With the comprehensive development of national fitness, men, women, young, and old in China have joined the ranks of fitness. In order to increase the understanding of human movement, many researches have designed a lot of software or hardware to realize the analysis of human movement state. However, the recognition efficiency of various systems or platforms is not high, and the reduction ability is poor, so the recognition information processing system based on LSTM recurrent neural network under deep learning is proposed to collect and recognize human motion data. The system realizes the collection, processing, recognition, storage, and display of human motion data by constructing a three-layer human motion recognition information processing system and introduces LSTM recurrent neural network to optimize the recognition efficiency of the system, simplify the recognition process, and reduce the data missing rate caused by dimension reduction. Finally, we use the known dataset to train the model and analyze the performance and application effect of the system through the actual motion state. The final results show that the performance of LSTM recurrent neural network is better than the traditional algorithm, the accuracy can reach 0.980, and the confusion matrix results show that the recognition of human motion by the system can reach 85 points to the greatest extent. The test shows that the system can recognize and process the human movement data well, which has great application significance for future physical education and daily physical exercise.

中文翻译：

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基于LSTM递归神经网络算法的人体动作识别信息处理系统的设计与实现

随着全民健身的全面发展，我国的男女老少都加入了健身的行列。为了增加对人体运动的理解，许多研究设计了很多软件或硬件来实现对人体运动状态的分析。但各种系统或平台的识别效率不高，还原能力较差，因此提出了深度学习下基于LSTM循环神经网络的识别信息处理系统来采集和识别人体运动数据。系统通过构建三层人体动作识别信息处理系统，实现人体动作数据的采集、处理、识别、存储和显示，并引入LSTM循环神经网络，优化系统的识别效率，简化识别流程，并减少降维导致的数据丢失率。最后，我们利用已知的数据集来训练模型，并通过实际的运动状态来分析系统的性能和应用效果。最终结果表明，LSTM循环神经网络的性能优于传统算法，准确率可以达到0.980，混淆矩阵结果表明系统对人体运动的识别最大程度可以达到85分。测试表明，该系统能够很好地识别和处理人体运动数据，对今后的体育教学和日常体育锻炼具有重要的应用意义。