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Related Experiment Video

Updated: May 28, 2025

Design and Analysis for Fall Detection System Simplification
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The machine learning algorithm based on decision tree optimization for pattern recognition in track and field sports.

Guomei Cui1, Chuanjun Wang1

  • 1College of Physical Education, Shandong Sport University, Rizhao, China.

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|February 13, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces an optimized decision tree algorithm for enhanced sprint pattern recognition, achieving 94.9% accuracy. This method improves athlete training and competition strategies by increasing recognition accuracy and efficiency.

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Area of Science:

  • Sports Science
  • Machine Learning
  • Biomechanics

Background:

  • Existing sprint pattern recognition methods lack accuracy and efficiency.
  • Optimizing training and competition strategies requires precise sprint analysis.

Purpose of the Study:

  • To develop an optimized machine learning algorithm for accurate and efficient sprint pattern recognition.
  • To improve athlete performance through enhanced training and competition strategies.

Main Methods:

  • Utilized high-precision sensors and computer simulation for biomechanical data (step frequency, stride length, acceleration).
  • Developed an optimized decision tree algorithm combining Random Forest (RF) and Gradient Boosting Tree (GBT) with adaptive feature selection and ensemble learning.
  • Employed cross-validation and grid search for hyperparameter optimization.

Main Results:

  • The optimized decision tree algorithm achieved 94.9% accuracy, outperforming Support Vector Machine (SVM) at 87.0% and Convolutional Neural Network (CNN) at 92.0%.
  • Demonstrated superior computational efficiency compared to SVM and CNN.
  • Reduced overfitting and improved generalization ability through adaptive techniques.

Conclusions:

  • The optimized decision tree algorithm significantly enhances sprint pattern recognition accuracy and efficiency.
  • This approach offers a promising tool for optimizing athlete training and competition strategies.
  • Future research should validate the model with real-world data to confirm generalization capabilities.