CN-120436613-B - Respiratory frequency detection method based on sound wave FMCW

CN120436613BCN 120436613 BCN120436613 BCN 120436613BCN-120436613-B

Abstract

The invention discloses a respiratory rate detection method based on sound wave FMCW, belonging to the technical field of sound wave signal processing; the method comprises the steps of generating a section of sound wave signals with continuously-changing frequency by using a mobile phone loudspeaker to form a continuous sound wave detection sequence, synchronously receiving the reflected sound wave signals by using a mobile phone microphone, filtering the received reflected sound wave signals, performing autocorrelation operation on a transmitting signal and the reflected signal to generate a distance change curve of the chest along with respiratory motion, performing smoothing treatment on the distance change curve, obtaining a main frequency through fast Fourier change, and calculating respiratory frequency. According to the invention, by utilizing the advantages of the FMCW technology, the interference of environmental noise is effectively inhibited, the autocorrelation coefficient of the reflected signal is analyzed, the chest motion information related to respiration is accurately extracted, the accurate detection of the respiratory frequency of a human body is realized, and the characteristics of easier peak finding and easier tracking are utilized by utilizing the autocorrelation function, so that the convenience of a user is greatly improved.

Inventors

Hu Yaoxuan
Yuan Chenbo
GU JIALIN
HAN CHONG
Yang Angcheng
LIANG XIUQI
GUO JIAN
SUN LIJUAN

Assignees

南京邮电大学

Dates

Publication Date: 20260512
Application Date: 20250425

Claims (5)

1. A respiratory rate detection method based on sound wave FMCW is characterized by comprising the following steps: Step S1, generating a section of sound wave signal with continuously-changing frequency by using a mobile phone loudspeaker, wherein the initial frequency is low frequency, gradually rises to high frequency, and circularly transmitting the sound wave signal to form a continuous sound wave detection sequence; S2, synchronously receiving the reflected sound wave signals by using a microphone of the mobile phone, and performing time alignment with the original transmitted signals; S3, filtering the received reflected sound wave signals; Step S4, performing autocorrelation operation on the emission signal and the reflection signal to generate a distance change curve of the thoracic cavity along with respiratory motion, wherein the distance change curve is specifically expressed as follows: Step S4-1, performing autocorrelation operation on the reflected signal and the transmitted signal to obtain an autocorrelation function : ; Wherein, the For the initial frequency of the mobile phone transmission, The echo signals are screened out by a Butterworth filter band-pass filter; Refers to the time delay variable due to propagation time; representing the integration time window length; step S4-2, finding the time difference between the reflected signal and the transmitted signal, and generating a distance change curve of the chest along with respiratory motion, wherein the distance change curve specifically comprises the following steps: In the formula From the time delay, a clearer contour image is obtained by using Hilbert transformation, and the transformed formula is as follows: ; Wherein, the Is the analytic signal after Hilbert transformation; Is a time delay variable; The waveform chart after Hilbert transformation clearly shows a first peak value and a second peak value, and the time of the horizontal axis of the coordinate axis is calculated by the following formula: ; Wherein, the Represents a distance from the object to be inspected, The result of subtracting the distance corresponding to the highest point of the peak value from the distance corresponding to the second highest point of the peak value is the chest distance; S5, performing smoothing treatment on the distance change curve to remove bursty interference; And S6, processing the thoracic movement waveform, obtaining a main frequency through fast Fourier change, and calculating the respiratory frequency.
2. The method for detecting respiratory rate based on FMCW of claim 1, wherein the initial frequency in the step S1 is low frequency and gradually increases to high frequency, specifically: The low frequency is not more than 17kHz, the high frequency is not less than 23kHz, and the calculation formula is as follows: ; Wherein, the Is the amplitude of the acoustic wave signal, Is the initial frequency of the signal and, Is the final frequency of the signal and, Representing the time period of the linear sweep, t represents the time elapsed after emission of the transmitted signal.
3. The method for detecting respiratory rate based on acoustic wave FMCW according to claim 1, wherein the filtering process is performed on the received reflected acoustic wave signal in the step S3, specifically: the received signal with high-frequency noise removed is obtained by a low-pass filter, and echo data is obtained by a band-pass filter : ; Wherein, the Refers to the time delay variable due to propagation time; is the amplitude of the acoustic wave signal, Is the initial frequency of the signal and, Is the final frequency of the signal and, Representing the time period of the linear sweep, Indicating the time that has elapsed since the transmission of the signal.
4. The method for detecting respiratory rate based on acoustic wave FMCW according to claim 1, wherein the step S5 is characterized by performing a smoothing process on a distance change curve, specifically: The method comprises the steps of carrying out smoothing treatment on the thoracic displacement data by adopting a moving average algorithm in a signal processing stage, carrying out smoothing treatment on the distance change curve data by adopting a sliding window average method, setting the window size to be 5 continuous sampling points, wherein each sampling point corresponds to a time interval of 0.05 seconds, calculating the average value of the data in each window from the first sampling point in sequence, carrying out symmetrical zero padding treatment on the data boundary, outputting the smoothed data to be a new distance change curve, verifying the treatment effect by comparing the waveform diagrams before and after smoothing, and if the smoothing effect is insufficient, adjusting the window size to be recalculated.
5. The method for detecting respiratory rate based on acoustic FMCW according to claim 1, wherein the step S6 is performed to process the thoracic movement waveform, obtain the main frequency through fast Fourier transform, and calculate the respiratory rate, specifically: The signal is subjected to spectrum analysis by adopting fast Fourier transform, and the formula is as follows: ; Wherein, the Is an imaginary unit; is the signal frequency; is the time elapsed since the emission of the signal; Is a waveform of the movement of the thorax over time.

