Journal of Applied Science and Engineering

Published by Tamkang University Press

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Study On The Impact Of AI-Based Elderly Health Monitoring System On Community Elderly Care Models In Highly Aging Countries

 2026

 Volume 35

Xiaodong Wang1, Cuicui Li2, Dianlong Wu3

1Department of Applied Japanese, Changchun Humanities and Sciences College, Changchun 130117, China

2Department of Japanese, Hebei North University, Zhangjiakou, Hebei 075000, China

3School of Foreign Languages, Changchun Normal University, Changchun, 130012, China

Received: April 09, 2026
Accepted: August 14, 2026
Publication Date: September 25, 2026

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Average Sentiment Across Themes in AI-Based Elderly Health Monitoring System

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

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The aging population poses significant challenges to elderly care, including caregiver burnout, high healthcare costs, and poor management of chronic conditions. AI-based health monitoring systems offer a promising solution by enabling real-time health tracking and predictive analytics. This study investigates the impact of AI-assisted monitoring on elderly care models, focusing on health outcomes, caregiver burden, satisfaction, and cost-effectiveness. Data were collected from 570 participants including elderly individuals, caregivers, and medical staff using questionnaires, interviews, and wearable devices. Quantitative results showed significant improvements: health outcomes (β=0.557, p<0.001), caregiver burden (R²=0.918, P<0.001), elderly satisfaction (β=0.570, P<0.001), and cost-effectiveness (β=0.540, P<0.001), with 32% fewer hospital visits, 3-4 hours daily caregiver time saved, and 60,000-75,000 savings per patient over five years. Quantitative analysis employed regression and predictive modeling, while qualitative data from 220 participants were analyzed thematically[cite: 6]. Findings indicate that AI-based systems improve health outcomes, reduce caregiver burden, and enhance elderly satisfaction, with significant long-term cost-saving potential. AI technologies are poised to transform community-based elderly care, improving quality of life and alleviating caregiver strain, though future work must address technological and ethical challenges.

Keywords: AI-based health monitoring, elderly care, caregiver burden, health outcomes, cost-effectiveness

  1. [1] A. Paulauskaite-Taraseviciene et al., (2023) “Geriatric Care Management System Powered by the IoT and Computer Vision Techniques” Healthcare 11(8): 1152. DOI: https://doi.org/10.3390/healthcare11081152.
  2. [2] A. A. Mir, A. S. Khalid, S. Musa, M. F. A. Fauzi, N. N. A. Razak, and T. B. Tang, (2025) “Machine Learning in Ambient Assisted Living for Enhanced Elderly Healthcare: A Systematic Literature Review” IEEE Access 13: 110508-110527. DOI: https://doi.org/10.1109/ACCESS.2025.3580961.
  3. [3] C. M. Rosca and A. Stancu, (2025) “Anomaly Detection in Elderly Health Monitoring via IoT for Timely Interventions” Applied Sciences 15(13): 7272. DOI: https://doi.org/10.3390/app15137272.
  4. [4] A. Pinto et al., (2025) “Artificial Intelligence in the Management of Infectious Diseases in Older Adults: Diagnostic, Prognostic, and Therapeutic Applications” Biomedicines 13(10): 2525. DOI: https://doi.org/10.3390/biomedicines13102525.
  5. [5] M. A. I. Mozumder et al., (2023) “Metaverse for Digital Anti-Aging Healthcare: Overview of AI, Blockchain, IoT Technologies, Challenges, and Future Directions” Applied Sciences 13(8): 5127. DOI: https://doi.org/10.3390/app13085127.
  6. [6] A. Ali et al., (2025) “An Innovative IoT and Edge Intelligence Framework for Monitoring Elderly People Using Anomaly Detection on Data from Non-Wearable Sensors” Sensors 25(6): 1735. DOI: https://doi.org/10.3390/s25061735.
  7. [7] K. Y. Lin, K. H. Chang, Y. W. Lin, and M. J. Wu, (2025) “Exploring Key Considerations for Artificial Intelligence Robots in Home Healthcare Using the Unified Theory of Acceptance and Use of Technology and the Fuzzy Analytical Hierarchy Process Method” Systems 13(1): 25. DOI: https://doi.org/10.3390/systems13010025.
  8. [8] J. Luo, K. Zhang, Q. Huang, S. Jiang, and Y. Pan, (2025) “From Acceptance to Dependence: Exploring Influences of Smart Healthcare on Continuous Use Intention of Mobile Health Services Among Older Adults with Chronic Illnesses in China” Behavioral Sciences 15(1): 19. DOI: https://doi.org/10.3390/bs15010019.
  9. [9] E. Mikołajewska, U. Rogalla-Ładniak, J. Masiak, E. Panas, and D. Mikołajewski, (2026) “Cyber-Physical Systems in Healthcare Based on Medical and Social Research Reflected in AI-Based Digital Twins of Patients” Applied Sciences 16(1): 318. DOI: https://doi.org/10.3390/app16010318.
  10. [10] S. H. Jeong and Y. G. Nam, (2024) “The Paradox of Digital Health: Why Middle-Aged Adults Outperform Young Adults in Health Management Utilization via Technology” Healthcare 12(22): 2261. DOI: https://doi.org/10.3390/healthcare12222261.
  11. [11] M. M. Baig, H. Gholam Hosseini, and M. Jämsä, (2017) “A Systematic Review of Wearable Patient Monitoring Systems Current Challenges and Opportunities for Clinical Adoption” Journal of Medical Systems 41(7): 233. DOI: https://doi.org/10.1007/s10916-017-0760-1.
  12. [12] A. Alsadoon, G. Al-Naymat, and O. D. Jerew, (2024) “An Architectural Framework of Elderly Healthcare Monitoring and Tracking through Wearable Sensor Technologies” Multimedia Tools and Applications 83(26): 67825-67870. DOI: https://doi.org/10.1007/s11042-024-18177-0.
  13. [13] R. Gayathri, S. Maheswari, S. K. Mathivanan, B. D. Shivahare, R. R. Chandan, and M. A. Shah, (2024) “A Comprehensive Health Assessment Approach Using Ensemble Deep Learning Model for Remote Patient Monitoring with IoT” Scientific Reports 14(1): 15661. DOI: https://doi.org/10.1038/s41598-024-66427-w.
  14. [14] M. Y. Ghafour, M. Hosseinzadeh, A. M. Ahmed, P. Asghari, H. Pourasghari, J. Koohpayehzadeh, A. Souri, and A. Rezapour, (2020) “An Elderly Health Monitoring System Based on Biological and Behavioral Indicators in Internet of Things” Journal of Ambient Intelligence and Humanized Computing 11(14): 5085-5095. DOI: https://doi.org/10.1007/s12652-020-02579-7.
  15. [15] M. N. Shabbir and D. T. Linh, (2024) “AI and Machine Learning Applications in Wearable Health Devices” Wearable Technology 5(1): 3123. DOI: https://doi.org/10.54517/wt3123.
  16. [16] W. Xue, A. Duan, Y. Zhao, H. Zuo, and H. F. Isleem, (2025) “AI-Enabled Multimodal Monitoring for Enhanced Safety and Functional Recovery in Elderly and Post-Stroke Care: A Mixed-Methods Study” Digital Health 11: 20552076251384834. DOI: https://doi.org/10.1177/20552076251384834.
  17. [17] M. Abadir, W. Dineen, D. Myers, S. Yu, and P. Phan, (2025) “Navigating the Future of Artificial Intelligence Technologies for Improving the Care of Older Adults” Innovation in Aging 9(Supplement_1): S24-S32. DOI: https://doi.org/10.1093/geroni/igaf092.