To overcome these challenges, future research can focus on developing lightweight multimodal frameworks that leverage advancements in deep learning and model compression techniques. These frameworks could prioritize efficient feature extraction and representation, allowing for faster processing while retaining the benefits of multimodal integration. Additionally, exploring attention mechanisms and transfer learning could help enhance the model’s ability to focus on relevant features from different modalities without overwhelming computational demands. Furthermore, the incorporation of generative models may provide a way to simulate additional contextual information based on limited skeletal data, helping to fill in gaps that traditional methods struggle with. This could lead to a more holistic understanding of human actions, improving classification performance in complex scenarios. Creating benchmark datasets that include diverse multimodal data can help standardize evaluations and drive progress in the field. As the demand for accurate and efficient action recognition systems continues to grow, innovative solutions that balance multimodal integration with computational efficiency will be critical in advancing this area of research.