Abstract
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.
Highlights
With the development of information technology, information processing system became a common form of human-computer interaction, widely used in all walks of life, and an important tool for various fields to obtain and store information, so the current research on information processing system is increasing [1]
Mai et al proposed a pedestrian tracking and detection system, which combined convolutional neural network (CNN) and color information to locate the pedestrians in the video frame, and assigned the detected pedestrians to the corresponding path according to the similarity of color distribution [3]
According to the analysis of neural network, it is found that LSTM recurrent neural network can well solve the problem of long-term dependence. erefore, in response to the national fitness program of our country, this research will perform the design of human motion recognition information processing system based on LSTM recurrent neural network algorithm, hoping to build an intelligent sports ecosystem and promote the development of China’s sports industry
Summary
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. E 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. E 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.
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