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A Novel Audio-Perception-Based Algorithm for Physiological Monitoring

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Pubblicato in:Sensors vol. 25, no. 12 (2025), p. 3582-3607
Autore principale: Zhang Zixuan
Altri autori: Jin Wenxuan, Huang Dejiao, Sun, Zhongwei
Pubblicazione:
MDPI AG
Soggetti:
Apple Inc
Physiology
Physical fitness
Smartphones
Exercise intensity
Headphones
Neural networks
Signal processing
Sensors
Microphones
Wearable computers
Noise
Methods
Algorithms
Monitoring systems
Respiration
Sound
Heart rate
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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.
ISSN:1424-8220
DOI:10.3390/s25123582
Fonte:Health & Medical Collection