Modern health technologies have shifted from reactive care to proactive prevention by utilizing personal sensors that continuously monitor vital signs like pulse, heat, and oxygen levels. By treating these data streams as evolving trajectories rather than isolated data points, memory-based algorithms can filter out environmental noise and identify subtle irregularities—such as heart rhythm shifts—long before physical symptoms emerge. These systems achieve high precision by learning an individual’s unique biological rhythms over time and integrating live data with historical medical records to form a comprehensive health profile. To ensure safety and trust, the process relies on strict privacy measures like decentralized training and full encryption, which protect personal information while allowing the AI to refine its accuracy. Ultimately, this intelligent extraction of meaning from daily activity bridges the gap between everyday routines and clinical care, empowering users with clearer insights and strengthening global health systems through earlier, more accurate interventions.

In conclusion, the proposed Public Health Recommendation System using wearable sensor data analytics provides a practical and forward-looking approach to healthcare by focusing on prevention rather than treatment. By continuously tracking vital signs and daily activity through wearable devices, the system can recognize patterns, detect potential health risks early, and offer personalized suggestions that help individuals make healthier choices. At the same time, it can support public health efforts by analysing broader trends from anonymized data, helping authorities plan better health strategies. The combination of wearable technology, intelligent analytics, and cloud based systems shows how modern tools can work together to improve both personal and community health. While issues such as privacy, security, and system reliability must be carefully addressed, further real-world testing and technological refinement could make this system an important part of future digital healthcare, benefiting individuals, healthcare providers, and society as a whole.

[1]E. Sezgin and S. Özkan Yıldırım, (2013) “A systematic literature review on health recommender systems,” in Proc. 4th IEEE Int. Conf. E-Health Bioeng. (EHB), Iasi, Romania, pp. 1–6. [2]R. De Croon, L. Van Houdt, N. N. Htun, G. Štiglic, V. Vanden Abeele and K. Verbert, (2021) “Health Recommender Systems: Systematic review,” J. Med. Internet Res., vol. 23, no. 6, Y. Cai, F. [3]Yu, M. Kumar, R. Gladney and J. Mostafa, (2022) “Health Recommender Systems Development, Usage, and Evaluation from 2010 to 2022: A Scoping Review,” Int. J. Environ. Res. Public Health, vol. 19, no. 22, Art. no. 15115. [4]Z. Wang, Y. Sun, J. Zhou et al. (2023) “Development and Evaluation of Health Recommender Systems: Systematic Scoping Review and Evidence Mapping,” J. Med. Internet Res., vol. 25, Art. no. e38184. [5]A. Ananthakrishnan et al. (2025) “The evaluation of health recommender systems: A scoping review,” Int. J. Med. Inform., vol. 195, Mar., Art. no. 105697. [6]A systematic review of healthcare recommender systems (2023): Open issues, challenges, and techniques, Expert Syst. Appl., vol. 213, Art. no. 118823. [7]J. Su, Y. Guan, Y. Li, W. Chen, H. Lv and Y. Yan (2020) “Do recommender systems function in the health domain: A system review,” arXiv, Jul. [8]J. Chen, T. K. Houston and J. M. Faro et al. (2021) “Evaluating the use of a recommender system for selecting optimal messages for smoking cessation: Patterns and effects of user-system engagement,” BMC Public Health, vol. 21 [9]M, Pfeifer D (2014) “Health recommender systems Concepts, requirements, technical basics and challenges”. International Journal of Environmental Research and Public Health. [10]Calero Valdez A, Ziefle M. (2018) The users’ perspective on the privacy–utility trade-offs in health recommender systems. arXiv preprint. [11]Abdullah Aljabr A, Kumar K (2024) “Recommender system for e-health”. [12]Bobadilla J, Ortega F, Hernando A, Gutiérrez A (2013) “Recommender systems survey & Knowledge-Based Systems”. [13]Kumar MS, Prabhu J (2018). “Healthcare recommender system based on smart health routes”. Proceedings of International Conference on Emerging Current Trends in Engineering and Management. [14]Dey A, Goswami D, Roy R, Ghosh S, Zhang YS, Chan JH (2022). “Mutual information assisted ensemble recommender system for identifying critical risk factors in healthcare prognosis”. arXiv preprint. 2022 [15]Hors-Fraile S, et al (2018). “Tailoring motivational health messages for smoking cessation using an mHealth recommender system”.

© 2025 International Journal of Engineering and Techniques (IJET).

Digital Object Identifier (DOI)

IJET assigns a unique DOI to every accepted article via Zenodo for persistent linking and citation. Your article’s DOI: https://doi.org/{{doi}}

Open DOI About Zenodo DOI

Close