Simulation of infant suction vacuum pressure using WGAN

In this study, it is aimed to simulate infant suction vacuums by replicating infant suction vacuum pressures with the Wasserstein Generative Adversarial Network (WGAN) model. To understand the physiology of infants' feeding behavior, it is very important to understand the physiology of sucking. One of the most important elements of feeding physiology is the infant suction vacuum. In this context, the infant suction vacuum pressure produced by the WGAN model was successfully simulated. Data preprocessing and hyperparameter optimization were performed to ensure the most accurate simulation of the WGAN model used in the study. As a result, the low RMSE and high R2 value between the real data and the generated data show that the model works successfully. This method is promising in terms of better understanding the sucking behavior of infants and contributing to solving problems in the field of infant feeding. In the future, infant sucking simulation will be more successful if the model is supported with larger data and other elements of infant sucking physiology such as swallowing and respiration are considered. This kind of simulation will pave the way for new applications in the fields of infant feeding and breastfeeding.