CN111493874B - Human respiratory rate measurement system and intelligent seat with same - Google Patents
Human respiratory rate measurement system and intelligent seat with same Download PDFInfo
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/08—Detecting, measuring or recording devices for evaluating the respiratory organs
- A61B5/0816—Measuring devices for examining respiratory frequency
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
- A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
- A61B5/1101—Detecting tremor
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
- A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
- A61B5/113—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb occurring during breathing
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/68—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient
- A61B5/6887—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient mounted on external non-worn devices, e.g. non-medical devices
- A61B5/6891—Furniture
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7203—Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal
- A61B5/7207—Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal of noise induced by motion artifacts
- A61B5/721—Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal of noise induced by motion artifacts using a separate sensor to detect motion or using motion information derived from signals other than the physiological signal to be measured
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7235—Details of waveform analysis
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
- Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation
Abstract
The invention relates to a human respiratory rate measurement system and an intelligent seat provided with the measurement system, wherein the measurement system at least comprises at least one sensor for collecting a first signal caused by physiological activities of a human body in a contact manner and a central processing unit for performing operation processing on the first signal to obtain respiratory rate parameters of the human body, and the central processing unit is configured to: selecting n peak points closest to the origin of the time axis coordinates in a manner of f in percentage of the total peak points to calculate the average value of the respiratory rate
Wherein D is the total spacing between the first peak point and the nth peak point; and redefining the working mode of the peak point with the residual percentage ratio of 1-f in the operation result based on the average value of the respiratory frequency.
Description
The invention relates to a physiological parameter measuring system and a divisional application of an intelligent seat with the measuring system, wherein the application number is 201810833110.4, the application date is 2018, 7 and 25, the application type is the invention.
Technical Field
The invention belongs to the technical field of measurement, and particularly relates to a physiological parameter measurement system and an intelligent seat with the measurement system.
Background
As an important human body physical sign parameter, how to accurately measure the parameter by a non-contact measurement mode is always one of the hot spots of research in biomedical engineering and instrument fields. The imaging type photoplethysmography technology is a non-contact physiological parameter detection technology developed on the basis of PPG, and the technology utilizes imaging equipment to carry out video acquisition on information containing a detected part, and realizes a biomedical detection method for extracting physiological parameters such as heart rate, respiratory rate, blood oxygen saturation and the like by processing a sensitive area of a video image. The IPPG technology has the advantages of low cost, non-contact, safety, continuous measurement, simple operation and the like, and provides a new solution and scheme for the research of non-contact physiological signal measurement and remote medical monitoring.
In the prior art, in the research aspect of extracting heart rate indexes of human bodies based on the IPPG technology, mainly, an extraction method of heart rate and respiratory rate is direct application or improvement optimization of research ideas. The heart rate is mainly extracted from a G channel of an observation signal generated by facial videos, or is extracted by three-channel blind source separation based on a classical ICA algorithm such as JADE and the like, and the respiration rate is further extracted from spectrum analysis of the heart rate. In addition, part of the methods incorporate facial video tracking algorithms to overcome noise interference problems. In most cases, the effect of using the G-channel method is indeed good when the interference noise is small, but the method has drawbacks when there is more interference noise, and more noise sources affect the separation effect of the three-channel ICA algorithm. Although the face video tracking algorithm can solve the problem of the movement of the face of the subject to a certain extent, noise interference caused by factors such as local fine changes of the face, weak changes of light and the like cannot be overcome well. Meanwhile, the existing method is basically based on the power spectrum after FFT for judging the source signal channel after ICA separation, and also faces the interference problem of a noise source, and under the condition of more separation channels, the judging accuracy of the source channel is very important for the robustness of an algorithm. In addition, synchronous extraction of heart rate and respiratory signals cannot be achieved in the existing method, and synchronous measurement of heart rate and respiratory is achieved.
Patent document publication No. CN105147293a discloses a system and method for realizing respiratory rate measurement, including a blood flow change acquisition module for illuminating the skin surface with a white LED lamp and acquiring a blood flow change signal caused by human heart beating; the respiratory wave signal acquisition module is used for acquiring respiratory wave signals from the blood flow wave change signal processing; the respiratory frequency calculation module is used for processing the respiratory wave signals to obtain respiratory frequency. The system and the method for realizing the measurement of the respiratory rate by adopting the structure can be used for applying the functional module to various positions of a human body suitable for measurement, and the real-time respiratory rate is obtained through white light optical irradiation and blood flow change monitoring. In the process of measuring the respiratory rate, a user is required to fix the data acquisition module at a designated position of the body in a wearing manner, and the operation is complex and is easily influenced by the environment.
Disclosure of Invention
The term "module" as used herein describes any hardware, software, or combination of hardware and software capable of performing the functions associated with the "module".
In view of the deficiencies of the prior art, the present invention provides a physiological parameter measuring system comprising at least a data acquisition unit communicatively coupled to a central processing unit, the data acquisition unit acquiring a first signal caused by physiological activity of a human body based on at least one sensor in a contact-wise manner, wherein the central processing unit is configured to perform at least the following steps The first signal is subjected to operation processing to obtain respiratory frequency parameters of a human body: filtering the first signal in a mode of filtering signals of a specific frequency band to obtain a second signal, performing operation processing on the second signal based on an autocorrelation function, and determining peak points in an operation result along a time axis according to a set sequence; selecting n peak points closest to the origin of the time axis coordinates in a manner of f in percentage of the total peak points to calculate the average value of the respiratory rate
Where D is the total spacing between the first peak point and the nth peak point.
According to a preferred embodiment, the central processing unit is further configured to redetermine the operation mode of the remaining percentage 1-f peak points in the operation result based on the average value of the respiratory frequency, wherein redetermining the remaining percentage 1-f peak points comprises at least the following steps: in the time range of [0, T]The point with the maximum operation result value is selected as the first peak point in the operation results of (1), and t is used 1 Indicating its time of occurrence. When the h peak point is confirmed and the occurrence time corresponding to the h peak point is t h In the case of being in the time range of
The point with the maximum value of the filtering operation result is taken as the h+1st peak point, and t is used h+1 Indicating the occurrence time of the method, wherein h is an integer greater than or equal to 2.
