A Comprehensive Literature Review on the Use of Restricted Boltzmann Machines and Deep Belief Networks for Human Action Recognition

This literature review provides a comprehensive synthesis of research on the use of Restricted Boltzmann Machines (RBMs) and Deep Belief Networks (DBNs) for human action recognition (HAR) from 2012 to the present. The review begins by introducing the theoretical foundations of RBMs and DBNs, detailing their architectures, training algorithms (notably contrastive divergence), and various extensions—including convolutional and recurrent adaptations—that have been developed to better capture the spatial–temporal dynamics inherent in video data. Key contributions in the field are systematically analyzed, with emphasis on hybrid models that integrate RBM/DBN pretraining with modern deep learning techniques to enhance feature extraction and improve recognition accuracy. The review also examines the major benchmark datasets used in HAR research (such as KTH, HMDB51, UCF101, NTU RGB+D, and Kinetics), discussing preprocessing strategies, evaluation metrics, and the challenges associated with overfitting, computational complexity, and model interpretability. In addition, recent trends such as the incorporation of attention mechanisms, self-supervised learning, and multi-modal data fusion are explored. By highlighting both the historical significance and the evolving advancements of RBM/DBN methodologies, this review provides insights into the current state of HAR research and outlines promising directions for future investigation, including the integration of generative pretraining with emerging architectures for robust and efficient real-time action recognition.