As pose estimation has garnered considerable attention for kinematic analysis, numerous pose estimation models have been developed in recent times. A pose estimation model is a trained neural network that predicts human body landmarks from an image. Each model contains different strong and weak points, which make it difficult for users to decide which model to use for kinematic analysis. The accuracy of the model can be one big factor for model selection, but there are not many studies investigating this critical point. Therefore, this study aims to investigate the accuracy of different models and variants by comparing the measurements from the models and variants against reference measurements. Five male participants were invited to this study. Each participant was asked to perform five exercises: squat, squat jump, counter movement jump, walk, and jog while being recorded by twelve normal RGB cameras (Contemplas) and ten marker-based tracking cameras (VICON). The video recordings from the Contemplas were processed by six different pose estimation models and variants: Mediapipe, MeTRAbs Small, MeTRAbs X Large, YOLO, MoveNet Lightning, and MoveNet Thunder to detect joint positions. From the detected joint positions, four joint angles, left hip, right hip, left knee, and right knee, were calculated. Three-way repeated measures ANOVA and Tukey HSD post-hoc analysis were applied to compare the pose estimation models with VICON measurements. The ANOVA result showed that exercise and model factors had a significant impact on the measurement errors although angle factor did not. In the post-hoc analysis, knee joint angle errors from YOLO, MoveNet Lightning, and MoveNet Thunder in jog and walk were significantly higher than those from Mediapipe, MeTRAbs Small, and MeTRAbs X Large. In conclusion, differentiated recommendations can be given for optimum model and variant choice in different conditions in kinematic analyses.
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