CN114642409A

CN114642409A - Human body pulse wave sensing, heart rate monitoring and blood pressure monitoring method and related device

Info

Publication number: CN114642409A
Application number: CN202210057209.6A
Authority: CN
Inventors: 周安福; 马华东; 梁雨萌; 温昕哲; 黄渭
Original assignee: Beijing University of Posts and Telecommunications
Current assignee: Beijing University of Posts and Telecommunications
Priority date: 2022-01-19
Filing date: 2022-01-19
Publication date: 2022-06-21
Anticipated expiration: 2042-01-19
Also published as: CN114642409B

Abstract

The application provides a human pulse wave perception method, a heart rate monitoring method, a blood pressure monitoring method and a related device, wherein the human pulse wave perception method comprises the following steps: acquiring blood volume change information of the target human body monitoring point based on the signal intensity of the millimeter waves reflected by the target human body monitoring point; calculating a second derivative of the blood volume change information with respect to time to obtain an acceleration signal for representing the blood vessel volume change of the target human body monitoring point; and filtering the acceleration signal to obtain a fine-grained pulse wave signal of the target human body within the current monitoring time, wherein the fine-grained pulse wave signal can be used for extracting a pulse counterpulsation wave. This application can effectively improve the perception ability to human pulse ripples on the basis of realizing the human pulse ripples perception of non-contact, and then can effectively improve the monitoring accuracy of human rhythm of the heart and blood pressure data.

Description

Human body pulse wave sensing, heart rate monitoring and blood pressure monitoring method and related device

Technical Field

The application relates to the technical field of blood pressure measurement, in particular to a human body pulse wave sensing method, a heart rate monitoring method, a blood pressure monitoring method and a human body pulse wave sensing device.

Background

Accurate measurement of human body pulse is one of the execution bases of accurate measurement of heart rate and blood pressure, and is a main means for assessing blood pressure level, diagnosing hypertension and observing blood pressure reduction curative effect. Especially for the elderly, the measurement method needs to be more reliable and convenient, and the pulse and the like must be continuously measured without interruption in order to realize the monitoring of the vital signs. Therefore, the target human body can be measured in a non-contact pulse measurement mode.

At present, the existing non-contact pulse measurement method generally uses an intelligent monitoring device to measure characteristic data such as phase and angle of the neck of a human body, and then finally determines the pulse of the human body according to the relationship between the characteristic data and the pulse data. This way a contactless pulse measurement is achieved. However, since the sensing ability of the pulse wave using the characteristic data such as phase and angle is limited, the pulse wave data cannot be accurately measured.

Disclosure of Invention

In view of this, embodiments of the present application provide a human pulse wave sensing method, a heart rate monitoring method, a blood pressure monitoring method, and a human pulse wave sensing apparatus, so as to obviate or mitigate one or more of the drawbacks of the prior art.

One aspect of the present application provides a human pulse wave sensing method, including:

acquiring blood volume change information of the target human body monitoring point based on the signal intensity of the millimeter waves reflected by the target human body monitoring point;

calculating a second derivative of the blood volume change information with respect to time to obtain an acceleration signal for representing the blood vessel volume change of the target human body monitoring point;

and filtering the acceleration signal to obtain a fine-grained pulse wave signal of the target human body within the current monitoring time, wherein the fine-grained pulse wave signal can be used for extracting a pulse counterpulsation wave.

In some embodiments of the present application, the target human monitoring point comprises: the wrist of the target human body.

In some embodiments of the present application, before obtaining the blood volume change information of the target human body monitoring point based on the signal intensity of the millimeter waves reflected by the target human body monitoring point, the method further includes:

receiving a millimeter wave original monitoring signal of a target human body in real time;

acquiring corresponding relation data between a monitoring distance and the intensity of a reflected signal from the original millimeter wave monitoring signal;

based on the corresponding relation data between the monitoring distance and the intensity of the reflected signal, searching the monitoring distance corresponding to the maximum intensity of the reflected signal, and determining the monitoring point of the target human body as the monitoring point of the target human body;

and extracting the reflected signal intensity of the millimeter wave corresponding to the target human body monitoring point.

Another aspect of the application provides a heart rate monitoring method comprising:

calculating the interval of each main peak of a fine-grained pulse wave signal, wherein the fine-grained pulse wave signal is obtained by applying the human pulse wave sensing method in advance;

and carrying out histogram statistics on the interval of each main peak, and determining the heart rate of the target human body in the current monitoring time according to the corresponding statistical result.

Another aspect of the present application provides a blood pressure monitoring method, including:

acquiring at least two pulse feature data of the target human body in the current monitoring time based on a fine-grained pulse wave signal, wherein the pulse feature data comprise pulse dicrotic wave arrival time and other feature data, and the fine-grained pulse wave signal is acquired by applying the human body pulse wave sensing method in advance;

and inputting all the pulse characteristic data into a preset neural network model so that the neural network model outputs blood pressure monitoring result data of the target human body within the current monitoring time.

In some embodiments of the present application, the obtaining at least two pulse feature data of the target human body at the current monitoring time based on the fine-grained pulse wave signal includes: calculating the interval of each main peak and the interval of each echo peak of the fine-grained pulse wave signals;

histogram statistics is carried out on the main peak intervals and the echo peak intervals, and the pulse dicrotic wave arrival time and other characteristic data of the target human body in the current monitoring time are respectively determined according to corresponding statistical results.

In some embodiments of the present application, after performing histogram statistics on each of the main peak intervals and the echo peak intervals, the method further includes:

acquiring a main peak interval and an echo peak interval according to a statistical result corresponding to the histogram statistics;

correspondingly, the other characteristic data includes: at least one of pulse frequency data, amplitude of the main peak and amplitude of the echo peak.