Description

Respiratory frequency detection method based on sound wave FMCW Technical Field The invention belongs to the technical field of sound wave signal processing, and particularly relates to a respiratory frequency detection method based on sound wave FMCW. Background Respiratory rate is one of the important physiological indicators for assessing the health condition of the human body, and abnormal respiratory rate is often associated with various diseases, such as sleep apnea syndrome, chronic obstructive pulmonary disease, and the like. Therefore, monitoring respiratory rate in real time, conveniently and accurately is of great importance for disease prevention, diagnosis and treatment. The traditional respiratory rate detection method is mainly divided into a contact type and a non-contact type. Contact methods, such as chest straps, nasal airflow sensors, etc., although highly accurate, require equipment to be worn, are less comfortable, and may affect normal breathing, not suitable for long-term monitoring. Non-contact methods, such as cameras, radars and the like, are not required to contact a human body, but are easily affected by factors such as ambient light, shielding and the like, and have higher equipment cost and are difficult to popularize. In recent years, along with popularization of smart phones and development of acoustic wave technology, an acoustic wave sensing technology based on smart phones is becoming a research hotspot. The technology utilizes a built-in loudspeaker and microphone of the smart phone to realize the perception of the surrounding environment by transmitting and receiving sound wave signals. Compared with the traditional respiratory rate detection method, the sound wave sensing technology based on the smart phone has the advantages of low cost, good portability, no need of additional equipment and the like, can realize non-contact measurement, and has wide application prospect. However, existing smart phone based acoustic wave sensing techniques still present some challenges in respiratory rate detection. For example, environmental noise, human body movement, and the like may affect the reception and processing of acoustic wave signals, resulting in a decrease in detection accuracy. In addition, how to extract the characteristic information related to respiration from the complex acoustic wave signals is also a key problem to be solved. Therefore, how to extract the characteristic information related to respiration from the complex acoustic wave signal and improve the detection accuracy is a technical problem to be solved by the invention. Disclosure of Invention The invention aims to provide a respiratory rate detection method based on sound wave FMCW so as to solve the problems in the background art. The invention aims at realizing the method based on the breathing frequency detection of the sound wave FMCW, which is characterized by comprising the following steps: Step S1, generating a section of sound wave signal with continuously-changing frequency by using a mobile phone loudspeaker, wherein the initial frequency is low frequency, gradually rises to high frequency, and circularly transmitting the sound wave signal to form a continuous sound wave detection sequence; S2, synchronously receiving the reflected sound wave signals by using a microphone of the mobile phone, and performing time alignment with the original transmitted signals; S3, filtering the received reflected sound wave signals; Step S4, performing autocorrelation operation on the emission signal and the reflection signal to generate a distance change curve of the chest along with respiratory motion; S5, performing smoothing treatment on the distance change curve to remove bursty interference; And S6, processing the thoracic movement waveform, obtaining a main frequency through fast Fourier change, and calculating the respiratory frequency. Preferably, in the step S1, the initial frequency is low, and gradually increases to high frequency, specifically: The low frequency is not more than 17kHz, the high frequency is not less than 23kHz, and the calculation formula is as follows: where A is the acoustic signal amplitude, f min is the signal initial frequency, f max is the signal final frequency, T is the time period of the linear sweep, and T is the time elapsed since the emission of the signal. Preferably, in the step S3, filtering processing is performed on the received reflected acoustic wave signal, which specifically includes: The receiving signal with high-frequency noise removed is obtained through a low-pass filter, and echo data r (t) is obtained through a band-pass filter: Where τ is the time delay due to propagation time, A is the acoustic signal amplitude, f min is the signal initial frequency, f max is the signal final frequency, T is the time period of the linear sweep, and T is the time elapsed after emission from the emitted signal. Preferably, in the step S4, the self-correlation operation i