Respiratory frequency detection method based on sound wave FMCW

Abstract

The invention discloses a respiratory rate detection method based on sound wave FMCW, and belongs to the technical field of sound wave signal processing. The method comprises the following steps: 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; the microphone of the mobile phone synchronously receives the reflected sound wave signal; carrying out filtering processing on the received and reflected sound wave signal; performing self-correlation operation on the transmitted signal and the reflected signal to generate a distance change curve of the thoracic cavity along with breathing movement; smoothing the distance change curve; the main frequency is obtained through fast Fourier transform, and the respiratory frequency is calculated. By utilizing the advantages of the FMCW technology, the environmental noise interference is effectively inhibited, the autocorrelation coefficient of the reflected signal is analyzed, the chest movement information related to respiration is accurately extracted, and the accurate detection of the human body respiration frequency is realized; the wave crest can be found more easily by using the self-correlation function, and the convenience of a user is greatly improved by using the property that the wave crest can be tracked more easily.

Description

Technical Field

[0001] The present invention belongs to the technical field of acoustic wave signal processing, and in particular relates to a respiratory frequency detection method based on acoustic wave FMCW. Background Art

[0002] Respiratory rate is one of the important physiological indicators for assessing human health. Abnormal respiratory rate is often associated with a variety of diseases, such as sleep apnea syndrome and chronic obstructive pulmonary disease. Therefore, real-time, convenient, and accurate monitoring of respiratory rate is of great significance for disease prevention, diagnosis, and treatment.

[0003] Traditional respiratory rate detection methods are primarily categorized as contact and non-contact. Contact methods, such as chest straps and nasal airflow sensors, while highly accurate, require wearing the device, which is uncomfortable and may interfere with normal breathing, making them unsuitable for long-term monitoring. Non-contact methods, such as cameras and radar, while not requiring contact with the human body, are susceptible to factors such as ambient light and obstructions. Furthermore, their high equipment cost makes them difficult to popularize.

[0004] In recent years, with the increasing popularity of smartphones and the development of acoustic wave technology, smartphone-based acoustic wave sensing technology has become a research hotspot. This technology utilizes the smartphone's built-in speaker and microphone to transmit and receive acoustic wave signals, enabling awareness of the surrounding environment. Compared to traditional respiratory rate detection methods, smartphone-based acoustic wave sensing offers advantages such as low cost, portability, and the absence of additional equipment. Furthermore, it enables contactless measurement, promising broad application prospects.

[0005] However, existing smartphone-based acoustic wave sensing technology still faces several challenges in detecting respiratory rate. For example, factors such as environmental noise and human movement can affect the reception and processing of acoustic wave signals, resulting in reduced detection accuracy. Furthermore, extracting respiratory-related feature information from complex acoustic wave signals remains a key issue that needs to be addressed.

[0006] Therefore, how to extract characteristic information related to breathing from complex sound wave signals and improve detection accuracy is the technical problem that the present invention aims to solve. Summary of the Invention

[0007] The purpose of the present invention is to provide a respiratory rate detection method based on acoustic wave FMCW to solve the problems raised in the above background technology.

[0008] The object of the present invention is achieved by: a method for detecting respiratory rate based on acoustic wave FMCW, characterized in that the method comprises the following steps:

[0009] Step S1: Use the mobile phone speaker to generate a sound wave signal with a continuously changing frequency, starting from a low frequency and gradually increasing to a high frequency, and cyclically transmit the sound wave signal to form a continuous sound wave detection sequence;

[0010] Step S2: Use the mobile phone microphone to synchronously receive the reflected sound wave signal and align it with the original transmitted signal in time;

[0011] Step S3: filtering the received reflected sound wave signal;

[0012] Step S4: performing autocorrelation calculation on the transmitted signal and the reflected signal to generate a distance variation curve of the chest cavity with respiratory movement;

[0013] Step S5: Smoothing the distance change curve to remove sudden interference;

[0014] Step S6: Process the chest movement waveform, obtain the main frequency through fast Fourier transform, and calculate the respiratory frequency.

[0015] Preferably, the starting frequency in step S1 is low frequency and gradually increases to high frequency, specifically:

[0016] The low frequency is no more than 17kHz, and the high frequency is no less than 23kHz. The calculation formula is:

[0017]

[0018] Where A is the amplitude of the sound wave signal, f min is the initial frequency of the signal, f max is the final frequency of the signal, T is the time period of the linear frequency sweep, and t is the time elapsed after the transmission signal is sent.

