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.

翻译：近年来，融入体育教育的基本运动技能评估聚焦于教学实践与评估可行性。评估对象从多年龄段细化为特定年龄段，评估内容则从复杂耗时转向简洁高效。为此，我们将深度学习应用于体能评估，提出基于加拿大灵敏与运动技能评估（CAMSA）的体能评价系统（CPFES）。该系统依据CAMSA标准评估儿童体能，并根据CPFES评分提供建议以促进儿童发展。我们设计了关键点检测模块与姿态估计模块，同时针对CAMSA准则开发了姿态评估模块，能够有效评估受测儿童的动作。实验结果表明，所提系统具有较高准确性。