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智能图像仿真与识别技术与老年人健康素养及生活质量的关系。

The Relationship between Intelligent Image Simulation and Recognition Technology and the Health Literacy and Quality of Life of the Elderly.

机构信息

Yanbian University, Yanji, China.

Shandong First Medical University and Shandong Academy of Medical Sciences, Taian, China.

出版信息

Contrast Media Mol Imaging. 2022 Feb 23;2022:9984873. doi: 10.1155/2022/9984873. eCollection 2022.

DOI:10.1155/2022/9984873
PMID:35280704
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8890847/
Abstract

In order to explore the relationship between intelligent image recognition technology and the mentality and quality of life of the elderly, this paper combines intelligent image simulation technology to identify the behavior of the elderly, protect the safety of the elderly, and provide timely feedback on the adverse conditions of the elderly. Moreover, this paper improves the traditional intelligent image recognition algorithm, verifies the research method of this paper through experimental research, and puts forward corresponding suggestions. Through investigation and research, we can see that the level of health literacy of elderly patients with chronic diseases is low. Therefore, in the future health education, we should strengthen health education for elderly patients with chronic diseases, use different mass media to propagate health knowledge, and promote the formation of healthy lifestyles and behaviors for elderly patients with chronic diseases. At the same time, the experiment also verified that the intelligent image recognition technology proposed in this paper has a positive effect in improving the mentality and quality of life of the elderly.

摘要

为了探究智能图像识别技术与老年人心理和生活质量之间的关系，本文结合智能图像模拟技术识别老年人的行为，保护老年人的安全，并对老年人的不良状况提供及时反馈。此外，本文改进了传统的智能图像识别算法，通过实验研究验证了本文的研究方法，并提出了相应的建议。通过调查研究可以看出，慢性病老年患者健康素养水平较低。因此，在今后的健康教育中，应加强对慢性病老年患者的健康教育，利用不同的大众媒体传播健康知识，促进慢性病老年患者形成健康的生活方式和行为。同时，实验还验证了本文提出的智能图像识别技术对改善老年人心理和生活质量具有积极作用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b969/8890847/d583adba033b/CMMI2022-9984873.005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b969/8890847/6ae0bb4bbb91/CMMI2022-9984873.001.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b969/8890847/d583adba033b/CMMI2022-9984873.005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b969/8890847/6ae0bb4bbb91/CMMI2022-9984873.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b969/8890847/1759eded6418/CMMI2022-9984873.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b969/8890847/ef9441ce1f52/CMMI2022-9984873.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b969/8890847/97eb6ca9584e/CMMI2022-9984873.004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b969/8890847/d583adba033b/CMMI2022-9984873.005.jpg

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