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Human motion recognition information processing system based on LSTM Recurrent Neural Network Algorithm

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Abstract

Human motion recognition has attracted the attention of many global media platforms in video feature analysis, and has evolved into a branch of AI technology. In order to understand the human motion recognition process, improve the recognition efficiency of human motion, and provide reference for future researchers. This paper studies the human motion recognition system based on LSTM neural system, mainly referring to the action recognition theory of Facebook, and expanding the types and scope of the recognized images. This system uses the bottom-up idea to recognize the human body in the picture or image set. Firstly, human joints are identified, and then multiple joints are combined as a node in the system. Due to the use of LSTM, this method can recognize the actions of different regions without human recognition, and then combine them. In theory, this method does not rely on the recognition of human body, so the recognition speed and accuracy will be improved. The research shows that the algorithm based on LSTM recurrent neural network achieves a high recognition rate. In the experiments of human motion information database, four groups of recognition rates are higher than the three methods, and the average recognition rate is 92.64%, higher than the other three methods. In the three groups of cross validation experiments, the average recognition rate is 86.97%, higher than the four methods. The recognition rate of the utkinect action 3D database is 95.96%, indicating that the deep learning algorithm has the ability to extract deep features of human action features that are useful for action recognition.

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Data availability statements

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

This work was supported by 1. Hunan Province Education Science “the 13th Five-Year Plan” 2019 general project project (Project Number: XJK19BTW004), 2. Hunan Social Science achievements Review Committee 2020 project (Project Number: XSP20YBC119), 3. Ministry of Education Humanities and Social Sciences Foundation Project, (Project Number: 71802119), 4. Natural Science Foundation Fund Youth Project (Project Number: 18YJC620002), 5. Subject of Education Department of Hunan Province (Project Number: 19C0740)

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Authors and Affiliations

  1. School of Education and Science, Hunan Normal University, Changsha, 410081, Hunan, China

    Xieji li

  2. School of PE, Hunan University of Science and Technology, Xiangtan, 411201, Hunan, China

    Xieji li

  3. School of Business Administration, Shanxi University of Finance and Economics, Taiyuan, 030006, Shanxi, China

    Xia Cao

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  1. Xieji li
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  2. Xia Cao
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Correspondence to Xia Cao.

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li, X., Cao, X. Human motion recognition information processing system based on LSTM Recurrent Neural Network Algorithm. J Ambient Intell Human Comput 14, 8509–8521 (2023). https://doi.org/10.1007/s12652-021-03614-x

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