Another aspect of the present application provides a human pulse wave sensing device, including:

the blood volume change information acquisition module is used for acquiring the blood volume change information of the target human body monitoring point based on the signal intensity of the millimeter waves reflected by the target human body monitoring point;

the acceleration signal acquisition module is used for calculating a second derivative of the blood volume change information with respect to time to obtain an acceleration signal for representing the blood vessel volume change of the target human body monitoring point;

and the fine-grained pulse wave signal acquisition module is used for filtering the acceleration signal to acquire a fine-grained pulse wave signal of the target human body within the current monitoring time, wherein the fine-grained pulse wave signal can be used for extracting pulse counterpulsation waves.

Another aspect of the present application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the human pulse wave sensing method, the heart rate monitoring method, or the blood pressure monitoring method when executing the computer program; the electronic equipment is in communication connection with the millimeter wave radar so as to receive millimeter wave original monitoring signals of a target human body, which are acquired by the millimeter wave radar in real time.

Another aspect of the present application provides a computer-readable storage medium on which a computer program is stored, which, when being executed by a processor, implements the human pulse wave sensing method, the heart rate monitoring method, or the blood pressure monitoring method. According to the human body pulse wave sensing method, the blood volume change information of a target human body monitoring point is obtained through the signal intensity of millimeter waves reflected by the target human body monitoring point; calculating a second derivative of the blood volume change information with respect to time to obtain an acceleration signal for representing the blood vessel volume change of the target human body monitoring point; the acceleration signals are filtered to obtain fine-grained pulse wave signals of the target human body within the current monitoring time, which can extract pulse counterpulsation waves, so that the sensing capability of the human body pulse waves can be effectively improved on the basis of realizing non-contact human body pulse wave sensing, and the monitoring accuracy of the human body heart rate and blood pressure data can be effectively improved.

Additional advantages, objects, and features of the application will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the application. The objectives and other advantages of the application may be realized and attained by the structure particularly pointed out in the written description and drawings.

It will be appreciated by those skilled in the art that the objects and advantages that can be achieved with the present application are not limited to the specific details set forth above, and that these and other objects that can be achieved with the present application will be more clearly understood from the detailed description that follows.

Drawings

The accompanying drawings, which are included to provide a further understanding of the application, are incorporated in and constitute a part of this application, and are not intended to limit the application. The components in the figures are not necessarily to scale, emphasis instead being placed upon illustrating the principles of the application. For purposes of illustrating and describing certain portions of the present application, the drawings may have been enlarged, i.e., may be larger, relative to other features of the exemplary devices actually made in accordance with the present application. In the drawings:

fig. 1 is a general flowchart of a human pulse wave sensing method according to an embodiment of the present application.

Fig. 2 is a graph of the relationship between signal intensity and blood volume and the millimeter wave RSS numbering over time.

Fig. 3 is a schematic flowchart illustrating a method for sensing a human pulse wave according to an embodiment of the present application.

Fig. 4 is a schematic flow chart of a heart rate monitoring method according to an embodiment of the present application.

Fig. 5 is a schematic general flow chart of a blood pressure monitoring method according to an embodiment of the present application.

Fig. 6 is a flowchart illustrating a blood pressure monitoring method according to an embodiment of the present application.

Fig. 7 is an exemplary diagram of the signal reflection intensity provided in the application example of the present application.

Fig. 8 is a schematic logic flow diagram of a blood pressure monitoring method provided in an application example of the present application.

Fig. 9 is a schematic diagram of an effect of a waveform recovery algorithm provided in an application example of the present application.

Fig. 10 is a flowchart of obtaining pulse data from millimeter wave signal strength information according to an application example of the present application.

Fig. 11 is a schematic structural diagram of a human pulse wave sensing device according to another embodiment of the present application.

Fig. 12 is a schematic structural diagram of a heart rate monitoring device according to another embodiment of the present application.

Fig. 13 is a schematic structural diagram of a blood pressure monitoring device according to another embodiment of the present application.

Detailed Description

In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be described in further detail below with reference to the accompanying drawings. The exemplary embodiments and descriptions of the present application are provided to explain the present application and not to limit the present application.

Here, it should be further noted that, in order to avoid obscuring the present application with unnecessary details, only the structures and/or processing steps closely related to the scheme according to the present application are shown in the drawings, and other details not so relevant to the present application are omitted.

It should be emphasized that the term "comprises/comprising" when used herein, is taken to specify the presence of stated features, elements, steps or components, but does not preclude the presence or addition of one or more other features, elements, steps or components.

It is also noted herein that the term "coupled," if not specifically stated, may refer herein to not only a direct connection, but also an indirect connection in which an intermediate is present.

Hereinafter, embodiments of the present application will be described with reference to the accompanying drawings. In the drawings, the same reference numerals denote the same or similar parts, or the same or similar steps.

Accurately measuring the pulse, heart rate, blood pressure and the like of a human body is one of the keys for judging the health of the human body. Taking blood pressure as an example:

hypertension, a novel disease, is increasingly prevalent in human life as the standard of living increases, and is particularly evident in the adult population. On average, there is one hypertensive out of every 5 adults, and the proportion of hypertensive patients is rising year by year. Hypertension can cause serious negative effects on human health, has no specific symptoms, even some hypertensive patients do not have any discomfort by themselves, and can cause persistent damage to organs of the whole body when not detected by people, thereby causing various difficult-to-cure and life-threatening complications, such as overstrain or mental stress which can cause heart failure or stroke. Accurate blood pressure measurement is a precondition for symptomatic medication, and is a main means for assessing blood pressure level, diagnosing hypertension and observing the curative effect of blood pressure reduction. For the elderly, the measurement method needs to be more reliable and convenient, and the blood pressure must be measured continuously without interruption in order to monitor vital signs.

