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WARNING: A Wearable Inertial-Based Sensor Integrated with a Support Vector Machine Algorithm for the Identification

Juri Taborri1, Eduardo Palermo2, Stefano Rossi1

  • 1Department of Economics, Engineering, Society and Business Organization, University of Tuscia, 01110 Viterbo, Italy.

Sensors (Basel, Switzerland)
|June 10, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces WARNING, an AI-powered wearable sensor system that accurately detects race-walking faults using machine learning. The technology shows potential to assist referees by objectively identifying illegal techniques in competitions.

Keywords:
artificial intelligenceinertial wearable sensorsrace walkingsportssupport vector machine

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Area of Science:

  • Sports Science
  • Biomechanical Engineering
  • Artificial Intelligence

Background:

  • Subjectivity in race walking officiating can lead to questioned results.
  • Artificial intelligence (AI) offers a potential solution for objective fault detection.
  • Wearable sensor technology can capture biomechanical data for analysis.

Purpose of the Study:

  • To present WARNING, an inertial-based wearable sensor system integrated with AI.
  • To automatically identify race-walking faults using machine learning algorithms.
  • To evaluate the performance of different machine learning algorithms for fault detection.

Main Methods:

  • Ten expert race walkers performed legal and illegal race walking techniques.
  • Two WARNING inertial sensors captured 3D linear acceleration data from the shanks.
  • Thirteen machine learning algorithms (decision tree, SVM, k-NN) were trained and tested using an inter-athlete procedure.
  • Performance was assessed by accuracy, F1 score, G-index, and prediction speed.

Main Results:

  • The quadratic support vector machine (SVM) classifier achieved over 90% accuracy.
  • The system demonstrated a high prediction speed of 29,000 observations/s when using data from both shanks.
  • Performance significantly decreased when analyzing data from only one leg.

Conclusions:

  • The WARNING system, utilizing AI and wearable sensors, can reliably detect race-walking faults.
  • This technology has strong potential as an objective assistant for referees in race walking.
  • WARNING can be valuable for both competition officiating and athlete training.