Portable multi-physiological-parameter measuring device and method

Abstract

The invention discloses a portable multi-physiological-parameter measuring device and method, and the device comprises a signal collection and preprocessing module which is used for collecting PPG signals and three-axis acceleration signal changes in the breathing process; the PPG derivative telecentric end respiratory signal extraction module is used for acquiring a peak point envelope signal of the PPG signal as a telecentric end respiratory signal; the acceleration derivative proximal end respiration signal extraction module is used for extracting the intrinsic mode with the maximum power spectrum density in the preset range as a proximal end respiration signal; the time delay calculation module is used for calculating the time delay between the near-end breathing signal and the far-end breathing signal; the blood pressure parameter calculation module is used for inputting the time delay into the RDT mapping model and calculating to obtain blood pressure parameters; the breathing parameter extraction module is used for calculating to obtain breathing parameters according to the far-heart end breathing signals and the near-heart end breathing signals; and the vegetative nerve evaluation module is used for carrying out quantitative evaluation on sympathetic nerves and vagus nerves. The method has the advantages of low measurement cost, many parameters and accurate result.

Description

Technical Field

[0001] The present invention relates to a physiological parameter measurement technology, and in particular to a portable multi-physiological parameter measurement device and method. Background Art

[0002] With the increasing problem of population aging and the continuous improvement of national health awareness, the importance of home health measurement and management has become increasingly prominent. Traditional professional medical devices are bulky and expensive, and it is difficult to meet the portability and economic requirements of daily health monitoring. Therefore, smart wearable devices that integrate multiple sensors are becoming the main choice for public health management due to their advantages such as lightness, high efficiency and low cost.

[0003] At present, most wristbands on the market are equipped with photoplethysmography (PPG) and accelerometers, which can monitor physiological parameters such as heart rate and blood oxygen, but still face challenges in accurately measuring autonomic nerve activity, blood pressure, respiration and complex parameters derived from them. In addition, considering the availability and widespread popularity of mobile devices, health monitoring using sensors such as built-in cameras, accelerometers, and ultra-wideband radars (UWB) in mobile phones is also gradually emerging. Existing technologies can already detect heart rate through a combination of cameras and flashlights, but there are few solutions for measuring parameters such as respiration, blood pressure, and autonomic nerve activity based on mobile devices.

[0004] In terms of respiratory parameter measurement, there are currently two methods for mainstream wearable devices to infer respiratory parameters: one is to use PPG signals to indirectly infer respiratory parameters, but this method has the following main limitations: the parameter acquisition is single, usually only the respiratory timing characteristics can be extracted, and it is difficult to provide more respiratory parameters and pattern information; the measurement site is limited, and only local respiratory changes are reflected. The other is to combine acceleration signals and PPG signals to infer respiratory parameters. For example, the patent document with application number 202110619645.3 discloses a wearable device and its monitoring method and monitoring device. The patented technical solution decomposes the motion component and respiratory component in the acceleration signal to calibrate and monitor the respiratory signal. However, the wrist is less affected by respiratory fluctuations in the natural state, and the respiratory signal extracted from the acceleration signal is easily submerged by noise, resulting in inaccurate measurements.

[0005] In terms of blood pressure measurement, current wearable devices with blood pressure monitoring functions mainly use three technologies: the first is direct measurement by pressurization. The pressurized measurement method requires repeated pressurization during the measurement process, which causes obvious discomfort to the user. The second is to infer blood pressure parameters based on PPG signals. For example, the patent document with application number 202210300668.2 discloses a wearable physiological parameter detection system. This patent scheme estimates blood pressure through the waveform characteristics of the PPG signal, but this method needs to identify other waveform features besides the peak point. These features are difficult to capture, and require high resolution of the PPG signal. It is also greatly affected by differences in individual physiological parameters, and the measurement results are inaccurate and the actual operability is not strong. The third is an indirect measurement method based on the pulse transit time (PTT) of electrocardiogram (ECG)-PPG fusion. For example, patent document No. 97199737 discloses a non-invasive and condom-free blood pressure measurement method. This method estimates blood pressure by calculating the time delay between the ECG signal and the PPG signal. However, this method relies on ECG sensors, has high hardware requirements and measurement costs, and is susceptible to environmental and motion interference. Poor signal quality of either ECG or PPG will significantly affect the accuracy of the final measurement result.

[0006] In terms of autonomic nervous system activity assessment, wearable devices currently mainly conduct brief assessments of the autonomic nervous system based on heart rate variability, without further integrating multiple system parameters such as respiration and blood pressure to quantitatively assess sympathetic and vagus nerve activity, making it impossible to achieve accurate analysis of the autonomic nervous system. Summary of the invention

[0007] In view of the problems existing in the prior art, the object of the present invention is to provide a portable multi-physiological parameter measurement device and method with lower cost, more comprehensive measurement results and higher accuracy.

[0008] In order to achieve the above-mentioned object of the invention, the present invention provides the following technical solutions:

[0009] A portable multi-physiological parameter measuring device, comprising:

[0010] The signal acquisition and preprocessing module is used to collect the user's PPG signal and the changes in the three-axis acceleration signal derived from the ups and downs of the chest, abdomen or head during breathing, and filter out the low-frequency drift and high-frequency noise in the collected signal;

[0011] A PPG derived distal end respiratory signal extraction module, used to obtain the peak point envelope signal of the PPG signal as the distal end respiratory signal;

[0012] The acceleration-derived proximal respiratory signal extraction module is used to perform modal decomposition on the triaxial acceleration signal to obtain multiple eigenmodes, and extract the eigenmode with the largest power spectrum density within a preset frequency range as the proximal respiratory signal;

[0013] A time delay calculation module is used to perform millisecond-level timing alignment of the proximal respiratory signal and the distal respiratory signal, and calculate the time delay between the proximal respiratory signal and the distal respiratory signal;

[0014] a blood pressure parameter calculation module, used to input the time delay as a delay parameter into a pre-built respiratory diffusion time (RDT) mapping model to calculate the blood pressure parameter, wherein the RDT mapping model is used to calculate the blood pressure parameter according to the delay parameter of the respiratory diffusion from the proximal end to the distal end;

[0015] The breathing parameter extraction module is used to calculate the breathing parameters, including breathing depth, breathing mode matching, breathing frequency, and inhalation-exhalation ratio, based on the distal end breathing signal and the proximal end breathing signal.

[0016] The autonomic nerve assessment module is used to integrate blood pressure parameters, respiratory parameters, and heart rate variability characteristics to achieve quantitative assessment of the sympathetic and vagus nerves.

