CPFES: Physical Fitness Evaluation Based on Canadian Agility and Movement Skill Assessment
This work addresses physical fitness assessment for children, offering a more efficient and age-specific evaluation method, though it appears incremental as it applies existing deep learning techniques to a new domain.
The authors tackled the problem of physical fitness evaluation for children by developing CPFES, a system that uses deep learning to assess movement skills based on the Canadian Agility and Movement Skill Assessment (CAMSA), achieving high accuracy in experiments.
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.