According to a preferred embodiment, for a time sequence x (t), the autocorrelation function is defined by the following formula:
(x t+h -x’)(x t -x ') 0.ltoreq.h.ltoreq.n, where n represents the number of sampling points, h represents the time interval between sampling points, x' represents the mean value of the sampling points and is defined by the following formula: />
According to a preferred embodiment, the first signal is a harmonic signal composed of at least a vibration wave signal generated by limb movement and a vibration wave signal generated by respiration, and a plurality of the sensors are arranged in a matrix form of at least three orders in a manner of being spaced from each other to acquire the first signal, wherein the specific frequency band is set to 6Hz to 10Hz to filter out the vibration wave signal generated by limb movement; or the specific frequency band is determined at least according to the following steps: and determining a first frequency domain, a second frequency domain and a third frequency domain in which the amplitudes of the first signals acquired by at least three sensors on the same side of the matrix are in an increasing trend along the extending direction parallel to the leg when the human body sits, wherein the specific frequency band is determined by the maximum left end point and the minimum right end point among the end points of the first frequency domain, the second frequency domain and the third frequency domain when the first frequency domain, the second frequency domain and the third frequency domain are intersected.
According to a preferred embodiment, determining peak points in the operation result sequentially along the time axis in order includes at least the following steps: and (3) carrying out peak initial judgment on the sample data after smooth filtering, acquiring all the sample data between a rising edge starting point and a falling edge ending point of the peak when the sample data meets the preset peak requirement, and recording the peak channel number and the peak sequence. And carrying out weighted average calculation on all the acquired sampling data between the rising edge starting point and the falling edge ending point, obtaining and storing an accurate value of the peak value position.
According to a preferred embodiment, the physiological parameter measuring system further comprises an ac amplifier for amplifying a signal, an analog-to-digital converter for converting an analog signal into a digital signal, and a filter for filtering out signals of a specific frequency band, wherein the first signal is transmitted to the central processing unit in such a manner that the first signal is processed by the ac amplifier, the analog-to-digital converter and the filter in sequence; the alternating current amplifier is configured to have an operation mode of a first-stage amplification circuit having a gain of 10 and a second-stage amplification circuit having a maximum gain of 20 and being set to an operation mode in which the gain can be increased or decreased.
According to a preferred embodiment, the physiological parameter measuring system is further configured to pre-warn of a fall in the form of a fall with a time advance of the sitting person according to the following steps:
determining the time T when the data signals acquired by the first sensor and the second sensor are equal to zero in the first set time period A for the first time 1 And time T 2 The method comprises the steps of carrying out a first treatment on the surface of the At omega 1 <0 and T 2 -T 1 >In case of alpha, the data signal acquired at the third sensor is for the first time smaller than the data acquired at the second sensor for a time period T 2 -T 1 Time T of the mean value in 3 Generating pre-warning information of forward tilting and falling.
In addition to the aforementioned physiological parameter measuring system, the present invention also provides an intelligent seat comprising at least a seat cushion and a backrest. The intelligent seat is not only provided with the physiological parameter measuring system for collecting respiratory frequency data of sitting people, but also provided with a plurality of sensors on the cushion and the backrest for collecting the first signals.
According to a preferred embodiment, the intelligent seat further comprises at least a sitting position recognition unit and an identification unit for identifying the identity information of the sitting person, both communicatively coupled to the central processing unit, wherein the identification unit is configured to be able to determine the working mode of the sitting person's identity based at least on fingerprint recognition, weight recognition and/or sitting behavior recognition; wherein the sitting posture recognition unit is configured to recognize the sitting posture of the sitting person according to the following working modes: establishing a three-dimensional coordinate system by taking the geometric center of the cushion as the origin of coordinates; acquiring acceleration values a along the x-axis direction, the y-axis direction and the z-axis direction respectively x 、a y And a z The method comprises the steps of carrying out a first treatment on the surface of the Acquiring angular velocity values w along the x-axis direction, the y-axis direction and the z-axis direction, respectively x 、w y And w z The method comprises the steps of carrying out a first treatment on the surface of the Determining a rotation angle θ based on the acceleration value and the angular velocity value 0 Pitch angle omega 0 And roll angle
Based on the rotation angle theta 0 Pitch angle omega 0 And roll angle->
The sitting posture of the sitting person is identified as leaning forward, backward, left, right or rotated.
According to a preferred embodiment, the cushion is provided with a plurality of sensors which are arranged at intervals according to a matrix form, wherein the sensors at the edge of the matrix collect the first signals in a mode that the sampling frequency is smaller than that of the sensors at the central part of the matrix; in the height direction along the backrest, a first sensor corresponding to the shoulder and back of the human body, a second sensor corresponding to the chest of the human body and a third sensor corresponding to the waist and back of the human body are arranged in a spaced manner, wherein the second sensor is arranged to acquire the first signals in a manner that the number of the second sensors is greater than that of the first sensors or the second sensors.
The beneficial technical effects of the invention are as follows:
(1) In the process of analyzing the respiratory rate, the data signals acquired by the sensor are extremely easy to be influenced by interference signals such as limb movement of a human body and vibration in the surrounding environment, and the interference signals with specific frequency are filtered through the filter, so that the vibration signals caused by respiration can be effectively obtained.
(2) In the process of determining the peak value of the autocorrelation function based on the peak finding algorithm to determine the respiratory rate, the invention only intercepts the first 20% of calculation results to calculate the average respiratory rate, and determines the rest 80% of peak points in the initial data again by taking the calculated average respiratory rate as a reference, thereby being capable of effectively avoiding calculation result drift caused by the quasi-periodic characteristic of respiration.
(3) According to the intelligent seat disclosed by the invention, the breathing frequency of a sitting person is analyzed and calculated in a contact mode, so that the inconvenience caused by the fact that the sitting person collects the breathing frequency by wearing special equipment is avoided, meanwhile, the back and/or neck of the sitting person can be prevented from leaning against the backrest when sitting, the measurement of the breathing frequency can be realized, and the intelligent seat is more in line with the usual use state of the seat.
Drawings
FIG. 1 is a modular schematic view of a preferred physiological parameter measuring system of the present invention;
FIG. 2 is a schematic diagram of a fast Fourier transform processed vibration wave signal obtained by beating a mattress once in 30 seconds;
FIG. 3 is a schematic diagram of the FFT operation processed vibration wave signals obtained by beating the mattress once every 30 seconds;
FIG. 4 is a schematic diagram of a fast Fourier transform processed vibration wave signal obtained by continuously scratching a mattress for a set period of time;
FIG. 5 is a schematic view of the construction of a preferred smart seat of the present invention;
FIG. 6 is a schematic diagram of a peak captured based on a preferred peak finding algorithm of the present invention;
FIG. 7 is a schematic flow chart of the preferred initial judgment of the peak value in the invention;
FIG. 8 is a schematic diagram of the operation of a preferred sitting position identification unit of the invention; and
fig. 9 is a schematic circuit diagram of a preferred ac amplifier of the present invention.