The most accurate blood pressure measurement method at present is invasive arterial pressure measurement through a vascular cannula, which places a trocar in an artery (usually radial artery, femoral artery, dorsalis pedis artery or brachial artery) to directly measure arterial pressure, but the measurement inevitably causes a wound, and the specific operation needs direct or guided completion by a professional, so that the method cannot be widely applied in daily life.

The common non-invasive pulse and blood pressure measurement method in life is through cuff measurement. The method is generally applied to daily measurement, but the method cannot provide portable continuous blood pressure measurement, so that the use scene is limited. Many methods of measuring blood pressure by daily wearable devices have recently been derived on a cuff-based basis. For example, eBP technology, which replaces cuff compression with in-ear compression, uses optical signals for sensing to detect arterial vibration. This improves the portability of measurements using cuffs, but pressurization in the ear may cause discomfort to the user during the measurement and may be associated with safety issues. In addition, sensing is performed using light signals so that the received signal is affected by the user's skin tone and ambient light.

In another method, the time PTT required for the pulse oscillations in the artery to travel from one location to another is measured, and the blood pressure is calculated from the time difference and the blood pressure in a fixed relationship, which requires that we detect two different locations in the artery. For example, using a smart bracelet to measure heart-to-wrist PTT requires the device to be brought into close proximity to the heart while using the device to detect wrist pulse oscillations, where the space occupied by human manipulation can cause measurement errors. Also specially designed glasses (measuring nose to eye PTT), shorts with sensors (measuring waist to leg PTT) etc. Without exception, these wearable devices cannot conveniently measure blood pressure at all times of the day, for example, it is not suitable to wear glasses when sleeping at night, and it is not possible to wear briefs at all times in daily life. The blood flow in the artery meets the bifurcation to generate an echo in the opposite direction, and the time interval RWTT between the main wave in the forward direction and the echo in the opposite direction is used for calculating the blood pressure. The existing intelligent watch measuring method is based on the principle, only a certain fixed position on an artery is detected, and the operation is simplified to a certain extent. However, the measurement process still uses the technique of using PPG (photoplethysmography) sensor to measure pulse, which is affected by ambient light and skin color of user, and the pressure of the wrist strap needs to be adjusted to prevent light leakage.

In addition, the existing non-contact pulse measurement method generally measures characteristic data such as phase and angle of the neck of a human body by using an intelligent monitoring device, and then finally determines the pulse of the human body according to the relationship between the characteristic data and the pulse data. This way a contactless pulse measurement is achieved. However, the sensing ability of the pulse wave by using the characteristic data such as the phase, the angle and the like is limited, and the pulse wave data cannot be accurately measured.

In summary, the prior related art has the following limitations: professional staff is needed to participate in the measurement process, the use scene is limited due to low portability, and the wearable device with high portability is uncomfortable to use and cannot be suitable for people with different skin colors. That is, long-term persistence is difficult and the user uses accurate pulse, heart rate or blood pressure monitoring that is well experienced.

Based on this, this application has provided a technique that uses millimeter wave non-contact ground to detect human pulse, rhythm of the heart and blood pressure, can be applied to providing comfortable, quick, accurate contactless pulse and blood pressure measurement in scenes such as smart watch, smart mobile phone, smart home.

Specifically, in order to solve the technical problems that the sensing capability of the existing non-contact pulse measuring mode for the pulse is limited, the pulse wave data cannot be accurately measured, and the like, the human pulse wave sensing method provided by the application acquires the blood volume change information of the target human monitoring point based on the signal intensity of the millimeter waves reflected by the target human monitoring point; calculating a second derivative of the blood volume change information with respect to time to obtain an acceleration signal for representing the blood vessel volume change of the target human body monitoring point; and filtering the acceleration signal to obtain a fine-grained pulse wave signal of the target human body within the current monitoring time, wherein the fine-grained pulse wave signal can be used for extracting a pulse counterpulsation wave.

Further, in order to solve the technical problems that the existing non-contact heart rate measurement mode is limited in heart rate monitoring capability and cannot accurately measure and obtain heart rate data, the heart rate monitoring method provided by the application adopts the fine-grained pulse wave signals obtained in the human body pulse wave sensing method, calculates each main peak interval of the fine-grained pulse wave signals, performs histogram statistics on each main peak interval, and determines the heart rate of the target human body in the current monitoring time according to the corresponding statistical result.

Furthermore, aiming at the technical problems that the blood pressure of a human body is determined by only adopting single characteristic data in the existing non-contact blood pressure measurement mode, the finally obtained blood pressure monitoring result is not accurate enough and the like due to factors such as the inaccurate acquisition of the special data, the blood pressure monitoring method provided by the application adopts the fine-grained pulse wave signals obtained in the human body pulse wave sensing method, obtains at least two pulse characteristic data of the target human body in the current monitoring time based on the fine-grained pulse wave signals, wherein the pulse characteristic data comprise the arrival time of pulse dicrotic waves and other characteristic data, and inputs the pulse characteristic data into a preset neural network model so that the neural network model outputs the blood pressure monitoring result data of the target human body in the current monitoring time. Specifically, the blood pressure monitoring method provided by the application senses the slight pulsation of the pulse on the wrist by using the wireless signals, so that the pulse and blood pressure information of the human body can be obtained in a non-contact manner, and at least two pulse characteristic data are obtained to obtain the blood pressure monitoring result. On the basis of realizing non-contact blood pressure monitoring and improving the convenience of blood pressure monitoring, the accuracy and the intelligent degree of blood pressure monitoring can be effectively improved, and the user experience of the tested personnel can be effectively improved. On the basis, the wireless signal sending and receiving device has portability and low cost. The non-contact blood pressure measurement has better use experience without pressing the wrist. In addition, due to the fact that blood pressure continuity detection can be achieved, the method and the device have wide use scenes, and provide more comfortable and accurate real-time blood pressure monitoring for the elderly with serious insomnia.