[0019] Preferably, in step S3, filtering is performed on the received reflected sound wave signal, specifically:

[0020] The received signal is filtered through a low-pass filter to remove high-frequency noise, and then the echo data r(t) is obtained through a band-pass filter:

[0021]

[0022] Where τ is the time delay caused by the propagation time; A is the amplitude of the sound wave signal, and f min is the initial frequency of the signal, f max is the final frequency of the signal, T is the time period of the linear frequency sweep, and t is the time elapsed after the transmission signal is sent.

[0023] Preferably, in step S4, the transmitted signal and the reflected signal are autocorrelated to generate a distance change curve of the chest cavity with respiratory movement, specifically:

[0024] Step S4-1: Perform autocorrelation operation on the reflected signal and the transmitted signal to obtain the autocorrelation function R(τ):

[0025]

[0026] Where s(t) is the starting frequency emitted by the mobile phone, r(t) is the echo signal filtered by the Butterfly bandpass filter; τ is the time delay variable caused by the propagation time; L is the length of the integration time window;

[0027] Step S4-2: Find the time difference between the reflected signal and the transmitted signal, and generate a distance change curve of the chest cavity with breathing movement, specifically:

[0028] In the formula, r(t) has its own time delay. Using Hilbert transform, we can get a clearer contour image. The transformed formula is as follows:

[0029]

[0030] Where H(t) is the analytical signal after Hilbert transform; τ is the time delay variable;

[0031] The waveform after Hilbert transform will clearly show the first peak and the second peak. The horizontal axis time is calculated by the formula:

[0032]

[0033] Where d represents distance and v represents the speed of sound; the distance corresponding to the second highest peak minus the distance corresponding to the highest peak is the chest distance.

[0034] Preferably, the distance change curve is smoothed in step S5, specifically:

[0035] In the signal processing stage, a moving average algorithm was used to smooth the chest displacement data, and a sliding window averaging method was used to smooth the distance change curve data. The window size was set to 5 consecutive sampling points, and each sampling point corresponded to a 0.05-second time interval. Starting from the first sampling point, the average value of the data in each window was calculated in sequence, and the data boundaries were processed symmetrically with zero padding. The smoothed data were output as a new distance change curve, and the processing effect was verified by comparing the waveforms before and after smoothing. If the smoothing effect was insufficient, the window size was adjusted and recalculated.

[0036] Preferably, in step S6, the chest movement waveform is processed, the main frequency is obtained by fast Fourier transform, and the respiratory frequency is calculated, specifically:

[0037] Fast Fourier transform is used to perform spectrum analysis on the signal. The formula is as follows:

[0038]

[0039] Where j is the imaginary unit; f is the signal frequency; t is the time elapsed after the transmission signal is sent; and x(t) is the waveform of the chest movement over time.

[0040] Compared with the prior art, the present invention has the following improvements and advantages:

[0041] 1. By leveraging the advantages of FMCW technology, it can effectively suppress environmental noise interference. By analyzing the autocorrelation coefficient of the reflected signal, it can accurately extract chest movement information related to breathing, thereby achieving precise detection of human respiratory rate. At the same time, the autocorrelation function makes it easier to find the peak and thus track the property, determine the time difference between the received transmitted signal and the reflected signal, and calculate the distance from the chest to the mobile phone, greatly increasing user convenience.

[0042] 2. By using Hilbert transform, peak tracking is more stable and convenient, and visualization also provides users with excellent visual experience; at the same time, using a smartphone as a detection device, there is no need to wear any sensors or contact the human body, avoiding the discomfort of traditional contact devices. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 Schematic diagram of the process of the present invention.

[0044] Figure 2 Schematic diagram of the transmitted signal waveform.

[0045] Figure 3 Schematic diagram of the echo signal waveform.

[0046] Figure 4 This is a schematic diagram of the autocorrelation waveform after processing.

[0047] Figure 5 Schematic diagram of the chest waveform.

[0048] Figure 6 Schematic diagram of the correlation function-distance comparison in the algorithm. DETAILED DESCRIPTION

[0049] The present invention is further summarized below with reference to the accompanying drawings.

[0050] like Figure 1As shown, a respiratory rate detection method based on acoustic wave FMCW includes the following steps:

[0051] Step S1: Use the mobile phone speaker to generate a sound wave signal with a continuously changing frequency, starting from a low frequency and gradually increasing to a high frequency, and cyclically transmit the sound wave signal to form a continuous sound wave detection sequence;

[0052] The mobile phone is used to continuously output a Chirp signal with a starting frequency of low frequency and gradually increasing to high frequency. The duration of a single signal is about 0.05 seconds, which includes a 0.025-second signal segment and a 0.025-second window period. The low frequency is no more than 17kHz and the high frequency is no less than 23kHz. The calculation formula is:

[0053]

[0054] Where A is the amplitude of the sound wave signal, f min is the initial frequency of the signal, f max is the final frequency of the signal, T is the time period of the linear frequency sweep, and t is the time elapsed after the transmission signal is sent.

