CN216629572U - Pressure reduction system and equipment based on respiratory training method - Google Patents
Pressure reduction system and equipment based on respiratory training method Download PDFInfo
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Abstract
The utility model relates to a pressure reduction system based on a respiratory training method, which comprises a controller, a first signal acquisition module and a second signal acquisition module, wherein the first signal acquisition module and the second signal acquisition module are electrically connected with the controller; the first signal acquisition module comprises a photoelectric probe and a lamp group capable of emitting specific wavelength, and the lamp group is annularly arranged on the periphery of the photoelectric probe; the second signal acquisition module comprises a processor and a motion sensor electrically connected with the processor, and the output end of the processor is electrically connected with the controller. The utility model can measure the blood pressure and the electrocardiosignal of the user in real time, train and guide the breath of the user, is convenient for the patient to use outside, and the processor calculates the breath rate of the user according to the motion signal received by the motion sensor, thereby meeting the actual application requirement.
Description
Technical Field
The utility model relates to the technical field of medical instrument rings, in particular to a blood pressure reducing system and equipment based on a respiratory training method.
Background
Hypertension is a clinical syndrome characterized by an increase in systemic arterial blood pressure, which may be accompanied by functional or organic lesions in organs such as heart, brain, and kidney. If the blood pressure can not be effectively controlled for a long time, the heart will be more and more burdened, and finally the heart failure of the human body can be caused.
At present, the hypertension treatment method mainly comprises drug treatment and voltage and current stimulation. But the drug treatment is easy to increase the dependence of the human body and can also bring burden to the body and the economy after long-term administration; the voltage and current stimulation cannot be fundamentally treated, and the treatment effect is not clear, so that the user experience is greatly influenced.
SUMMERY OF THE UTILITY MODEL
In order to solve the above problems, an object of the present invention is to provide a blood pressure lowering system and device based on a respiration training method, which can measure the blood pressure and the electrocardiographic signal of a user in real time, train and guide the respiration of the user, and facilitate the patient to use outdoors.
A pressure reduction system based on a respiratory training method comprises a controller, a first signal acquisition module and a second signal acquisition module, wherein the first signal acquisition module and the second signal acquisition module are electrically connected with the controller; the first signal acquisition module comprises a photoelectric probe and a lamp group capable of emitting specific wavelength, and the lamp group is annularly arranged on the periphery of the photoelectric probe; the second signal acquisition module comprises a processor and a motion sensor electrically connected with the processor, the output end of the processor is electrically connected with the controller, and the processor calculates the breathing rate of the user according to the motion signal received by the motion sensor.
In addition, the decompression system based on the breathing training method provided by the utility model can also have the following additional technical characteristics:
furthermore, the first signal acquisition module further comprises a photoelectric signal processing circuit electrically connected with the photoelectric probe.
Furthermore, the photoelectric signal processing circuit comprises an amplifier, a band-pass filter, an integrator and a high-speed ADC which are connected in sequence; wherein, the input end of the amplifier is electrically connected with the output end of the photoelectric probe.
Further, the lamp group consists of three LED lamps emitting specific wavelengths, and each lamp group module comprises green light, red light and infrared light; the central wavelength of the green light is 525nm, the central wavelength of the red light is 660nm, and the central wavelength of the red light is 940 nm.
Furthermore, the photoelectric signal processing circuit also comprises a filtering unit capable of carrying out 50Hz power frequency filtering.
Furthermore, the system also comprises a display screen and a voice auxiliary module which are electrically connected with the controller; wherein the voice assistance module comprises a microphone electrically connected with the controller through an audio processing circuit.
Furthermore, the system also comprises an alarm module, wherein the alarm module comprises an alarm indicator light and a mobile terminal or a cloud server which is communicated with the controller through a communication module.
In order to solve the above technical problem, an embodiment of the present invention further provides a pressure reduction device based on a respiratory training method, where the device is a wearable or portable device, and the wearable or portable device includes but is not limited to a watch, a bracelet, or a shell of a finger stall; the wearable or portable device can be adapted to the above-described breath training based depressurization system.