[0017] Optionally, the signal acquisition and preprocessing module can be specifically a wearable device, which is provided with a photoelectric unit, an accelerometer and a preprocessing unit, the photoelectric unit is used to collect the user's PPG signal, the accelerometer is used to collect the changes in the three-axis acceleration signal derived from the rise and fall of the chest, abdomen or head during breathing, and the preprocessing unit is used to perform bandpass, phase-shift-free, and finite impulse response filtering on the PPG signal and the three-axis acceleration signal in sequence.

[0018] Optionally, the signal acquisition and preprocessing module may also be a mobile device, which is provided with a flash, a camera, an accelerometer and a data acquisition processing unit. Under the control of the data acquisition processing unit, the flash and the camera flash at a predetermined frequency and the camera takes a picture of the measurement part. The accelerometer is used to collect changes in three-axis acceleration signals derived from the rise and fall of the chest, abdomen or head during breathing. The data acquisition processing unit is used to extract the user's PPG signal based on the picture, and perform bandpass, phase-shift-free, and finite impulse response filtering on the PPG signal and the three-axis acceleration signal in sequence.

[0019] Furthermore, the PPG-derived distal respiratory signal extraction module specifically includes:

[0020] A segmentation unit, used for dividing the collected PPG signal into a plurality of PPG signal segments;

[0021] The standard deviation calculation unit is used to calculate the standard deviation of the PPG signal segment and the template waveform according to the following formula based on the pre-constructed PPG template waveform:

[0022]

[0023] Where SD is the standard deviation, S(n) is the sampled PPG signal, T(n) is the PPG template waveform, and N is the total number of sampling points in the PPG signal segment;

[0024] A judging unit, used to judge whether SD is less than a set threshold SDT, if so, the current PPG signal segment is judged to be valid, otherwise, the current PPG signal segment is judged to be invalid;

[0025] The distal end breathing signal generating unit is used to search for the peak points of the valid PPG signal segment in the PPG signal, connect all the peak points, and fill the data of the invalid PPG signal segment by interpolation to obtain the processed PPG signal, and then resample the processed PPG signal to obtain the distal end breathing signal R1(n).

[0026] Furthermore, the acceleration-derived proximal respiratory signal extraction module specifically includes:

[0027] The modal decomposition unit is used to perform modal decomposition on the triaxial acceleration signal to obtain the intrinsic mode set {IMF i ,i=1,2,…,M},IMF i is the i-th eigenmode obtained after modal decomposition of the triaxial acceleration signal, and M is the number of eigenmodes;

[0028] A power spectrum density calculation unit, used to calculate the power spectrum density of each eigenmode in the frequency domain;

[0029] The proximal end respiration signal generating unit is used to calculate the proximal end respiration signal according to the following formula:

[0030]

[0031] Where R2(n) represents the proximal respiratory signal, PSD(IMF i (f)) represents IMF i Power spectral density at frequency f.

[0032] Furthermore, the time delay calculation module specifically includes:

[0033] A time alignment unit is used to obtain the signal sampling timestamp of the signal acquisition and preprocessing module, so as to align the timing of the distal end respiratory signal with the proximal end respiratory signal at the millisecond level;

[0034] A peak extraction unit, used for extracting peak points of the distal end respiratory signal and the proximal end respiratory signal;

[0035] The time delay unit is used to calculate the time delay between the distal end respiratory signal and the proximal end respiratory signal at the peak points as the time delay between the distal end respiratory signal and the proximal end respiratory signal in the current respiratory cycle.

[0036] Furthermore, the blood pressure parameter calculation module specifically includes:

[0037] A morphological parameter extraction unit is used to search for valley points before and after the peak point of the proximal respiratory signal, obtain the start and end time of the current respiratory cycle according to the peak point and the valley point, and search the PPG signal segment within the start and end time of the respiratory cycle in the PPG signal, and calculate the average rise and fall time of all PPG waveforms in the signal segment;

[0038] The blood pressure calculation unit is used to input the time delay and the average heart rate into a pre-built RDT mapping model to calculate the blood pressure parameters, wherein the pre-built RDT mapping model is specifically:

[0039]

[0040] In the formula, SBP and DBP are systolic blood pressure and diastolic blood pressure respectively, a S , b S 、c S d S 、a D , b D 、c D d D is the adjustment coefficient obtained by fitting the historical data using the multivariate linear regression function, τ is the time delay, UT is the average rise time of the PPG waveform, and DT is the average fall time of the PPG waveform.

[0041] Furthermore, the breathing parameter extraction module specifically includes:

[0042] The breathing depth calculation unit is used to calculate the average amplitude A1 of the distal end breathing signal, and the breathing depth is calculated according to the average amplitude A1:

[0043] D=k·A1

[0044] Among them, k is the individualized parameter, D is the breathing depth;

[0045] The breathing mode matching degree calculation unit is used to calculate the average amplitude A2 of the proximal end breathing signal, and combine it with the average amplitude A1 of the distal end breathing signal to calculate the breathing mode matching degree according to the following formula:

[0046]

[0047] Where μ represents the phase synchronization coefficient, N represents the number of sampling points, ψ(R1(n)) represents the phase of the distal respiratory signal R1(n), ψ(R2(n)) represents the phase of the proximal respiratory signal R2(n), and M represents the matching degree of the respiratory mode.

[0048] The respiratory frequency calculation unit is used to identify the peak point of the proximal respiratory signal, calculate the average value S of the interval time between the two peak points, and calculate the respiratory frequency according to the following formula:

[0049]

[0050] Where, f represents the respiratory rate;

[0051] The I / O ratio calculation unit is used to search for valley points before and after each peak point of the proximal respiratory signal, calculate the average value I of the interval between the front valley point and the peak point, and the average value E of the interval between the rear valley point and the peak point, and obtain the I / O ratio:

[0052]

[0053] Where R represents the inspiration-expiration ratio.