[0055] Step S2: Use the mobile phone microphone to synchronously receive the reflected sound wave signal and align it with the original transmitted signal in time, specifically:

[0056] When a mobile phone microphone records ambient sound, it marks the start time of the transmitted sound wave, and then locates the obvious starting point and amplitude increase point of the reflected wave in the received signal to align the time reference of the transmitted and received signals.

[0057] Step S3: filtering the received reflected sound wave signal, specifically:

[0058] like Figure 2 As shown, after receiving the reflected signal, the received signal is filtered through a sixth-order Butterworth low-pass filter to remove high-frequency noise, and then filtered through a sixth-order Butterworth band-pass filter to obtain the final clear echo data r(t). The echo signal is as shown in Figure 3 As shown:

[0059]

[0060] Where τ is the time delay caused by the propagation time; A is the amplitude of the sound wave signal, and f min is the initial frequency of the signal, f max is the final frequency of the signal, T is the time period of the linear frequency sweep, and t is the time elapsed after the transmission signal is sent.

[0061] Step S4: Perform autocorrelation calculation on the transmitted signal and the reflected signal to generate a distance change curve of the chest cavity with respiratory movement, specifically:

[0062] like Figure 4 As shown, step S4-1: perform autocorrelation operation on the reflected signal and the transmitted signal to obtain the autocorrelation function R(τ):

[0063]

[0064] Where s(t) is the starting frequency emitted by the mobile phone, r(t) is the echo signal filtered by the Butterfly bandpass filter; τ is the time delay variable caused by the propagation time; L is the length of the integration time window;

[0065] Step S4-2: Find the time difference between the reflected signal and the transmitted signal, and generate a distance change curve of the chest cavity with breathing movement, specifically:

[0066] In the formula, r(t) has its own time delay. Using Hilbert transform, we can get a clearer contour image. The transformed formula is as follows:

[0067]

[0068] Where H(t) is the analytical signal after Hilbert transform; τ is the time delay variable;

[0069] The waveform after Hilbert transform will clearly show the first peak and the second peak. The horizontal axis time is calculated by the formula:

[0070]

[0071] Where d represents distance and v represents the speed of sound; the distance corresponding to the second highest peak minus the distance corresponding to the highest peak is the chest distance;

[0072] Since the initial peak is large, the target peak is smaller than the initial peak and is difficult to observe. We use the gain function y(s) to process the value corresponding to the vertical axis correlation function:

[0073] y(d)=0.65ln(d);

[0074] We then tracked the second peak over time to obtain a waveform of the thorax.

[0075] Step S5: Smoothing the distance change curve to remove sudden interference;

[0076] like Figure 5As shown in the figure, in the signal processing stage, the moving average algorithm is used to smooth the chest displacement data, and the sliding window averaging method is used to smooth the distance change curve data. The window size is set to 5 consecutive sampling points, and each sampling point corresponds to a 0.05 second time interval. Starting from the first sampling point, the average value of the data in each window is calculated in sequence, and the data boundary is processed by symmetrical zero padding. The smoothed data is output as a new distance change curve, and the processing effect is verified by comparing the waveforms before and after smoothing. If the smoothing effect is insufficient, the window size is adjusted and recalculated.

[0077] Step S6: Process the chest movement waveform, obtain the main frequency through fast Fourier transform, and calculate the respiratory frequency.

[0078] Fast Fourier transform is used to perform spectrum analysis on the signal. The formula is as follows:

[0079]

[0080] Where j is the imaginary unit; f is the signal frequency; t is the time elapsed after the transmission signal is sent; and x(t) is the waveform of the chest movement over time.

[0081] A method for detecting human respiratory rate based on acoustic FMCW technology uses a mobile phone to transmit an acoustic chirp signal to the human chest, generating a reflected signal. The autocorrelation function of the reflected and transmitted signals is then calculated to determine the time difference between the transmitted and reflected signals, thereby calculating the distance from the chest to the phone. The second maximum value of the autocorrelation coefficient is then tracked, and the left and right movement of the second peak on the distance spectrum is observed to generate a waveform diagram of the chest movement. Finally, the period of the chest waveform is analyzed to determine the target person's respiratory rate.

