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

What is a Mode?01:07

What is a Mode?

25.1K
The mode is one of the commonly used measures of a central tendency. It is defined as the most frequent value in a data set.
There can be more than one mode in a data set if multiple values have the same highest frequency. For instance, suppose that the Statistics exam scores of 20 students are: 50; 53; 59; 59; 63; 63; 72; 72; 72; 72; 72; 76; 78; 81; 83; 84; 84; 84; 90; 93. Here, the mode is 72, as it occurs most frequently, five times.
A data set with two modes is called bimodal. For example,...
25.1K
Ventilatory Modes01:14

Ventilatory Modes

1.4K
Mechanical ventilators are life-saving devices that support or replace spontaneous breathing. They deliver breaths to patients through varying methods known as ventilator modes. Understanding these modes is critical for healthcare providers managing patients with respiratory failure.
There are three ventilatory modes: full support, partial support, and spontaneous. These are described below.
Full Support Modes
Full support modes include controlled mechanical ventilation, continuous mandatory...
1.4K
MOSFET: Enhancement Mode01:22

MOSFET: Enhancement Mode

801
Enhancement-mode MOSFETs are pivotal components in electronics, distinguished by their capacity to act as highly efficient switches. They are part of the larger family of metal-oxide Semiconductor Field-Effect Transistors (MOSFETs). They are available in two types: p-channel and n-channel, each tailored to specific polarity operations.
In their basic form, enhancement-mode MOSFETs are typically non-conductive when the gate-source voltage (Vgs) is zero. This default 'off' state means no...
801
Modes of Standing Waves: II01:04

Modes of Standing Waves: II

1.6K
The starting point for expressing the modes of standing waves is understanding the boundary conditions that the waves must follow. The boundary conditions are derived from the physical understanding of how the standing waves are sustained, that is, how the vibrating particles of the medium behave at the boundaries imposed on them.
For a tube open at one end and closed at the other filled with air, the modes are such that there is always an antinode at the open end and a node at the closed end....
1.6K
Modes of Standing Waves - I01:03

Modes of Standing Waves - I

3.9K
A close look at earthquakes provides evidence for the conditions appropriate for resonance, standing waves, and constructive and destructive interference. A building may vibrate for several seconds with a driving frequency matching the building's natural frequency of vibration; this produces a resonance that results in one building collapsing while the neighboring buildings do not. Often, buildings of a certain height are devastated, while other taller buildings remain intact. This...
3.9K
Modes of Operations of BJT01:21

Modes of Operations of BJT

2.0K
A Bipolar Junction Transistor (BJT) is a versatile component in electronics, functioning in four distinct modes based on the biasing of its junctions: active, saturation, cut-off, and inverted modes.
Active Mode: The most common mode for amplification, the active mode features a forward-biased emitter-base junction and a reverse-biased base-collector junction. This setup enables electrons to be injected from the emitter to the base while blocking the majority carriers at the collector. The...
2.0K

You might also read

Related Articles

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

Sort by
Same author

A Data-Driven and Personalized Stance Symmetry Controller for Robotic Ankle-Foot Prostheses: A Preliminary Investigation.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society·2023
Same author

Using Deep Learning Models to Predict Prosthetic Ankle Torque.

Sensors (Basel, Switzerland)·2023
Same author

Autoencoder-based myoelectric controller for prosthetic hands.

Frontiers in bioengineering and biotechnology·2023
Same author

Learning to operate a high-dimensional hand via a low-dimensional controller.

Frontiers in bioengineering and biotechnology·2023
Same author

Optimizing Representations of Multiple Simultaneous Attributes for Gait Generation Using Deep Learning.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society·2023
Same author

The effect of diabetes and tissue depth on adipose chamber size and plantar soft tissue features.

Foot (Edinburgh, Scotland)·2023

Related Experiment Video

Updated: Jan 21, 2026

A Structured Rehabilitation Protocol for Improved Multifunctional Prosthetic Control: A Case Study
06:58

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

Published on: November 6, 2015

10.2K

A Framework For Mode-Free Prosthetic Control For Unstructured Terrains.

Vijeth Rai, Eric Rombokas

    IEEE ... International Conference on Rehabilitation Robotics : [Proceedings]
    |August 4, 2019
    PubMed
    Summary

    Researchers developed a Recurrent Neural Network (RNN) to predict prosthetic ankle movement using body motion. This advanced prosthetic limb controller adapts to continuous, real-world locomotion without needing predefined modes.

    More Related Videos

    Customizing a Cryolite Glass Prosthetic Eye
    08:04

    Customizing a Cryolite Glass Prosthetic Eye

    Published on: October 31, 2019

    11.3K
    Engineering Platform and Experimental Protocol for Design and Evaluation of a Neurally-controlled Powered Transfemoral Prosthesis
    11:16

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

    Published on: July 22, 2014

    16.7K

    Related Experiment Videos

    Last Updated: Jan 21, 2026

    A Structured Rehabilitation Protocol for Improved Multifunctional Prosthetic Control: A Case Study
    06:58

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

    Published on: November 6, 2015

    10.2K
    Customizing a Cryolite Glass Prosthetic Eye
    08:04

    Customizing a Cryolite Glass Prosthetic Eye

    Published on: October 31, 2019

    11.3K
    Engineering Platform and Experimental Protocol for Design and Evaluation of a Neurally-controlled Powered Transfemoral Prosthesis
    11:16

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

    Published on: July 22, 2014

    16.7K

    Area of Science:

    • Biomedical Engineering
    • Robotics
    • Human Motion Analysis

    Background:

    • Current prosthetic limb controllers use discrete modes for specific activities like walking or stairs.
    • Human locomotion is continuous and adaptive, making mode-based control insufficient for complex, real-world scenarios.
    • Inter-joint coordination in human movement allows prediction of individual joint trajectories from overall body motion.

    Purpose of the Study:

    • To develop a prosthetic joint trajectory generation system for unstructured human locomotion.
    • To apply a Recurrent Neural Network (RNN) for predicting ankle kinematics during complex, non-modal activities.
    • To assess the model's robustness to subject-specific variations and sensor configurations.

    Main Methods:

    • Ten healthy subjects wore a full-body motion capture suit to record kinematics during various activities (obstacle avoidance, sidestepping, cone weaving, backward walking).
    • A Recurrent Neural Network (RNN) was trained to predict the right ankle angle trajectory using data from other body joints.
    • The model's performance was evaluated across different activities and sensor subsets, analyzing robustness to variations in walking speed and step length.

    Main Results:

    • The RNN successfully predicted ankle kinematics for unstructured locomotion activities, demonstrating adaptability beyond predefined modes.
    • The prediction model showed robustness to subject-specific variations, including walking speed and step length.
    • Performance varied with different activities and sensor subsets, providing insights into optimal system configuration.

    Conclusions:

    • Body motion prediction using RNNs offers a viable method for generating prosthetic joint reference trajectories in real-time.
    • This approach enables prosthetic control that fluidly adapts to continuous, unpredictable human locomotion without explicit terrain or event detection.
    • The study highlights the potential for advanced robotic rehabilitation devices that leverage natural body dynamics for intuitive control.