[0054] Furthermore, the autonomic nerve assessment module specifically includes:

[0055] The heart rate calculation unit is used to identify the peak points in the PPG signal, obtain the time interval between the two peak points, and calculate the heart rate sequence by the following formula:

[0056]

[0057] Where HR(m) represents the mth heart rate value, FM is the total number of peak points of the PPG signal, and PP(m) represents the time interval between the mth peak point and the m+1th peak point;

[0058] The sympathetic nerve assessment unit is used to integrate blood pressure fluctuations, long-term heart rate fluctuations, and respiratory-blood pressure coupling strength to achieve quantitative assessment of the sympathetic nerves according to the following formula:

[0059] SAI=w1·HRV LF +w2·SPV+w3·DPV+w4·(1-PLV RB )+w5·Gain LF

[0060]

[0061] In the formula, SAI represents sympathetic nerve activity, SPV is the standard deviation of the systolic blood pressure series, DPV is the standard deviation of the diastolic blood pressure series, and HRV is LF is the power spectrum of the low-frequency part of the heart rate, PLVRB is the phase locking value between the proximal respiratory signal and the systolic pressure sequence, HR(f) is the Fourier discrete spectrum of HR(m), PSD(HR(f)) represents the power spectrum density of HR(f), ψ(R2(n)) represents the phase of the proximal respiratory signal R2(n), ψ(SBP(n)) represents the phase of the systolic pressure sequence SBP(n), j is an imaginary unit, e is a natural constant, Gain LF is the transfer function gain, P RB (f) is the cross spectral density between the proximal respiratory signal and the systolic pressure sequence at frequency f, P RR represents the autospectral density of the proximal respiratory signal at frequency f, w1, w2, w3, w4, w5 are the variable coefficients determined by calibration; N represents the number of sampling points;

[0062] The vagus nerve assessment unit is used to integrate short-term heart rate fluctuations and respiratory-heart rate coupling strength to perform quantitative assessment of the vagus nerve according to the following formula:

[0063] PAI=v1·HRV HF +v2·(1-PLV RH )+v3·Gain HF

[0064]

[0065]

[0066] Where PAI represents vagus nerve activity, HRV HF is the power spectrum of the high frequency part of the heart rate, PLV RH is the phase lock value between the proximal respiratory signal and the heart rate sequence, ψ(R2(n)) represents the phase of the proximal respiratory signal R2(n), ψ(HR(n)) represents the phase of the heart rate sequence HR(n), P RH (f) is the cross spectral density between the proximal respiratory signal and the heart rate sequence at frequency f, P RR (f) represents the autospectral density of the proximal respiratory signal at frequency f, and v1, v2, and v3 are variable coefficients determined by calibration. A portable multi-physiological parameter measurement method comprises:

[0067] Collect the user's PPG signal and the changes in the three-axis acceleration signal derived from the ups and downs of the chest, abdomen or head during breathing, and filter out the low-frequency drift and high-frequency noise in the collected signal;

[0068] Obtain the peak point envelope signal of the PPG signal as the distal end respiration signal;

[0069] Perform modal decomposition on the triaxial acceleration signal to obtain multiple eigenmodes, and extract the eigenmode with the largest power spectrum density within a preset frequency range as the proximal respiratory signal;

[0070] Perform millisecond-level timing alignment on the proximal respiratory signal and the distal respiratory signal, and calculate the time delay between the proximal respiratory signal and the distal respiratory signal;

[0071] Inputting the time delay as a delay parameter into a pre-constructed RDT mapping model to calculate the blood pressure parameter, wherein the RDT mapping model is used to calculate the blood pressure parameter according to the delay parameter;

[0072] According to the distal and proximal respiratory signals, the breathing depth, breathing pattern matching, respiratory rate, and inhalation-exhalation ratio are calculated.

[0073] The blood pressure parameters, respiratory parameters and heart rate variability characteristics are integrated to achieve quantitative evaluation of the sympathetic and vagus nerves.

[0074] Compared with the prior art, the present invention has the following beneficial effects:

[0075] (1) From a technical perspective, the present invention extracts the distal respiratory signal based on the PPG signal, extracts the proximal respiratory signal based on the acceleration signal, and realizes the calculation of multiple respiratory parameters based on the distal respiratory signal and the proximal respiratory signal, and the calculation results are more accurate. The present invention also uses the time delay between the distal respiratory signal and the proximal respiratory signal to calculate the blood pressure parameters according to the RDT model. The calculation model is derived based on the respiratory diffusion process and has more advantages than the PTT model in terms of continuous acquisition, environmental requirements, and types of signal sources;

[0076] (2) From an economic perspective, the present invention has relatively low requirements on hardware and sensor types and is suitable for basic wearable devices and most mobile devices. Without introducing other sensors (such as ECG sensors, wrist pulse pressure sensors, respiratory pressure sensors, etc.), it can achieve accurate measurement of multiple physiological parameters such as respiration and blood pressure, effectively reducing the cost of personal health management. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] Figure 1 is a schematic structural diagram of a portable multi-physiological parameter measuring device provided by an embodiment of the present invention;

[0078] Figure 2 It is a schematic diagram of the principle of signal acquisition and respiratory signal extraction of the present invention;

[0079] Figure 3 It is a schematic diagram of the blood pressure parameter calculation principle of the present invention;

[0080] Figure 4It is a schematic diagram of the breathing parameter calculation principle of the present invention. DETAILED DESCRIPTION

[0081] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0082] Embodiment 1

[0083] The embodiment of the present invention provides a portable multi-physiological parameter measuring device, such as Figure 1 As shown, including:

[0084] The signal acquisition and preprocessing module is used to collect the user's PPG signal and the changes in the three-axis acceleration signal derived from the ups and downs of the chest, abdomen or head during breathing, and filter out the low-frequency drift and high-frequency noise in the collected signal;

[0085] A PPG derived distal end respiratory signal extraction module, used to obtain the peak point envelope signal of the PPG signal as the distal end respiratory signal;

[0086] The acceleration-derived proximal respiratory signal extraction module is used to perform modal decomposition on the triaxial acceleration signal to obtain multiple eigenmodes, and extract the eigenmode with the largest power spectrum density within a preset frequency range as the proximal respiratory signal;

[0087] A time delay calculation module is used to perform millisecond-level timing alignment of the proximal respiratory signal and the distal respiratory signal, and calculate the time delay between the proximal respiratory signal and the distal respiratory signal;

[0088] A blood pressure parameter calculation module, used to input the time delay as a delay parameter into a pre-built RDT mapping model to calculate the blood pressure parameter, wherein the RDT mapping model is used to calculate the blood pressure parameter according to the delay parameter;

[0089] The breathing parameter extraction module is used to calculate the breathing parameters, including breathing depth, breathing mode matching, breathing frequency, and inhalation-exhalation ratio, based on the distal end breathing signal and the proximal end breathing signal.

[0090] The autonomic nerve assessment module is used to integrate blood pressure parameters, respiratory parameters, and heart rate variability characteristics to achieve quantitative assessment of the sympathetic and vagus nerves.

