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.

中文翻译：

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医学运动康复中基于深度学习的高级时空提取模型，用于运动分析和数据处理

目前，范围广泛的未标记和最小样式数据显着降低了当前运动序列的重用能力。数据重用的一个重要方法是成功的分类和碎片分离，这在本研究中已经讨论过。本文重点讨论这些特定问题以及深度学习在设计和符号领域的巨大进步。有限玻尔兹曼模型 (LBM) 理论基于高级时空提取模型 (ASTEM)，该模型已用于分析人体骨骼的生理运动。研究结果主要有三个方面。(1) 为构建半组合模型，使用堆栈因子分解作为时空模型函数和 LBM 判别。(2), 优化算法用于利用权重分解思想创建三通道生成 LBM 模型，然后提取原始运动序列的基于时间和空间的抽象属性。(3)通过3D卷积LBM，利用人机交互的感知建立无监督的帧检测相关模型。运动数据的医学分析和提取的一个重要研究方向被适当地用于解释和从运动分析中获得有价值的信息和知识。实验结果表明，该技术为实现真正的基于云的融合系统提供了技术帮助和指导。(3)通过3D卷积LBM，利用人机交互的感知建立无监督的帧检测相关模型。运动数据的医学分析和提取的一个重要研究方向被适当地用于解释和从运动分析中获得有价值的信息和知识。实验结果表明，该技术为实现真正的基于云的融合系统提供了技术帮助和指导。(3)通过3D卷积LBM，利用人机交互的感知建立无监督的帧检测相关模型。运动数据的医学分析和提取的一个重要研究方向被适当地用于解释和从运动分析中获得有价值的信息和知识。实验结果表明，该技术为实现真正的基于云的融合系统提供了技术帮助和指导。