List of reference numerals
1: sensor 2: and a filter 3: cushion for sitting
4: backrest 5: sitting posture recognition unit 6: identity recognition unit
7: storage unit 8: the central processing unit 9: fingerprint input unit
10: ac amplifier 11: analog-to-digital converter 12: mobile terminal
13: data acquisition unit
101: the first sensor 102: the second sensor 103: third sensor
Detailed Description
The following detailed description refers to the accompanying drawings.
Example 1
The embodiment provides a method for measuring the respiratory rate of a human body, which at least comprises the following steps:
s1: collecting data signals related to a human body through a data collector;
S2: filtering the data signal to obtain sample data;
s3: determining a sample autocorrelation function based on the sample data;
s4: capturing and measuring a peak value of a sample autocorrelation function;
s5: respiratory rate is extracted based on the peak.
For ease of understanding, the following is discussed in detail based on the steps.
S1: and collecting data signals related to the human body through a data collector.
When a person inhales, the thoracic diaphragm contracts and causes the chest to expand. Natural expansion forces air into both lungs to balance the pressure inside and outside the body. Expiration begins when inspiration ends. During exhalation, the thoracic diaphragm relaxes, which in turn causes the chest to contract, and air is expelled from the lungs. During respiration and inspiration, the chest diaphragm contracts regularly to vibrate the human body slightly up and down. The small vibrations are hardly visible to the human eye but can be collected based on sensor technology. For example, in the case where a human body is seated on a seat, a plurality of sensors 1 may be provided on a seat cushion of the seat that contacts buttocks of the human body to collect slight up-down vibration of the buttocks. The sensor may be at least one of a geophone, a pressure sensor or a vibration sensor for monitoring seismic waves. For example, in the case where the user is resting on a sofa or mattress at night, the above-described sensors may be provided in an interlayer of the sofa or mattress to collect data signals related to the human body.
Preferably, the data signals related to the human body include at least a vibration wave signal caused by respiration, a vibration wave signal caused by movement of a limb of the human body, and a vibration wave signal caused by an external object within a certain range from the human body, wherein the vibration wave signal caused by respiration, the vibration wave signal caused by movement of the limb of the human body, and the vibration wave signal caused by the external object within a certain range from the human body cannot be individually acquired by means of only the sensor. The vibration signal collected by the sensor is a mixed signal of the three vibration wave signals. The sensor 1 may be arranged to acquire the vibration signal in a sampling period of a certain frequency.
S2: the data signal is filtered to obtain sample data.
In the case where the sensor 1 collects vibration data for a period of time, the vibration data is subjected to a filtering process based on the filter 2 to filter out disturbance data therein. For example, the present invention is directed to determining the respiratory rate of a human body by analyzing minute vibrations caused during the respiration of the human body, and thus, disturbance data are vibration wave signals caused by the movement of limbs of the human body collected by a sensor and vibration wave signals caused by external objects within a certain range from the human body. The filter 2 allows a data signal to be obtained which is generated entirely by the vibrations of the human breath.
Preferably, the filter may be configured to perform the filtering process in such a manner as to filter out the signal of the set frequency. The frequency of signals to be filtered can be determined by performing contrast tests, for example, when vibration wave signals caused by the movement of the limbs of the human body need to be filtered, three sets of contrast tests are set and vibration caused by the movement of the limbs of the human body is simulated in different manners. Specifically, test group a was set to a mode of operation in which the mattress was tapped once during a certain period of time and vibration wave signals were acquired by the sensor. The test group B is set to an operation mode in which the mattress is tapped at intervals during a certain period of time and vibration wave signals are acquired by the sensor. The test group C is set to an operation mode in which the mattress is scratched continuously for a certain period of time and vibration wave signals are collected by the sensor. The acquired vibration wave signals are displayed after being processed by fast Fourier transform operation. For example, fig. 2 shows the fft-processed vibration wave signal obtained by beating the mattress once in 30 seconds, fig. 3 shows the fft-processed vibration wave signal obtained by beating the mattress once every 30 seconds, and fig. 4 shows the fft-processed vibration wave signal obtained by continuously scratching the mattress for a set period of time. It is clear from the vibration wave signals shown in fig. 2, 3 and 4 that the frequency of the vibration wave signal caused by the movement of the limb of the human body is generally greater than 6hz, and a steep increase trend occurs at 8 hz. The filter is set to be in an operation mode for filtering out vibration wave signals with the frequency of 6 Hz-10 Hz, so that the vibration wave signals caused by the limbs of the human body can be filtered out. Preferably, the frequency is set to 8Hz to 8.4Hz.
Preferably, the specific frequency band may be further determined according to the following steps: and determining a first frequency domain, a second frequency domain and a third frequency domain in which amplitudes of first signals acquired by at least three sensors positioned on the same side of the matrix are in an increasing trend along an extending direction parallel to legs of a human body when sitting, wherein when the first frequency domain, the second frequency domain and the third frequency domain range have intersections with each other, a specific frequency band is determined by a maximum left end point and a minimum right end point among end points of the first frequency domain, the second frequency domain and the third frequency domain. As shown in fig. 5, when 9 sensors are mounted on the seat cushion of the seat in such a manner as to constitute a 3-stage matrix, the seated person sits and is in a standard sitting posture with the buttocks thereof fully contacting the 9 sensors at the same time. The three sensors on the left and right parts of the matrix correspond to the left and right legs of the human body, respectively, in the extending direction along the thighs. At this time, most of the vibration wave signals caused by the human body limbs come from the legs, and the sensors at the left and right sides of the matrix are most directly affected by the legs, most responsive to the real frequency domain of the vibration wave signals caused by the legs. The sensor in the middle of the matrix is most affected by human respiration, and the acquired data has higher accuracy for calculating the respiratory frequency, and the vibration wave signal of the specific frequency of the sensor in the middle of the matrix can be filtered based on the specific frequency confirmed by the side of the matrix. For example, after the data collected by the three sensors corresponding to the left leg of the human body on the left side of the matrix are processed to form a curve with frequency on the abscissa and amplitude on the ordinate similar to that of fig. 2, frequency domains in which the amplitudes of the three sensors tend to increase are respectively [10, 20], [15, 30], [25, 40], wherein the value 10, the value 15 and the value 25 respectively represent the left end points of the three sensors, the value 20, the value 30 and the value 40 respectively represent the right end points of the three sensors, and the specific frequency band [20, 25] can be determined by selecting the maximum left end point 25 and the minimum right end point 20. The filter is set to an operating mode for filtering out vibration wave signals having frequencies of 20Hz to 25 Hz. The specific frequency band determined in the above manner is always maintained in the real frequency range which is most representative of the vibration wave signal caused by the movement of the human body limb. For example, the frequency domains acquired by the three sensors can represent the amplitude increase caused by the movement of the limbs of the human body to a certain extent, the frequency domains [15, 30] representing the frequency domains with the minimum error can be determined by adopting a method taking the intermediate value, obviously, the frequency domains [10, 20] and [25, 40] have errors but can reflect the real frequency of the vibration wave signals caused by the movement of the limbs, and the new frequency domains [20, 25] formed by further shrinking the frequency domains [15, 30] by using the right end point of the frequency domains [10, 20] and the left end point of the frequency domains [25, 40] have higher accuracy.