In order to solve the above technical problem, an embodiment of the present invention further provides a method for lowering blood pressure based on a respiratory training method, where the method includes:
when a user activates equipment, acquiring blood pressure information and related human body parameters of the user, establishing an individualized model aiming at the user at a cloud end, and recommending a corresponding respiratory training scheme by the individualized model according to the related information of the user;
the personalized breathing training scheme guides a user to carry out breathing training;
after the breathing training of the current period is finished, the breathing frequency and the breathing time of the next stage of breathing training are adjusted according to the effect of the breathing training.
Further, the method of guiding a user to breath training according to a selected breath training regimen comprises:
measuring the respiratory frequency of a user in real time, and judging whether the current respiratory frequency of the user is lower than a first preset threshold value or not;
if so, prompting the user to breathe according to the rhythm;
if not, prompting the user to keep the breathing rhythm.
The pressure reduction system based on the respiratory training method comprises a first signal acquisition module and a second signal acquisition module which are electrically connected with a controller; the first signal acquisition module comprises a photoelectric probe and a lamp group capable of emitting specific wavelength, and the lamp group is annularly arranged on the periphery of the photoelectric probe; the second signal acquisition module comprises a processor and a motion sensor electrically connected with the processor, the output end of the processor is electrically connected with the controller, and the processor calculates the breathing rate of the user according to the motion signal received by the motion sensor. The utility model can conduct slow breathing training guidance for the hypertension user and is convenient for the patient to use when going out, thereby greatly improving the user experience and meeting the practical application requirements.
Drawings
Fig. 1 is a block diagram of a pressure reduction system based on a respiratory training method according to an embodiment of the present invention;
FIG. 2 is a block diagram of a detailed structure of the first signal acquisition module in FIG. 1;
FIG. 3 is a schematic diagram of the optoelectronic signal processing circuit and the optoelectronic probe of FIG. 2;
FIG. 4 is a schematic view of the position of the photoelectric probe and the lamp set in FIG. 2;
FIG. 5 is a circuit diagram of a filtering unit in the optoelectronic signal processing circuit;
FIG. 6 is a schematic diagram of an application of a pressure reduction device based on breathing training according to another embodiment of the present invention;
fig. 7 is a flowchart of a pressure reduction method based on a respiratory training method according to another embodiment of the present invention.
The following detailed description will further illustrate the utility model in conjunction with the above-described figures.
Detailed Description
In order to make the objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in detail below. Several embodiments of the utility model are presented in the drawings. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete.
It will be understood that when an element is referred to as being "secured to" another element, it can be directly on the other element or intervening elements may also be present. When an element is referred to as being "connected" to another element, it can be directly connected to the other element or intervening elements may also be present. As used herein, the terms "vertical," "horizontal," "left," "right," "up," "down," and the like are for illustrative purposes only and do not indicate or imply that the referenced device or element must be in a particular orientation, constructed or operated in a particular manner, and is not to be construed as limiting the present invention.
In the present invention, unless otherwise expressly specified or limited, the terms "mounted," "connected," "secured," and the like are to be construed broadly and can, for example, be fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood by those skilled in the art according to specific situations. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.
As shown in fig. 1 to fig. 6, in view of the above problems, an embodiment of the present invention discloses a pressure reduction system based on a respiratory training method, which includes a controller 10, a first signal acquisition module 20 electrically connected to the controller 10, a second signal acquisition module 30, a display screen 40, and a voice auxiliary module 50.
Further, the controller 10 employs a low power consumption MCU controller such as STM32 series, which has an ADC capable of collecting voltage signals processed by each module, and is provided with a plurality of communication interfaces supporting different protocols, such as SPI interface, I/O interface, UART interface, etc., which can be used for connection and communication of a plurality of modules.
Further, the first signal acquisition module 20 includes a photoelectric probe 21, a photoelectric signal processing circuit 22 electrically connected to the photoelectric probe 21, and a lamp set 23 capable of emitting a specific wavelength.