In one or more embodiments of the present application, frequency Modulated Continuous wave fmcw (frequency Modulated Continuous wave) refers to a Continuous wave radar, such as a weather radar, that transmits a frequency Modulated by a particular signal. The frequency modulation continuous wave radar obtains the distance information of the target by comparing the difference between the frequency of the echo signal at any moment and the frequency of the transmitting signal at the moment, and the distance is proportional to the frequency difference between the two frequencies. The radial speed and the distance of the target can be obtained by processing the measured frequency difference between the two.

In one or more embodiments of the present application, the Signal Strength (RSS) may also be referred to as the Received Signal Strength.

Based on the above content, the present application further provides a human pulse wave sensing device for implementing the human pulse wave sensing method provided in one or more embodiments of the present application, where the human pulse wave sensing device may be a processor, a server, a controller, or the like, and the human pulse wave sensing device may be in communication connection with millimeter wave radars installed in wearable devices or the like by itself or through a third-party server or the like in sequence, so as to receive millimeter wave monitoring signals sent by the millimeter wave radars in the wearable devices worn by users, and then the human pulse wave sensing device obtains blood volume change information of a target human monitoring point based on signal intensity of millimeter waves reflected by the target human monitoring point acquired in real time; calculating a second derivative of the blood volume change information with respect to time to obtain an acceleration signal for representing the blood vessel volume change of the target human body monitoring point; the acceleration signals are filtered to obtain fine-grained pulse wave signals of the target human body within the current monitoring time, the fine-grained pulse wave signals can be extracted to pulse counterpulsation waves, then the human body pulse wave sensing device can send pulse information corresponding to the fine-grained pulse wave signals to a display screen of the wearable equipment to be displayed or to pre-authorized client equipment to be displayed, and the like, so that wearing personnel, nursing personnel and the like can conveniently and efficiently know the current pulse data of the personnel wearing the wearable equipment.

Based on the above, the present application further provides a heart rate monitoring device for implementing the heart rate monitoring method provided in one or more embodiments of the present application, where the heart rate monitoring device may be a processor, a server, a controller, or the like, and the heart rate monitoring device may be in communication connection with a millimeter wave radar and/or the human pulse wave sensing device installed in a wearable device or the like by itself or through a third-party server or the like in sequence, so as to receive millimeter wave monitoring signals sent by the millimeter wave radar in the wearable device worn by a user and then calculate fine-grained pulse wave signals, or directly receive fine-grained pulse wave signals that have been calculated by the human pulse wave sensing device, and then the heart rate monitoring device calculates main peak intervals of the fine-grained pulse wave signals, and performs histogram statistics on the main peak intervals, and determining the heart rate of the target human body within the current monitoring time according to the corresponding statistical result. Then human pulse wave perception device can with this rhythm of the heart information send show in wearable equipment's the display screen or send and show etc. to show on the customer end equipment authorized in advance to make wearing personnel and nursing staff etc. can convenient and efficient learn the current rhythm of the heart data of the personnel who wear wearable equipment.

Based on the above, the present application further provides a blood pressure monitoring device for implementing the blood pressure monitoring method provided in one or more embodiments of the present application, where the blood pressure monitoring device may be a processor, a server, a controller, or the like, and the blood pressure monitoring device may be in communication connection with a millimeter wave radar and/or the human pulse wave sensing device installed in a wearable device or the like by itself or through a third-party server or the like in sequence, so as to receive a millimeter wave monitoring signal sent by the millimeter wave radar in the wearable device worn by a user and then calculate a fine-grained pulse wave signal, or directly receive the fine-grained pulse wave signal calculated by the human pulse wave sensing device, and then the blood pressure monitoring device may obtain at least two pulse feature data of the target human body in the current monitoring time based on the fine-grained pulse wave signal, the pulse characteristic data comprises pulse dicrotic wave arrival time and other characteristic data; and inputting all the pulse characteristic data into a preset neural network model so that the neural network model outputs blood pressure monitoring result data of the target human body within the current monitoring time. Then the blood pressure monitoring device can send the blood pressure monitoring result data to the display screen of the wearable equipment for display or to the pre-authorized client equipment for display and the like, so that the wearing personnel, the nursing personnel and the like can conveniently and efficiently know the current blood pressure monitoring result data of the personnel wearing the wearable equipment. In addition, the human body pulse wave sensing device, the heart rate monitoring device and the blood pressure monitoring device can also be in communication connection with client equipment (including the wearable equipment) held by a user by themselves or through a third-party server and the like, and the monitoring result data measured by the human body pulse wave sensing device, the heart rate monitoring device and the blood pressure monitoring device are sent to the client equipment to be displayed or risk prompted and the like.

It can be understood that, in a preferred aspect of the present application, the human pulse wave sensing device, the heart rate monitoring device, and the blood pressure monitoring device may be integrated together, for example, may be embodied as a same processor, and the processor may implement the steps of the human pulse wave sensing method, the heart rate monitoring method, and the blood pressure monitoring method mentioned in the embodiment of the present application.