Preferably, the time domain signal acquired by the sensor is converted into a sinusoidal signal based on the frequency domain based on a fast fourier algorithm. For example, for an aperiodic continuous-time signal x (n), the continuous-spectrum signal x (k) calculated by the fourier transform algorithm can be expressed as follows:
wherein k is an integer between 0 and N-1,
x (N) is an input sequence of column length N, i.e. slice data in the time domain acquired by the sensor. x (k) is the output sequence of column length N, i.e. the data in the frequency domain after fast fourier transformation.
S3: a sample autocorrelation function is determined based on the sample data.
The autocorrelation function of the samples is arranged to extract a circadian rhythm from a time series. For a time series x (t), the sample autocorrelation function is defined by the following formula:
where n represents the number of sampling points, h represents the time interval, and x' represents the average of the samples, defined by the following formula:
when the time interval h is zero, the vibration power signal is perfectly aligned with itself, and the autocorrelation reaches a maximum. When the time delay interval starts to increase, the first vibration power signal remains the same, while the second vibration power signal is shifted to the right. The mismatch between the two signals results in a decrease of the sample autocorrelation function value. In case the time interval is equal to an integer multiple of the detection interval of the respiratory oscillation, the oscillation pulses in the first oscillation power signal and the oscillation pulses in the second oscillation power signal are well matched to produce a larger autocorrelation function value. The inference of vibration frequency can be achieved by detecting peaks in the sample autocorrelation function calculation.
Preferably, the second signal is subjected to operation processing based on the autocorrelation function, and peak points in the operation result are determined according to a set sequence along a time axis, wherein the set sequence may be according to a time sequence, so that the peak points on the whole time axis are sequentially determined. Preferably, the second signal with the set sequence can be divided into a plurality of parts along the time axis, the peak value of each subdivision part is obtained by parallel calculation of a plurality of processors, and the obtained peak value is restored to the whole time axis according to the time sequence, so that the running processing time of the data can be effectively shortened.
S4: peak capture and measurement of sample autocorrelation function. The position and the value of the peak value of the sample autocorrelation function are determined based on a peak finding algorithm. Fig. 7 shows a schematic flow chart of initial judgment of the peak value, and as shown in fig. 7, the initial judgment of the peak value can be performed by the following steps:
s401: and (3) carrying out peak initial judgment on the data after smooth filtering, acquiring all sampling data between a rising edge starting point and a falling edge ending point of the peak when the data meets the preset peak requirement, and recording the peak channel number and the peak sequence. For example, peak initiation of data can be achieved as follows. When the sampling data of one channel is received, the maximum value of the sampling data in the channel can be found by a comparison mode. The peak value judgment can be realized in a running water mode when running, and the judgment principle is as follows: setting the current value as 0, assigning the current value when the current value is larger than the current value, and obtaining the maximum value when the running water is finished. When the maximum value is determined, the maximum value is taken as the peak point and extends downwards by a preset value, if the maximum value extends downwards by 15dB to obtain a reference value, the maximum value is recorded as a peak value, and if the maximum value is larger than the reference value, judgment is not made that the maximum value is smaller than the reference value, when the sampled data sequentially rises and is higher than the reference value, the rising edge of the peak value is represented, and when the sampled data sequentially falls and is higher than the reference value, the falling edge of the peak value is represented. All sampled data between the peak rising edge start and falling edge end are acquired and the peak channel number and peak order are recorded.
S402: and carrying out weighted average calculation on all the acquired sampling data between the rising edge starting point and the falling edge ending point, obtaining and storing an accurate value of the peak value position. For example, a set of rising edge start to falling edge end data for the same channel is read. Setting the starting point abscissa of the rising edge as zero, multiplying the read ordinate by the corresponding abscissa and accumulating to obtain data Y. And accumulating the read ordinate to obtain data T. Calculating the ratio Y/T of the data Y and the data T, wherein the ratio Y/T is the accurate value of the peak position.
S5: respiratory rate is extracted based on the peak. The spacing between two adjacent peaks is the breathing frequency.
As shown in fig. 6, the peak value after processing the sample data can be obtained based on the peak finding algorithm. The first 20% of the total peak count is averaged to obtain an average of the peak spacing to represent the respiratory rate. For example, in the case of a total of 13 peak points as shown in fig. 6, only the intervals between the first 3 peak points are selected for average calculation, so that the situation that the calculation result deviates due to the quasi-periodic characteristic of the peak finding algorithm can be effectively avoided.
Preferably, the number of peak points determined based on the peak finding algorithm should be theoretically the same as the number of breaths in a specified period of time. The quasi-periodic nature of the peak finding algorithm causes errors in the calculation results, so that the occurrence of deviations is confirmed based on the positions of the peak points determined after a certain calculation amount. The correct position of the remaining 80% of the peak points can be determined again by:
S501: assuming that n peak points are the first 20% of the total peak points, the sum of the distances between the first and nth peak points is D, the average respiratory rate can be used
A representation;
s502: based on calculating the estimated respiratory frequency average value T, finding out the respiratory frequency average value within the time range of [0, T from the sample data by searching the maximum amplitude value]The 1 st peak point in the spectrum is used as t 1 Indicating the time of occurrence thereof;
s503: when h peak points have been confirmed and the occurrence time corresponding to the h peak point is t h In the case of (2) by finding the maximum amplitude value from the sample data in a time range
The h+1th peak point in the inner part; />
S504: step S503 is repeated until all remaining 80% peak points are determined.