[0082] In order to prove the effectiveness of the present invention, the existing technology using radar as the detection device is compared with the present invention using a mobile phone as the detection device; Figure 6 As shown, the method of the present invention mainly uses time domain operations on signals, that is, a method of performing time domain transformation of the signal by autocorrelation function and Hilbert transform. This method is simpler, easier to understand, and easier to operate and implement.

[0083] The foregoing is merely an embodiment of the present invention and is not intended to limit the present invention. It will be apparent to those skilled in the art that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention are intended to be included within the scope of the claims of the present invention.

Claims

1. A respiratory rate detection method based on acoustic wave FMCW, characterized by: The method comprises the following steps: Step S1: Use the mobile phone speaker to generate a sound wave signal with a continuously changing frequency, starting from a low frequency and gradually increasing to a high frequency, and cyclically transmit the sound wave signal to form a continuous sound wave detection sequence; Step S2: Use the mobile phone microphone to synchronously receive the reflected sound wave signal and align it with the original transmitted signal in time; Step S3: filtering the received reflected sound wave signal; Step S4: performing autocorrelation calculation on the transmitted signal and the reflected signal to generate a distance variation curve of the chest cavity with respiratory movement; Step S5: Smoothing the distance change curve to remove sudden interference; Step S6: Process the chest movement waveform, obtain the main frequency through fast Fourier transform, and calculate the respiratory frequency.

2. The respiratory rate detection method based on acoustic FMCW according to claim 1, characterized in that: In step S1, the starting frequency is low and gradually increases to high, specifically: The low frequency is no more than 17kHz, and the high frequency is no less than 23kHz. The calculation formula is: Where A is the amplitude of the sound wave signal, f min is the initial frequency of the signal, f max is the final frequency of the signal, T is the time period of the linear frequency sweep, and t is the time elapsed after the transmission signal is sent.

3. The respiratory rate detection method based on acoustic FMCW according to claim 1, characterized in that: In step S3, the received reflected sound wave signal is filtered, specifically: The received signal is filtered through a low-pass filter to remove high-frequency noise, and then the echo data r(t) is obtained through a band-pass filter: Where τ is the time delay variable caused by the propagation time; A is the amplitude of the sound wave signal, f min is the initial frequency of the signal, f max is the final frequency of the signal, T is the time period of the linear frequency sweep, and t is the time elapsed after the transmission signal is sent.

4. The respiratory rate detection method based on acoustic FMCW according to claim 1, characterized in that: In step S4, the transmitted signal and the reflected signal are autocorrelated to generate a distance change curve of the chest cavity with respiratory movement, specifically: Step S4-1: Perform autocorrelation operation on the reflected signal and the transmitted signal to obtain the autocorrelation function R(τ): Where s(t) is the starting frequency emitted by the mobile phone, r(t) is the echo signal filtered by the Butterfly bandpass filter; τ is the time delay variable caused by the propagation time; L is the length of the integration time window; Step S4-2: Find the time difference between the reflected signal and the transmitted signal, and generate a distance change curve of the chest cavity with breathing movement, specifically: In the formula, r(t) has its own time delay. Using Hilbert transform, we can get a clearer contour image. The transformed formula is as follows: Where H(t) is the analytical signal after Hilbert transform; τ is the time delay variable; The waveform after Hilbert transform will clearly show the first peak and the second peak. The horizontal axis time is calculated by the formula: Where d represents distance and v represents the speed of sound; the distance corresponding to the second highest peak minus the distance corresponding to the highest peak is the chest distance.

5. The respiratory rate detection method based on acoustic FMCW according to claim 1, characterized in that: In step S5, the distance change curve is smoothed, specifically: In the signal processing stage, a moving average algorithm was used to smooth the chest displacement data, and a sliding window averaging method was used to smooth the distance change curve data. The window size was set to 5 consecutive sampling points, and each sampling point corresponded to a 0.05-second time interval. Starting from the first sampling point, the average value of the data in each window was calculated in sequence, and the data boundaries were processed symmetrically with zero padding. The smoothed data were output as a new distance change curve, and the processing effect was verified by comparing the waveforms before and after smoothing. If the smoothing effect was insufficient, the window size was adjusted and recalculated.

6. The respiratory rate detection method based on acoustic FMCW according to claim 1, characterized in that: In step S6, the chest movement waveform is processed, the main frequency is obtained by fast Fourier transform, and the respiratory frequency is calculated, specifically: Fast Fourier transform is used to perform spectrum analysis on the signal. The formula is as follows: Where j is the imaginary unit; f is the signal frequency; t is the time elapsed after the transmission signal is sent; and x(t) is the waveform of the chest movement over time.

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