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

Force Classification

Force Classification

Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...

You might also read

Related Articles

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

Sort by

Same author

From Light to Energy: Machine Learning Algorithms for Position and Energy Deposition Estimation in Scintillator-SiPM Detectors.

Sensors (Basel, Switzerland)·2026

Same author

Visual Measurements of Breathing Parameters in Children With a Particular Focus on Phase Angle: A Pilot Study.

Cureus·2025

Same author

Incorporating artificial intelligence in portable infrared thermal imaging for the diagnosis and staging of nonalcoholic fatty liver disease.

Journal of biophotonics·2024

Same author

A wearable sensor and machine learning estimate step length in older adults and patients with neurological disorders.

NPJ digital medicine·2024

Same author

Non-ionizing measurement and quantification of bell-shaped chests in spinal muscular atrophy: a pilot study.

Frontiers in pediatrics·2024

Same author

Smartphone-based detection of COVID-19 and associated pneumonia using thermal imaging and a transfer learning algorithm.

Journal of biophotonics·2024

Search research articles

Related Experiment Video

Updated: May 5, 2026

Multifunctional Setup for Studying Human Motor Control Using Transcranial Magnetic Stimulation, Electromyography, Motion Capture, and Virtual Reality

Multifunctional Setup for Studying Human Motor Control Using Transcranial Magnetic Stimulation, Electromyography, Motion Capture, and Virtual Reality

Published on: September 3, 2015

Classification of hand and wrist movements via surface electromyogram using the random convolutional kernels

Daniel Ovadia¹, Alex Segal², Neta Rabin³

¹Department of Biomedical Engineering, Tel Aviv University, Tel Aviv, Israel.

Scientific Reports

|February 20, 2024

Summary

This summary is machine-generated.

This study introduces a new machine learning method using Random Convolutional Kernel Transform (ROCKET) to improve prosthetic control. The approach enhances Surface Electromyography (sEMG) signal classification for better prosthetic functionality and user satisfaction.

More Related Videos

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

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision

Published on: March 28, 2025

Related Experiment Videos

Last Updated: May 5, 2026

Multifunctional Setup for Studying Human Motor Control Using Transcranial Magnetic Stimulation, Electromyography, Motion Capture, and Virtual Reality

Multifunctional Setup for Studying Human Motor Control Using Transcranial Magnetic Stimulation, Electromyography, Motion Capture, and Virtual Reality

Published on: September 3, 2015

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

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision

Published on: March 28, 2025

Area of Science:

Biomedical Engineering
Rehabilitation Technology
Machine Learning

Background:

High rejection rates (~30%) for electric upper-limb prostheses stem from functional, control, and reliability issues.
Effective human-machine interfaces are critical for user acceptance and improving amputees' quality of life.
Surface Electromyography (sEMG) signal classification offers a promising avenue for natural prosthetic control.

Purpose of the Study:

To enhance the classification accuracy of hand and wrist movements using sEMG time series data.
To develop a robust, computationally efficient, and accurate method for prosthetic control.
To improve user satisfaction with prosthetic devices through better control.

Main Methods:

Utilized a novel approach combining a variation of the Random Convolutional Kernel Transform (ROCKET) for feature extraction.
Employed a cross-validation ridge classifier for sEMG signal classification.
Tested the algorithm on established datasets: UCI sEMG hand movement, Ninapro DB5, and DB7.

Main Results:

Achieved high discrimination accuracy in classifying hand and wrist movements from sEMG signals.
Demonstrated that ROCKET combined with a simple linear classifier yields state-of-the-art results with reduced computational complexity.
Showcased minimal parameter tuning requirements for the proposed method.

Conclusions:

The proposed ROCKET-based feature extraction and ridge classification method offers a significant advancement in prosthetic control.
This approach provides a reliable, cost-effective solution for improving prosthetic functionality and user acceptance.
Further development could lead to more intuitive and responsive prosthetic devices, enhancing amputees' autonomy.