Specifically, the lamp group 23 may be composed of three LED lamps emitting specific wavelengths, and each lamp group module includes green light, red light, and infrared light. The green light central wavelength is 525nm and is used for calculating the heart rate, the red light central wavelength is 660nm, and the red light central wavelength is 940nm and is used for calculating the blood oxygen content. The three lamp sets are distributed on the periphery of the photoelectric probe 21 and are arranged at the bottom of the portable device, and the bottom of the portable device can be tightly attached to the surface of the skin of a human body, so that light signals can enter the skin more easily while light leakage is reduced, and the photoelectric signals can be absorbed by the human body more easily. The photoelectric detection head 21 adopts a photodiode and is used for absorbing photoelectric signals fed back by human tissues. When the user wears the user equipment, the three lamp groups acquire data in a coordinated mode, and the controller adjusts different acquisition modes according to the quality of the acquired signals. In a common mode, the three LED lamp components are started in a time-sharing mode, the controller compares the quality of different channel signals collected in a time-sharing mode, and the channel with the best signal is selected for calculation. If the signals generated by the three LED lamp groups in the common mode are poor, the controller controls to start the special mode, and the three LED lamp groups are started and collected simultaneously. The two signal acquisition modes are cooperatively used, so that the power consumption is saved, the signal acquisition range of the equipment on the skin surface is expanded, the condition of poor signal quality caused by local scars or wrinkles on the skin surface of a human body is better avoided, and the usability of the equipment is enhanced.
The photoelectric signal processing circuit 22 comprises an amplifier with an input end electrically connected with the output end of the photoelectric probe 21, a band-pass filter with an input end electrically connected with the output end of the amplifier, an integrator with an input end electrically connected with the output end of the band-pass filter, and a high-speed ADC with an input end electrically connected with the output end of the integrator. Wherein the amplifier is a transimpedance amplifier.
It can be understood that the light with specific wavelength emitted by the LED lamp group penetrates the surface of the skin of the human body, the photoelectric probe absorbs the photoelectric signal reflected by the surface of the human body, and sends the photoelectric signal into the photoelectric signal processing circuit to perform signal processing, and then extracts an effective signal, and transmits the effective signal to the controller, and the controller obtains a heart rate value, a respiration rate value, a blood oxygen value, and a blood pressure value according to the signal.
Furthermore, the second signal acquisition module comprises a processor and a motion sensor electrically connected with the processor, and the output end of the processor is electrically connected with the controller. The processor calculates the user's breathing rate from the motion signals received by the motion sensor.
Specifically, the calculation of the respiration rate mainly comprises a signal preprocessing part and a feature extraction part. The premise of the establishment of the respiration rate measurement based on the accelerometer sensor (three axes) is low noise interference or no noise interference, because the accelerometer sensor can sense weak abdominal cavity fluctuation and can also sense macroscopic motions such as wrist overturning translation and the like; considering that the noise signals generated by non-respiratory motion can interfere the accuracy rate of respiratory measurement, the utility model introduces the signals of the gyroscope sensors to jointly form six-axis sensor signals to perform fusion perception decision on the respiratory rate.
In the signal preprocessing stage:
in the prior art, a band-pass filter with fixed parameters is mostly adopted for stable denoising, and the method cannot effectively filter noise interference caused by wrist movement. In order to achieve the purpose of wrist movement noise adaptive filtering, the utility model adopts a special adaptive filter, specifically,
firstly, acquiring instantaneous motion noise frequency contained in a gyroscope signal by an analytic signal method (HHT, Hilbert-Huang transform), and then carrying out notch filtering processing on a motion signal acquired by an accelerometer to finally achieve the purpose of filtering wrist motion noise.
Characteristic extraction:
it is well known in the art of feature extraction to perform peak-finding on the original triaxial acceleration signal to extract some time-domain features, which is efficient under stable signals but is too single in dimension for daily use and thus less robust.
The utility model respectively performs time domain characteristic extraction and frequency domain comprehensive characteristic extraction on the filtered single-channel accelerometer signals.
Specifically, in the aspect of time domain feature extraction, for an acceleration signal with a period of time t, Fs is a sampling rate, and for accurately detecting each peak point fed back due to respiration fluctuation, adaptive iteration peak searching is performed in combination with main frequency information of the signal to obtain a reliable peak feature. The iteration is as follows,
setting an initial peak finding width w1 based on the historical respiration rate;
the first iteration will output a new breath rate breath interval s1 (the time difference between two breaths, i.e., the inverse of the instantaneous breath rate);
carrying out short-time Fourier transform on the signal to obtain frequency domain front three-peak frequency points f1, f2 and f 3;
finding the frequency domain peak closest to the breathing frequency (namely the frequency point with the minimum absolute value difference with 1/s 1), and assuming that the closest frequency point is f2, the new peak searching width is (Fs/f2+ s1 x Fs)/2;
carrying out secondary peak searching according to the new peak searching width to obtain a breathing interval s 2;
judging whether the 1/s2 is closer to the f2 and the f 3556/s 1, and assuming that the closer is 1/s1, the new peak-seeking width is (s2 × Fs + s1 × Fs)/2;
carrying out three peak seeking according to the new peak seeking width to obtain a breathing interval s 3;
judging whether the 1/s3 is closer to the 1/s2 and 1/s1, and assuming that the closer is 1/s2, and the new peak-seeking width is (s2 x Fs + s3 x Fs)/2;
stopping searching peaks until the difference value between the new peak searching width and the last peak searching width is less than 1;
and obtaining the real-time respiration rate characteristic according to the finally obtained respiration interval.
