IoT-Enabled Wearable Healthcare Device with Real Time ECG Monitoring and Cloud Analytics

International Journal of Electronics and Communication Engineering

© 2025 by SSRG - IJECE Journal

Volume 12 Issue 12

Year of Publication : 2025

Authors : V. Saravanan, Selvamani Indrajith, Nisha J C, D. Kalaiyarasi, S. Gopinath, K. Srilakshmi

10.14445/23488549/IJECE-V12I12P108

How to Cite?

V. Saravanan, Selvamani Indrajith, Nisha J C, D. Kalaiyarasi, S. Gopinath, K. Srilakshmi, "IoT-Enabled Wearable Healthcare Device with Real Time ECG Monitoring and Cloud Analytics," SSRG International Journal of Electronics and Communication Engineering, vol. 12,  no. 12, pp. 88-97, 2025. Crossref, https://doi.org/10.14445/23488549/IJECE-V12I12P108

Abstract:

The conventional ECG methods used to monitor vital signs are limited by their reliance on hospital equipment, restricted accessibility, and the late onset of diagnosis. To overcome these obstacles, an IoT-based wearable healthcare device is suggested to track the real-time ECG and analytics in the cloud. The product combines wearable ECG, SpO2, and heart rate variability sensors with an IoT microcontroller, backed by optimized communication protocols and cloud storage. A hybrid CNN-LM Deep Learning Model, based on arrhythmia classification, is employed, and mathematical models are utilized to compare energy efficiency and latency. In experimental testing, an accuracy of 98.6%, a sensitivity of 97.9%, a specificity of 98.2%, a precision of 98.3%, a F1-score of 98.1%, an average latency of 45 ms, a packet delivery ratio of 99.2%, and an energy consumption of 18.7 mW were achieved. These findings support the efficiency of the developed system in providing scalable, energy-efficient, and accurate real-time cardiac monitoring to support innovative healthcare applications.

Keywords:

Analog to digital converter, Bluetooth Low Energy, Heart Rate Variability, Least Mean Square, Message Queuing Telemetry Transport, Packet Delivery Ratio, Peripheral capillary oxygen saturation.

References:

[1] Yuguang Ye et al., “Hybrid CNN-BLSTM Architecture for Classification and Detection of Arrhythmia in ECG Signals,” Scientific Reports, vol. 15, pp. 1-17, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Yazeed Yasin Ghadi et al., “Integration of Wearable Technology and Artificial Intelligence in Digital Health for Remote Patient Care,” Journal of Cloud Computing: Advances, Systems and Applications, vol. 14, pp. 1-25, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Christiana Chamon, Abhijit Sarkar, and A. Lynn Abbott, “Noise-Driven AI Sensors: Secure Healthcare Monitoring with PUFs,” arXiv Preprint, pp. 1-5, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Sumei Fan et al., “On-device Computation of Single-lead ECG Parameters for Real-time Remote Cardiac Health Assessment: A Real world Validation Study,” arXiv Preprint, pp. 1-22, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Achinta Brata Roy Tonmoy, Selim Sultan, and Suparna Rani Bristy, “IoT-Based Portable ECG and Heart Rate Monitoring System with Minimal Cost,” 2025 1st International Conference on AIML-Applications for Engineering & Technology (ICAET), Pune, India, pp. 1-6, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[6] H. Anu, S. Rathnakara, and S. Mallikarjunaswamy, “Enhanced ECG Signal Classification with CNN-LSTM Networks using Aquila Optimization (AQO),” Engineering, Technology & Applied Science Research (ETASR), vol. 15, no. 3, pp. 23461-23466, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[7] V. Vaithianathan et al., “Smart Agriculture-Based Food Quality Analysis with Healthcare Security System Using Cloud Machine Learning Model,” Remote Sensing in Earth Systems Sciences, vol. 7, pp. 389-398, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Adarsha Bhattarai, and Dongming Peng, “An Intelligent Wearable ECG Sensor in Intra-medical Virtual Chain Network and Inter-Medical Virtual Chain Network,” SN Computer Science, vol. 5, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Ekta Singh Dahiya et al., “Wearable Technology for Monitoring Electrocardiograms (ECGs) in Adults: A Scoping Review,” Sensors, vol. 24, no. 4, pp. 1-16, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Shoaib Sattar et al., “Cardiac Arrhythmia Classification Using Advanced Deep Learning Techniques on Digitized ECG Datasets,” Sensors, vol. 24, no. 8, pp. 1-23, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Yanxing Suo et al., “Wearable Sensors for Vital Signs: A Review of 20 Years and Future Directions,” Integrated Circuits and Systems, vol. 1, no. 4, pp. 215-226, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Zhadyra Alimbayeva et al., “Wearable ECG Device and Machine Learning for Heart Monitoring,” Sensors, vol. 24, no. 13, pp. 1-20, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Wang Yunquan, Bi Xun, and Zhao Ying, “Design and Development of ECG Monitoring Cloud Platform Based on the Internet of Things Electrocardiograph,” Chinese Journal of Medical Instrumentation, vol. 48, no. 2, pp. 228-231, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Abdullah A. Al-Atawi et al., “Stress Monitoring Using Machine Learning, IoT and Wearable Sensors,” Sensors, vol. 23, no. 21, pp. 1-15, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Alhassan E. Alattar, and Saeed Mohsen, “A Survey on Smart Wearable Devices for Healthcare Applications,” Wireless Personal Communications, vol. 132, pp. 775-783, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[16] S. Karthiga, and A.M. Abirami, “Deep Learning Convolutional Neural Network for ECG Signal Classification Aggregated Using IoT,” Computer Systems Science and Engineering, vol. 42, no. 3, pp. 851-866, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Hiren Kumar Thakkar, and Prasan Kumar Sahoo, “IoT Based ECG-SCG Big Data Analysis Framework for Continuous Cardiac Health Monitoring in Cloud Data Centers,” Predictive Analytics in Cloud, Fog, and Edge Computing, pp. 177-184, 2022.[CrossRef] [Google Scholar] [Publisher Link]