Given their status as unwritten visual-gestural languages, research on the automatic recognition of sign languages has increasingly implemented multisource capturing tools for data collection and processing. This paper explores advancements in Italian Sign Language (LIS) recognition using a multimodal dataset in the medical domain: the MultiMedaLIS Dataset. We investigate the integration of RGB frames, depth data, optical flow, and skeletal information to develop and evaluate two computational models: Skeleton-Based Graph Convolutional Network (SL-GCN) and Spatiotemporal Separable Convolutional Network (SSTCN). RADAR data was collected but not included in the testing phase. Our experiments validate the effectiveness of these models in enhancing the accuracy and robustness of isolated LIS signs recognition. Our findings highlight the potential of multisource approaches in computational linguistics to improve linguistic accessibility and inclusivity for members of the signing community.