It can be understood that the human pulse wave sensing device, the heart rate monitoring device and the blood pressure monitoring device can be integrated with the millimeter wave radar and arranged in wearable equipment, other non-wearable medical monitoring equipment or client equipment such as a mobile terminal, or the human pulse wave sensing device, the heart rate monitoring device and the blood pressure monitoring device can also be independently arranged in a server and used for processing the received data, and sending out the processed result to reduce the manufacturing cost of the client equipment such as the wearable equipment.

That is, the aforementioned monitoring parts of the human pulse wave sensing device, the heart rate monitoring device and the blood pressure monitoring device can be executed in the server, the controller or the processor as described above, and in another practical application case, all the operations can be completed in the client device. The selection may be specifically performed according to the processing capability of the client device, the limitation of the user usage scenario, and the like. This is not a limitation of the present application. If all the operations are completed in the client device, the client device may further include a processor for specific processing of human body pulse wave sensing, heart rate monitoring, and blood pressure monitoring.

It is understood that the client device may include any mobile device capable of loading applications, such as a smart phone, a tablet electronic device, a network set-top box, a portable computer, a Personal Digital Assistant (PDA), a vehicle-mounted device, a smart wearable device, and the like. Wherein, intelligence wearing equipment can include intelligent glasses, intelligent wrist-watch, intelligent bracelet etc..

The client device may have a communication module (i.e., a communication unit), and may be communicatively connected to a remote server to implement data transmission with the server. The server may include a server on the task scheduling center side, and in other implementation scenarios, the server may also include a server on an intermediate platform, for example, a server on a third-party server platform that has a communication link with the task scheduling center server. The server may include a single computer device, or may include a server cluster formed by a plurality of servers, or a server structure of a distributed apparatus.

The server and the client device may communicate using any suitable network protocol, including a network protocol that has not been developed at the filing date of the present application. The network protocol may include, for example, a TCP/IP protocol, a UDP/IP protocol, an HTTP protocol, an HTTPS protocol, or the like. Of course, the network Protocol may also include, for example, an RPC Protocol (Remote Procedure Call Protocol), a REST Protocol (Representational State Transfer Protocol), and the like used above the above Protocol.

The following embodiments and application examples are specifically and individually described in detail.

In order to solve the technical problems that the sensing capability of the existing non-contact pulse measurement mode for the pulse is limited, the pulse wave data cannot be accurately measured, and the like, the application provides an embodiment of a human pulse wave sensing method, and referring to fig. 1, the human pulse wave sensing method executed based on the human pulse wave sensing device specifically includes the following contents:

step 110: and obtaining the blood volume change information of the target human body monitoring point based on the signal intensity of the millimeter waves reflected by the target human body monitoring point.

It is understood that the blood volume change information specifically includes the following:

(1) when the heart contracts, the blood injected from the ventricle enters the artery, the blood volume in the artery increases, and the side pressure is generated on the blood vessel wall, so that the blood pressure is maximum; the pressure on the inner wall of the arterial vessel is then called the systolic pressure, also called the high pressure.

(2) At the end of diastole, blood temporarily stops shooting into the artery, the blood volume in the artery is reduced, the arterial blood continues flowing by the elasticity and tension of the blood vessel wall, the blood vessel wall is still pressed, and the blood pressure is called diastolic pressure. Also known as low pressure.

(3) Thus systolic pressure corresponds to a segment of the arterial vessel where the blood volume increases rapidly and diastolic pressure corresponds to a segment where the blood volume decreases gradually.

In view of the above, the specific principle of reflecting the blood volume change information by the millimeter wave signal intensity is as follows:

according to lambert beer's law, the degree to which light and electromagnetic wave signals impinging on the liquid layer are absorbed is related to the thickness of the liquid layer, with the thicker the liquid layer, the more light signals are absorbed. Therefore, an increase in blood volume in an arterial blood vessel (an increase in arterial blood thickness) results in a greater absorption of electromagnetic signals, i.e., a decrease in signal strength of reflected electromagnetic waves; conversely, a smaller blood volume in the arterial blood vessel (a smaller arterial blood thickness) results in a weaker absorption capacity for the electromagnetic signal, i.e. a higher signal strength of the reflected electromagnetic wave.

Therefore, the blood volume change information of the target human body monitoring point can be represented by the intensity of the millimeter wave reflected signal when the millimeter wave irradiates the artery blood vessel. Furthermore, by monitoring the change of blood volume, the contraction and expansion time of the heart can be accurately positioned in the pulse wave signal, so as to divide the heart beating period (heart rate).

Step 120: and calculating a second derivative of the blood volume change information with respect to time to obtain an acceleration signal for representing the blood vessel volume change of the target human body monitoring point.

Step 130: and filtering the acceleration signal to obtain a fine-grained pulse wave signal of the target human body within the current monitoring time, wherein the fine-grained pulse wave signal can be used for extracting a pulse counterpulsation wave.

It is understood that the fine-grained pulse wave signals are specifically described as follows:

referring to fig. 2, besides the heart beat cycle, other characteristic information with finer granularity than the heart rate can be obtained from the pulse wave signal. During the reduction of the arterial blood volume, the blood flowing through the branch at the end of the blood vessel causes backflow, the backflow blood temporarily increases the blood volume, and the backflow causes a slight change such as a peak in the intensity of the millimeter wave received signal. The time interval between the time when this flashback occurs and the time when blood enters a pulse from a ventricle is called the pulse-dicrotic wave arrival time (RWTT), which has a high correlation with the blood pressure of the human body.

