Deep Learning Based Advanced Spatio-Temporal Extraction Model in Medical Sports Rehabilitation for Motion Analysis and Data Processing

Presently, A wide range of unlabeled and minimal style data significantly decreases the current motion sequence's reuse ability. An important method of data reuse has a successful classification and fragment separation, which has been discussed in this research. This paper focuses on these particular problems and the tremendous progress of deep learning in design and symbolic fields. A Limited Boltzmann Model (LBM) theory is based on the Advanced Spatio-Temporal Extraction Model (ASTEM), which has been used for analyzing the physiological motion of human skeletons. There are primarily three aspects to the results of the study. (1) For constructing a semi-combination model, the stack factor decomposition is used as a spatiotemporal model function and LBM discrimination. (2), Optimized algorithm used to create the three-channel generative LBM model using the weight decomposition idea and then extract the time and space-based abstract properties of the original motion series. (3) The unsupervised related model of frame detection is built using the perception of human interaction through 3D convolution LBM. A significant research direction of the medical analysis and extraction of sports data is used appropriately to interpret and gain valuable information and knowledge from motion analyses. Experimental outcomes show that this technique offers technical assistance and guidance for implementing a real cloud-based fusion system.