ESP32-Powered PPG Signal Acquisition: Open-Source Hardware and Software for Research and Education

To support the understanding of cardiovascular monitoring and physiological signal processing, we present a portable, open-source photoplethysmography (PPG) acquisition platform developed for educational and research applications. The system is built entirely with commercial off-the-shelf components and centers around an ESP32 microcontroller, which performs high-speed analog signal acquisition at 500 samples per second, alongside real-time control, and wireless communication. A cross-platform, Python-based graphical user interface enables real-time signal visualization, peak detection, and the computation of heart rate variability (HRV) metrics, including RMSSD and SDNN, during offline analysis. All hardware and software resources are openly available to enable replication and further development. This project emphasizes accessibility, transparency, and hands-on learning in biomedical signal acquisition. System functionality is validated offline through controlled data collection from human subjects, demonstrating results consistent with established HRV benchmarks.

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Publication Details

Journal
Hardware
Published
2025-11-12
DOI
https://doi.org/10.3390/hardware3040015
Citations
1
Primary Topic
Non-Invasive Vital Sign Monitoring
Type
article
Field-Weighted Citation Impact
0.67
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ESP32-Powered PPG Signal Acquisition: Open-Source Hardware and Software for Research and Education

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ESP32-Powered PPG Signal Acquisition: Open-Source Hardware and Software for Research and Education

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article en
1 citations

Abstract

To support the understanding of cardiovascular monitoring and physiological signal processing, we present a portable, open-source photoplethysmography (PPG) acquisition platform developed for educational and research applications. The system is built entirely with commercial off-the-shelf components and centers around an ESP32 microcontroller, which performs high-speed analog signal acquisition at 500 samples per second, alongside real-time control, and wireless communication. A cross-platform, Python-based graphical user interface enables real-time signal visualization, peak detection, and the computation of heart rate variability (HRV) metrics, including RMSSD and SDNN, during offline analysis. All hardware and software resources are openly available to enable replication and further development. This project emphasizes accessibility, transparency, and hands-on learning in biomedical signal acquisition. System functionality is validated offline through controlled data collection from human subjects, demonstrating results consistent with established HRV benchmarks.

HardwareVol. 3(4)
Universidad Autónoma de Baja California (MX), Mexicali Institute of Technology (MX), Tecnológico Nacional de México (MX)
Openalex Percentile: Top 25%
Non-Invasive Vital Sign Monitoring
0.67
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