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Related Experiment Video

Updated: May 31, 2025

An Inertial Measurement Unit Based Method to Estimate Hip and Knee Joint Kinematics in Team Sport Athletes on the Field
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Action Recognition in Basketball with Inertial Measurement Unit-Supported Vest.

Hamza Sonalcan1, Enes Bilen1, Bahar Ateş2

  • 1Computer Engineering Department, Engineering Faculty, Aydın Adnan Menderes University, Aydın 09100, Türkiye.

Sensors (Basel, Switzerland)
|January 25, 2025
PubMed
Summary

This study developed an accurate action recognition system using a single Inertial Measurement Unit (IMU) sensor for basketball training. The system achieved 96.9% accuracy, offering a low-cost, high-performance solution for athletes.

Keywords:
action recognitionbasketball traininginertial measurement unit (IMU)machine learningwearable sensors

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Area of Science:

  • Sports Science
  • Biomechanical Engineering
  • Machine Learning Applications

Background:

  • Basketball training relies on accurate movement analysis.
  • Current methods for action recognition in sports can be costly or cumbersome.
  • Wearable sensor technology offers a potential solution for unobtrusive athlete monitoring.

Purpose of the Study:

  • To develop and evaluate an action recognition system for fundamental basketball movements.
  • To enhance basketball training with a high-performance, low-cost wearable solution.
  • To minimize athlete discomfort during movement data collection.

Main Methods:

  • Utilized a single Inertial Measurement Unit (IMU) sensor in a wearable vest.
  • Collected data from 21 collegiate basketball players performing various movements.
  • Applied machine learning algorithms (KNN, decision tree, Random Forest, AdaBoost, XGBoost) after data preprocessing and feature extraction.

Main Results:

  • The XGBoost algorithm achieved the highest accuracy of 96.6% with specific parameters (window size 250, 75% overlap).
  • The overall system demonstrated a classification accuracy of 96.9%.
  • The developed system outperformed other single-sensor action recognition systems.

Conclusions:

  • The study presents a novel dataset for basketball action recognition.
  • It compares the efficacy of different feature extraction and machine learning techniques.
  • A scalable, efficient, and accurate action recognition system for basketball has been developed.