Machine Learning Human Behavior Detection Mechanism Based on Python Architecture

Jinnuo Zhu, S. B. Goyal (), Chaman Verma (), Maria Simona Raboaca and Traian Candin Mihaltan ()
Additional contact information
Jinnuo Zhu: Nanchang Institute of Science & Technology, Nanchang 330108, China
S. B. Goyal: Faculty of Information Technology, City University, Petaling Jaya 46100, Malaysia
Chaman Verma: Department of Media and Educational Informatics, Faculty of Informatics, Eötvös Loránd University, 1053 Budapest, Hungary
Maria Simona Raboaca: ICSI Energy Department, National Research and Development Institute for Cryogenics and Isotopic Technologies, 240050 Ramnicu Valcea, Romania
Traian Candin Mihaltan: Faculty of Building Services, Technical University of Cluj-Napoca, 40033 Cluj-Napoca, Romania

Mathematics, 2022, vol. 10, issue 17, 1-31

Abstract: Human behavior is stimulated by the outside world, and the emotional response caused by it is a subjective response expressed by the body. Humans generally behave in common ways, such as lying, sitting, standing, walking, and running. In real life of human beings, there are more and more dangerous behaviors in human beings due to negative emotions in family and work. With the transformation of the information age, human beings can use Industry 4.0 smart devices to realize intelligent behavior monitoring, remote operation, and other means to effectively understand and identify human behavior characteristics. According to the literature survey, researchers at this stage analyze the characteristics of human behavior and cannot achieve the classification learning algorithm of single characteristics and composite characteristics in the process of identifying and judging human behavior. For example, the characteristic analysis of changes in the sitting and sitting process cannot be for classification and identification, and the overall detection rate also needs to be improved. In order to solve this situation, this paper develops an improved machine learning method to identify single and compound features. In this paper, the HATP algorithm is first used for sample collection and learning, which is divided into 12 categories by single and composite features; secondly, the CNN convolutional neural network algorithm dimension, recurrent neural network RNN algorithm, long- and short-term extreme value network LSTM algorithm, and gate control is used. The ring unit GRU algorithm uses the existing algorithm to design the model graph and the existing algorithm for the whole process; thirdly, the machine learning algorithm and the main control algorithm using the proposed fusion feature are used for HATP and human beings under the action of wearable sensors. The output features of each stage of behavior are fused; finally, by using SPSS data analysis and re-optimization of the fusion feature algorithm, the detection mechanism achieves an overall target sample recognition rate of about 83.6%. Finally, the research on the algorithm mechanism of machine learning for human behavior feature classification under the new algorithm is realized.

Keywords: human behavior; machine learning; convolutional neural networks; human–computer interaction; wearable sensors (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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