A Raspberry Pi Local Server-Based Wearable Device System for Heart Health Monitoring

Main Article Content

Alamul iman
Juju Juhaeriyah

Abstract

Cardiovascular diseases, including arrhythmia and heart failure, remain major causes of death worldwide. Cloud-based ECG monitoring systems may face challenges related to dependence on internet connectivity and data transmission. This study aimed to develop a prototype of a local-network-based wearable ECG monitoring system using a Raspberry Pi as a local server without relying on public internet connectivity. A modified Research and Development (R&D) method was applied through requirements analysis, system design, prototype development, and system evaluation. The system integrated an AD8232 ECG sensor, an ESP8266 microcontroller, and a Raspberry Pi. ECG data were transmitted in real time through a local Wi-Fi network using HTTP over TCP/IP and stored in a MySQL database. BPM measurements were validated against a pulse oximeter using 10 measurements for each of six participants aged 14–87 years, resulting in 60 measurements in total. The system achieved an average accuracy of 97.75% and an average error of 2.25%. The prototype successfully acquired and recorded raw ECG and BPM data, classified heart rate into predefined normal, bradycardia, and tachycardia categories, and provided corresponding LED indicators. The system is intended for ECG and heart-rate monitoring and does not provide clinical diagnosis of cardiac conditions.

Article Details

How to Cite
1.
iman A, Juhaeriyah J. A Raspberry Pi Local Server-Based Wearable Device System for Heart Health Monitoring. telsinas [Internet]. 2026Sep.1 [cited 2026Sep.1];9(2):162-74. Available from: https://journal.undiknas.ac.id/index.php/teknik/article/view/7779
Section
Articles
Author Biographies

Alamul iman, Universitas Swadaya Gunung Jati, Indonesia

Electrical Engineering Study Program, Universitas Swadaya Gunung Jati, Indonesia

Juju Juhaeriyah, Universitas Swadaya Gunung Jati, Indonesia

Electrical Engineering Study Program, Universitas Swadaya Gunung Jati, Indonesia

References

G. Prieto-Avalos, N. A. Cruz-Ramos, G. Alor-Hernndez, J. L. Snchez-Cervantes, L. Rodrguez-Mazahua, and L. R. Guarneros-Nolasco, Wearable Devices for Physical Monitoring of Heart: A Review, May 01, 2022, MDPI. doi: 10.3390/bios12050292.

J. D. Huang, J. Wang, E. Ramsey, G. Leavey, T. J. A. Chico, and J. Condell, Applying Artificial Intelligence to Wearable Sensor Data to Diagnose and Predict Cardiovascular Disease: A Review, Sensors, vol. 22, no. 20, Oct. 2022, doi: 10.3390/s22208002.

M. I. Saputra, M. M. Robih, and J. Juhaeriyah, Design and Development of Heart Vest for Heart health Monitoring Based on Internet of Things (IoT), vol. 4, no. 10, 2025, doi: 10.58344/jws.v4i10.1525.

M. Moshawrab, M. Adda, A. Bouzouane, H. Ibrahim, and A. Raad, Smart Wearables for the Detection of Cardiovascular Diseases: A Systematic Literature Review, Jan. 01, 2023, MDPI. doi: 10.3390/s23020828.

L. Neri et al., Electrocardiogram Monitoring Wearable Devices and Artificial-Intelligence-Enabled Diagnostic Capabilities: A Review, May 01, 2023, MDPI. doi: 10.3390/s23104805.

X. Zhang, Y. Zhan, X. Wang, and J. Yang, A Chest Strap-Based System for Electrocardiogram Monitoring, Applied Sciences (Switzerland), vol. 15, no. 11, Jun. 2025, doi: 10.3390/app15115920.

A. B. Nigusse, D. A. Mengistie, B. Malengier, G. B. Tseghai, and L. Van Langenhove, Wearable smart textiles for longterm electrocardiography monitoringa review, Sensors, vol. 21, no. 12, Jun. 2021, doi: 10.3390/s21124174.

E. S. Dahiya, A. M. Kalra, A. Lowe, and G. Anand, Wearable Technology for Monitoring Electrocardiograms (ECGs) in Adults: A Scoping Review, Feb. 01, 2024, Multidisciplinary Digital Publishing Institute (MDPI). doi: 10.3390/s24041318.

F. Li, H. Tang, S. Shang, K. Mathiak, and F. Cong, Classification of heart sounds using convolutional neural network, Applied Sciences (Switzerland), vol. 10, no. 11, Jun. 2020, doi: 10.3390/app10113956.

