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

Search research articles

Related Experiment Video

Updated: May 25, 2026

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

Power independent EMG based gesture recognition for robotics.

Ling Li¹, David Looney, Cheolsoo Park

¹Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK. ling.li206@imperial.ac.uk

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|January 19, 2012

Summary

Related Concept Videos

You might also read

Related Articles

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

Sort by

Same author

Fall-from-Bed Risk Prediction Using Physics-Based Bed Simulation.

Sensors (Basel, Switzerland)·2026

Same author

Quaternion-based CNN for heart rate prediction from PPG.

Neural networks : the official journal of the International Neural Network Society·2026

Same author

A novel backpropagation algorithm based on negated kurtosis loss for training shallow, convolutional, and deep neural networks.

Neural networks : the official journal of the International Neural Network Society·2026

Same author

How to Evaluate Signal Quality of ear-ECG?

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

Multi-task learning for estimation of remote PPG and respiration signals with complex valued convolutional neural network.

Scientific reports·2025

Same author

Impact of Pulmonary Vein Isolation on Atrial Fibrillation Organisation: Correlation of Intracardiac and Surface Electrocardiogram Measures.

Journal of cardiovascular electrophysiology·2025

This study introduces a new method for detecting muscle contractions using surface Electromyograph (EMG) signals to control robots with hand gestures. The system enhances accuracy and robustness for real-time robotic applications.

Area of Science:

Biomedical Engineering
Robotics
Signal Processing

Background:

Surface Electromyograph (EMG) signals are crucial for understanding muscle activity.
Controlling robotic systems with hand gestures requires accurate and robust muscle activity detection.
Existing methods may lack robustness to variations in electrode placement and impedance.

Purpose of the Study:

To present a novel method for detecting muscle contractions.
To develop a system for identifying four distinct hand gestures for robot control.
To enhance the accuracy and robustness of EMG-based gesture recognition.

Main Methods:

Utilizing surface Electromyograph (EMG) measurements from arm muscles.
Applying a multivariate extension of Empirical Mode Decomposition (EMD) for simultaneous EMG channel processing.

More Related Videos

The Bionic Clicker Mark I & II

The Bionic Clicker Mark I & II

Published on: August 14, 2017

Related Experiment Videos

Last Updated: May 25, 2026

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

The Bionic Clicker Mark I & II

The Bionic Clicker Mark I & II

Published on: August 14, 2017

Employing phase synchrony measures to ensure robustness against variations in electrode placement and impedance.

Main Results:

Successfully identified four discrete gestures (flexion, extension, pronation, supination) based on muscle synchrony.
Demonstrated enhanced accuracy and robustness in real-time robot control simulations.
The proposed methodology effectively preserves cross-information from EMG signals.

Conclusions:

The novel EMG-based method offers a robust and accurate approach for hand gesture recognition.
This technique facilitates improved control of robotic systems in real-time.
The integration of EMD and phase synchrony measures addresses key challenges in EMG signal processing for human-robot interaction.