A Novel Audio-Perception-Based Algorithm for Physiological Monitoring
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| 出版年: | Sensors vol. 25, no. 12 (2025), p. 3582-3607 |
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| 第一著者: | |
| その他の著者: | , , |
| 出版事項: |
MDPI AG
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| 主題: | |
| オンライン･アクセス: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 024 | 7 | |a 10.3390/s25123582 |2 doi | |
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| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231630 |2 nlm | ||
| 100 | 1 | |a Zhang Zixuan |u College of Science, Qingdao University of Technology, Qingdao 266520, China; 7711cgoswd@gmail.com (Z.Z.); huangdejiao28@163.com (D.H.) | |
| 245 | 1 | |a A Novel Audio-Perception-Based Algorithm for Physiological Monitoring | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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. | |
| 610 | 4 | |a Apple Inc | |
| 653 | |a Physiology | ||
| 653 | |a Physical fitness | ||
| 653 | |a Smartphones | ||
| 653 | |a Exercise intensity | ||
| 653 | |a Headphones | ||
| 653 | |a Neural networks | ||
| 653 | |a Signal processing | ||
| 653 | |a Sensors | ||
| 653 | |a Microphones | ||
| 653 | |a Wearable computers | ||
| 653 | |a Noise | ||
| 653 | |a Methods | ||
| 653 | |a Algorithms | ||
| 653 | |a Monitoring systems | ||
| 653 | |a Respiration | ||
| 653 | |a Sound | ||
| 653 | |a Heart rate | ||
| 700 | 1 | |a Jin Wenxuan |u College of Computer Science and Technology, Ocean University of China, Qingdao 266100, China; 22020036024@ouc.edu.cn | |
| 700 | 1 | |a Huang Dejiao |u College of Science, Qingdao University of Technology, Qingdao 266520, China; 7711cgoswd@gmail.com (Z.Z.); huangdejiao28@163.com (D.H.) | |
| 700 | 1 | |a Sun, Zhongwei |u College of Information and Control Engineering, Qingdao University of Technology, Qingdao 266520, China | |
| 773 | 0 | |t Sensors |g vol. 25, no. 12 (2025), p. 3582-3607 | |
| 786 | 0 | |d ProQuest |t Health & Medical Collection | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3223941736/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3223941736/fulltextwithgraphics/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3223941736/fulltextPDF/embedded/6A8EOT78XXH2IG52?source=fedsrch |