The infant stage is crucial for human development, with fidgeting playing a key role in the development of balance and coordination. Recent studies have developed machine learning algorithms that utilize body posture estimation to detect fidgety movements in babies. However, research on optimal hyperparameters for infant posture estimation is still limited. Without a reference to the optimal configuration, research on infant-based pose estimation could be prolonged and deviate from its main goal of detecting infant growth through movement.This paper employs a computer vision approach to enhance the accuracy of predicting fidgety movements in babies. Evaluating the hyperparameters of the Convolutional Neural Network (CNN) model for Baby Pose Estimation can significantly improve its performance. The synthetic and real infant pose (SyRIP) dataset, along with the high-resolution net (HRnet) and distribution-aware coordinate representation of keypoin (DARKPose) models, is utilized for the infant pose estimation dataset. The hyperparameter values were exploited to identify the most optimal results in this research. Among the 37 scenarios, the following hyperparameter combinations yielded the best results: Batch Size combinations of 2 and 4, train epochs of 15 and 150, lambda value of 0.0001, learning rate of 0.00005, learning rate factor of 0.1, learning rate steps of 60 and 120, weight decay of 0.00005, gamma of 0.95, and momentum of 0.9. Increasing the epochs and pre-epochs has proven to enhance the model's performance. Lambda values show a positive correlation with model performance. Conversely, values such as Learning Rate and its factor, steps, gamma, momentum, and weight decay demonstrate a negative correlation.