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
Manual Monitoring Constraints – Inconsistent supervision, high cost
Lack of Immediate Alerts – Delayed response to health deterioration
Cost-Prohibitive Devices – Expensive and inaccessible equipment
Limited Remote Monitoring – Poor integration and high deployment cost in rural regions
Technical Architecture
Three-layer IoT model:
Perception Layer: Sensors + ESP32 for data collection
Network Layer: Wi-Fi + MQTT/HTTP for secure data transmission
Application Layer: Blynk app for visualization and alerts
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