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

You might also read

Related Articles

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

Sort by
Same author

Concept of a new Medical Data-Driven Health Care Model based on Remote Patient Monitoring.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2023
Same author

Using Tactile Sensing to Improve the Sample Efficiency and Performance of Deep Deterministic Policy Gradients for Simulated In-Hand Manipulation Tasks.

Frontiers in robotics and AI·2021
Same author

Approach bias retraining through virtual reality in smokers willing to quit smoking: A randomized-controlled study.

Behaviour research and therapy·2021
Same author

Promoting smoking abstinence in smokers willing to quit smoking through virtual reality-approach bias retraining: a study protocol for a randomized controlled trial.

Trials·2020
Same author

A wearable EIT system for detection of muscular activity in the extremities.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2020
Same author

Retraining automatic action tendencies for smoking using mobile phone-based approach-avoidance bias training: A study protocol for a randomized controlled study.

Trials·2019
Same journal

DSPE-ViT: a lightweight vision transformer with dynamic sparse positional encoding for dense small object detection in UAV imagery.

Frontiers in neurorobotics·2026
Same journal

ST-HONet: Spatio-Temporal Hierarchical Network for long-horizon bimanual visuomotor imitation.

Frontiers in neurorobotics·2026
Same journal

ST-HADP: Spatio-Temporal hierarchical attention diffusion policy for long-horizon generalizable bimanual visuomotor imitation.

Frontiers in neurorobotics·2026
Same journal

EQISP: efficient quantized image signal processing with multi-scale pyramid fusion for resource constrained embodied perception.

Frontiers in neurorobotics·2026
Same journal

Research on embodied agent multimodal perception and real-time path planning algorithms for complex unstructured environments.

Frontiers in neurorobotics·2026
Same journal

NL-YOLOv5: a model with a larger receptive field and the ability to globally acquire features.

Frontiers in neurorobotics·2026
See all related articles

Related Experiment Video

Updated: Oct 22, 2025

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision
08:15

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

Published on: March 28, 2025

857

Toward More Robust Hand Gesture Recognition on EIT Data.

David P Leins1, Christian Gibas2, Rainer Brück2

  • 1Research Institute Cognitive Interaction Technology, Bielefeld University, Bielefeld, Germany.

Frontiers in Neurorobotics
|August 30, 2021
PubMed
Summary
This summary is machine-generated.

This study enhances control for prosthetic hands using electrical impedance tomography (EIT) by developing deep learning models. New methods significantly improve cross-session gesture recognition accuracy for prosthetic hand control.

Keywords:
artificial intelligencedata analysisdeep learningelectrical impedance tomographygesture recognitionneural networks

More Related Videos

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping
09:41

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping

Published on: April 21, 2023

1.8K
Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

4.5K

Related Experiment Videos

Last Updated: Oct 22, 2025

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision
08:15

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

Published on: March 28, 2025

857
Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping
09:41

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping

Published on: April 21, 2023

1.8K
Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

4.5K

Area of Science:

  • Biomedical Engineering
  • Rehabilitation Technology
  • Machine Learning in Healthcare

Background:

  • Electrical impedance tomography (EIT) shows potential for monitoring muscle activity in prosthetic hand control.
  • Existing EIT-based gesture recognition methods lack generalization across users and sessions.
  • Significant inter-session and inter-user variability, along with signal drift, challenge EIT signal analysis.

Purpose of the Study:

  • To analyze an existing EIT dataset for multi-fingered hand prostheses.
  • To develop advanced machine learning architectures to overcome EIT signal variability.
  • To improve cross-session and cross-user classification accuracy for hand gesture recognition.

Main Methods:

  • Utilized t-SNE analysis to investigate EIT data variance.
  • Developed novel deep learning architectures to differentiate signal variations.
  • Implemented three calibration methods based on data analysis.

Main Results:

  • Deep learning architectures improved cross-session accuracy from 19.55% to 30.45%.
  • Calibration methods further boosted cross-session accuracy to 39.01%, 55.37%, and 56.34%.
  • The study identified and addressed key challenges in EIT signal processing for prosthetics.

Conclusions:

  • Deep learning and novel calibration techniques significantly enhance EIT-based prosthetic hand control.
  • The developed methods offer a pathway to more robust and natural prosthetic limb functionality.
  • Further research can leverage these findings for improved human-machine interfaces in prosthetics.