Example 2
This embodiment is a further improvement of embodiment 1, and the repeated contents are not repeated.
The invention also provides an intelligent seat, which at least comprises a seat cushion 3 and a backrest 4, wherein a plurality of sensors 1 are arranged on the seat cushion and the backrest to collect data signals related to a human body. The sensor 1 is preferably a capacitive sensor and is arranged on the seat cushion and the backrest in such a way that it is connected to a resistor to form a resistance-capacitance circuit, so that the current use state of the intelligent seat can be identified by measuring the change of the capacitance of the sensor.
Preferably, as shown in fig. 5, the intelligent seat uses 16 sensors 1 in total to form a data acquisition unit 13 for acquiring data signals related to a human body, wherein 9 sensors are arranged on a seat cushion and 7 sensors are arranged on a backrest. The 9 sensors are arranged on the cushion in a manner forming a shape similar to a third-order matrix, wherein the sensors at the edge of the matrix are sampled at a first sampling frequency and the sensors at the middle of the matrix are sampled at a second sampling frequency. The first sampling frequency is smaller than the second sampling frequency to save energy consumption. Meanwhile, the sensor positioned in the middle of the matrix is influenced by the up-and-down vibration caused by the respiration of the human body to the greatest extent, so that the calculation accuracy of the respiration frequency of the human body can be improved by collecting data at a higher sampling frequency. In the height direction along the seat, 7 sensors are arranged on the backrest in a manner of being divided into three rows which are arranged in parallel at intervals, wherein two sensors are arranged on a first row closest to the seat cushion and a third row farthest from the seat cushion, and three sensors are arranged on a second row positioned in the middle of the first row and the third row so as to correspond to the lung positions of a human body. In the case of a standard sitting posture, the second row can correspond to the lung position of the human body. The sensors with a larger number on the second row can collect more comprehensive data related to the back fluctuation state, and then the respiratory rate of a human body can be better calculated through periodic fluctuation of the back.
Preferably, after the sensor is connected with the resistor to form the impedance circuit, it is possible to determine whether the seat is in the sitting state or the idle state of the user based on a change in the capacitance value of the sensor. By the following meansA kind of electronic device with high-pressure air-conditioning system
Modeling the discharge process of the sensor, where V (t) represents the voltage across the sensor at time t, V 0 Is the voltage across the sensor at the initial moment, R represents a resistance with a fixed resistance value and its resistance value may preferably be 2mΩ. The calculation formula of the sensor capacitance can be obtained by solving the modeling formula of the sensor discharge process in reverse>
Preferably, the sitting posture change of the sitting person can be analyzed and monitored based on the capacitance signal acquired by the sensor 1. When the user sits on the smart seat in a stationary state, such as sleeping, the gap between the human skin and the sensor is equal to the thickness of the clothing being worn. When the sitting posture of the sitting person changes, for example, the sitting posture is changed from left inclination to right inclination, the gap between the human skin and the sensors can change remarkably, and the current sitting posture of the sitting person can be identified by matching the change rule of the capacitance signals of the 9 sensors on the cushion.
Preferably, the intelligent seat further comprises a sitting posture recognition unit 5 communicatively coupled to the sensor, the sitting posture recognition unit 5 being adapted to receive a capacitance signal of the sensor on the seat cushion and/or the backrest and to process the change in the capacitance signal to determine the current sitting posture of the sitting person.
Preferably, the intelligent seat is configured with an operation mode in which the breathing frequency of the sitting person is detected according to the method of measuring the breathing frequency of the human body of embodiment 1.
For ease of understanding, the specific working principle of the sitting posture recognition unit 5 will be discussed in detail below.
Fig. 8 shows a schematic diagram of the working principle of the sitting posture recognition unit. As shown in fig. 8, the pressure data collected by the sensor is converted into a capacitance valueThe represented digital signals are transmitted to a sitting posture recognition unit 5 for processing, the sitting posture recognition unit 5 establishes a coordinate system xyz by taking the position of a sensor positioned at the center of a matrix on a cushion as a coordinate origin, and the sitting posture recognition unit 5 is internally provided with an accelerometer and a gyroscope in an integrated mode, wherein acceleration values a in three axial directions of xyz are measured through the accelerometer x 、a y And a z Calculating angular velocity values w in three axial directions of xyz through a gyroscope x 、w y And w z . By matching w z The rotation angle theta can be obtained by integral calculation 0 . The pitch angle omega can be calculated by the following formula based on the acceleration value 0 And roll angle
Wherein the rotation angle theta 0 Pitch angle omega 0 And roll angle
Positive in the direction indicated by the arrow shown in fig. 8. The sensor is arranged to sample the pressure data based on a certain sampling frequency, which may be performed once per time interval T. For example, the time interval T may be set to 1 minute, and the rotation angle, pitch angle, and flip angle detected at the previous time of the time interval T are respectively θ 1 、ω 1 And->
The rotation angle, pitch angle and flip angle detected at a later time of the time interval T are respectively theta 2 、ω 2 And->
By presetting rotation angle, pitch angle and turnover angle respectivelyThreshold value theta max 、ω max And
the magnitude of the inclination, for example of the sitting posture, can be determined, for example, in +.>
In the case of (2), it can be preliminarily judged that the user has a tendency to lean left, in +.>
In the case of (2), it can be determined that the sitting posture of the user is greatly inclined left. At omega 1 <0, it can be initially determined that the user has a tendency to lean forward, at ω 1 <-ω max In the case of (2), it is possible to judge that the sitting posture of the user is greatly inclined forward. At theta 2 <θ 1 In the case of (2), it can be judged that the user has a left-hand rotation tendency at |θ 2 -θ 1 |>θ max In the above case, it can be determined that the sitting posture of the user is greatly rotated left.
