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

Neural Control of Respiration01:18

Neural Control of Respiration

4.9K
The neural regulation of respiration is a meticulously coordinated process primarily controlled by the respiratory centers located within the brainstem. These centers, composed of specialized neurons, transmit nerve impulses that control the contraction and relaxation of our respiratory muscles.
Respiratory Centers in the Brainstem
Two primary areas comprise the respiratory center: the medullary respiratory center in the medulla oblongata and the pontine respiratory group in the pons. The...
4.9K
Convolution Properties II01:17

Convolution Properties II

590
The important convolution properties include width, area, differentiation, and integration properties.
The width property indicates that if the durations of input signals are T1 and T2, then the width of the output response equals the sum of both durations, irrespective of the shapes of the two functions. For instance, convolving two rectangular pulses with durations of 2 seconds and 1 second results in a function with a width of 3 seconds.
The area property asserts that the area under the...
590
Nuclear Fusion02:45

Nuclear Fusion

33.9K
The process of converting very light nuclei into heavier nuclei is also accompanied by the conversion of mass into large amounts of energy, a process called fusion. The principal source of energy in the sun is a net fusion reaction in which four hydrogen nuclei fuse and ultimately produce one helium nucleus and two positrons.
A helium nucleus has a mass that is 0.7% less than that of four hydrogen nuclei; this lost mass is converted into energy during the fusion. This reaction produces about...
33.9K
Convolution Properties I01:20

Convolution Properties I

616
Convolution computations can be simplified by utilizing their inherent properties.
The commutative property reveals that the input and the impulse response of an LTI (Linear Time-Invariant) system can be interchanged without affecting the output:
616
Protein Networks02:26

Protein Networks

4.6K
An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
4.6K
Network Covalent Solids02:18

Network Covalent Solids

16.2K
Network covalent solids contain a three-dimensional network of covalently bonded atoms as found in the crystal structures of nonmetals like diamond, graphite, silicon, and some covalent compounds, such as silicon dioxide (sand) and silicon carbide (carborundum, the abrasive on sandpaper). Many minerals have networks of covalent bonds.
To break or to melt a covalent network solid, covalent bonds must be broken. Because covalent bonds are relatively strong, covalent network solids are typically...
16.2K

You might also read

Related Articles

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

Sort by
Same author

Temporomandibular joint disorder caused by autoimmune diseases: A retrospective analysis.

Asian journal of surgery·2024
Same author

Toward Efficient Utilization of Photogenerated Charge Carriers in Photoelectrochemical Systems: Engineering Strategies from the Atomic Level to Configuration.

Chemical reviews·2024
Same author

Exploring Pathogenic Genes in Frozen Shoulder through weighted gene co-expression network analysis and Mendelian Randomization.

International journal of medical sciences·2024
Same author

Understanding the active site in chameleon-like bifunctional catalyst for practical rechargeable zinc-air batteries.

Nature communications·2024
Same author

PFOA/PFOS Facilitated Intestinal Fatty Acid Absorption by Activating the PPARα Pathway: Insights from Organoids Model.

Environment & health (Washington, D.C.)·2024
Same author

Neutrophil-to-Lymphocyte Ratio is Associated with Clinical Outcomes in Patients Treated with Mechanical Thrombectomy for Posterior Circulation Large Vessel Occlusion.

World neurosurgery·2024

Related Experiment Video

Updated: Feb 7, 2026

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

10.0K

Sensor Fusion for Myoelectric Control Based on Deep Learning With Recurrent Convolutional Neural Networks.

Weiming Wang1, Biao Chen1, Peng Xia1

  • 1School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China.

Artificial Organs
|July 14, 2018
PubMed
Summary

A novel recurrent convolutional neural network (RCNN) model improves electromyogram (EMG) signal decoding for myoelectric control. Sensor fusion enhances accuracy and robustness, making RCNNs promising for real-world applications.

Keywords:
Deep learningElectromyogramRecurrent convolutional neural networksSensor fusionTransfer learning

More Related Videos

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

1.1K
Deep Learning-Based Segmentation of Cryo-Electron Tomograms
10:25

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

10.9K

Related Experiment Videos

Last Updated: Feb 7, 2026

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

10.0K
Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

1.1K
Deep Learning-Based Segmentation of Cryo-Electron Tomograms
10:25

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

10.9K

Area of Science:

  • Biomedical Engineering
  • Machine Learning
  • Neuroscience

Background:

  • Electromyogram (EMG) signal decoding is crucial for myoelectric control systems.
  • Traditional machine learning methods struggle with the complexity and robustness required for real-life EMG signal processing.
  • Existing systems lack sufficient adaptability to dynamic conditions like arm movements.

Purpose of the Study:

  • To propose a novel Recurrent Convolutional Neural Network (RCNN) model for enhanced EMG signal decoding and hand movement classification.
  • To evaluate the performance of the RCNN model against traditional methods like Support Vector Machines (SVM) and Convolutional Neural Networks (CNNs).
  • To investigate the impact of sensor fusion (EMG and acceleration data) on improving system adaptability and robustness.

Main Methods:

  • Development and application of a deep architecture RCNN model for processing complex time-series EMG data.
  • Utilizing transfer learning and parameter transferring techniques to optimize model training and prevent overfitting.
  • Implementing sensor fusion by combining EMG signals with acceleration data as multimodal input.
  • Comparative analysis of RCNN performance against SVM and CNN on a non-invasive EMG dataset.

Main Results:

  • The RCNN model demonstrated superior classification accuracy compared to SVM and CNNs.
  • Time-domain input and 1-dimensional convolution yielded higher accuracy within the RCNN architecture.
  • Sensor fusion significantly improved model performance, particularly under conditions involving arm movements.
  • Parameter transferring accelerated training and mitigated overfitting issues.

Conclusions:

  • The proposed RCNN model is a highly effective and promising approach for EMG signal decoding in myoelectric control.
  • Sensor fusion is a key strategy for enhancing the accuracy and robustness of myoelectric control systems.
  • The RCNN model offers significant advantages in handling complex EMG data for advanced human-machine interfaces.