An Integrated Web-Based Wearable System for Real-Time Multi- Parameter Health Monitoring

device. The comparative SpO2 measurements are illustrated in Figure 14. IJEMT Figure 14: Comparison of HMS and SME SpO2 measurements 3.1.6 Error Analysis The resulting error indices are summarized in Table 3. Heart rate exhibited the highest measurement deviation, with a MAPE of 14.84% and an RMSE of 14.28 bpm. Body temperature recorded the lowest error, with a MAPE of 1.37% and an RMSE of 0.57 °C, while the SpO2 measurements produced a MAPE of 2.66% and an RMSE of 2.85 percentage points. Table 3. Error analysis of the health monitoring system Error Analysis Heart Rate Temperature SpO2 Level MAPE 14.84% 1.37% 2.66% RMSE 14.28 bpm 0.57 °C 2.85 % points 3.2 Discussion At the system level, the successful integration of physiological and environmental sensing, OLED visualization, and local web-based access demonstrates the practical value of the developed platform. The system continuously acquired sensor data and presented measurements simultaneously on the OLED and web interface, confirming the functional integration of sensing, processing, display, and wireless communication. This aligns with previous network-enabled health monitoring systems designed to extend real-time observation beyond conventional clinical settings (Mostafa et al., 2022). The local web interface further reduces dependence on external cloud infrastructure and dedicated mobile applications. For measurement performance, body temperature showed the closest agreement with the reference device, with mean values of 36.49 °C and 36.54 °C for the HMS and SME, respectively, together with a MAPE of 1.37% and an RMSE of 0.57 °C. SpO2 also showed comparatively good agreement, with a MAPE of 2.66% and an RMSE of 2.85 percentage points. However, the consistently higher HMS SpO2 readings suggest a possible positive measurement bias requiring further calibration. These findings are consistent with previous studies showing that wearable and IoT-enabled platforms can support real-time acquisition of physiological parameters (Wan et al., 2018). Heart-rate measurements exhibited greater point-to-point variation despite similar mean values of 88.89 bpm and 88.00 bpm for the HMS and SME, respectively. The MAPE of 14.84% and RMSE of 14.28 bpm indicate lower stability compared with body temperature and SpO2. This is consistent with reports that optical pulse-sensing systems may be influenced by signal quality and implementation conditions (Wu et al., 2020). Further calibration and signal-processing refinement are therefore required. Since the evaluation involved only three subjects under resting conditions, the findings should be IJEMT regarded as preliminary, and broader validation across more participants and activity conditions is required. 4. Conclusion This study developed and evaluated a web-based wearable health monitoring system for real- time measurement of heart rate, blood oxygen saturation (SpO2), body temperature, ambient temperature, and relative humidity. The system integrated physiological and environmental sensing, local OLED visualization, and browser-based wireless access within a compact battery-powered platform. Comparative evaluation against standard medical equipment showed close agreement for body temperature and SpO2, with MAPE values of 1.37% and 2.66% and RMSE values of 0.57 °C and 2.85 percentage points, respectively. Heart-rate measurement showed greater deviation, with a MAPE of 14.84% and an RMSE of 14.28 bpm, indicating the need for improved calibration and signal processing. Overall, the system demonstrates potential as a low-cost platform for continuous personal health monitoring without dependence on cloud infrastructure. Future work should focus on broader validation under diverse participants and activity conditions, improved heart-rate processing, long-term performance assessment, and intelligent analysis of collected health data. 5. Acknowledgement The authors acknowledge the technical assistance provided by the laboratory staff of the Department of Electrical and Electronics Engineering, Olusegun Agagu University of Science and Technology. 