Preferably, as shown in fig. 5, the first sensor 101, the second sensor 102, and the third sensor 103 are disposed to be spaced apart from each other in the height direction of the seat, wherein the third sensor may be disposed at a lower portion of the backrest corresponding to the lumbar region of the human body, the second sensor may be disposed at a middle portion of the backrest corresponding to the thoracic region of the human body, and the first sensor may be disposed at an upper portion of the backrest corresponding to the shoulder region of the human body. The sitting posture recognition unit 5 is also configured to early warn of a forward tilting fall of the sitting person in a manner with a time advance based on a sensor on the seat back. Specifically, the sitting posture recognition unit 5 performs early warning on the forward tilting fall of the sitting person in a manner with a time advance according to at least the following steps:
s1: determining a time T at which the data signals acquired by the first sensor 101 and the second sensor 102, respectively, are equal to zero for the first time within the first set time period a 1 And time T 2 。
Sensor acquisitionA data signal of zero for the set indicates that the seated person has been out of contact with the sensor and that the person is not exerting pressure on the sensor. The back of the seated person is fully in contact with the third sensor, the second sensor and the first sensor when the human body is not in the forward leaning state. In the process of leaning forward of the human body caused by fatigue of sitting personnel, the first sensor corresponding to the shoulder and back of the human body, the second sensor corresponding to the chest of the human body and the third sensor corresponding to the waist and back of the human body are gradually separated from contact with the human body according to the sequence. The first sensor and the second sensor being less than zero for the first set time period a are sufficient to indicate that the human body is in a forward leaning state. The person is separated from the back of the backrest for a short time and is attached to the backrest again, so that the sitting person is in a waking state, and the current mental state of the sitting person can be judged by setting the first set time period A only through conscious behaviors for adjusting sitting postures or treating special conditions. S2: at omega 1 <0 and T 2 -T 1 >In case of alpha, the data signal acquired at the third sensor 103 is for the first time smaller than the data acquired at the second sensor for a time period T 2 -T 1 Time T of the mean value in 3 Generating pre-warning information of forward tilting and falling.
T 2 -T 1 >Alpha indicates that the sitting person is in a slow forward tilting process, e.g. in the case of an elderly person watching tv at night, the forward tilting of his body due to fatigue is slow. When T is 2 -T 1 >Alpha and omega 1 <When the method is 0, the situation that the forward tilting of the body of the sitting person is likely to be an unconscious behavior caused by fatigue can be primarily judged, and the sitting person has a great forward tilting and falling risk under the condition that the behavior is not prevented by early warning. When the data acquired by the third sensor corresponding to the waist and back of the human body is zero, the whole sitting person is completely separated from the backrest and is in a completely forward tilting state, and early warning is obviously performed on the sitting person. In the process of unconscious forward tilting of the sitting person caused by fatigue, the reduced change trend of the data acquired by the second sensor is approximately the same as the change trend of the third sensor, and the data acquired by the second sensor is used for a time period T 2 -T 1 The average value in the seat is used as a standard for judging that the body of the sitting person is completely separated from the backrest, and early warning information is generated at the moment, so that the early warning has a certain time advance, and forward tilting and falling caused by untimely early warning are avoided.
Preferably, at average respiratory rate
Less than beta and T 2 -T 1 >In case of alpha, the data signal acquired at the third sensor 103 is for the first time smaller than the data acquired at the second sensor for a time period T 2 -T 1 Time T of the mean value in 3 Generating pre-warning information of forward tilting and falling.
The human body has different breathing frequencies when the person is in a waking state and tired to enter a light sleep state, for example, a sitting person with snoring can not snore in the waking state, the breathing frequency is at a normal value, when the person snores, due to nose dyspnea, the person can breathe through the mouth to obtain sufficient oxygen, the breathing frequency is reduced, and the sitting person can be primarily judged to be in a conscious and unconscious state under the condition that the average breathing frequency is smaller than beta. The specific value of β may be set according to the specific situation, and may be set as a respiratory rate value of the sitting person in an awake state, for example.
Example 3
This embodiment is a further improvement of embodiment 1 and embodiment 2, and the repeated description is omitted.
The intelligent seat of the present invention further comprises an identification unit 6. The identification unit is configured to learn an operational mode of sitting behavior of the sitting person based on a machine learning algorithm. For example, in a process of contacting a seat to sit down and fully sitting steady, different sitting persons make the duration of the process different based on different sitting habits. For example, the sitting person can form distinct sitting steps according to sitting habits, and for young people with younger ages, the sitting is often performed by directly sitting down, so that the instantaneous impact force on the seat is very high, and the pressure data collected by the sensor can be displayed in a form of abrupt increase at a certain moment after being processed through a time window. For older middle-aged and elderly people, the sitting is usually performed by slowly sitting down, and the pressure data collected by the sensor is displayed through a time window after being processed, so that the pressure data is continuously increased in a period of time. For example, a seated person may experience the same sitting position adjustment process from part to part during sitting in order to find a sitting position that fits his or her buttocks, leg length, and/or spine morphology. The sitting person may take a sitting position and then stand up and pull the seat back and forth to find a comfortable sitting position, or may take a sitting position by shaking the body left and right or shaking the body back and forth after sitting. The identification unit memorizes and learns different sitting behaviors of different sitting persons by means of a machine learning algorithm, based on differences in the sitting behaviors from each other to distinguish identities of family members, which are, for example, in the same family.
Preferably, the identity recognition unit 6 is further configured with an operation mode capable of recognizing the identity of the seated person based on the body weight of the seated person. When the smart seat is used in the range of family members in parents and two children, the smart seat may be further provided to include a storage unit 7 for storing weight data and identity data of the family members. After purchasing the intelligent seat, the identity information of the family member is input into the storage unit 7 by means of keyboard input or voice input, and the storage unit stores the identity information of, for example, "son" and "40 Kg" in quality in a corresponding and interrelated manner. When sitting personnel sits on the intelligent seat, the intelligent seat collects pressure data through the sensor and transmits the pressure data to the central processing unit 8 for processing calculation, so that quality data of the sitting personnel can be obtained, and the identity of the sitting personnel can be obtained after the quality data is matched with the quality data stored in the storage unit. After the identification of the seated person is completed, the quality data of the seated person stored in the storage unit may be replaced with quality data calculated by the central processing unit 8.
Preferably, the identity recognition unit 6 is further configured to be an operation mode capable of recognizing the identity of the seated person based on fingerprint information of the seated person. The intelligent seat further comprises a fingerprint entry unit 9. After the intelligent seat is purchased, the fingerprint of the family member is recorded through the fingerprint recording unit and stored through the storage unit 7, and identity information corresponding to the fingerprint is recorded when the fingerprint is recorded, and the fingerprint and the identity information are stored in the storage unit in a correlated storage mode. When sitting personnel sit on intelligent seat, the sitting personnel unlock the intelligent seat through the fingerprint input unit, and under the condition that the input fingerprint is matched with the fingerprint stored by the storage unit, the intelligent seat is electrified to work and the identity of the sitting personnel is identified.
