Smart Embedded System for Physiological Monitoring Using Machine Learning and Sensor Fusion
Keywords:
Smart embedded system, physiological monitoring, machine learning, sensor fusion, heart rate variability, wearable health devices, real-time classificationAbstract
The increasing demand for continuous, real-time health monitoring has driven advancements in intelligent embedded systems that integrate physiological sensing, machine learning, and sensor fusion. This study presents the design and evaluation of a smart embedded system capable of capturing and classifying multiple physiological signals—including heart rate, SpO₂, body temperature, respiration rate, and activity level—for early detection of health anomalies. A suite of machine learning models, including Logistic Regression, Random Forest, Support Vector Machine (SVM), Convolutional Neural Network (CNN), and K-Nearest Neighbors (KNN), were trained and tested using features extracted from the fused sensor data. CNN demonstrated the highest classification accuracy (93.5%), while Logistic Regression recorded the best AUC (0.80), highlighting different strengths across models. Feature importance analysis revealed heart rate variability (HRV), SpO₂ mean, and temperature trend as the most influential predictors. Additionally, correlation analysis emphasized the synergistic relationships between physiological parameters, reinforcing the value of sensor fusion in signal interpretation. The proposed system offers a portable, efficient, and scalable solution for real-time physiological monitoring, with potential applications in remote healthcare, fitness tracking, and wearable technologies
Downloads
Metrics
References
Ali, F., El-Sappagh, S., Islam, S. R., Kwak, D., Ali, A., Imran, M., & Kwak, K. S. (2020). A smart healthcare monitoring system for heart disease prediction based on ensemble deep learning and feature fusion. Information Fusion, 63, 208-222.
Anikwe, C. V., Nweke, H. F., Ikegwu, A. C., Egwuonwu, C. A., Onu, F. U., Alo, U. R., & Teh, Y. W. (2022). Mobile and wearable sensors for data-driven health monitoring system: State-of-the-art and future prospect. Expert Systems with Applications, 202, 117362.
Begum, S., Barua, S., & Ahmed, M. U. (2014). Physiological sensor signals classification for healthcare using sensor data fusion and case-based reasoning. Sensors, 14(7), 11770-11785.
Bianchi, V., Bassoli, M., Lombardo, G., Fornacciari, P., Mordonini, M., & De Munari, I. (2019). IoT wearable sensor and deep learning: An integrated approach for personalized human activity recognition in a smart home environment. IEEE Internet of Things Journal, 6(5), 8553-8562.
Diab, M. S., & Rodriguez-Villegas, E. (2022). Embedded machine learning using microcontrollers in wearable and ambulatory systems for health and care applications: A review. IEEE Access, 10, 98450-98474.
Ding, S., & Wang, X. (2020). Medical remote monitoring of multiple physiological parameters based on wireless embedded internet. IEEE Access, 8, 78279-78292.
Gedam, S., & Paul, S. (2023, September). Multi-sensor Data Fusion and Deep Machine Learning Models-Based Mental Stress Detection System. In International Conference on Advances in Data-driven Computing and Intelligent Systems (pp. 205-217). Singapore: Springer Nature Singapore.
Ha, N., Xu, K., Ren, G., Mitchell, A., & Ou, J. Z. (2020). Machine learning‐enabled smart sensor systems. Advanced Intelligent Systems, 2(9), 2000063.
Issa, M. E., Helmi, A. M., Al-Qaness, M. A., Dahou, A., Abd Elaziz, M., & Damaševičius, R. (2022, June). Human activity recognition based on embedded sensor data fusion for the internet of healthcare things. In Healthcare (Vol. 10, No. 6, p. 1084). MDPI.
Jacob Rodrigues, M., Postolache, O., & Cercas, F. (2020). Physiological and behavior monitoring systems for smart healthcare environments: A review. Sensors, 20(8), 2186.
John, A., Nundy, K. K., Cardiff, B., & John, D. (2021). Multimodal multiresolution data fusion using convolutional neural networks for IoT wearable sensing. IEEE Transactions on Biomedical Circuits and Systems, 15(6), 1161-1173.
Kanagamalliga, S., Nath, S. S., & Nivetha, T. (2024, December). Enhancing Heavy Vehicle Safety with Intelligent Driver Behaviour Monitoring via Multi-Sensor Fusion and Embedded Systems. In 2024 International Conference on System, Computation, Automation and Networking (ICSCAN) (pp. 1-5). IEEE.
Kanjo, E., Younis, E. M., & Ang, C. S. (2019). Deep learning analysis of mobile physiological, environmental and location sensor data for emotion detection. Information Fusion, 49, 46-56.
King, R. C., Villeneuve, E., White, R. J., Sherratt, R. S., Holderbaum, W., & Harwin, W. S. (2017). Application of data fusion techniques and technologies for wearable health monitoring. Medical engineering & physics, 42, 1-12.
Lee, J., Stanley, M., Spanias, A., & Tepedelenlioglu, C. (2016, December). Integrating machine learning in embedded sensor systems for Internet-of-Things applications. In 2016 IEEE international symposium on signal processing and information technology (ISSPIT) (pp. 290-294). IEEE.
Mendes Jr, J. J. A., Vieira, M. E. M., Pires, M. B., & Stevan Jr, S. L. (2016). Sensor fusion and smart sensor in sports and biomedical applications. Sensors, 16(10), 1569.
Nancy, A. A., Ravindran, D., Raj Vincent, P. D., Srinivasan, K., & Gutierrez Reina, D. (2022). Iot-cloud-based smart healthcare monitoring system for heart disease prediction via deep learning. Electronics, 11(15), 2292.
Phatak, A. A., Wieland, F. G., Vempala, K., Volkmar, F., & Memmert, D. (2021). Artificial intelligence based body sensor network framework—narrative review: proposing an end-to-end framework using wearable sensors, real-time location systems and artificial intelligence/machine learning algorithms for data collection, data mining and knowledge discovery in sports and healthcare. Sports Medicine-Open, 7(1), 79.
Rajan Jeyaraj, P., & Nadar, E. R. S. (2022). Smart-monitor: Patient monitoring system for IoT-based healthcare system using deep learning. IETE Journal of Research, 68(2), 1435-1442.
Rashid, N., Mortlock, T., & Al Faruque, M. A. (2023). Stress detection using context-aware sensor fusion from wearable devices. IEEE Internet of Things Journal, 10(16), 14114-14127.
Refaee, E. A., & Shamsudheen, S. (2022). A computing system that integrates deep learning and the internet of things for effective disease diagnosis in smart health care systems. The Journal of Supercomputing, 78(7), 9285-9306.
Vyas, N., Farringdon, J., Andre, D., & Stivoric, J. I. (2012). Machine learning and sensor fusion for estimating continuous energy expenditure. AI Magazine, 33(2), 55-55.
Wang, M., Wang, T., Luo, Y., He, K., Pan, L., Li, Z., ... & Chen, X. (2021). Fusing stretchable sensing technology with machine learning for human–machine interfaces. Advanced Functional Materials, 31(39), 2008807.
Zhang, S., Suresh, L., Yang, J., Zhang, X., & Tan, S. C. (2022). Augmenting sensor performance with machine learning towards smart wearable sensing electronic systems. Advanced Intelligent Systems, 4(4), 2100194.
Zhang, Y., Zheng, X. T., Zhang, X., Pan, J., & Thean, A. V. Y. (2024). Hybrid integration of wearable devices for physiological monitoring. Chemical Reviews, 124(18), 10386-10434.
..
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
You are free to:
- Share — copy and redistribute the material in any medium or format
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
Terms:
- Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