The emphasis on the fine granularity of the pulse wave signal is to emphasize that the pulse wave signal calculated by the present patent contains fine granularity features such as the arrival time of the pulse dicrotic wave in addition to the systolic and diastolic time. That is, the fine-grained pulse wave signal means a pulse wave signal with fine-grained features such as a pulse dicrotic wave that can be extracted. Of course, there are other available fine-grained features in the pulse wave signal besides RWTT, such as the change of the pulse wave signal intensity when blood is injected into the artery from the ventricle, the change of the pulse wave signal intensity when blood flows back, and the like.

From the above description, the human pulse wave sensing method provided by the embodiment of the application can effectively improve the sensing capability of the human pulse wave on the basis of realizing non-contact human pulse wave sensing, and further can effectively improve the monitoring accuracy of the human heart rate and the blood pressure data.

In order to further improve the convenience of non-contact human pulse wave sensing, in an embodiment of the human pulse wave sensing method provided by the application, the target human monitoring points in the human pulse wave sensing method can also include wrists of a target human body besides necks and the like. Therefore, compared with the prior art, the human body pulse wave sensing method provided by the application can be integrated on a smart phone, a watch or other wearable devices, focuses on a wrist of a user to obtain pulse waves, and allows the user to perform daily activities with higher degree of freedom while continuously measuring.

In order to further improve the convenience and efficiency of acquiring the signal intensity, in an embodiment of the human pulse wave sensing method provided by the present application, referring to fig. 3, before step 110 of the human pulse wave sensing method, the following contents are further specifically included:

step 010: receiving a millimeter wave original monitoring signal of a target human body in real time;

step 020: acquiring corresponding relation data between a monitoring distance and the intensity of a reflected signal from the original millimeter wave monitoring signal;

step 030: based on the corresponding relation data between the monitoring distance and the intensity of the reflected signal, searching the monitoring distance corresponding to the maximum intensity of the reflected signal, and determining the monitoring point of the target human body as the monitoring point of the target human body;

step 040: and extracting the reflected signal intensity of the millimeter wave corresponding to the target human body monitoring point.

Further, in order to solve the technical problems that the monitoring capability of the heart rate is limited, the heart rate data cannot be accurately measured, and the like in the existing non-contact heart rate measurement mode, the present application provides an embodiment of a heart rate monitoring method, referring to fig. 4, the heart rate monitoring method executed after step 130 and based on the heart rate monitoring device specifically includes the following contents:

step 210: and calculating the interval of each main peak of the fine-grained pulse wave signals, wherein the fine-grained pulse wave signals are obtained by applying the human pulse wave sensing method in advance.

Step 220: and carrying out histogram statistics on the interval of each main peak, and determining the heart rate of the target human body in the current monitoring time according to the corresponding statistical result.

According to the above description, the human body pulse wave sensing method provided by the embodiment of the application can effectively improve the monitoring capability of the heart rate on the basis of realizing non-contact heart rate monitoring, and further can effectively improve the monitoring accuracy of the human body heart rate.

In order to solve the problem that the accuracy of the blood pressure monitoring result cannot be guaranteed in the existing non-contact blood pressure monitoring method, the present application provides an embodiment of a blood pressure monitoring method, and referring to fig. 5, the blood pressure monitoring method executed after step 130 and executed based on the blood pressure monitoring device specifically includes the following contents:

step 310: at least two pulse feature data of the target human body in the current monitoring time are obtained based on fine-grained pulse wave signals, the pulse feature data comprise pulse dicrotic wave arrival time and other feature data, and the fine-grained pulse wave signals are obtained by applying the human body pulse wave sensing method in advance.

In an example, the monitoring time may be preset according to a user requirement, or may be set by a default value, for example, the monitoring time may be any value between 1 second and 1 hour, and is preferably 1 minute.

It is understood that the pulse characteristic data refers to data capable of representing pulse characteristics, and may include at least pulse dicrotic wave arrival time and pulse frequency data. The pulse dicrotic wave arrival time refers to data of echo generated by signal reflection at the bifurcation of a blood vessel when pulse waves are transmitted in the blood vessel, and is specifically represented as an arrival time interval RWTT of a main wave peak and an echo peak in each pulse period. The pulse frequency data refers to the number of pulses of the tested person in the monitoring period, such as: 96 times per minute.

Step 320: and inputting all the pulse characteristic data into a preset neural network model so that the neural network model outputs blood pressure monitoring result data of the target human body within the current monitoring time.

It can be understood that the neural network model may be a fully-connected neural network model, and is obtained by performing model training in advance according to multiple sets of historical data with marks, and the historical data at least includes two pulse feature data. For example, the training may be performed in advance by using the measurement data of the person to be measured for a plurality of times, the feature vector composed of the pulse frequency, the RWTT value, the amplitude of the main peak and the amplitude of the echo peak, etc. is input, and the blood pressure low pressure value and the blood pressure high pressure value of the person to be measured are output through derivation.

After step 320, the blood pressure monitoring result data may also be output, for example, sent to the wearable device or the mobile terminal for displaying the blood pressure monitoring result data, or determine whether to perform a risk prompt of too high or too low blood pressure at the wearable device or the mobile terminal according to a comparison result between the threshold and the blood pressure monitoring result data.

As can be seen from the above description, the blood pressure monitoring method provided in the embodiment of the present application obtains the blood pressure monitoring result by obtaining and using at least two pulse characteristic data, and can effectively improve the accuracy and the intelligent degree of blood pressure monitoring and the user experience of the person to be measured on the basis of realizing non-contact blood pressure monitoring and improving the convenience of blood pressure monitoring.

