Real-Time Monitoring of Vagal Modulation through Sensor-Based Breathing Analysis

Authors: R. Yuvaraj, R. Sakthi, P. Ragul, D. Divakar, Dr. M. Prabhu

DOI Link: https://doi.org/10.22214/ijraset.2025.71230

Abstract

This project introduces a real-time sensor-based monitoring system designed to evaluate physiological responses during controlled breathing exercises. By integrating heart rate variability (HRV), respiration patterns, and oxygen saturation (SpO2) data, the system provides a comprehensive assessment of autonomic nervous system regulation, particularly vagal modulation.The primary objective is to explore the influence of slow and deep breathing techniques on enhancing parasympathetic activity, promoting relaxation, and supporting overall well-being. The system architecture is built around an ESP32 microcontroller that interfaces with multiple biosensors, including the MAX30102 for HRV and SpO2 monitoring and a dedicated respiration sensor. Real-time data is displayed via an LCD screen, with an integrated buzzer alert mechanism to notify users of abnormal physiological readings. Through a series of structured experiments, participants perform various breathing techniques, during which the system continuously captures and analyses physiological metrics. These experiments aim to quantify the impact of breathing exercises on autonomic balance, oxygen efficiency, and stress modulation. Result: voltage regulation in a linear supplycan result in low efficiency. Data analysis involves time-based comparisons of HRV parameters, respiration rates, and SpO2 levels to evaluate changes in sympathetic and parasympathetic activity. In addition to objective data, participants’ subjective experiences are recorded to validate system effectiveness. Result:voltage regulation in a linear supply can result in low efficiency.

Introduction

This project develops a non-invasive, real-time system to monitor vagal nerve activity via breathing patterns, aiming to improve mental and physical health by influencing the autonomic nervous system. Using sensors like respiratory belts, heart rate, SpO2, and ECG, the system captures key biosignals processed by a microcontroller and analyzed with machine learning to classify vagal modulation levels. Experimental results show that slow, deep breathing enhances vagal activity, confirmed by respiratory and heart rate markers. The system offers live feedback for stress and health monitoring, supporting clinical and wellness applications. It uses low-cost sensors under controlled conditions, with plans to expand capabilities through wearable tech and additional physiological data.

Conclusion

The proposed sensor-based system offers an efficient and real-time solution for monitoring physiological parameters associated with breathing exercises. By integrating heart rate variability (HRV), respiration rate, and SpO? levels through sensors connected to an ESP32 microcontroller, the system effectively captures data reflecting autonomic nervous system activity. This enables users and researchers to analyze how controlled breathing—especially slow and deep techniques—can influence vagal modulation and support better physiological balance.

References

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Copyright

Copyright © 2025 R. Yuvaraj, R. Sakthi, P. Ragul, D. Divakar, Dr. M. Prabhu. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Paper Id : IJRASET71230

Publish Date : 2025-05-19

ISSN : 2321-9653

Publisher Name : IJRASET

DOI Link : Click Here