Design and Implementation of Elderly Health Monitoring Glasses Based on Intelligent Vision
DOI:
https://doi.org/10.62051/ijcsit.v6n1.08Keywords:
Smart glasses, Health monitoring, Aging-friendly design, Senseless monitoring, Fall detectionAbstract
With the acceleration of the aging process in China, the elderly generally face the double challenges of eyesight decline and health care. Traditional health monitoring devices require extra wear and are complex to operate, making it difficult to meet daily needs. Therefore, this study innovatively combined the health monitoring function with daily glasses, and proposed a design scheme of elderly health monitoring glasses based on intelligent vision. Through the deep integration of the micro sensor and intelligent algorithm, based on preserving the function of vision correction, the non-inductive monitoring of vital signs such as heart rate and blood oxygen is realized, and the multi-modal pose analysis technology is used to identify the fall behavior in real time and automatically trigger the emergency help mechanism. The system adopts lightweight structure and voice interaction design to ensure that elderly users "ready to wear and use" and effectively solve the pain points of "cumbersome operation" of traditional equipment. The program uses daily wearable devices as the carrier to build an invisible health management system, provide new ideas for smart elderly care, and significantly improve the safety and independence of life for the elderly.
Downloads
References
[1] Xiang Xin, Wang Yi. Current situation, characteristics, causes and countermeasures of population aging in China [J]. Chinese Journal of Gerontology, 201, 41(18):4149-4152. (in Chinese)
[2] Li Nan, Xu Lei, Liu Liyan, et al. Application research of wearable devices for health monitoring of the elderly [J]. Knitting Industry, 2022, (06):60-63.
[3] Chen Sutong, Hou Chengyi, Li Yaogang, et al. The present situation and the tendency of the development of intelligent glasses [J]. Journal of glass enamel and glasses, 2021, 49 (07): 29-36. DOI: 10.13588 / j.carol carroll nki g.e. 2096-7608.2021.07.005.
[4] Zhao Shifan. Research on smart wearable design for the elderly based on multi-modal interaction [D]. Jiangnan university, 2024. DOI: 10.27169 /, dc nki. Gwqgu. 2024.000471.
[5] Li Na. Research on wearable health monitoring system based on human motion status recognition [D]. Beijing University of Technology, 2013.
[6] Wu Tian-hao. Elderly fall recognition based on 3-axis acceleration sensor and gyroscope [D]. Beijing University of Technology, 2013.
[7] Shi Weisong, Sun Hui, Cao Jie, et al. Edge Computing: A new computing model in the Internet of Everything era [J]. Journal of Computer Research and Development, 2017, 54(05):907-924.
[8] Qi Youjie, Wang Qi. Review [J]. Journal of multi-source data fusion algorithm and space electronic countermeasure, 2017 (6): 37-41. DOI: 10.16328 / j.h tdz8511.2017.06.009.
[9] Sun X J. Research on Human behavior recognition technology based on spatiotemporal feature fusion network [D]. Shandong university, 2022. DOI: 10.27272 /, dc nki. Gshdu. 2022.003163.
[10] LI Yi. Research on Adaptive Filtering and Filtering Algorithm [D]. Northwestern Polytechnical University, 2003.
[11] Wang Yuejun. Respiratory rate monitoring based on photoelectric volume pulse wave [D]. Beijing Institute of Technology, 2015.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 International Journal of Computer Science and Information Technology

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.