Further, referring to fig. 5, the optoelectronic signal processing circuit further includes a filtering unit capable of performing 50Hz power frequency filtering. The Q value of the unit is increased along with the increase of the feedback coefficient beta (0< beta <1), and the relation between the Q value and the beta is as follows: q-1/4 (1- β), and adjusting the ratio of R16 to R17 changes the Q value. It can be understood that the power frequency noise is a main noise source, and the power frequency noise is overlapped with the frequency band of the electrocardiosignal, so that the analysis of the electrocardiosignal is seriously influenced. Therefore, the photoelectric signal processing circuit and the motion signal processing circuit are both provided with 50Hz power frequency filtering, and the power frequency signal interference is eliminated.
Furthermore, the system also comprises a display screen and a voice auxiliary module which are electrically connected with the controller; wherein the voice assistance module comprises a microphone electrically connected with the controller through an audio processing circuit.
The audio circuit has two realization modes including that a loudspeaker is arranged on the equipment and is controlled by an MCU (microprogrammed control unit); or the loudspeaker or the voice system of the mobile equipment is controlled to work through the APP at the mobile equipment terminal. The user can breathe the adjustment according to voice prompt, especially when lying and breathe the training, need not to watch equipment display screen, lets the user can adjust breathing rhythm the very first time, makes the user obtain better breathing training step-down effect.
It can be understood that the display screen and the voice auxiliary module form a human-computer interaction module through the controller, the display screen is used for initiating breathing training and displaying a training result, and when the voice auxiliary module is used for breathing training, the user is reminded to adjust breathing according to the breathing frequency detected in real time.
Furthermore, the system also comprises an alarm module, wherein the alarm module comprises an alarm indicator light and a mobile terminal or a cloud server which is communicated with the controller through a communication module.
It can be understood that the alarm module can be used for carrying out remote early warning when the blood pressure of the user is abnormal, so that related personnel can be reminded to take rescue measures in time, and the actual application requirements are met.
The utility model provides a pressure reduction system based on a respiratory training method, which comprises a controller, a first signal acquisition module and a second signal acquisition module, wherein the first signal acquisition module and the second signal acquisition module are electrically connected with the controller; the first signal acquisition module comprises a photoelectric probe and a lamp group capable of emitting specific wavelength, and the lamp group is annularly arranged on the periphery of the photoelectric probe; the second signal acquisition module comprises a processor and a motion sensor electrically connected with the processor, and the output end of the processor is electrically connected with the controller. The utility model can conduct slow breathing training guidance for the hypertension user and is convenient for the patient to use when going out, thereby greatly improving the user experience and meeting the practical application requirements.
The embodiment of the utility model also provides a pressure reduction device based on a respiratory training method, the device is a wearable or portable device, and the wearable or portable device comprises but is not limited to a watch, a bracelet or a shell of a finger stall; the wearable or portable device can be adapted to the breath training based depressurization system described above. It can be understood that the pressure reduction system can be carried on the portable equipment, so that the user can conveniently carry out breathing training anytime and anywhere, and the user experience is greatly improved.
Referring to fig. 7, an embodiment of the present invention further provides a method for lowering blood pressure based on a respiratory training method, where the method includes steps S31 to S33:
step S31, when the user activates the device, the blood pressure information and the relevant human body parameters of the user are obtained, a personalized model for the user is established at the cloud end, and the personalized model recommends the corresponding breathing training scheme according to the relevant information of the user.