[0091] In specific implementation, Figure 2As shown, the signal acquisition and preprocessing module can be specifically a wearable device, and the wearable device is provided with a photoelectric unit, an accelerometer and a preprocessing unit, the photoelectric unit is used to collect the user's PPG signal, the accelerometer is used to collect the changes in the three-axis acceleration signal derived from the ups and downs of the chest, abdomen or head during breathing, and the preprocessing unit is used to perform bandpass, phase-shift-free, and finite impulse response filtering on the PPG signal and the three-axis acceleration signal in sequence, wherein the PPG signal is bandpassed at 0.05-5Hz, and the three-axis acceleration signal is bandpassed at 0.05-2Hz. The wearable device can be a smart watch, a smart bracelet, a smart ring, a smart headset, a smart glasses, a smart fabric or other wearable device with PPG detection and acceleration detection functions, or any combination of a wearable device with a PPG detection function and a wearable device with an acceleration detection function; the photoelectric unit can be realized based on the fusion of one or more visible or non-visible light such as red light, green light, infrared light, etc. Specifically, for limb-type wearable devices such as smart watches, smart bracelets, and smart rings, after the user puts on the wearable device and places the limbs on the chest, abdomen, or head, the PPG signal and the ups and downs of the chest, abdomen, or head during breathing are collected based on the device's photoelectric module and built-in three-axis accelerometer; for head-mounted wearable devices such as smart headphones and smart glasses, after the user puts on the device, the PPG signal and the ups and downs of the head during breathing are collected based on the device's photoelectric module and built-in three-axis accelerometer. The PPG-derived distal end breathing signal extraction module, the acceleration-derived proximal end breathing signal extraction module, the time delay calculation module, the blood pressure parameter calculation module, and the breathing parameter extraction module can be integrated into an application and installed on the wearable device, or installed on other electronic devices, such as mobile phones and tablets.

[0092] In other embodiments, the signal acquisition and preprocessing module can also be a mobile device, or any combination of a mobile device and a mobile device, or a mobile device and a wearable device; the mobile device is provided with a flash, a camera, an accelerometer and a data acquisition processing unit, and the flash and the camera are controlled by the data acquisition processing unit, and the flash flashes at a predetermined frequency and the camera takes a picture of the measurement part, and the accelerometer is used to collect the changes of the three-axis acceleration signal derived from the ups and downs of the chest, abdomen or head during breathing. The data acquisition processing unit can be a program installed on the mobile device, which is used to extract the user's PPG signal based on the picture, and perform bandpass, phase-shift-free, and finite impulse response filtering on the PPG signal and the three-axis acceleration signal in turn. During the acquisition, such as Figure 2As shown, the mobile device is placed on the chest and abdomen of the user, and the PPG measurement part is placed at the flash and camera. When it is detected that the user places the mobile device on the chest / abdomen and aligns the PPG measurement part with the flash and camera in a specified manner, the acceleration signal is collected at a frequency of 20Hz to obtain the chest / abdomen undulation three-axis acceleration signal; at the same time, the flash is configured to be in pulse modulation (PWM) mode, the luminous intensity of the LED light is adjusted to 50%-70% (to avoid overexposure / underexposure), and the original imaging parameters of the camera sensor are collected at a frequency of 20Hz; the RGB three channels are separated from the original photo data, and the green channel is selected as the PPG main signal source; the brightness data is normalized to eliminate the interference of the ambient light baseline, and the PPG signal is obtained:

[0093]

[0094] The mobile device can be a smart phone, a tablet computer or any mobile device equipped with a photoelectric module and a three-axis accelerometer. The PPG measurement site can be a finger, a wrist or any body part with significant hemodynamics. The flash can be green light, white light or any color light different from the color of blood. The original imaging parameters of the camera can be obtained by measuring with a single camera or by fusing the original imaging parameters sampled by multiple cameras. The PPG-derived distal respiratory signal extraction module, the acceleration-derived proximal respiratory signal extraction module, the time delay calculation module, the blood pressure parameter calculation module and the respiratory parameter extraction module can be integrated into an application and installed on the mobile device.

[0095] The PPG derived distal end respiratory signal extraction module specifically includes:

[0096] A segmentation unit, used for dividing the collected PPG signal into a plurality of PPG signal segments;

[0097] The standard deviation calculation unit is used to calculate the standard deviation of the PPG signal segment and the template waveform according to the following formula based on the pre-constructed PPG template waveform:

[0098]

[0099] Where SD is the standard deviation, S(n) is the sampled PPG signal, T(n) is the PPG template waveform, and N is the total number of sampling points in the PPG signal segment;

[0100] A judging unit, used to judge whether SD is less than a set threshold SDT, if so, the current PPG signal segment is judged to be valid, otherwise, the current PPG signal segment is judged to be invalid;

[0101] The distal end breathing signal generating unit is used to search for the peak points of the valid PPG signal segment in the PPG signal, connect all the peak points, and fill the data of the invalid PPG signal segment through cubic spline interpolation to obtain the processed PPG signal, and then resample the processed PPG signal to obtain the distal end breathing signal R1(n).

[0102] The acceleration-derived proximal respiratory signal extraction module specifically includes:

[0103] The modal decomposition unit is used to perform modal decomposition on the triaxial acceleration signal to obtain the intrinsic mode set {IMF i ,i=1,2,…,M},IMF i is the i-th eigenmode obtained after modal decomposition of the triaxial acceleration signal, and M is the number of eigenmodes; wherein, modal decomposition can be performed using EMD decomposition;

[0104] A power spectrum density calculation unit, used to calculate the power spectrum density of each eigenmode in the frequency domain;

[0105] The proximal end respiration signal generating unit is used to calculate the proximal end respiration signal according to the following formula:

[0106]

[0107] Where R2(n) represents the proximal respiratory signal, PSD(IMF i (f)) represents IMF i Power spectral density at frequency f.

[0108] like Figure 3 As shown, the time delay calculation module specifically includes:

[0109] The time alignment unit is used to obtain the signal sampling timestamp of the crystal oscillator on the signal acquisition and preprocessing module device, so as to align the timing of the distal end respiratory signal with the proximal end respiratory signal at the millisecond level;

[0110] A peak extraction unit, used for extracting peak points of the distal end respiratory signal and the proximal end respiratory signal;

[0111] The time delay unit is used to calculate the time delay between the distal end respiratory signal and the proximal end respiratory signal at the peak point as the time delay between the distal end respiratory signal and the proximal end respiratory signal in the current respiratory cycle, that is, the blood conduction time.

