Real-Time Health Monitoring Using Power BI and Heart Rate Monitoring Devices

  • Authors

    • Supriya S. Thombre Department of Computer Technology, Yeshwantrao Chavan College of Engineering, Nagpur, India
    • Vaibhaw R. Doifode Department of Electrical Engineering, Yeshwantrao Chavan College of Engineering, Nagpur, India
    • Sakshi H. Kokardekar KPIT Technologies Limited, Hinjewadi, Pune, India
    • Abhiruchi A Patil Bhagat Department of CSE, Yeshwantrao Chavan College of Engineering, Nagpur, India
    • Sunil Prayagi Department of Mechanical Engineering, Yeshwantrao Chavan College of Engineering, Nagpur, India
    • Rajesh Khobragade Department of Electronics Engineering, Ramdeobaba University, Nagpur, India
    https://doi.org/10.14419/a2y09781

    Received date: June 19, 2025

    Accepted date: August 1, 2025

    Published date: August 12, 2025

  • Microsoft Copilot, Power BI, Real-time health monitoring, IoT-enabled wearable devices, AI-powered forecasting, Anomaly detection

  • Abstract

    With the growing demand for real-time health monitoring, integrating Microsoft Power BI with IoT-enabled heart rate monitoring devices offers a dynamic and intelligent approach to cardiovascular health analysis. This research explores the feasibility of streaming, visualizing, and predicting heart rate trends using Power BI, with a novel integration of Microsoft Copilot to provide personalized insights and proactive health recommendations. The study employs a real-time data pipeline where heart rate readings are collected from wearable devices, processed through Azure IoT and SQL databases, and dynamically visualized in Power BI dashboards. Through AI-powered forecasting and anomaly detection, the system identifies stress patterns, activity-based heart rate fluctuations, and potential health risks. The introduction of Microsoft Copilot enhances user engagement by offering natural language summaries, interactive Q&A, and AI-driven health suggestions based on real-time and historical data. Experimental results indicate an 85% accuracy in anomaly detection and a 40% improvement in user health awareness. The implemented system showcases the potential of AI-driven business intelligence tools in remote health monitoring, fitness tracking, and preventive healthcare sectors. Screenshots of the Power BI dashboards and Copilot interactions validate the findings, proving the feasibility of an automated, scalable, and intelligent health analytics system.

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  • How to Cite

    Thombre , S. S. ., Doifode , V. R. ., Kokardekar , S. H. ., Bhagat , A. A. P. . ., Prayagi , S. ., & Khobragade , R. . (2025). Real-Time Health Monitoring Using Power BI and Heart Rate Monitoring Devices. International Journal of Basic and Applied Sciences, 14(SI-2), 175-182. https://doi.org/10.14419/a2y09781