In the aspect of personalized model construction, the model is constructed in a multi-step iteration mode according to the received existing user information;
firstly, unsupervised clustering of samples is carried out according to an existing cloud sample library, a preliminary unsupervised clustering model is established, and the samples comprise multi-dimensional physiological information characteristics such as age, BMI index, blood pressure, blood sugar, maximum oxygen uptake for exercise, resting heart rate, sleeping heart rate, blood oxygen saturation and the like.
And (3) once model construction, namely constructing an unsupervised clustering model A according to the characteristics, wherein the model comprises but is not limited to a self-encoder, DBSCAN, Kmeans and the like.
After clustering is completed, classification can be customized according to multi-dimensional physiological data of age, BMI index, blood pressure, blood sugar, maximum oxygen uptake of exercise, resting heart rate, sleeping heart rate, blood oxygen saturation and the like of each classification.
After scheme customization is completed, a proper breathing training scheme can be recommended to users with different classifications in the cloud for the first time in an oriented mode.
After a period of breathing course is completed, the satisfaction degree of the user is counted, category solidification is carried out on the user with the satisfaction degree being very satisfactory, a large amount of breathing training category label data are obtained in the step, and the labels are breathing training scheme categories.
And sending the obtained data into a new model for model training.
Specifically, the method comprises the following steps:
the acquired respiration rate dataset is the following, which is a set of label data that can be used for model training.
A tag data set:
wherein, X is a label row, Y is a label column, X can be represented as Am × n, Am × n is a feature matrix with m rows and n columns, m represents the number of samples, and n represents the feature dimension.
Reducing the dimension of the features, which mainly aims to reduce the dimension of the existing features (reduce the features from multiple dimensions (e.g. 35 dimensions) to fewer dimensions (e.g. 15 dimensions)), inputting fewer redundant features into the next model, so as to effectively reduce the risk of overfitting the model in the training process, wherein the Principal Component Analysis (PCA) is used for reducing the dimension, and for the feature matrix a to be reduced, a sample covariance matrix a needs to be foundTA, the maximum d eigenvectors are used for low-dimensional projection dimensionality reduction by using a matrix consisting of the maximum d eigenvectors (a)
Where t is the score vector and p is the load vector), to give an extreme example if the principal component number takes 1,
then A is only in
Projecting in the direction of (a);
and (3) carrying out dimension reduction processing on the original features (the features are reduced to k dimensions), wherein: k is the designated characteristic number after dimension reduction, and the reconstructed label data set after dimension reduction is
Inputting: an original physiological information data set T;
and (3) outputting: reduced dimension dataset Tr。
And (4) constructing a secondary model, wherein a GBDT model which is better in performance on a medium-sized structured data set is adopted to construct a GBDT initial model, and the GBDT initial model is trained by using the label data set after dimension reduction. In particular, the method comprises the following steps of,
the input is as follows: t isr(wherein the loss function is
)；
The output is: strong learner f (x) (ultimately generating a mature GBDT model that can be used for prediction).
The weak learner model is initialized first: (estimating a constant value to minimize the loss function, which is a tree with only one root node, the square loss function is generally the mean of the nodes, and the absolute loss function is the median of the node samples.)
And then, updating the iteration, wherein for (representing the iteration number, namely the number of the generated weak learners):
(a) for sample i-1, 2, …, the negative gradient of the m-computation loss function takes it as an estimate of the residual at the value of the current model.
(b) To pair
Fitting a regression tree to obtain leaf node regions R of the mth treeejJ is 1,2,3, …, where J represents the number of leaf nodes per tree.
(c) Estimating the values of leaf node regions using a linear search, minimizing a loss function, and computing J as 1,2,3, …, J
(d) Updating the weight value:
the final regression tree (personalized model) is obtained
Where e is the number of trees, J is 1,2,3, …, where J represents the number of leaf nodes per tree, cejRepresents the weight corresponding to the leaf node on the e-th tree, I (x belongs to R)ej) Is an indicator function, if x ∈ RejThen I is 1, otherwise I is 0.
The classification result (breathing training scheme number) can be finally obtained by PCA and mature GBDT on the newly entered individual data of the cloud, so that the corresponding breathing training scheme can be accurately recommended through the model under the condition of acquiring the relevant physiological information of the user.
Specifically, the blood pressure information of the user can be acquired through the portable device carried by the user, and the blood pressure information can be the hypertension diagnosed by the user or the measurement result of the portable device. By performing blood pressure detection when the user is not at risk of hypertension; when the user has hypertension risk, a corresponding breathing training scheme is formed according to the hypertension information of the user, and the training scheme is recommended to the user, so that the user is guided to carry out breathing training.