[0112] The blood pressure parameter calculation module specifically includes:

[0113] A morphological parameter extraction unit is used to search for valley points before and after the peak point of the proximal respiratory signal, obtain the start and end time of the current respiratory cycle according to the peak point and the valley point, and search the PPG signal segment within the start and end time of the respiratory cycle in the PPG signal, and calculate the average rise and fall time of all PPG waveforms in the signal segment;

[0114] The blood pressure calculation unit is used to input the time delay and the average heart rate into a pre-built RDT mapping model to calculate the blood pressure parameters, wherein the pre-built RDT mapping model is specifically:

[0115]

[0116] In the formula, SBP and DBP are systolic blood pressure and diastolic blood pressure respectively, a S , b S 、c S d S 、a D , b D 、c D d D is the adjustment coefficient obtained by fitting the historical data using the multivariate linear regression function, τ is the time delay, UT is the average rise time of the PPG waveform, and DT is the average fall time of the PPG waveform.

[0117] like Figure 4 As shown, the breathing parameter extraction module specifically includes:

[0118] The breathing depth calculation unit is used to calculate the average amplitude A1 of the distal end breathing signal, and the breathing depth is calculated according to the average amplitude A1:

[0119] D=k·A1

[0120] Among them, k is an individualized parameter, which is related to height, weight, vital capacity, tightness of the bracelet, etc., and can be obtained through calibration, and D is the breathing depth;

[0121] The breathing mode matching degree calculation unit is used to calculate the average amplitude A2 of the proximal end breathing signal, and combine it with the average amplitude A1 of the distal end breathing signal to calculate the breathing mode matching degree according to the following formula:

[0122]

[0123] Where μ represents the phase synchronization coefficient, N represents the number of sampling points, ψ(R1(n)) represents the phase of the distal respiratory signal R1(n), ψ(R2(n)) represents the phase of the proximal respiratory signal R2(n), and M represents the matching degree of the respiratory mode.

[0124] The respiratory frequency calculation unit is used to identify the peak point of the proximal respiratory signal, calculate the average value S of the interval time between the two peak points, and calculate the respiratory frequency according to the following formula:

[0125]

[0126] Where, f represents the respiratory rate;

[0127] The I / O ratio calculation unit is used to search for valley points before and after each peak point of the proximal respiratory signal, calculate the average value I of the interval between the front valley point and the peak point, and the average value E of the interval between the rear valley point and the peak point, and obtain the I / O ratio:

[0128]

[0129] Where R represents the inspiration-expiration ratio.

[0130] The autonomic nerve assessment module specifically includes:

[0131] The heart rate calculation unit is used to identify the peak points in the PPG signal, obtain the time interval between the two peak points, and calculate the heart rate sequence by the following formula:

[0132]

[0133] Where HR(m) represents the mth heart rate value, FM is the total number of peak points of the PPG signal, and PP(m) represents the time interval between the mth peak point and the m+1th peak point;

[0134] The sympathetic nerve assessment unit is used to integrate blood pressure fluctuations, long-term heart rate fluctuations, and respiratory-blood pressure coupling strength to achieve quantitative assessment of the sympathetic nerves according to the following formula:

[0135] SAI=w1·HRV LF +w2·SPV+w3·DPV+w4·(1-PLV RB )+w5·Gain LF

[0136]

[0137] In the formula, SAI represents sympathetic nerve activity, SPV is the standard deviation of the systolic blood pressure series, DPV is the standard deviation of the diastolic blood pressure series, and HRV is LF is the power spectrum of the low-frequency part of the heart rate, PLV RBis the phase locking value between the proximal respiratory signal and the systolic pressure sequence, HR(f) is the Fourier discrete spectrum of HR(m), PSD(HR(f)) represents the power spectrum density of HR(f), ψ(R2(n)) represents the phase of the proximal respiratory signal R2(n), ψ(SBP(n)) represents the phase of the systolic pressure sequence SBP(n), j is an imaginary unit, e is a natural constant, Gain LF is the transfer function gain, P RB (f) is the cross spectral density between the proximal respiratory signal and the systolic pressure sequence at frequency f, P RR represents the autospectral density of the proximal respiratory signal at frequency f, w1, w2, w3, w4, w5 are the variable coefficients determined by calibration; N represents the number of sampling points;

[0138] The vagus nerve assessment unit is used to integrate short-term heart rate fluctuations and respiratory-heart rate coupling strength to perform quantitative assessment of the vagus nerve according to the following formula:

[0139] PAI=v1·HRV HF +v2·(1-PLV RH )+v3·Gain HF

[0140]

[0141] Where PAI represents vagus nerve activity, HRV HF is the power spectrum of the high frequency part of the heart rate, PLV RH is the phase lock value between the proximal respiratory signal and the heart rate sequence, ψ(R2(n)) represents the phase of the proximal respiratory signal R2(n), ψ(HR(n)) represents the phase of the heart rate sequence HR(n), P RH (f) is the cross spectral density between the proximal respiratory signal and the heart rate sequence at frequency f, P RR (f) represents the autospectral density of the proximal respiratory signal at frequency f, and v1, v2, and v3 are the variable coefficients determined by calibration.

[0142] It is worth noting that in the embodiment of the above-mentioned device, the various units and modules included are only divided according to functional logic, but are not limited to the above-mentioned division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention.

[0143] The embodiments described above are only illustrative, wherein the modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical modules, i.e., they may be located in one place, or they may be distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, or of course, it can be implemented only by hardware, as long as the function or effect can be achieved.

[0144] Embodiment 2

[0145] The embodiment of the present invention provides a portable multi-physiological parameter measurement method, comprising:

[0146] Collect the user's PPG signal and the changes in the three-axis acceleration signal derived from the ups and downs of the chest, abdomen or head during breathing, and filter out the low-frequency drift and high-frequency noise in the collected signal;

[0147] Obtain the peak point envelope signal of the PPG signal as the distal end respiration signal;

[0148] Perform modal decomposition on the triaxial acceleration signal to obtain multiple eigenmodes, and extract the eigenmode with the largest power spectrum density within a preset frequency range as the proximal respiratory signal;

[0149] Perform millisecond-level timing alignment on the proximal respiratory signal and the distal respiratory signal, and calculate the time delay between the proximal respiratory signal and the distal respiratory signal;

[0150] Inputting the time delay as a delay parameter into a pre-constructed RDT mapping model to calculate the blood pressure parameter, wherein the RDT mapping model is used to calculate the blood pressure parameter according to the delay parameter;

[0151] According to the distal and proximal respiratory signals, the breathing depth, breathing pattern matching, respiratory rate, and inhalation-exhalation ratio are calculated.

