Wearable Device-Based Health Monitoring System with AI-Driven Predictive Analytics for Real-Time and Preventive Healthcare
Keywords:
Wearable Health Monitoring, AI in Healthcare, Predictive Analytics, Internet of Things (IoT), Real-Time Health MonitoringAbstract
This study proposes a wearable device-based health monitoring system integrated with artificial intelligence (AI) predictive analytics to enable continuous, real-time, and proactive healthcare management. The system utilizes wearable sensors to collect physiological and activity data, including heart rate, blood oxygen saturation (SpO?), body temperature, and movement patterns. These data are transmitted through IoT-based communication to a cloud platform, where they undergo preprocessing, feature extraction, and analysis using machine learning and deep learning models. The proposed approach incorporates algorithms such as Random Forest, Support Vector Machine (SVM), and Long Short-Term Memory (LSTM) networks to perform disease prediction, anomaly detection, and risk scoring. Experimental results demonstrate that the models achieve high performance across multiple evaluation metrics, including accuracy, precision, recall, F1-score, and ROC-AUC, with LSTM showing superior performance in handling time-series data. The system effectively supports real-time monitoring, enabling early detection of potential health risks and providing timely alerts to users and healthcare providers. Compared to existing systems, the proposed framework offers enhanced predictive capabilities, improved responsiveness, and better integration of wearable technology with AI-driven analytics. The findings highlight the significant potential of combining wearable devices and AI in advancing healthcare innovation, particularly in remote patient monitoring, telemedicine, and preventive medicine. Despite challenges related to data privacy, device limitations, and computational requirements, this research demonstrates a scalable and intelligent solution for modern healthcare systems, emphasizing the critical role of predictive analytics in the future of preventive healthcare.
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References
Alanazi, R. (2022). Identification and prediction of chronic diseases using machine learning approach. Journal of Healthcare Engineering, 2022(1), 2826127.
Albahri, O. S., Zaidan, A. A., Zaidan, B. B., Hashim, M., Albahri, A. S., & Alsalem, M. A. (2018). Real-time remote health-monitoring Systems in a Medical Centre: A review of the provision of healthcare services-based body sensor information, open challenges and methodological aspects. Journal of Medical Systems, 42(9), 164.
Bai, Y., Gu, B., & Tang, C. (2025). Enhancing real-time patient monitoring in intensive care units with deep learning and the internet of things. Big Data.
Boateng, E. Y., Otoo, J., & Abaye, D. A. (2020). Basic tenets of classification algorithms K-nearest-neighbor, support vector machine, random forest and neural network: A review. Journal of Data Analysis and Information Processing, 8(4), 341–357.
Bubulac, L., Georgescu, T., Zivari, M., Popescu-Spineni, D.-M., Albu, C.-C., Bobu, A., Nemeth, S. T., Bogdan-Andreescu, C.-F., Gurghean, A., & Alecu, A. A. (2025). An Integrative Review of Computational Methods Applied to Biomarkers, Psychological Metrics, and Behavioral Signals for Early Cancer Risk Detection. Bioengineering, 12(11), 1259.
Cho, S., Ensari, I., Weng, C., Kahn, M. G., & Natarajan, K. (2021). Factors affecting the quality of person-generated wearable device data and associated challenges: rapid systematic review. JMIR MHealth and UHealth, 9(3), e20738.
De Pretis, F., Van Gils, M., Varheenmaa, M., Tiihonen, M., & Forsberg, M. M. (2025). Innovative approaches to collecting, aggregating, and analyzing adverse drug events in smart hospitals. International Journal of Risk & Safety in Medicine, 36(4), 289–301.
Dias, D., & Paulo Silva Cunha, J. (2018). Wearable health devices—vital sign monitoring, systems and technologies. Sensors, 18(8), 2414.
Elsts, A. (2013). A Framework to Facilitate Wireless Sensor Network Application Development.
Ferrara, E. (2024). Large language models for wearable sensor-based human activity recognition, health monitoring, and behavioral modeling: a survey of early trends, datasets, and challenges. Sensors, 24(15), 5045.
Franklin, A., Gantela, S., Shifarraw, S., Johnson, T. R., Robinson, D. J., King, B. R., Mehta, A. M., Maddow, C. L., Hoot, N. R., & Nguyen, V. (2017). Dashboard visualizations: Supporting real-time throughput decision-making. Journal of Biomedical Informatics, 71, 211–221.
Gadiyar, R., Zhang, T., & Sankaranarayanan, A. (2018). Artificial intelligence software and hardware platforms. In Artificial intelligence for autonomous networks (pp. 165–188). Chapman and Hall/CRC.
Gonzalez, I., Calderón, A. J., & Folgado, F. J. (2022). IoT real time system for monitoring lithium-ion battery long-term operation in microgrids. Journal of Energy Storage, 51, 104596.
Kang, M., & Tian, J. (2018). Machine learning: Data pre?processing. Prognostics and Health Management of Electronics: Fundamentals, Machine Learning, and the Internet of Things, 111–130.
Kibria, M. G., Nguyen, K., Villardi, G. P., Zhao, O., Ishizu, K., & Kojima, F. (2018). Big data analytics, machine learning, and artificial intelligence in next-generation wireless networks. IEEE Access, 6, 32328–32338.
Malasinghe, L. P., Ramzan, N., & Dahal, K. (2019). Remote patient monitoring: a comprehensive study. Journal of Ambient Intelligence and Humanized Computing, 10(1), 57–76.
Nayak, J., Naik, B., & Behera, H. S. (2015). A comprehensive survey on support vector machine in data mining tasks: applications & challenges. International Journal of Database Theory and Application, 8(1), 169–186.
Roomkham, S., Lovell, D., Cheung, J., & Perrin, D. (2018). Promises and challenges in the use of consumer-grade devices for sleep monitoring. IEEE Reviews in Biomedical Engineering, 11, 53–67.
Samadbeik, M., Engstrom, T., Lobo, E. H., Kostner, K., Austin, J. A., Pole, J. D., & Sullivan, C. (2024). Healthcare dashboard technologies and data visualization for lipid management: A scoping review. BMC Medical Informatics and Decision Making, 24(1), 352.
Sarkar, V., Harrod, W., & Snavely, A. E. (2009). Software challenges in extreme scale systems. Journal of Physics: Conference Series, 180(1), 12045.
?tefan, A.-M., Rusu, N.-R., Ovreiu, E., & Ciuc, M. (2024). Empowering healthcare: a comprehensive guide to implementing a robust medical information system—components, benefits, objectives, evaluation criteria, and seamless deployment strategies. Applied System Innovation, 7(3), 51.
Teh, H. Y., Kempa-Liehr, A. W., & Wang, K. I.-K. (2020). Sensor data quality: A systematic review. Journal of Big Data, 7(1), 11.
Vijayan, V., Connolly, J. P., Condell, J., McKelvey, N., & Gardiner, P. (2021). Review of wearable devices and data collection considerations for connected health. Sensors, 21(16), 5589.
Wang, Y., An, X., & Xu, W. (2023). Intelligent medical IoT health monitoring system based on VR and wearable devices. Journal of Intelligent Systems, 32(1), 20220291.
Wu, W., May, R. J., Maier, H. R., & Dandy, G. C. (2013). A benchmarking approach for comparing data splitting methods for modeling water resources parameters using artificial neural networks. Water Resources Research, 49(11), 7598–7614.
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