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Toward Modeling Psychomotor Performance in Karate Combats Using Computer Vision Pose Estimation.

Jon Echeverria1, Olga C Santos2

  • 1Computer Science School, Universidad Nacional de Educación a Distancia (UNED), 28040 Madrid, Spain.

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|December 28, 2021
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Summary
This summary is machine-generated.

This study uses AI-powered pose estimation to model Karate movements, analyzing psychomotor performance in kumite. The approach effectively models complex, two-person interactions for skill assessment.

Keywords:
OpenPosecomputer visiondeep learninghuman activity recognition (HAR)human pose estimation (HPE)karatemartial arts

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Area of Science:

  • Sports Science
  • Artificial Intelligence
  • Biomechanics

Background:

  • Technological advancements facilitate human-computer interaction across diverse fields, including sports.
  • Martial arts, like Karate, offer a structured system of movements governed by physics, making them suitable for motor skill learning analysis.
  • Artificial intelligence (AI) and computer vision algorithms show potential in modeling human movements for performance analysis and learning progression.

Purpose of the Study:

  • To explore the application of AI-driven pose estimation for modeling psychomotor performance in Karate kumite.
  • To address the challenge of modeling dyadic interactions in kumite, moving beyond individual movement analysis.
  • To compare the effectiveness of different data mining algorithms in classifying Karate movements based on extracted features.

Main Methods:

  • Utilized a pose estimation algorithm to extract features from predefined movements in Ippon Kihon kumite.
  • Focused on modeling the joint interaction between two participants during kumite.
  • Compared classification metrics of four distinct data mining algorithms applied to the extracted movement data.

Main Results:

  • The pose estimation approach successfully extracted relevant features from Karate movements.
  • The study achieved high classification metrics when applying data mining algorithms to the analyzed kumite data.
  • The developed model demonstrated effectiveness in analyzing the psychomotor performance within a two-person Karate combat scenario.

Conclusions:

  • AI-based pose estimation is a viable method for modeling psychomotor performance in Karate kumite.
  • The approach can effectively analyze complex, dyadic interactions characteristic of martial arts.
  • This research provides a foundation for advanced AI applications in sports science and motor skill acquisition.