The respiratory rate is one of the crucial indicators for monitoring human physiological health. The purpose of this paper was to introduce a head-mounted respiratory monitoring solution based on electrical impedance sensing. Firstly, we constructed a finite element model to analyze the feasibility of using head impedance for respiratory sensing based on the physiological changes in the pharynx. After that, we developed a circuit module that could be integrated into a head-mounted respiratory monitoring device using a bioelectrical impedance sensor. Furthermore, we combined adaptive filtering and respiratory tracking algorithms to develop an app for a mobile phone. Finally, we conducted controlled experiments to verify the effectiveness of this electrical impedance sensing system for extracting respiratory rate. We found that the respiration rates measured by the head-mounted electrical impedance respiratory monitoring system were not significantly different from those of commercial respiratory monitoring devices by a paired t-test (p > 0.05). The results showed that the respiratory rates of all subjects were within the 95% confidence interval. Therefore, the head-mounted respiratory monitoring scheme proposed in this paper was able to accurately measure respiratory rate, indicating the feasibility of this solution. In addition, this respiratory monitoring scheme helps to achieve real-time continuous respiratory monitoring, which can provide new insights for personalized health monitoring.

In the prevention and rehabilitation stage of low back diseases, it is necessary to detect the state of muscles to avoid excessive fatigue and injury caused by continuous exertion of lumbar muscles. In this paper, an in vivo experimental platform for measuring low back muscle impedance was established in order to detect the different force states of muscle and study the corresponding relationship between muscle force state and muscle impedance. In the experiment, Biering-Sorensen (BS) test was used to make the low back muscle contract continuously, and different weights (5 kg, 2.5 kg, 0 kg) were used to distinguish the force state of muscle. The impedance analyzer was used to measure the muscle impedance parameters under different load in the subject's low back muscle for studying the electrical impedance characteristics of different low back muscle states. The results showed that the relative resistance $R^{\prime}$ of EIM was a downward trend with time and the 5 kg load had the fastest decline; The relative reactance $X_{c}^{\prime}$ of EIM was an upward trend with time, the 5 kg load also had the fastest rise. The slope $k$ of the fitting curve of $R^{\prime}$ and $X_{c}^{\prime}\text{were}-5.8\times 10^{-4}$ and $8.76 \times 10^{-4}$ respectively. Therefore, EIM can effectively detect the state of low back muscles.

To verify the feasibility of head-mounted impedance respiratory monitoring, we proposed a pharynx impedance measurement platform based on a four-electrode bioelectrical impedance method. The impedance characteristics of the human body at different breathing states within 50 kHz electrical signals were measured. The excitation signal is designed with LabVIEW, and the signal output unit and data acquisition module with recording function are designed by adopting DAQmx driver function and data acquisition(DAQ) card. The raw signal is obtained with the help of instrumentation amplifier, and the respiratory waveform is extracted from the raw signal using wavelet noise elimination and Gaussian filtering algorithms. Experiments were conducted through the above set-up and the feasibility of the four-electrode method for pharynx impedance measurement and the effectiveness of the platform for respiratory monitoring were demonstrated by experimental results. The results show that the approach can accurately monitor the respiratory waveform with 100% peak detection accuracy, meeting the need of actual respiratory monitoring, which provides a novel method for the wearable respiratory monitoring equipment.