Example 4
This embodiment is a further improvement of the foregoing embodiment, and the repeated contents are not repeated.
The intelligent seat further comprises an ac amplifier 10 for amplifying an ac signal and an analog-to-digital converter 11 for converting an analog signal into a digital signal. The data signal acquired by the sensor is first transmitted into an ac amplifier 10 for amplification. As shown in fig. 9, the ac amplifier includes at least two stages of amplifying circuits, wherein the first stage amplifying circuit and the second stage amplifying circuit are each provided with an RC band-pass filter that allows only waves of a specific frequency band to pass therethrough, and the specific frequency band of the RC band-pass filter is set to 0.25Hz to 10kHz. The gain of the first stage amplification circuit is 10 to reduce some interference from the amplification circuit itself, the maximum gain of the second stage amplification circuit is 20 and the gain of the second stage amplification circuit is set to an operating mode that can be dynamically adjusted by resistor R7. The maximum total gain of the entire ac amplifier is 200.
Preferably, the signal processed by the ac amplifier 10 is transmitted to an analog-to-digital converter and converted into a digital signal for subsequent processing.
Example 5
This embodiment is a further improvement of the foregoing embodiment, and the repeated contents are not repeated.
As shown in fig. 1, the present invention further provides a physiological parameter measurement system, which at least includes a sensor 1, a filter 2, a storage unit 7, an ac amplifier 10, an analog-to-digital converter 11 and a mobile terminal 12, all of which are communicatively coupled to a central processing unit 8, wherein a data signal collected by the sensor 1 is sequentially processed by the ac amplifier and the analog-to-digital converter in a first stage and then is transmitted to the filter for processing so as to filter an interference signal with a specific frequency, and the first data processed by the filter is transmitted to the central processing unit for processing in a second stage.
Preferably, the central processing unit is configured to perform the second stage of processing on the first data in the following manner.
A1: determining a sample autocorrelation function of the first data based on the first data, wherein for a time sequence x (t), the sample autocorrelation function is defined as:
where n represents the number of sampling points, h represents the time interval, x' represents the mean of the samples, where, 
A2: peak capture and measurement of sample autocorrelation function. The position and the value of the peak value of the sample autocorrelation function are determined based on a peak finding algorithm. Wherein the peak finding algorithm is defined by at least the following steps:
a201: and (3) carrying out peak initial judgment on the data after smooth filtering, acquiring all sampling data between a rising edge starting point and a falling edge ending point of the peak when the data meets the preset peak requirement, and recording the peak channel number and the peak sequence. For example, peak initiation of data can be achieved as follows. When the sampling data of one channel is received, the maximum value of the sampling data in the channel can be found by a comparison mode. The peak value judgment can be realized in a running water mode when running, and the judgment principle is as follows: setting the current value as 0, assigning the current value when the current value is larger than the current value, and obtaining the maximum value when the running water is finished. When the maximum value is determined, the maximum value is taken as the peak point and extends downwards by a preset value, if the maximum value extends downwards by 15dB to obtain a reference value, the maximum value is recorded as a peak value, and if the maximum value is larger than the reference value, judgment is not made that the maximum value is smaller than the reference value, when the sampled data sequentially rises and is higher than the reference value, the rising edge of the peak value is represented, and when the sampled data sequentially falls and is higher than the reference value, the falling edge of the peak value is represented. All sampled data between the peak rising edge start and falling edge end are acquired and the peak channel number and peak order are recorded.
A202: and carrying out weighted average calculation on all the acquired sampling data between the rising edge starting point and the falling edge ending point, obtaining and storing an accurate value of the peak value position. For example, a set of rising edge start to falling edge end data for the same channel is read. Setting the starting point abscissa of the rising edge as zero, multiplying the read ordinate by the corresponding abscissa and accumulating to obtain data Y. And accumulating the read ordinate to obtain data T. Calculating the ratio Y/T of the data Y and the data T, wherein the ratio Y/T is the accurate value of the peak position.
A3: respiratory rate is extracted based on the peak. The spacing between two adjacent peaks is the breathing frequency. The method of peak-based extraction of respiratory rate is defined by at least the following steps:
a301: assuming that n peak points are the first 20% of the total peak points, the sum of the distances between the first and nth peak points is D, the average respiratory rate can be used
A representation;
a302: based on calculating the estimated respiratory frequency average value T, finding out the respiratory frequency average value within the time range of [0, T from the sample data by searching the maximum amplitude value]The 1 st peak point in the spectrum is used as t 1 Indicating the time of occurrence thereof;
a303: when h peak points have been confirmed and the occurrence time corresponding to the h peak point is t h In the case of (2) by finding the maximum amplitude value from the sample data in a time range
The h+1th peak point in the inner part;
a304: step a303 is repeated until all remaining 80% peak points are determined.
Preferably, the detection system may further comprise a sitting position identification unit 5 for identifying the sitting position of the sitting person, an identity identification unit 6 for identifying the identity of the sitting person by the user and a fingerprint entry unit 9 for gathering a fingerprint of the sitting person, all communicatively coupled to the central processing unit.
Preferably, the storage unit 7 is configured to be at least capable of storing the fingerprint data entered by the fingerprint entry unit, the identity data identified by the identity recognition unit, the sitting posture data identified by the sitting posture recognition unit, and the breathing frequency data obtained by processing by the central processing unit.
Preferably, the mobile terminal 12 is configured to invoke the operation mode of display of the stored data in the storage unit 7. Through the mobile terminal, the sitting person and/or the third party person can intuitively view the sitting data of the sitting person.
It should be noted that the above-described embodiments are exemplary, and that a person skilled in the art, in light of the present disclosure, may devise various solutions that fall within the scope of the present disclosure and fall within the scope of the present disclosure. It should be understood by those skilled in the art that the present description and drawings are illustrative and not limiting to the claims. The scope of the invention is defined by the claims and their equivalents.
Claims (9)
1. A human respiratory rate measurement system comprises at least one sensor (1) for acquiring a first signal caused by physiological activity of a human body in a contact manner and a central processing unit (8) for performing an arithmetic processing on the first signal to obtain respiratory rate parameters of the human body,
characterized in that the central processing unit (8) is configured to:
filtering the first signal in a mode of filtering signals of a specific frequency band to obtain a second signal, performing operation processing on the second signal based on an autocorrelation function, and determining peak points in an operation result along a time axis according to a set sequence; selecting n peak points closest to the origin of the time axis coordinates in a manner that the percentage of the second signal to the total peak point is f to calculate the average value of the respiratory frequency
Wherein D is the total spacing between the first peak point and the nth peak point;
and redefining the working mode of the peak point with the residual percentage ratio of 1-f in the operation result based on the average value of the respiratory frequency.