A. Rancea, I. Anghel, and T. Cioara, Edge Computing in Healthcare: Innovations, Opportunities, and Challenges, Sep. 01, 2024, Multidisciplinary Digital Publishing Institute (MDPI). doi: 10.3390/fi16090329.

Y. Ansari, O. Mourad, K. Qaraqe, and E. Serpedin, Deep learning for ECG Arrhythmia detection and classification: an overview of progress for period 20172023, 2023, Frontiers Media SA. doi: 10.3389/fphys.2023.1246746.

Y. M. Obeidat and A. M. Alqudah, An Embedded System Based on Raspberry Pi for Effective Electrocardiogram Monitoring, Applied Sciences (Switzerland), vol. 13, no. 14, Jul. 2023, doi: 10.3390/app13148273.

A. B. Nigusse, B. Malengier, and L. Van Langenhove, Development and Evaluation of a Wearable ECG Monitoring System , Engineering Proceedings, vol. 52, no. 1, 2024, doi: 10.3390/engproc2023052009.

Z. Alimbayeva, C. Alimbayev, K. Ozhikenov, N. Bayanbay, and A. Ozhikenova, Wearable ECG Device and Machine Learning for Heart Monitoring, Sensors, vol. 24, no. 13, Jul. 2024, doi: 10.3390/s24134201.

K. Sharma and R. Eskicioglu, DEEP LEARNING-BASED ECG CLASSIFICATION ON RASPBERRY PI USING A TENSORFLOWLITE MODEL BASED ON PTB-XL DATASET, International Journal of Artificial Intelligence and Applications (IJAIA), vol. 13, no. 4, 2022, doi: 10.5121/ijaia.2022.1340455.

X. Huai, L. Jiang, C. Wang, P. Chen, and H. Li, Heart sound classification based on convolutional neural network with convolutional block attention module, Front. Physiol., vol. 16, 2025, doi: 10.3389/fphys.2025.1596150.

M. D. Nguyen et al., A Comparative Study of Wi-Fi Technologies in Wireless Sensor Networks, Computer Networks and Communications, pp. 7587, Feb. 2025, doi: 10.37256/cnc.3120256070.

S. Ghousia Begum, E. Priyadarshi, S. Pratap, S. Kulshrestha, and V. Singh, Automated Detection of Abnormalities in ECG signals using Deep Neural Network, Biomedical Engineering Advances, vol. 5, p. 100066, Jun. 2023, doi: 10.1016/j.bea.2022.100066.

R. Dhuny, A. A. I. Peer, N. A. Mohamudally, and N. Nissanke, Performance evaluation of a portable single-board computer as a 3-tiered LAMP stack under 32-bit and 64-bit Operating Systems, Array, vol. 15, Sep. 2022, doi: 10.1016/j.array.2022.100196.

M. Alghieth, DeepECG-Net: a hybrid transformer-based deep learning model for real-time ECG anomaly detection, Sci. Rep., vol. 15, no. 1, Dec. 2025, doi: 10.1038/s41598-025-07781-1.

M. Akter, N. Islam, A. Ahad, M. A. Chowdhury, F. F. Apurba, and R. Khan, An Embedded System for Real-Time Atrial Fibrillation Diagnosis Using a Multimodal Approach to ECG Data, Eng, vol. 5, no. 4, pp. 27282751, Dec. 2024, doi: 10.3390/eng5040143.

V. Randazzo, J. Ferretti, and E. Pasero, Anytime ecg monitoring through the use of a low-cost, user-friendly, wearable device, Sensors, vol. 21, no. 18, Sep. 2021, doi: 10.3390/s21186036.

Z. Wu and C. Guo, Deep learning and electrocardiography: systematic review of current techniques in cardiovascular disease diagnosis and management, Dec. 01, 2025, BioMed Central Ltd. doi: 10.1186/s12938-025-01349-w.

A. Mukhopadhyay, A. Remanidevi Devidas, V. P. Rangan, and M. V. Ramesh, A QoS-Aware IoT Edge Network for Mobile Telemedicine Enabling In-Transit Monitoring of Emergency Patients, Future Internet, vol. 16, no. 2, Feb. 2024, doi: 10.3390/fi16020052.

A. A. Fadhel and H. M. Hasan, Reducing Delay and Packets Loss in IoT-Cloud Based ECG Monitoring by Gaussian Modeling, International journal of online and biomedical engineering, vol. 19, no. 6, pp. 97113, 2023, doi: 10.3991/ijoe.v19i06.38581.