[0152] The blood pressure parameters, respiratory parameters and heart rate variability characteristics are integrated to achieve quantitative evaluation of the sympathetic and vagus nerves.

[0153] The calculation steps of the distal respiratory signal specifically include:

[0154] Dividing the collected PPG signal into a number of PPG signal segments;

[0155] Based on the pre-constructed PPG template waveform, the standard deviation of the PPG signal segment and the template waveform is calculated according to the following formula:

[0156]

[0157] Where SD is the standard deviation, S(n) is the PPG signal, T(n) is the PPG template waveform, and N is the total number of sampling points in the PPG signal segment;

[0158] Determine whether SD is less than a set threshold SDT, if so, the current PPG signal segment is determined to be valid, otherwise, the current PPG signal segment is determined to be invalid;

[0159] The peak points of the valid PPG signal segments in the PPG signal are searched, all the peak points are connected, and the data of the invalid PPG signal segments are supplemented by interpolation to obtain the processed PPG signal, and then the processed PPG signal is resampled to obtain the distal end respiratory signal R1(n).

[0160] The calculation steps of the proximal respiratory signal specifically include:

[0161] Perform modal decomposition on the triaxial acceleration signal and obtain the intrinsic mode set {IMF i ,i=1,2,…,M},IMF i is the i-th eigenmode obtained after modal decomposition of the triaxial acceleration signal, and M is the number of eigenmodes; wherein, modal decomposition can be performed using EMD decomposition;

[0162] Calculate the power spectral density of each eigenmode in the frequency domain;

[0163] The proximal respiration signal is calculated as follows:

[0164]

[0165] Where R2(n) represents the proximal respiratory signal, PSD(IMF i (f)) represents IMF i Power spectral density at frequency f.

[0166] The specific steps of time delay calculation include:

[0167] Obtain the signal sampling timestamp of the signal acquisition and preprocessing module, so as to align the timing of the distal respiratory signal with the proximal respiratory signal at the millisecond level;

[0168] Extract the peak points of the distal respiratory signal and the proximal respiratory signal;

[0169] The time delay between the distal end respiratory signal and the proximal end respiratory signal at the peak point is calculated as the time delay between the distal end respiratory signal and the proximal end respiratory signal in the current respiratory cycle, that is, the blood conduction time.

[0170] The steps for calculating the blood pressure parameters specifically include:

[0171] The valley points are searched before and after the peak point of the proximal respiratory signal, and the start and end time of the current respiratory cycle are obtained according to the peak point and the valley point. The PPG signal segment located within the start and end time of the respiratory cycle is searched in the PPG signal, and the average rise and fall time of all PPG waveforms in the signal segment is calculated to calibrate the indirect effect of PPG morphological changes on the time delay between the distal respiratory signal and the proximal respiratory signal.

[0172] The time delay and the average heart rate are input into a pre-constructed RDT mapping model to calculate the blood pressure parameters, wherein the pre-constructed RDT mapping model is specifically:

[0173]

[0174] In the formula, SBP and DBP are systolic blood pressure and diastolic blood pressure respectively, a S , b S 、c S d S 、a D , b D 、c D d D is the adjustment coefficient obtained by fitting the historical data using the multivariate linear regression function, τ is the time delay, UT is the average rise time of the PPG waveform, and DT is the average fall time of the PPG waveform.

[0175] The calculation step of the respiratory parameters specifically includes:

[0176] Calculate the average amplitude A1 of the distal respiratory signal, and calculate the respiratory depth based on the average amplitude A1:

[0177] D=k·A1

[0178] Among them, k is an individualized parameter, which is related to height, weight, vital capacity, tightness of the bracelet, etc., and can be obtained through calibration, and D is the breathing depth;

[0179] The average amplitude A2 of the proximal respiratory signal is calculated, and combined with the average amplitude A1 of the distal respiratory signal, the respiratory mode matching degree is calculated according to the following formula:

[0180]

[0181] Where μ represents the phase synchronization coefficient, N represents the number of sampling points, ψ(R1(n)) represents the phase of the distal respiratory signal R1(n), ψ(R2(n)) represents the phase of the proximal respiratory signal R2(n), and M represents the matching degree of the respiratory mode.

[0182] Identify the peak points of the proximal respiratory signal, calculate the average value S of the interval between the two peak points, and calculate the respiratory frequency according to the following formula:

[0183]

[0184] Where, f represents the respiratory rate;

[0185] The valley points are searched before and after each peak point of the proximal respiratory signal, and the average value I of the interval between the front valley point and the peak point, as well as the average value E of the interval between the rear valley point and the peak point are calculated to obtain the inspiration-expiration ratio:

[0186]

[0187] Where R represents the inspiration-expiration ratio.

[0188] The steps of the autonomic nerve assessment specifically include:

[0189] Identify the peak points in the PPG signal, get the time interval between the two peak points, and calculate the heart rate sequence using the following formula:

[0190]

[0191] Where HR(m) represents the mth heart rate value, FM is the total number of peak points of the PPG signal, and PP(m) represents the time interval between the mth peak point and the m+1th peak point;

[0192] By integrating blood pressure fluctuations, long-term heart rate fluctuations, and respiratory-blood pressure coupling strength, quantitative evaluation of sympathetic nerves can be achieved according to the following formula:

[0193] SAI=w1·HRV LF +w2·SPV+w3·DPV+w4·(1-PLV RB )+w5·Gain LF

[0194]

[0195] In the formula, SAI represents sympathetic nerve activity, SPV is the standard deviation of the systolic blood pressure series, DPV is the standard deviation of the diastolic blood pressure series, and HRV is LF is the power spectrum of the low-frequency part of the heart rate, PLV RB is the phase locking value between the proximal respiratory signal and the systolic pressure sequence, HR(f) is the Fourier discrete spectrum of HR(m), PSD(HR(f)) represents the power spectrum density of HR(f), ψ(R2(n)) represents the phase of the proximal respiratory signal R2(n), ψ(SBP(n)) represents the phase of the systolic pressure sequence SBP(n), j is an imaginary unit, e is a natural constant, Gain LFis the transfer function gain, P RB (f) is the cross spectral density between the proximal respiratory signal and the systolic pressure sequence at frequency f, P RR represents the autospectral density of the proximal respiratory signal at frequency f, w1, w2, w3, w4, w5 are the variable coefficients determined by calibration; N represents the number of sampling points;

