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Functional Classification of Joints01:09

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Functional Classification of Joints
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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.
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This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
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Updated: Oct 2, 2025

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping
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Ensemble-Based Bounding Box Regression for Enhanced Knuckle Localization.

Ritesh Vyas1, Bryan M Williams1, Hossein Rahmani1

  • 1School of Computing and Communications, Lancaster University, Lancaster LA1 4YW, UK.

Sensors (Basel, Switzerland)
|February 26, 2022
PubMed
Summary
This summary is machine-generated.

Knuckle creases on the dorsal hand offer a unique biometric for identifying individuals when facial data is unavailable. An ensemble object detection method accurately localizes these creases, enhancing identification capabilities.

Keywords:
ensembleforensicsknuckle localizationobject detector

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Area of Science:

  • Biometrics
  • Computer Vision
  • Forensic Science

Background:

  • Facial recognition is common, but not always available for identification.
  • Knuckle creases on the dorsal hand present a viable alternative biometric trait.
  • Accurate localization of knuckle creases is crucial for effective identification.

Purpose of the Study:

  • To develop an ensemble approach for accurate knuckle region localization on the dorsal hand.
  • To improve the efficacy of hand-based biometrics for forensic identification.
  • To evaluate the generalizability of the proposed localization method across diverse datasets.

Main Methods:

  • Utilized an ensemble of multiple object detector frameworks for knuckle region localization.
  • Tested the approach on two large-scale public hand databases and a novel proprietary dataset.
  • Developed novel performance metrics to assess the accuracy of knuckle region detection against ground truth.

Main Results:

  • The ensemble approach demonstrated superior performance in localizing knuckle regions compared to individual detectors.
  • The method showed effectiveness across varying backgrounds and finger positions.
  • Cross-dataset evaluation confirmed the generalizability and robustness of the proposed approach.

Conclusions:

  • Ensemble object detection provides a comprehensive and accurate method for dorsal hand knuckle localization.
  • This technique enhances the potential of hand-based biometrics in forensic applications.
  • The proposed approach offers a reliable solution for identifying individuals when other biometrics fail.