In order to improve the accuracy of obtaining the pulse characteristic data, in an embodiment of the blood pressure monitoring method provided by the present application, step 310 of the blood pressure monitoring method may further perform waveform recovery processing on the fine-grained pulse wave signal to obtain a target pulse signal including each main peak and each echo peak, and obtain at least two pulse characteristic data of the target human body in a current monitoring period according to the target pulse signal.

Specifically, the signal intensity of the millimeter waves reflected by the target human body monitoring point is an FMCW frequency modulation signal which is transmitted by a millimeter wave radar and reflected by targets in different distances, and the signal can obtain target reflection information in different distances after fast Fourier transform. Therefore, the FMCW radar can be relied upon to obtain range-signal strength information in the first place. Within a certain distance range, the distance corresponding to the position with the maximum signal intensity represents the position of the arm of the testee. Thus, the position of the target can be judged. Then, the signal intensity of the millimeter wave corresponding to the target position is extracted, the change of the signal intensity of the millimeter wave reflected by the target in a period of time is noticed, and the millimeter wave data with periodicity is obtained.

The millimeter wave data obtained through the above steps is mixed with low-frequency and high-frequency noise, such as breathing or arm unintentional vibration, which affects signal periodicity. Processing of data is therefore required to reduce the interference of noise. Specifically, the collected original signal is band-pass filtered to filter out low-frequency and high-frequency noise.

In order to further improve the accuracy and reliability of blood pressure monitoring, in an embodiment of the blood pressure monitoring method provided by the present application, the pulse characteristic data in the blood pressure monitoring method includes: pulse dicrotic wave arrival time; the pulse feature data further comprises: at least one of pulse frequency data, a dominant peak amplitude and an echo peak amplitude of the target pulse signal.

In an example, the step 320 of inputting each item of pulse feature data into a preset neural network model includes: and inputting the arrival time of the pulse counterpulsation wave, the pulse frequency data, and the amplitude of the main wave peak and the amplitude of the echo wave peak of the target pulse signal into a preset neural network model.

In order to improve the accuracy and the intelligence of obtaining at least two pulse characteristic data of the target human body in the current monitoring period according to the target pulse signal, in an embodiment of the blood pressure monitoring method provided by the present application, referring to fig. 6, step 310 of the blood pressure monitoring method specifically includes the following contents:

step 311: and calculating the interval of each main wave crest and the interval of the return wave crest of the fine-granularity pulse wave signal.

Step 312: and performing histogram statistics on the main peak intervals and the echo peak intervals, and respectively determining the pulse dicrotic wave arrival time and other characteristic data of the target human body within the current monitoring time according to corresponding statistical results.

The millimeter wave signal after filtering well embodies the periodicity of pulse vibration, the pulse frequency per minute can be calculated according to the period, and RWTT can be calculated according to the time interval between the echo peak and the main wave peak. However, in general, the main peak and the echo peak or the echo peak and the echo peak are mixed together, and it is not easy to accurately extract the pulse or RWTT information, so we need to recover the waveform. Here we choose to take the second derivative of the signal strength with respect to time to obtain acceleration information of amplitude changes. This clearly separates the main peak from the echo peak. The waveform periodicity after recovery is more obvious, and the waveform analysis is convenient to carry out.

The positions of each main peak and each echo peak can be found out according to the recovered periodic pulse wave signals, the interval of the main peak with the highest frequency of occurrence is calculated through a histogram, and a more accurate pulse value can be obtained after calculation. The pulse of the testee is 96 times/minute according to the histogram calculation.

First find the peak findpeaks present in the RSS data. The peak is defined here as: if the signal strength of one point is higher than the adjacent points on both the left and right sides, it is marked as a peak. The peak points found at this time include the main peak and the echo peak. In the next step we need to screen out the main peak. Considering that the peak value of the main peak is obviously higher than the echo peak, the peak value can be removed by performing one findpeaks process on the found peak point column (pink dot), and only the main peak is left. After the main peaks are found, as mentioned above, the Interval between the main peaks is counted, and the pulse period can be calculated by combining the signal sampling rate SampleRate. The specific calculation formula is as follows:

units are times/minute. Similarly, when extracting RWTT information, we only need to count the interval between the main peak and the first echo peak in each period. The amplitude values of the main peak and the echo peak can be further counted to be used as a basis for calculating the blood pressure value in the next step, and the operation of feature extraction is quite simple after the positions of the main peak and the echo peak are found.

In order to further improve the reliability, accuracy and intelligence of the blood pressure monitoring result, in an embodiment of the blood pressure monitoring method provided by the present application, referring to fig. 6, the following contents are further included after step 312 of the blood pressure monitoring method:

step 313: and acquiring a main peak interval and a return peak interval according to a statistical result corresponding to the histogram statistics.

Correspondingly, step 320 of the blood pressure monitoring method specifically includes the following contents:

step 321: and inputting the arrival time of the pulse counterpulsation wave, the pulse frequency data, and the amplitude of the main wave peak and the amplitude of the echo wave peak of the target pulse signal into a preset neural network model, so that the neural network model outputs the blood pressure monitoring result data of the target human body in the current monitoring period.

Specifically, features related to blood pressure values have been extracted from the millimeter wave signals, and the final step is to derive blood pressure values using these features. A fully-connected neural network model is designed for this purpose, multiple times of measurement data of a testee are used for training, a feature vector formed by pulse frequency, RWT value, main peak amplitude, echo peak amplitude and the like is input, and the low pressure value and the high pressure value of the blood pressure of the testee are output through derivation.