6. Declarations Ethics approval and consent to participate Ethical approval for this study was obtained from the Research Ethics Committee of Olusegun Agagu University of Science and Technology (OAUSTECH-REC), with approval number OAUSTECH/2026/001. Participation was voluntary, and informed consent was obtained from all participants. Competing interests The authors declare no competing interests. IJEMT References Aakesh, U., Rajasekaran, Y., & Sudhakar, T. (2023, January). Review on healthcare monitoring and tracking wristband for elderly people using ESP-32. In 2023 5th International Conference on Smart Systems and Inventive Technology (pp. 523–529). IEEE. https://doi.org/10.1109/ICSSIT55814.2023.10061134 Abdulhussain, S. H., Mahmmod, B. M., Alwhelat, A., Shehada, D., Shihab, Z. I., Mohammed, H. J., Abdulameer, T. H., Alsabah, M., Fadel, M. H., Ali, S. K., & Abbood, G. H. (2025). A comprehensive review of sensor technologies in IoT: Technical aspects, challenges, and future directions. Computers, 14(8), 1–54. https://doi.org/10.3390/computers14080342 Alshammari, H. H. (2023). The internet of things healthcare monitoring system based on MQTT protocol. Alexandria Engineering Journal, 69, 275–287. https://doi.org/10.1016/j.aej.2023.01.065 Babalola, A. D., Akingbade, K. F., Popoola, J. J., & Ubochi, B. C. (2025, April). Real-time physiological data monitoring with a wearable device and its application to cardiovascular health. In International Conference on Data Science and Artificial Intelligence (pp. 391–403). Springer Nature Singapore. https://doi.org/10.1007/978- 981-96-9083-1_30 El-Rashidy, N., El-Sappagh, S., Islam, S. R., M. El-Bakry, H., & Abdelrazek, S. (2021). Mobile health in remote patient monitoring for chronic diseases: Principles, trends, and challenges. Diagnostics, 11(4), 1–32. https://doi.org/10.3390/diagnostics11040607 Hamim, M., Paul, S., Hoque, S. I., Rahman, M. N., & Baqee, I. (2019). IoT-based remote health monitoring system for patients and elderly people. In Proceedings of the 2019 International Conference on Robotics, Electrical and Signal Processing Techniques (pp. 533–538). https://doi.org/10.1109/ICREST.2019.8644514 Hosain, M. N., Kwak, Y. S., Lee, J., Choi, H., Park, J., & Kim, J. (2024). IoT-enabled biosensors for real-time monitoring and early detection of chronic diseases. Physical Activity and Nutrition, 28(4), 60–69. https://doi.org/10.20463/pan.2024.0033 Iyaomolere, B. A., & Alamu, O. A. (2019). Design and performance analysis of an infra-red based heart rate monitoring system. International Journal of Engineering and Advanced Technology , 8(6), 534–540. https://doi.org/10.35940/ijeat.E7313.088619 Kale, H., Joshi, K., Potphode, I., Hiwarkar, A., Kawade, A., Ujjainkar, K., & Sahare, T. (2025). Design of microcontroller-based smart wristband for safety. In Technological Innovations and Applications in Industry 4.0 (pp. 239–244). CRC Press. Mamdiwar, S. D., R, A., Shakruwala, Z., Chadha, U., Srinivasan, K., & Chang, C. Y. (2021). Recent advances on IoT-assisted wearable sensor systems for healthcare monitoring. Biosensors, 11(10), 1–37. https://doi.org/10.3390/bios11100372 Mostafa, S. M. G., Zaki, M., Islam, M. M., Alam, M. S., & Ullah, M. A. (2022). Design and implementation of an IoT-based healthcare monitoring system. In Proceedings of the 2022 International Conference on Innovations in Science, Engineering, and Technology (pp. 362–366). https://doi.org/10.1109/ICISET54810.2022.9775850 Rehan, H. (2024). Enhancing early detection and management of chronic diseases with AI- driven predictive analytics on healthcare cloud platforms. Journal of AI-Assisted Scientific Discovery, 4(2), 1–38. Sadeghi, H., Ahmadi, M., & Rajabi, D. (2025). Design and prototyping of an AI-powered wearable device for continuous vital signs monitoring with intelligent alerting. Scientific Reports, 15(1), 1–19. https://doi.org/10.1038/s41598-025-28772-2 IJEMT Sathiya, A., Angel, D., Iswarya, M., Poonkodi, R., & Angelo, K. M. (2025, January). IoT enabled healthcare framework using edge AI and advanced wearable sensors for real time health monitoring. In 2025 International Conference on Multi-Agent Systems for Collaborative Intelligence (pp. 384–392). IEEE. https://doi.org/10.1109/ICMSCI62561.2025.10894492 Wan, J., Al-awlaqi, A. A. H., Li, M., O’Grady, M., Gu, X., Wang, J., & Cao, N. (2018). Wearable IoT enabled real-time health monitoring system. EURASIP Journal on Wireless Communications and Networking, 2018(1), 1–10. https://doi.org/10.1186/s13638-018-1308-x Wu, T., Wu, F., Qiu, C., Redouté, J. M., & Yuce, M. R. (2020). A rigid-flex wearable health monitoring sensor patch for IoT-connected healthcare applications. IEEE Internet of Things Journal, 7, 6932–6945. https://doi.org/10.1109/JIOT.2020.2977164