The work presented in this thesis can be divided into four main sections. • The first section focuses on data preprocessing. Irrelevant background in formation was removed from the raw depth images from the Azure Kinect acquisitions to reduce meaningless variability, and a fixed size square region of interest (ROI) containing the subject was extracted to standardize the input for the training of the model in the following steps. • The second section covers the development and validation of a DL framework designed for removing IR interference from depth images. Here, A latent-space generative diffusion model and a companion autoencoder capable to encode depth images into (and reconstruct them from) the same latent space in which the diffusion model operates, were trained on the preprocessed dataset to generate meaningful synthetic images of patients performing motor tasks analogous to those included in the construction set.