# A Novel Audio-Perception-Based Algorithm for Physiological Monitoring

## TL;DR

This paper introduces a new system that uses sound from inside the ear to monitor heart rate and breathing in real time during exercise.

## Contribution

A novel audio-perception-based system combining NMF and BiLSTM for real-time physiological monitoring with high accuracy.

## Key findings

- The system achieves 91% average accuracy in heart rate and respiration monitoring.
- It maintains over 90% accuracy across different environments and body weights.
- The BiLSTM model improves breathing phase detection and respiratory rate calculation.

## Abstract

Exercise metrics are critical for assessing health, but real-time heart rate and respiration measurements remain challenging. We propose a physiological monitoring system that uses an in-ear microphone to extract heart rate and respiration from faint ear canal signals. An improved non-negative matrix factorization (NMF) algorithm combines with a short-time Fourier transform (STFT) to separate physiological components, while an inverse Fourier transform (IFT) reconstructs the signal. The earplug effect enhances the low-frequency components, thereby improving the signal quality and noise immunity. Heart rate is derived from short-term energy and zero-crossing rate, while a BiLSTM-based model can refine the breathing phases and calculate indicators such as respiratory rate. Experiments have shown that the average accuracy can reach 91% under various conditions, exceeding 90% in different environments and under different weights, thus ensuring the system’s robustness.