Motor coordination is crucial for preschoolers’ development and is a key factor in assessing childhood development. Current diagnostic methods often rely on subjective manual assessments. This paper presents a machine vision-based approach aimed at improving the objectivity and adaptability of assessments. The method proposed involves the extraction of key points from the human skeleton through the utilization of a lightweight pose estimation network, thereby transforming video assessments into evaluations of keypoint sequences. The study uses different methods to handle static and dynamic actions, including regularization and Dynamic Time Warping (DTW) for spatial alignment and temporal discrepancies. A penalty-adjusted single-frame pose similarity method is used to evaluate actions. The lightweight pose estimation model reduces parameters by 85%, uses only 6.6% of the original computational load, and has an average detection missing rate of less than 1%. The average error for static actions is 0.071 with a correlation coefficient of 0.766, and for dynamic actions it is 0.145 with a correlation coefficient of 0.653. These results confirm the proposed method’s effectiveness, which includes customized visual components like motion waveform graphs to improve accuracy in pediatric healthcare diagnoses.