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Veins of Upper Limbs01:17

Veins of Upper Limbs

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The human circulatory system, a marvel of biological engineering, is a complex network of vessels that transport blood throughout the body. Among these, the veins responsible for carrying blood from the upper limbs are divided into two categories: deep and superficial.
The deep venous system is primarily composed of the ulnar and radial veins. The ulnar vein, which drains the fingers through the superficial palmar venous arches, and the radial vein, which serves the palms via the deep palmar...
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Arteries of the Upper Limbs01:12

Arteries of the Upper Limbs

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The subclavian artery transitions into the axillary artery as it exits the chest and enters the axillary region. This artery is critical for supplying blood to the shoulder area, including the head of the humerus, through the humeral circumflex arteries. As the vessel continues into the upper arm or brachium, it becomes the brachial artery. This artery plays a key role in vascularizing the brachial region and bifurcates at the elbow into several branches. These branches include the deep...
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Bones of the Upper Limb: Ulna01:15

Bones of the Upper Limb: Ulna

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The ulna and radius are parallel bones of the antebrachium or the forearm. The ulna lies medially and consists of a bony tip called the olecranon process at its proximal end. This hook-like projection articulates with the olecranon fossa of the humerus and forms the "hinged" ulnohumeral part of the elbow joint. This joint facilitates forearm extension and flexion while preventing its hyperextension. Similarly, the coronoid process, another bony projection on the proximal/anterior side...
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Bones of the Upper Limb: Radius01:09

Bones of the Upper Limb: Radius

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The radius is longer of the two bones that make up the human antebrachium or forearm. At the proximal end, the radius articulates with the capitulum of the humerus and the radial notch of the ulna to form the elbow joint. At the distal end, the radius articulates with the ulna via the ulnar notch, forming the distal radioulnar joint. Distally, the radius also attaches to the carpal wrist bones (scaphoid and lunate) to form the radiocarpal joint.
The radius has a nail-shaped head, and a...
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Bones of the Upper Limb: Humerus01:19

Bones of the Upper Limb: Humerus

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The upper limb consists of the arm, forearm, wrist, and hand bones. The humerus is the single bone of the upper arm region. Proximally, it has a large, spherical, smooth head that articulates with the glenoid cavity of the scapula to form the glenohumeral or shoulder joint. The margin of the head is the anatomical neck, a residual epiphyseal plate. Laterally it extends to form bony projections called the greater tubercle and the lesser tubercle. Next to the tubercles is the surgical neck, a...
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Reliability and Validity01:29

Reliability and Validity

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Reliability and validity are two important considerations that must be made with any type of data collection. Reliability refers to the ability to consistently produce a given result. In the context of psychological research, this would mean that any instruments or tools used to collect data do so in consistent, reproducible ways.
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Related Experiment Video

Updated: Jan 26, 2026

Asthma Detection Research Based on Voice Signal Processing and Machine Learning
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Asthma Detection Research Based on Voice Signal Processing and Machine Learning

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Open Database for Accurate Upper-Limb Intent Detection Using Electromyography and Reliable Extreme Learning Machines.

Vinicius Horn Cene1, Mauricio Tosin2, Juliano Machado3

  • 1Programa de Pós-Graduação em Engenharia Elétrica da Universidade Federal do Rio Grande do Sul, Avenue Osvaldo Aranha 103, Porto Alegre 90035-190, Brazil. vinicius.cene@gmail.com.

Sensors (Basel, Switzerland)
|April 21, 2019
PubMed
Summary

This study introduces a new method for processing surface electromyography (sEMG) signals using reliable Extreme Learning Machines (ELM) classifiers. The approach enhances control for artificial limbs and assistive devices by improving signal classification accuracy.

Keywords:
EMGextreme learning machinesfeedforward neural networksnon-iterative classifierprosthetic handreliability

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Area of Science:

  • Biomedical Engineering
  • Signal Processing
  • Machine Learning

Background:

  • Surface Electromyography (sEMG) signal processing is crucial for natural human-machine interfaces.
  • Current sEMG control systems face limitations due to unreliable signal prediction and lack of standardized processing.
  • Advancements are needed to improve the robustness and accuracy of sEMG-based control.

Purpose of the Study:

  • To present and validate a novel sEMG database and reliable Extreme Learning Machine (ELM) classifiers.
  • To improve the consistency and accuracy of sEMG signal classification for enhanced control.
  • To establish a benchmark for sEMG signal processing and classification.

Main Methods:

  • Utilized a stochastic filter based on the Antonyan Vardan Transform (AVT) for signal processing.
  • Employed two variations of reliable ELM classifiers (R-ELM and R-Regularized ELM (RELM)) for signal classification.
  • Derived a reliability metric to autonomously select the most reliable samples for classification.
  • Validated the approach using the NINAPro database (DB1, DB2, DB6) and the IEE database.

Main Results:

  • The proposed sEMG database demonstrated consistent performance.
  • Reliable ELM classifiers achieved high classification accuracies, exceeding 99% on the IEE database.
  • Accuracies of 75.1%, 79.77%, and 69.83% were obtained for NINAPro DB1, DB2, and DB6, respectively.
  • The reliable ELM classifiers matched or outperformed existing methods in related studies.

Conclusions:

  • The developed reliable ELM classifiers offer a robust solution for sEMG signal processing.
  • This approach significantly improves the accuracy and reliability of biosignal classification for human-machine interfaces.
  • The validated database and classifiers provide a valuable resource for future research in assistive device control.