[0196] By integrating the short-term fluctuations of heart rate and the strength of respiratory-heart rate coupling, the quantitative assessment of the vagus nerve was performed according to the following formula:

[0197] PAI=v1·HRV HF +v2·(1-PLV RH )+v3·Gain HF

[0198]

[0199] Where PAI represents vagus nerve activity, HRV HF is the power spectrum of the high frequency part of the heart rate, PLV RH is the phase lock value between the proximal respiratory signal and the heart rate sequence, ψ(R2(n)) represents the phase of the proximal respiratory signal R2(n), ψ(HR(n)) represents the phase of the heart rate sequence HR(n), P RH (f) is the cross spectral density between the proximal respiratory signal and the heart rate sequence at frequency f, P RR (f) represents the autospectral density of the proximal respiratory signal at frequency f, and v1, v2, and v3 are the variable coefficients determined by calibration.

[0200] The method provided in the embodiment of the present invention is implemented based on the device of the first embodiment and has the corresponding functions and beneficial effects of the device.

[0201] It should be understood that the above embodiments and descriptions only describe the principles, main features and advantages of the present invention. Without departing from the spirit and scope of the present invention, the present invention may be subject to various changes and improvements, and these changes and improvements all fall within the scope of protection of the present invention.

Claims

1. A portable multi-physiological parameter measuring device, characterized in that: include: The signal acquisition and preprocessing module is used to collect the user's PPG signal and the changes in the three-axis acceleration signal derived from the ups and downs of the chest, abdomen or head during breathing, and filter out the low-frequency drift and high-frequency noise in the collected signal; A PPG derived distal end respiratory signal extraction module, used to obtain the peak point envelope signal of the PPG signal as the distal end respiratory signal; The acceleration-derived proximal respiratory signal extraction module is used to perform modal decomposition on the triaxial acceleration signal to obtain multiple eigenmodes, and extract the eigenmode with the largest power spectrum density within a preset frequency range as the proximal respiratory signal; A time delay calculation module is used to perform millisecond-level timing alignment of the proximal respiratory signal and the distal respiratory signal, and calculate the time delay between the proximal respiratory signal and the distal respiratory signal; A blood pressure parameter calculation module, used to input the time delay as a delay parameter into a pre-built RDT mapping model to calculate the blood pressure parameter, wherein the RDT mapping model is used to calculate the blood pressure parameter according to the delay parameter; A breathing parameter extraction module is used to calculate the breathing parameters, including breathing depth, breathing pattern matching, breathing frequency, and inhalation-exhalation ratio, based on the distal end breathing signal and the proximal end breathing signal; The autonomic nerve assessment module is used to integrate blood pressure parameters, respiratory parameters, and heart rate variability characteristics to achieve quantitative assessment of the sympathetic and vagus nerves.

2. The portable multi-physiological parameter measuring device according to claim 1, characterized in that: The signal acquisition and preprocessing module is specifically a wearable device, which is provided with a photoelectric unit, an accelerometer and a preprocessing unit. The photoelectric unit is used to collect the user's PPG signal, the accelerometer is used to collect the changes in the three-axis acceleration signal derived from the ups and downs of the chest, abdomen or head during breathing, and the preprocessing unit is used to perform bandpass, phase-shift-free, and finite impulse response filtering on the PPG signal and the three-axis acceleration signal in sequence.

3. The portable multi-physiological parameter measuring device according to claim 1, characterized in that: The signal acquisition and preprocessing module is specifically a mobile device, which is provided with a flash, a camera, an accelerometer and a data acquisition processing unit. Under the control of the data acquisition processing unit, the flash and the camera flash at a predetermined frequency and the camera takes a picture of the measurement part. The accelerometer is used to collect changes in three-axis acceleration signals derived from the ups and downs of the chest, abdomen or head during breathing. The data acquisition processing unit is used to extract the user's PPG signal based on the picture, and perform bandpass, phase-shift-free, and finite impulse response filtering on the PPG signal and the three-axis acceleration signal in sequence.

4. The portable multi-physiological parameter measuring device according to claim 1, characterized in that: The PPG derived distal end respiratory signal extraction module specifically includes: A segmentation unit, used for dividing the collected PPG signal into a plurality of PPG signal segments; The standard deviation calculation unit is used to calculate the standard deviation of the PPG signal segment and the template waveform according to the following formula based on the pre-constructed PPG template waveform: Where SD is the standard deviation, S(n) is the sampled PPG signal, T(n) is the PPG template waveform, and N is the total number of sampling points in the PPG signal segment; A judging unit, used to judge whether SD is less than a set threshold SDT, if so, the current PPG signal segment is judged to be valid, otherwise, the current PPG signal segment is judged to be invalid; The distal end breathing signal generating unit is used to search for the peak points of the valid PPG signal segment in the PPG signal, connect all the peak points, and fill the data of the invalid PPG signal segment by interpolation to obtain the processed PPG signal, and then resample the processed PPG signal to obtain the distal end breathing signal R1(n).

5. The portable multi-physiological parameter measuring device according to claim 1, characterized in that: The acceleration-derived proximal respiratory signal extraction module specifically includes: The modal decomposition unit is used to perform modal decomposition on the triaxial acceleration signal to obtain the intrinsic mode set {IMF i ,i=1,2,…,M},IMF i is the i-th eigenmode obtained after modal decomposition of the triaxial acceleration signal, and M is the number of eigenmodes; A power spectrum density calculation unit, used to calculate the power spectrum density of each eigenmode in the frequency domain; The proximal end respiration signal generating unit is used to calculate the proximal end respiration signal according to the following formula: Where R2(n) represents the proximal respiratory signal, PSD(IMF i (f)) represents IMF i Power spectral density at frequency f.

6. The portable multi-physiological parameter measuring device according to claim 1, characterized in that: The time delay calculation module specifically includes: A time alignment unit is used to obtain the signal sampling timestamp of the signal acquisition and preprocessing module, so as to align the timing of the distal end respiratory signal with the proximal end respiratory signal at the millisecond level; A peak extraction unit, used for extracting peak points of the distal end respiratory signal and the proximal end respiratory signal; The time delay unit is used to calculate the time delay between the distal end respiratory signal and the proximal end respiratory signal at the peak points as the time delay between the distal end respiratory signal and the proximal end respiratory signal in the current respiratory cycle.

