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Related Concept Videos

Functional Classification of Joints01:09

Functional Classification of Joints

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Functional Classification of Joints
The functional classification of joints is determined by the amount of mobility between the adjacent bones. Joints are functionally classified as a synarthrosis or immobile joint, an amphiarthrosis or slightly moveable joint, or as a diarthrosis, a freely moveable joint. Fibrous and cartilaginous joints can be functionally classified as either synarthroses  or amphiarthroses, whereas all synovial joints are classified as diarthroses.
Synarthrosis
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Related Experiment Video

Updated: Aug 6, 2025

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision
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A fuzzy granular logistic regression algorithm for sEMG-based cross-individual prosthetic hand gesture

Yanan Diao1,2,3, Qiangqiang Chen1,4, Yan Liu1

  • 1CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Research Center for Neural Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, People's Republic of China.

Journal of Neural Engineering
|March 14, 2023
PubMed
Summary

A new modified fuzzy granularized logistic regression (FG_LogR) algorithm improves cross-individual surface electromyography (sEMG) gesture classification accuracy. This advancement offers potential for more intelligent prosthetic control and reduced abandonment rates.

Keywords:
cross-individual gesture classificationfuzzy granulationlogistic regressionprosthetic systemssurface electromyography

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Area of Science:

  • Biomedical Engineering
  • Rehabilitation Technology
  • Machine Learning in Healthcare

Background:

  • Prosthetic systems enhance post-amputation patient quality of life.
  • Surface electromyography (sEMG)-based gesture classification is crucial for prosthetic control.
  • Current algorithms lack cross-individual accuracy, leading to high prosthetic system abandonment.

Purpose of the Study:

  • To propose a novel algorithm for significantly improving cross-individual gesture classification accuracy.
  • To address the limitations of existing algorithms in overcoming physiological variations between individuals.

Main Methods:

  • Recruited eight healthy adults for sEMG data collection of seven daily gestures.
  • Developed and applied a modified fuzzy granularized logistic regression (FG_LogR) algorithm for cross-individual classification.
  • Compared FG_LogR performance against standard logistic regression and five other classic algorithms.

Main Results:

  • The FG_LogR algorithm achieved average classification accuracies ranging from 79.7% to 86.1%.
  • FG_LogR demonstrated an accuracy improvement of 3.5% to 7.9% over standard logistic regression.
  • The proposed algorithm outperformed five other classic algorithms, with an average accuracy increase exceeding 5%.

Conclusions:

  • The FG_LogR algorithm enhances cross-individual gesture recognition accuracy through fuzzy feature granulation.
  • This algorithm shows significant potential for clinical application in prosthetic systems.
  • Improved gesture recognition can lead to more precise prosthetic control and lower abandonment rates.