2. The human respiratory rate measurement system according to claim 1, wherein the central processing unit (8) is configured to redetermine the peak points of the remaining percentage ratios 1-f at least as follows:
In the time range of [0, T]The point with the maximum operation result value is selected as the first peak point in the operation results of (1), and t is used 1 Indicating the time of occurrence thereof;
when the h peak point is confirmed and the occurrence time corresponding to the h peak point is t h In the case of being in the time range of
The point with the maximum value of the filtering operation result is taken as the h+1st peak point, and t is used h+1 Indicating the occurrence time of the method, wherein h is an integer greater than or equal to 2.
3. The human respiratory rate measurement system according to claim 2, wherein the central processing unit (8) is configured to: filtering the first signal to obtain a second signal in a mode of filtering signals of a specific frequency band, performing operation processing on the second signal based on an autocorrelation function, and determining peak points in an operation result along a time axis according to a set sequence, wherein,
the first signals are harmonic signals composed of at least vibration wave signals generated by limb movement and vibration wave signals generated by respiration, and the plurality of sensors (1) are arranged in a mode of being spaced from each other to form at least three-order matrix forms so as to acquire the first signals.
4. The human respiratory rate measurement system according to claim 3, wherein the specific frequency band is set to 6Hz to 10Hz to filter out the vibration wave signal generated by the limb movement;
or the specific frequency band is determined at least according to the following steps:
in a direction parallel to the extension direction of the legs of a human sitting, determining a first frequency domain, a second frequency domain and a third frequency domain in which the amplitudes of first signals acquired by at least three sensors (1) on the same side of the matrix as each other are in an increasing trend, wherein,
and determining the specific frequency band by using the maximum left endpoint and the minimum right endpoint in the endpoints of the first frequency domain, the second frequency domain and the third frequency domain when the intersection exists among the first frequency domain, the second frequency domain and the third frequency domain.
5. The human respiratory rate measurement system according to claim 4, wherein,
for a time sequence x (t), the autocorrelation function is defined by the following formula:
where n represents the number of sampling points, h represents the time interval between sampling points, and x' represents the average value of sampling points and is defined by the following formula:
6. the human respiratory rate measurement system according to claim 5, wherein determining peak points in the operation result sequentially in order along the time axis comprises at least the steps of:
Carrying out peak initial judgment on the sample data after smooth filtering, when the sample data meets the preset peak requirement, acquiring all sample data between a rising edge starting point and a falling edge ending point of the peak, and recording a peak channel number and a peak sequence;
and carrying out weighted average calculation on all the acquired sampling data between the rising edge starting point and the falling edge ending point, obtaining and storing an accurate value of the peak value position.
7. The human respiratory rate measurement system according to claim 6, further comprising an alternating current amplifier (10) for amplifying a signal, an analog-to-digital converter (11) for converting an analog signal into a digital signal, and a filter (2) for filtering out signals of a specific frequency band, wherein the first signal is transmitted into the central processing unit (8) in such a manner that it is processed sequentially through the alternating current amplifier (10), the analog-to-digital converter (11) and the filter (2);
the alternating current amplifier (10) is configured to have an operation mode of a first-stage amplification circuit and a second-stage amplification circuit, wherein the gain of the first-stage amplification circuit is 10, and the maximum gain of the second-stage amplification circuit is 20 and is set to an operation mode in which the gain thereof can be increased or decreased.
8. An intelligent seat, characterized in that it is equipped with a human respiratory rate measurement system according to one of the preceding claims for collecting respiratory rate data of at least sitting persons, said intelligent seat comprising at least a seat cushion (3) and a backrest (4), wherein a number of said sensors (1) are arranged in the seat cushion (3) and the backrest (4) for collecting said first signals.
9. The intelligent seat according to claim 8, characterized in that the intelligent seat at least further comprises a sitting posture recognition unit (5) and an identity recognition unit (6) for recognizing the identity information of the sitting person, which are both communicatively coupled to the central processing unit (8), wherein the identity recognition unit (6) is configured to be able to determine the working mode of the sitting person's identity based at least on fingerprint recognition, weight recognition and/or sitting behavior recognition; wherein, the liquid crystal display device comprises a liquid crystal display device,
the sitting posture recognition unit (5) is configured to recognize the sitting posture of the sitting person according to the following steps:
establishing a three-dimensional coordinate system by taking the geometric center of the cushion as the origin of coordinates;
acquiring acceleration values a along the x-axis direction, the y-axis direction and the z-axis direction respectively x 、a y And a z ；
Acquiring angular velocity values w along the x-axis direction, the y-axis direction and the z-axis direction, respectively x 、w y And w z ；
Determining a rotation angle θ based on the acceleration value and the angular velocity value 0 Pitch angle omega 0 And roll angle
Based on the rotation angle theta 0 Pitch angle omega 0 And roll angle
The sitting posture of the sitting person is identified as leaning forward, backward, left, right or rotated. />
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| CN109770878B (en) * | 2019-01-31 | 2024-04-02 | 浙江圣奥家具制造有限公司 | Intelligent seat, intelligent seat interaction system and intelligent seat interaction method |
| CN109805909B (en) * | 2019-01-31 | 2024-04-02 | 浙江圣奥家具制造有限公司 | System and method for measuring human body sign during sitting |
| CN111839514A (en) * | 2019-04-26 | 2020-10-30 | 西门子(深圳)磁共振有限公司 | Method and apparatus for interference correction in respiratory navigation and storage medium |
| ES2881862A1 (en) * | 2020-05-28 | 2021-11-30 | Dindog Tech S L | MONITORING DATA COMMUNICATION METHOD AND MONITORING SYSTEM (Machine-translation by Google Translate, not legally binding) |
| CN112494031A (en) * | 2020-11-26 | 2021-03-16 | 咸宁职业技术学院 | Respiration rate calculation method and device |
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| Publication number | Publication date |
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| CN108888271A (en) | 2018-11-27 |
| CN111493874A (en) | 2020-08-07 |
| CN111493875A (en) | 2020-08-07 |
| WO2020019358A1 (en) | 2020-01-30 |
| CN108888271B (en) | 2020-05-22 |
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