CPFES: Physical Fitness Evaluation Based on Canadian Agility and Movement Skill Assessment

TL;DR

This paper introduces CPFES, a deep learning-based system for assessing children's physical fitness using CAMSA, featuring modules for landmark detection, pose estimation, and action evaluation, achieving high accuracy.

Contribution

The paper presents a novel, efficient system combining deep learning modules for accurate physical fitness assessment based on CAMSA criteria.

Findings

High accuracy in pose and action evaluation

Effective assessment of children's physical fitness

Potential for practical application in physical education

Abstract

In recent years, the assessment of fundamental movement skills integrated with physical education has focused on both teaching practice and the feasibility of assessment. The object of assessment has shifted from multiple ages to subdivided ages, while the content of assessment has changed from complex and time-consuming to concise and efficient. Therefore, we apply deep learning to physical fitness evaluation, we propose a system based on the Canadian Agility and Movement Skill Assessment (CAMSA) Physical Fitness Evaluation System (CPFES), which evaluates children's physical fitness based on CAMSA, and gives recommendations based on the scores obtained by CPFES to help children grow. We have designed a landmark detection module and a pose estimation module, and we have also designed a pose evaluation module for the CAMSA criteria that can effectively evaluate the actions of the child being tested. Our experimental results demonstrate the high accuracy of the proposed system.