Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Feedback control systems

Feedback control systems

Feedback control systems are categorized in various ways based on their design, analysis, and signal types.
Linear feedback systems are theoretical models that simplify analysis and design. These systems operate under the principle that their output is directly proportional to their input within certain ranges. For instance, an amplifier in a control system behaves linearly as long as the input signal remains within a specific range. However, most physical systems exhibit inherent nonlinearity...

You might also read

Related Articles

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

Sort by

Same author

A Deep Learning Framework With Domain Generalization and Few-Shot Learning for Locomotion Mode Classification Across Users, Sessions, and Prostheses.

IEEE transactions on medical robotics and bionics·2026

Same author

Assessing Functional Changes With the Integration of Wrist Flexion Into a Myoelectric Prosthesis.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society·2025

Same author

Evaluating the impact of channel count and feature set on online pattern recognition control of a virtual arm.

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

Self-supervised representation learning with continuous training data improves the feel and performance of myoelectric control.

Computers in biology and medicine·2025

Same author

Pattern Recognition Control of a 2 Degree-of-Freedom Prosthetic Device.

IEEE ... International Conference on Rehabilitation Robotics : [proceedings]·2025

Same author

Ambulation With a Transfemoral Bone-Anchored Powered Knee-Ankle Prosthesis: a Case Study.

IEEE ... International Conference on Rehabilitation Robotics : [proceedings]·2025

Search research articles

Related Experiment Video

Updated: Jun 17, 2026

A Structured Rehabilitation Protocol for Improved Multifunctional Prosthetic Control: A Case Study

A Structured Rehabilitation Protocol for Improved Multifunctional Prosthetic Control: A Case Study

Published on: November 6, 2015

Multiple binary classifications via linear discriminant analysis for improved controllability of a powered

Levi J Hargrove¹, Erik J Scheme, Kevin B Englehart

¹Institute of Biomedical Engineering, University of New Brunswick, Fredericton, NB E3B5A3, Canada. l-hargrove@northwestern.edu

IEEE Transactions on Neural Systems and Rehabilitation Engineering : a Publication of the IEEE Engineering in Medicine and Biology Society

|January 15, 2010

Summary

A new pattern recognition myoelectric control system offers improved usability despite higher classification error. This system is computationally simple, making it suitable for real-time applications and clinical use.

More Related Videos

Engineering Platform and Experimental Protocol for Design and Evaluation of a Neurally-controlled Powered Transfemoral Prosthesis

Engineering Platform and Experimental Protocol for Design and Evaluation of a Neurally-controlled Powered Transfemoral Prosthesis

Published on: July 22, 2014

Surface Electromyographic Biofeedback as a Rehabilitation Tool for Patients with Global Brachial Plexus Injury Receiving Bionic Reconstruction

Surface Electromyographic Biofeedback as a Rehabilitation Tool for Patients with Global Brachial Plexus Injury Receiving Bionic Reconstruction

Published on: September 28, 2019

Related Experiment Videos

Last Updated: Jun 17, 2026

A Structured Rehabilitation Protocol for Improved Multifunctional Prosthetic Control: A Case Study

A Structured Rehabilitation Protocol for Improved Multifunctional Prosthetic Control: A Case Study

Published on: November 6, 2015

Engineering Platform and Experimental Protocol for Design and Evaluation of a Neurally-controlled Powered Transfemoral Prosthesis

Engineering Platform and Experimental Protocol for Design and Evaluation of a Neurally-controlled Powered Transfemoral Prosthesis

Published on: July 22, 2014

Surface Electromyographic Biofeedback as a Rehabilitation Tool for Patients with Global Brachial Plexus Injury Receiving Bionic Reconstruction

Surface Electromyographic Biofeedback as a Rehabilitation Tool for Patients with Global Brachial Plexus Injury Receiving Bionic Reconstruction

Published on: September 28, 2019

Area of Science:

Biomedical Engineering
Rehabilitation Engineering
Human-Computer Interaction

Background:

Conventional myoelectric control systems face challenges in usability and configuration.
Pattern recognition approaches offer potential for improved prosthetic control but require sophisticated implementation.
There is a need for robust, easily configurable, and highly usable myoelectric control systems for clinical applications.

Purpose of the Study:

To introduce a novel pattern recognition based myoelectric control system.
To evaluate the system's performance quantitatively and functionally against existing methods.
To assess the system's suitability for real-time embedded implementation and clinical viability.

Main Methods:

Developed a myoelectric control system utilizing parallel binary classification and class-specific thresholds.
Designed an intuitive configuration interface similar to conventional systems.
Assessed performance using a classification error metric and a virtual clothespin test, comparing against linear discriminant analysis and mode switching schemes.

Main Results:

The proposed system exhibited a higher classification error (p < 0.001) compared to benchmark systems.
Functional assessment via the virtual clothespin test demonstrated significantly improved controllability (p < 0.001).
The system proved computationally simple, suitable for real-time embedded implementation.

Conclusions:

The novel pattern recognition system offers enhanced usability and controllability in myoelectric control.
Despite a higher classification error, the system's functional performance is superior.
The system's simplicity and robustness provide a foundation for a clinically viable myoelectric control solution.