A Raspberry Pi-based digital stethoscope: Advancing real-time heart and lung sound diagnostics with IoT and machine learning integration

Nurul Azwaani Salehuddin Haqe
Faculty Computing Informatics, Multimedia University, Cyberjaya 63100, Malaysia
R Kanesaraj Ramasamy
Faculty Computing Informatics, Multimedia University, Cyberjaya 63100, Malaysia
Sivasutha Thanjappan
Hospital Canselor Tuanku Muhriz, Universiti Kebangsaan Malaysia, Kuala Lumpur 56000, Malaysia
Shamsuriani Md Jamal
Hospital Canselor Tuanku Muhriz, Universiti Kebangsaan Malaysia, Kuala Lumpur 56000, Malaysia
Faizal Amri Hamzah
Hospital Canselor Tuanku Muhriz, Universiti Kebangsaan Malaysia, Kuala Lumpur 56000, Malaysia
Venushini Rajendran
Faculty Computing Informatics, Multimedia University, Cyberjaya 63100, Malaysia

Article ID: 1707

Keywords: digital stethoscope, Raspberry Pi 3, heart sound monitoring, lung sound recording, real-time signal processing, medical diagnostics, IoT in healthcare, acoustic signal detection

Abstract

Digital transformation is reshaping auscultation by moving the stethoscope from a purely acoustic instrument to an intelligent, data-driven diagnostic platform. This review critically examines Raspberry Pi 3-based digital stethoscope technologies for heart and lung sound monitoring, with an emphasis on system architecture, sensor integration, signal acquisition, real-time processing, storage, playback, and connectivity. The paper synthesizes current designs and implementation strategies, highlighting how low-cost embedded platforms can improve the repeatability, accessibility, and objectivity of cardiopulmonary assessment compared with conventional acoustic stethoscopes. Particular attention is given to technical performance factors, including filtering, signal-to-noise ratio, latency, audio distortion, noise suppression, and time-frequency analysis, which are essential for detecting clinically significant acoustic features such as heart murmurs, crackles, and wheezes. The review also identifies key limitations affecting clinical translation, including power consumption, device portability, casing size, environmental noise, hardware constraints, and the need for robust validation in real-world settings. Future directions include optimized embedded processing, wireless and IoT-enabled tele-auscultation, cloud-assisted data management, and machine learning models for automated classification and decision support. Overall, Raspberry Pi 3-based digital stethoscopes represent a promising pathway toward affordable, scalable, and clinically meaningful cardiopulmonary monitoring, particularly for point-of-care, remote, and resource-limited healthcare environments. By integrating acoustic sensing, edge computing, and intelligent analytics, these systems can strengthen diagnostic workflows and support the next generation of connected digital healthcare.

Published

2026-07-22

How to Cite

Salehuddin Haqe, N. A., Ramasamy, R. K., Thanjappan, S., Md Jamal, S., Amri Hamzah, F., & Rajendran, V. (2026). A Raspberry Pi-based digital stethoscope: Advancing real-time heart and lung sound diagnostics with IoT and machine learning integration. Sound & Vibration, 60(4). https://doi.org/10.59400/sv1707

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Issue

Vol. 60 No. 4 (2026)

Section

Article

This work is licensed under a Creative Commons Attribution 4.0 International License.

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