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Psychological Stress Identification and Evaluation Method Based on Mobile Human-Computer Interaction Equipment.

作者信息

Zhang Na

机构信息

Department of Ideological and Political Education, Ulanqab Medical College, Ulanqab, 012000 Inner Mongolia, China.

出版信息

Appl Bionics Biomech. 2022 Apr 25;2022:6039789. doi: 10.1155/2022/6039789. eCollection 2022.

DOI:10.1155/2022/6039789
PMID:35510042
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9061069/
Abstract

Since the 1980s, the research of artificial neural networks in the field of artificial intelligence has become more and more common. It accepts nonlinear parallel processing, has strong learning and flexibility, and can be used for influencing factor analysis. The ideal power values and triggers are obtained in the Hopfield network model using genetic algorithm, which best avoids the drawbacks of the Hopfield network model instillation learning method. Through the BP of mobile human-computer interaction equipment, hereditary, genetic algorithms, and Hi-PLS regression method in the artificial neural network, the psychological pressure of college students is identified, evaluated, and predicted from three dimensions such as learning, life, and personal events. This makes it possible to understand the current physical and mental conditions of the students in a timely manner, guide to relieve anxiety and fear, and reach a safe psychological level. The three test results are less than 1%, which has high research significance and value.

摘要
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bcf2/9061069/8a1eb19d804f/ABB2022-6039789.011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bcf2/9061069/ecfe42743cae/ABB2022-6039789.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bcf2/9061069/65f74cf1fcdf/ABB2022-6039789.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bcf2/9061069/83b6f33972b7/ABB2022-6039789.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bcf2/9061069/b52e6180924e/ABB2022-6039789.004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bcf2/9061069/1604bd6dac30/ABB2022-6039789.005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bcf2/9061069/40c6a0fbb80e/ABB2022-6039789.006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bcf2/9061069/54cb57f33031/ABB2022-6039789.007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bcf2/9061069/5840608b8947/ABB2022-6039789.008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bcf2/9061069/adca23911d65/ABB2022-6039789.009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bcf2/9061069/c05a6a14e7d1/ABB2022-6039789.010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bcf2/9061069/8a1eb19d804f/ABB2022-6039789.011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bcf2/9061069/ecfe42743cae/ABB2022-6039789.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bcf2/9061069/65f74cf1fcdf/ABB2022-6039789.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bcf2/9061069/83b6f33972b7/ABB2022-6039789.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bcf2/9061069/b52e6180924e/ABB2022-6039789.004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bcf2/9061069/1604bd6dac30/ABB2022-6039789.005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bcf2/9061069/40c6a0fbb80e/ABB2022-6039789.006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bcf2/9061069/54cb57f33031/ABB2022-6039789.007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bcf2/9061069/5840608b8947/ABB2022-6039789.008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bcf2/9061069/adca23911d65/ABB2022-6039789.009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bcf2/9061069/c05a6a14e7d1/ABB2022-6039789.010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bcf2/9061069/8a1eb19d804f/ABB2022-6039789.011.jpg

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