7. The portable multi-physiological parameter measuring device according to claim 1, characterized in that: The blood pressure parameter calculation module specifically includes: A morphological parameter extraction unit is used to search for valley points before and after the peak point of the proximal respiratory signal, obtain the start and end time of the current respiratory cycle according to the peak point and the valley point, and search the PPG signal segment within the start and end time of the respiratory cycle in the PPG signal, and calculate the average rise and fall time of all PPG waveforms in the signal segment; The blood pressure calculation unit is used to input the time delay and the average heart rate into a pre-built RDT mapping model to calculate the blood pressure parameters, wherein the pre-built RDT mapping model is specifically: In the formula, SBP and DBP are systolic blood pressure and diastolic blood pressure respectively, a S 、b S 、c S d S 、a D 、b D 、c D d D is the adjustment coefficient obtained by fitting the historical data using the multivariate linear regression function, τ is the time delay, UT is the average rise time of the PPG waveform, and DT is the average fall time of the PPG waveform.

8. The portable multi-physiological parameter measuring device according to claim 1, characterized in that: The breathing parameter extraction module specifically includes: The breathing depth calculation unit is used to calculate the average amplitude A1 of the distal end breathing signal, and the breathing depth is calculated according to the average amplitude A1: D=k·A1 Among them, k is the individualized parameter, D is the breathing depth; The breathing mode matching degree calculation unit is used to calculate the average amplitude A2 of the proximal end breathing signal, and combine it with the average amplitude A1 of the distal end breathing signal to calculate the breathing mode matching degree according to the following formula: Where μ represents the phase synchronization coefficient, N represents the number of sampling points, ψ(R1(n)) represents the phase of the distal respiratory signal R1(n), ψ(R2(n)) represents the phase of the proximal respiratory signal R2(n), and M represents the matching degree of the respiratory mode. The respiratory frequency calculation unit is used to identify the peak point of the proximal respiratory signal, calculate the average value S of the interval time between the two peak points, and calculate the respiratory frequency according to the following formula: Where, f represents the respiratory rate; The I / O ratio calculation unit is used to search for valley points before and after each peak point of the proximal respiratory signal, calculate the average value I of the interval between the front valley point and the peak point, and the average value E of the interval between the rear valley point and the peak point, and obtain the I / O ratio: Where R represents the inspiration-expiration ratio.

9. The portable multi-physiological parameter measuring device according to claim 1, characterized in that: The autonomic nerve assessment module specifically includes: The heart rate calculation unit is used to identify the peak points in the PPG signal, obtain the time interval between the two peak points, and calculate the heart rate sequence by the following formula: Where HR(m) represents the mth heart rate value, FM is the total number of peak points of the PPG signal, and PP(m) represents the time interval between the mth peak point and the m+1th peak point; The sympathetic nerve assessment unit is used to integrate blood pressure fluctuations, long-term heart rate fluctuations, and respiratory-blood pressure coupling strength to achieve quantitative assessment of the sympathetic nerves according to the following formula: <h2 style=";text-align:left;direction:ltr">SAI = w1 HRV<h2 style=";text-align:left;direction:ltr"> LF <h2 style=";text-align:left;direction:ltr"> +w2 SPV+w3 DPV+w4 (1-PLV<h2 style=";text-align:left;direction:ltr"> RB <h2 style=";text-align:left;direction:ltr"> )+w5 Gain<h2 style=";text-align:left;direction:ltr"> LF In the formula, SAI represents sympathetic nerve activity, SPV is the standard deviation of the systolic blood pressure series, DPV is the standard deviation of the diastolic blood pressure series, and HRV is LF is the power spectrum of the low-frequency part of the heart rate, PLV RB is the phase locking value between the proximal respiratory signal and the systolic pressure sequence, HR(f) is the Fourier discrete spectrum of HR(m), PSD(HR(f)) represents the power spectrum density of HR(f), ψ(R2(n)) represents the phase of the proximal respiratory signal R2(n), ψ(SBP(n)) represents the phase of the systolic pressure sequence SBP(n), j is an imaginary unit, e is a natural constant, Gain LF is the transfer function gain, P RB (f) is the cross spectral density between the proximal respiratory signal and the systolic pressure sequence at frequency f, P RR represents the autospectral density of the proximal respiratory signal at frequency f, w1, w2, w3, w4, w5 are the variable coefficients determined by calibration; N represents the number of sampling points; The vagus nerve assessment unit is used to integrate short-term heart rate fluctuations and respiratory-heart rate coupling strength to perform quantitative assessment of the vagus nerve according to the following formula: PAI=v1·HRV HF +v2·(1-PLV RH )+v3·Gain HF Where PAI represents vagus nerve activity, HRV HF is the power spectrum of the high frequency part of the heart rate, PLV RH is the phase lock value between the proximal respiratory signal and the heart rate sequence, ψ(R2(n)) represents the phase of the proximal respiratory signal R2(n), ψ(HR(n)) represents the phase of the heart rate sequence HR(n), P RH (f) is the cross spectral density between the proximal respiratory signal and the heart rate sequence at frequency f, P RR (f) represents the autospectral density of the proximal respiratory signal at frequency f, and v1, v2, and v3 are the variable coefficients determined by calibration.

10. A portable multi-physiological parameter measurement method, characterized in that: include: Collect the user's PPG signal and the changes in the three-axis acceleration signal derived from the ups and downs of the chest, abdomen or head during breathing, and filter out the low-frequency drift and high-frequency noise in the collected signal; Obtain the peak point envelope signal of the PPG signal as the distal end respiration signal; Perform modal decomposition on the triaxial acceleration signal to obtain multiple eigenmodes, and extract the eigenmode with the largest power spectrum density within a preset frequency range as the proximal respiratory signal; Perform millisecond-level timing alignment on the proximal respiratory signal and the distal respiratory signal, and calculate the time delay between the proximal respiratory signal and the distal respiratory signal; Inputting the time delay as a delay parameter into a pre-constructed RDT mapping model to calculate the blood pressure parameter, wherein the RDT mapping model is used to calculate the blood pressure parameter according to the delay parameter; According to the distal and proximal respiratory signals, the breathing depth, breathing pattern matching, respiratory rate, and inhalation-exhalation ratio are calculated. The blood pressure parameters, respiratory parameters and heart rate variability characteristics are integrated to achieve quantitative evaluation of the sympathetic and vagus nerves.

Images