IoT Based Patient Health Monitoring System Using ESP32 and Blynk App

Abstract

With the continuous evolution of Internet of Things (IoT) technologies, healthcare systems are experiencing a shift toward smart, real-time patient monitoring solutions. This paper introduces a cost-effective and portable health monitoring system based on the ESP32 microcontroller, integrated with the Blynk platform for remote access. The system is designed to track essential health metrics such as heart rate (monitored using a pulse sensor), body temperature (measured by LM35 or DS18B20 sensors), and blood oxygen saturation (detected via an SpO? sensor). All collected data is wirelessly sent to the cloud using Wi-Fi, allowing healthcare professionals to monitor patient health remotely through the Blynk app. The application interface offers real-time data visualization and sends immediate alerts when abnormal values are detected. Due to its scalability and ease of use, the system is suitable for both home care and clinical use. Testing confirms the reliability of sensor outputs and stable cloud connectivity. This approach not only reduces the need for manual health checks but also ensures continuous observation, improving care for elderly and chronically ill patients.

Introduction

Overview

The paper presents an IoT-enabled patient health monitoring system using the ESP32 microcontroller and Blynk platform. Designed for continuous, remote tracking of vital health signs—heart rate, body temperature, and SpO? levels—it addresses key limitations of traditional healthcare like manual monitoring, high costs, and lack of real-time alerts.

Key System Features

• Sensors Used:

  • Pulse Sensor – Heart rate (BPM)

  • Temperature Sensor (LM35/DS18B20)

  • SpO? Sensor (MAX30100/MAX30102)

• Core Technologies:

  • ESP32: Low-power, Wi-Fi-enabled microcontroller.

  • Blynk IoT Platform: Provides a real-time mobile dashboard and alert system.

• Functions:

  • Real-time health data monitoring via cloud

  • Instant push notifications when parameters exceed thresholds

  • Accessible from remote locations via a mobile app

Target Users

? Elderly individuals
? Chronic disease patients
? Post-surgery patients
? People in remote/under-resourced areas

Problems Addressed

1. Manual Monitoring Constraints – Inconsistent supervision, high cost

2. Lack of Immediate Alerts – Delayed response to health deterioration

3. Cost-Prohibitive Devices – Expensive and inaccessible equipment

4. Limited Remote Monitoring – Poor integration and high deployment cost in rural regions

Technical Architecture

• Three-layer IoT model:

  1. Perception Layer: Sensors + ESP32 for data collection

  2. Network Layer: Wi-Fi + MQTT/HTTP for secure data transmission

  3. Application Layer: Blynk app for visualization and alerts

• Hardware:

  • ESP32 (controller)

  • Sensors (Pulse, Temp, SpO?)

  • Power source (USB or battery)

• Software:

  • Arduino IDE for development

  • Libraries: Blynk, MAX30100, DallasTemperature, WiFi

  • Blynk app widgets: Gauge, Graph, Notifications

Testing & Performance

• Test Setup: 5 participants, 48-hour continuous monitoring

• Accuracy:

  • HR: ±3 BPM

  • SpO?: ~1.8% deviation

  • Temp: ±0.3°C (DS18B20 more stable than LM35)

• Responsiveness:

  • Data latency: 0.8–1.2 seconds

  • Alerts triggered in under 5 seconds

• Power:

  • Battery life: ~9 hours on 2000mAh

  • Deep sleep mode extends usage

• False Alerts: Few, mostly due to sensor misplacement

Conclusion

The proposed IoT-based Patient Health Monitoring System, built using the ESP32 microcontroller and the Blynk IoT platform, effectively showcases how affordable, portable technology can revolutionize remote healthcare delivery. By integrating vital health sensors—including pulse, SpO?, and temperature sensors—this system enables continuous, real-time monitoring of patients, with data seamlessly accessible via mobile devices.

References

[1] IoT in Healthcare: Abstract: Dang, L. M., Piran, M. J., Han, D., Min, K., & Moon, H. (2020).A survey on Internet of Things and cloud computing for healthcare. Electronics, 9(10), 1719. https://ieeexplore.ieee.org/document/9279211 [2] ESP32Microcontroller: Espressif Systems. (2022). ESP32 technical reference manual. Retrieved from https://www.espressif.com/sites/default/files/documentation/esp32_technical_reference_manual_en.pdf. [3] Blynk IoT Platform: Abstract: Blynk. (2023). Official Blynk documentation. Retrieved from https://docs.blynk.io [4] Health Sensor Accuracy: Ram, M. R., Madhav, K. V., Krishna, E. H., & Reddy, E. V. (2019). A novel approach for motion artifact reduction in PPG signals. IEEE. Transactions on Biomedical Engineering, 66(5) ,1234–1244. https://doi.org/10.1109/TBME.2018.2872505 [5] Comparative Studies: Patel, S., Park, H., Bonato, P., Chan, L., & Rodgers, M. (2021). Low-cost IoT-based patient monitoring: A review. Journal of Medical Systems, 45(3), 45. https://doi.org/10.1007/s10916-021-01720-z [6] Data Security in IoT Healthcare: Kumar, P., Tripathi, R., & Singh, R. (2022). Secure data transmission in IoT-based healthmonitoring. IEEE Internet of Things Journal, 9(4)2567–2575. https://doi.org/10.1109/JIOT.2021.3096782 [7] Power Optimization Techniques: Abstract: Singh, R., & Lee, H. (2023). Energy-efficient IoT architectures for remote monitoring. Sustainable Computing: Informatics and Systems, 38, 100876.https://doi.org/10.1016/j.suscom.2023.100876

Copyright

Copyright © 2025 Himanshi Manjhi. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.