through Reinforcement Learning in a Game Environment.

Kilian Freitag, Yiannis Karayiannidis, Jan Zbinden

    IEEE Transactions on Bio-Medical Engineering
    |June 11, 2025
    PubMed
    Summary
    This summary is machine-generated.

    Reinforcement learning (RL) enhances myoelectric control by fine-tuning with usage-based muscle data, significantly improving prosthetic limb accuracy and reliability for bionic applications.

    More Related Videos

    Investigating Motor Skill Learning Processes with a Robotic Manipulandum
    07:52

    Investigating Motor Skill Learning Processes with a Robotic Manipulandum

    Published on: February 12, 2017

    8.7K
    Brain-Computer Interface-controlled Upper Limb Robotic System for Enhancing Daily Activities in Stroke Patients
    06:11

    Brain-Computer Interface-controlled Upper Limb Robotic System for Enhancing Daily Activities in Stroke Patients

    Published on: April 18, 2025

    276

    Related Experiment Videos

    Last Updated: Jun 13, 2025

    WheelCon: A Wheel Control-Based Gaming Platform for Studying Human Sensorimotor Control
    08:18

    WheelCon: A Wheel Control-Based Gaming Platform for Studying Human Sensorimotor Control

    Published on: August 15, 2020

    4.9K
    Investigating Motor Skill Learning Processes with a Robotic Manipulandum
    07:52

    Investigating Motor Skill Learning Processes with a Robotic Manipulandum

    Published on: February 12, 2017

    8.7K
    Brain-Computer Interface-controlled Upper Limb Robotic System for Enhancing Daily Activities in Stroke Patients
    06:11

    Brain-Computer Interface-controlled Upper Limb Robotic System for Enhancing Daily Activities in Stroke Patients

    Published on: April 18, 2025

    276

    Area of Science:

    • Biomedical Engineering
    • Neuroscience
    • Robotics

    Background:

    • Myoelectric controllers for bionic prosthetics face challenges in accurately decoding motor intent.
    • Current Supervised Learning (SL) methods require high-quality labeled muscle activity data, which is difficult to obtain during real-world use.
    • Improving the reliability of these controllers is crucial for advanced prosthetic limb functionality.

    Purpose of the Study:

    • To investigate the potential of Reinforcement Learning (RL) to enhance motor intent decoding in myoelectric controllers.
    • To incorporate usage-based electromyographic (EMG) data for improved controller performance.
    • To overcome limitations of traditional SL methods in acquiring representative training data.

    Main Methods:

    • A pre-trained SL control policy using static EMG data was fine-tuned with RL.
    • Dynamic EMG data was collected during interaction within a custom-designed game environment.
    • Real-time experiments were conducted to evaluate the RL-enhanced approach.

    Main Results:

    • The RL-based method demonstrated effective prediction of simultaneous finger movements.
    • Decoding accuracy during gameplay saw a two-fold increase.
    • A 39% improvement in accuracy was observed in a separate motion test.

    Conclusions:

    • Reinforcement Learning (RL) significantly improves the accuracy and robustness of myoelectric controllers.
    • Incorporating usage-based EMG data during RL fine-tuning is key to enhanced performance.
    • This approach shows great promise for advancing the reliability of bionic limbs.