  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.
