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Related Concept Videos

Aggregates Classification01:29

Aggregates Classification

Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
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Force Classification01:22

Force Classification

Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
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Functional Classification of Joints

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.
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Classification of Systems-I

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Horizontal motion, governed by the initial kick, maintains a constant velocity throughout the flight of the soccer ball.

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Design and Analysis for Fall Detection System Simplification
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Classification of vertical jump performance categories in futsal using machine learning algorithms.

Diana Ximena Martínez-Arce1,2, Laura Andrea Quintero-Palma1,2, Jessica Quiceno-Henao1

  • 1Laboratorio Integrado de Análisis del Movimiento, Institución Universitaria Escuela Nacional del Deporte, Santiago de Cali, Colombia.

Frontiers in Sports and Active Living
|July 16, 2026
PubMed
Summary

Machine learning accurately classified futsal athletes' vertical jump performance using force-time data. This framework aids talent identification and personalized training by analyzing explosive actions from dual force plates.

Keywords:
biomechanicsclassification algorithmsforce platesfutsalmachine learningvertical jump

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Area of Science:

  • Biomechanics
  • Sports Science
  • Machine Learning

Background:

  • Futsal demands explosive vertical jumps, generating complex force-time data.
  • Traditional analysis struggles with multidimensional biomechanical data from dual force plates.

Purpose of the Study:

  • Develop a supervised classification framework for futsal athletes' vertical jump performance.
  • Utilize independent force-time metrics from dual force plates for classification.

Main Methods:

  • Fifty-one male athletes performed countermovement jumps, squat jumps, and drop jumps.
  • Principal Component Analysis (PCA) derived four performance categories from six biomechanical variables.
  • Four machine learning algorithms were compared using athlete-level stratified cross-validation.

Main Results:

  • Logistic Regression achieved high accuracy (F1-Score=0.830, AUC-ROC=0.977) on the independent test set.
  • Cross-validation provided conservative estimates (F1=0.770 ± 0.069, AUC=0.941 ± 0.039).
  • Countermovement jump peak power and drop jump variables were most influential predictors.

Conclusions:

  • Machine learning, specifically Logistic Regression, accurately classifies futsal athletes' vertical jump performance.
  • The PCA and supervised classification framework offers a reproducible method for talent identification and training.
  • Rigorous validation demonstrates the framework's potential for longitudinal monitoring and individualized training programs.