At step S32, the personalized breathing training program guides the user to perform breathing training.
Specifically, after the training mode selected by the user is obtained, the respiratory frequency of the user is measured in real time in the training process, and whether the current respiratory frequency of the user is lower than a first preset threshold value is judged; if so, prompting the user to breathe according to the rhythm; if not, prompting the user to keep the breathing rhythm.
And step S33, after the breathing training of the current period is finished, adjusting the breathing frequency and the breathing time of the next stage of breathing training according to the effect of the breathing training.
The respiratory frequency is a respiratory frequency value which can effectively improve the blood pressure level of the user and is obtained through analysis according to the blood pressure level of the user. The breathing time is the time of the breathing frequency which can effectively improve the blood pressure level of the user and is at the current breathing frequency value, wherein the time is obtained by analyzing according to the blood pressure level of the user. That is, along with the accumulation of training, the personalized model of the system can correspondingly adjust and match the optimal training scheme according to the blood pressure measured by the user and the physical condition, and the personalized model can help the user to achieve the effect of reducing blood pressure in a targeted manner.
It should be understood that, although the steps in the above-described flowcharts are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least a portion of the steps in the above-described flowcharts may include multiple sub-steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of performing the sub-steps or the stages is not necessarily sequential, but may be performed alternately or alternatingly with other steps or at least a portion of the sub-steps or stages of other steps.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the utility model. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
All possible combinations of the technical features in the above embodiments may not be described for the sake of brevity, but should be considered as being within the scope of the present disclosure as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (8)
1. A pressure reduction system based on a respiratory training method is characterized by comprising a controller, a first signal acquisition module and a second signal acquisition module, wherein the first signal acquisition module and the second signal acquisition module are electrically connected with the controller; the first signal acquisition module comprises a photoelectric probe and a lamp group capable of emitting specific wavelength, and the lamp group is annularly arranged on the periphery of the photoelectric probe; the second signal acquisition module comprises a processor and a motion sensor electrically connected with the processor, the output end of the processor is electrically connected with the controller, and the processor calculates the breathing rate of the user according to the motion signal received by the motion sensor.
2. The respiratory training method-based depressurization system of claim 1 wherein the first signal acquisition module further comprises an optoelectronic signal processing circuit electrically connected to the optoelectronic probe.
3. The breath training method-based depressurization system of claim 2, wherein the optoelectronic signal processing circuit comprises an amplifier, a band-pass filter, an integrator and a high-speed ADC connected in sequence; wherein, the input end of the amplifier is electrically connected with the output end of the photoelectric probe.
4. The breath training method based depressurization system of claim 3 wherein the light group is composed of three LED lights emitting specific wavelengths, each light group module containing green light, red light and infrared light; the central wavelength of the green light is 525nm, the central wavelength of the red light is 660nm, and the central wavelength of the red light is 940 nm.
5. The breath training method based depressurization system of claim 4 wherein the optoelectronic signal processing circuit further comprises a filtering unit capable of 50Hz power frequency filtering.
6. The respiratory training method-based depressurization system of claim 1 further comprising a display screen and a voice assist module electrically connected to the controller; wherein the voice assistance module comprises a microphone electrically connected with the controller through an audio processing circuit.
7. The breath training method-based depressurization system of claim 1, further comprising an alarm module, wherein the alarm module comprises an alarm indicator light, and a mobile terminal or a cloud server in communication with the controller through a communication module.
8. A pressure reduction device based on a breathing exercise method, wherein the device is a wearable or portable device including but not limited to a watch, a bracelet or a shell of a finger cot; the wearable or portable device can be adapted to the breath training method based depressurization system of any one of claims 1 to 7.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115137322A (en) * | 2022-09-05 | 2022-10-04 | 深圳市景新浩科技有限公司 | Continuous monitoring system for dynamic blood pressure |
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| CN115137322A (en) * | 2022-09-05 | 2022-10-04 | 深圳市景新浩科技有限公司 | Continuous monitoring system for dynamic blood pressure |
| CN115137322B (en) * | 2022-09-05 | 2022-12-06 | 深圳市景新浩科技有限公司 | Continuous monitoring system for dynamic blood pressure |
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