Comparative study of deep learning models for action recognition based on skeleton data

In this study, we conducted a comparative analysis of three deep learning models—CNNs (90% accuracy), LSTMs (92% accuracy), and RNNs (95% accuracy)—for skeleton-based action recognition. The research focused on evaluating each model's ability to accurately classify a variety of human actions using a Kaggle dataset. Our findings revealed CNNs' strength in spatial recognition, LSTMs' proficiency in temporal dynamics, and RNNs' overall superiority in sequential processing. The study emphasizes the importance of model selection in action recognition tasks and lays groundwork for future exploration into hybrid models.