Zhang Zixuan, Jin Wenxuan, Huang Dejiao, Sun Zhongwei
College of Science, Qingdao University of Technology, Qingdao 266520, China.
College of Computer Science and Technology, Ocean University of China, Qingdao 266100, China.
Sensors (Basel). 2025 Jun 6;25(12):3582. doi: 10.3390/s25123582.
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.
运动指标对于评估健康状况至关重要，但实时心率和呼吸测量仍然具有挑战性。我们提出了一种生理监测系统，该系统使用入耳式麦克风从微弱的耳道信号中提取心率和呼吸信息。一种改进的非负矩阵分解（NMF）算法与短时傅里叶变换（STFT）相结合以分离生理成分，同时逆傅里叶变换（IFT）重建信号。耳塞效应增强了低频成分，从而提高了信号质量和抗噪声能力。心率由短期能量和过零率得出，而基于双向长短期记忆网络（BiLSTM）的模型可以细化呼吸阶段并计算呼吸频率等指标。实验表明，该系统在各种条件下的平均准确率可达91%，在不同环境和不同重量下均超过90%，从而确保了系统的鲁棒性。
