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 Concept Videos

Real Time RT-PCR02:57

Real Time RT-PCR

65.1K
Real-time reverse transcription-polymerase chain reaction, or Real-time RT-PCR, is an analytical tool used to determine the expression level of target genes. The method involves converting mRNA to complementary DNA with the help of an enzyme known as reverse transcriptase, followed by the PCR amplification of the cDNA. These two processes can be performed simultaneously in a single tube or separately as a two-step reaction.
The real-time quantification of the number of amplified products is...
65.1K
Virtual Work01:20

Virtual Work

1.4K
The principle of virtual work states that if a body is in static and dynamic equilibrium, then the sum of all the virtual work done by all external forces and couple moments for any given virtual displacement must be zero.
In static equilibrium, a body can experience an imaginary or virtual movement, such as displacement or rotation. The virtual work done by a force is equal to the dot product of force and virtual displacement in the direction of the force. When it comes to virtually rotating a...
1.4K
Time-Domain Interpretation of PD Control01:07

Time-Domain Interpretation of PD Control

404
Proportional-Derivative (PD) control is a widely used control method in various engineering systems to enhance stability and performance. In a system with only proportional control, common issues include high maximum overshoot and oscillation, observed in both the error signal and its rate of change. This behavior can be divided into three distinct phases: initial overshoot, subsequent undershoot, and gradual stabilization.
Consider the example of control of motor torque. Initially, a positive...
404
Time and frequency -Domain Interpretation of PI Control01:27

Time and frequency -Domain Interpretation of PI Control

411
Proportional-Integral (PI) controllers are essential in many control systems to improve stability and performance. They are commonly used in everyday devices like thermostats to enhance system damping and reduce steady-state error. When the zero in the controller's transfer function is optimally placed, the system benefits significantly in terms of stability and accuracy.
Acting as a low-pass filter, the PI controller slows the system's response and extends settling times. This requires...
411
Time and frequency -Domain Interpretation of Phase-lead Control01:24

Time and frequency -Domain Interpretation of Phase-lead Control

452
Phase-lead controllers are commonly used in various control systems to enhance response speed and stability. Adjusting the brightness on a television screen offers a practical example of phase-lead control. When contrast is enhanced, a phase-lead controller is employed. Mathematically, phase-lead control is identified when the first parameter is smaller than the second.
The design of phase-lead control involves the strategic placement of poles and zeros to balance steady-state error and system...
452
Time and frequency -Domain Interpretation of Phase-lag Control01:21

Time and frequency -Domain Interpretation of Phase-lag Control

416
Phase-lag controllers are widely used in control systems to improve stability and reduce steady-state errors. A dimmer switch controlling the brightness of a light bulb serves as a practical example of phase-lag control, gradually adjusting the bulb's brightness. Mathematically, phase-lag control or low-pass filtering is represented when the factor 'a' is less than 1.
Phase-lag controllers do not place a pole at zero, but instead influence the steady-state error by amplifying any...
416

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

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
Same author

Measuring metabolic energy expenditure with short duration walking tests for individuals with lower limb amputation.

PloS one·2025

Related Experiment Video

Updated: Jan 30, 2026

Controlled Rotation of Human Observers in a Virtual Reality Environment
09:11

Controlled Rotation of Human Observers in a Virtual Reality Environment

Published on: April 21, 2022

3.0K

Adapting myoelectric control in real-time using a virtual environment.

Richard B Woodward1,2, Levi J Hargrove3,4,5

  • 1Center for Bionic Medicine, Shirley Ryan Ability Lab, Chicago, IL, 60611, USA. rwoodward@northwestern.edu.

Journal of Neuroengineering and Rehabilitation
|January 18, 2019
PubMed
Summary
This summary is machine-generated.

Active training in virtual reality improves myoelectric prosthesis control. This study shows that active limb movement during data collection enhances pattern recognition system performance, with virtual reality offering an accessible training solution.

Keywords:
AmputeeElectromyographyMyoelectric controlPattern recognitionReal-time adaptationSerious gamingUpper-limb prosthesesVirtual guided trainingVirtual rehabilitation

More Related Videos

A Telemetric, Gravimetric Platform for Real-Time Physiological Phenotyping of Plant–Environment Interactions
15:30

A Telemetric, Gravimetric Platform for Real-Time Physiological Phenotyping of Plant–Environment Interactions

Published on: August 5, 2020

12.5K
Development of an Audio-based Virtual Gaming Environment to Assist with Navigation Skills in the Blind
09:01

Development of an Audio-based Virtual Gaming Environment to Assist with Navigation Skills in the Blind

Published on: March 27, 2013

14.9K

Related Experiment Videos

Last Updated: Jan 30, 2026

Controlled Rotation of Human Observers in a Virtual Reality Environment
09:11

Controlled Rotation of Human Observers in a Virtual Reality Environment

Published on: April 21, 2022

3.0K
A Telemetric, Gravimetric Platform for Real-Time Physiological Phenotyping of Plant–Environment Interactions
15:30

A Telemetric, Gravimetric Platform for Real-Time Physiological Phenotyping of Plant–Environment Interactions

Published on: August 5, 2020

12.5K
Development of an Audio-based Virtual Gaming Environment to Assist with Navigation Skills in the Blind
09:01

Development of an Audio-based Virtual Gaming Environment to Assist with Navigation Skills in the Blind

Published on: March 27, 2013

14.9K

Area of Science:

  • Biomedical Engineering
  • Rehabilitation Technology
  • Human-Computer Interaction

Background:

  • Pattern recognition technology enhances myoelectric prosthesis control but requires complex electromyographic data collection for training and retraining.
  • Clinician availability for guiding users through data collection can be a limitation for prosthesis users.

Purpose of the Study:

  • To develop and evaluate an interactive virtual reality (VR) environment for optimizing myoelectric controller training.
  • To compare the efficacy of active versus passive limb movement during data collection for training myoelectric controllers.
  • To assess the impact of computational adaptation via serious gaming on myoelectric controller performance.

Main Methods:

  • Development of an engaging VR environment for myoelectric controller training.
  • Comparison of active (limb movement) versus passive (static limb) data collection methods.
  • Evaluation of computational adaptation through serious gaming for performance enhancement.

Main Results:

  • Actively trained classifiers demonstrated significantly superior performance compared to passively trained classifiers in non-amputee subjects (P < 0.05).
  • Passive data collection combined with computational adaptation in a VR environment led to significant improvements in real-time myoelectric controller performance.

Conclusions:

  • Active movements during data collection are crucial for improving pattern recognition systems in myoelectric prostheses.
  • Virtual reality-guided serious gaming environments can effectively facilitate adaptation and enhance real-time performance of myoelectric controllers.