In recent years, there has been a growing interest in multimodal and multi-source data due to their ability to introduce heterogeneous information. Studies have demonstrated that combining such information enhances the performance of Recommender Systems across various scenarios. In the context of Health Recommendation Systems (HRS), different types of data are utilized, primarily focusing on patient-based information, but data from Pose Estimations (PE) are not incorporated. The objective of my Ph.D. is to investigate methods to design and develop HRS that treat the PE as one of the input sources, taking into account aspects such as privacy concerns and balancing the trade-off between system quality and responsiveness. By leveraging the combination of diverse information sources, I intend to create a new model in the area of HRS capable of providing more precise and explainable recommendations.