Based on this, for the embodiment of the blood pressure monitoring method, the present application further provides a specific application example of the blood pressure monitoring method for further explanation, and the application example of the present application proposes a technology for detecting the pulse and the blood pressure of a human body in a non-contact manner by using millimeter waves. The millimeter wave radar can sense the slight change of the human arm caused by the periodic flow of the human artery blood vessel, and the pulse and blood pressure information of the human body can be extracted by recording and analyzing the periodic vibration. The principle is as follows: cardiac periodic pump bleeding propagates in arterial blood vessels, and this periodic characteristic is reflected in the volume change of blood in the wrist arterial blood vessels. The millimeter wave signal can sense the volume change of blood: the increased volume of blood results in increased signal absorption of the millimeter waves, thereby reducing the signal strength of the reflected signal; conversely, the reduced volume of blood results in reduced signal absorption of the millimeter waves, thereby increasing the signal strength of the reflected signal. Therefore, the pulse period of the human body can be obtained by calculating the period of the signal intensity change. In addition, the application example of the application utilizes the strong correlation between pulse waves RWTT (echo return time) and blood pressure to calculate the blood pressure information of the human body by extracting the RWTT. The technology only uses a millimeter wave radar with the size of one coin for commercial application, and can easily realize the detection of pulse and blood pressure in daily life. Meanwhile, the detection is non-contact, namely the millimeter wave chip and the detected target do not need to be in direct contact, so that the arrangement is easier, and the clothes of the testee do not need to be embedded or the wrist does not need to be tightened.

In particular, the specific application example of the application needs to consider how to use millimeter waves to sense the information of the periodic change of the blood volume in the tiny human artery. For a target (such as an artery blood vessel of an arm) with a small reflection area and a weak vibration amplitude, the existing sensing algorithm based on the periodic phase change of the millimeter wave signal cannot accurately sense the vibration of the target. Aiming at the problem, the application example of the application focuses on the periodic change of the intensity of the reflected signal of the liquid by utilizing the characteristic that the liquid in the pipeline is periodically pumped out, and a series of algorithms are designed to extract the periodic information of the millimeter wave signal. The specific application example of the application also needs to consider how to accurately extract the RWT so as to calculate the blood pressure of the human body. Because of the weak nature of the signal, echoes are often mixed with harmonics and it is difficult to accurately calculate the RWTT. It is also challenging to get the user's exact high and low voltage values from RWTT. Aiming at the problem, a series of filtering algorithms are set, a neural network is designed to process signals, and high voltage and low voltage are obtained according to millimeter wave signal characteristics measured by a testee.

The concrete description is as follows:

basic principle

(1) Pulse period detection principle

As illustrated in fig. 7, the FMCW millimeter wave radar can transmit and receive millimeter wave signals reflected by an object. This periodic fluid volume change causes a corresponding change in the RSS (signal reflection intensity) as the blood is pumped periodically by the heart to flow in the arterial vessel. The periodicity of the received signal is calculated to reflect the frequency at which blood is pumped by the heart. This frequency can be used to calculate the periodicity information of the heart movement, i.e. the pulse of the arm.

(2) Principle of blood pressure calculation

When the pulse wave is transmitted in the blood vessel, the reflection of the signal occurs at the bifurcation of the blood vessel, thereby generating an echo. The blood flow velocity can be calculated by detecting the RWTT, which is the arrival time interval between the main peak and the echo peak in each pulse period, so as to obtain the information of the blood pressure. The calculation formula is as follows:

where K1 and K2 are fixed parameters that are user specific. Therefore, the change of the RWDT is calculated through the change of the signals sensed by the millimeter waves, and the blood pressure change of the user can be monitored in real time.

(II) Process flow, see FIG. 8:

(1) signal input and preprocessing

The collected millimeter wave signals are FMCW frequency modulation signals which are transmitted by a millimeter wave radar and reflected by targets in different distances, and the signals can obtain target reflection information in different distances after fast Fourier transform. Therefore, the FMCW radar can be relied upon to obtain range-signal strength information in the first place. Within a certain distance range, the distance corresponding to the position with the maximum signal intensity represents the position of the arm of the testee. Thus, the position of the target can be judged. And then, millimeter wave information corresponding to the target position is extracted, the change of the intensity of the millimeter wave signal reflected by the target in a period of time is concerned, and the periodic millimeter wave data is obtained.

The millimeter wave data obtained through the above steps is mixed with low-frequency and high-frequency noise, such as breathing or arm unintentional vibration, which affects signal periodicity. Therefore, processing of data is required to reduce the interference of noise. Specifically, the collected original signal is band-pass filtered to filter out low-frequency and high-frequency noise. The resulting periodic millimeter wave signal strength information after filtering is shown as the input signal in fig. 9.

(2) Pulse wave feature extraction

The millimeter wave signals after filtering well reflect the periodicity of pulse vibration, the pulse times per minute can be calculated according to the period, and RWTT can be calculated according to the time interval between the echo peak and the main peak. However, in general, the main peak and the echo peak or the echo peak and the echo peak are mixed together, and it is difficult to accurately extract the pulse or RWTT information, so that the waveform needs to be restored. The second derivative of the signal intensity with respect to time is selected to obtain the acceleration information of the amplitude change. This clearly separates the main and return peaks, the effect of which is schematically shown in figure 9. The waveform periodicity after recovery is more obvious, and the waveform analysis is convenient to carry out.

And aiming at the recovered periodic pulse wave signals, the positions of each main peak and each echo peak are found out, the interval of the main peaks with the highest occurrence frequency is calculated through a histogram, and a more accurate pulse value can be obtained after calculation. As shown in fig. 10, the pulse rate of the subject was found to be 96 times/minute by histogram calculation. The accurate finding of the peak position is a precondition for effective feature extraction, and the following briefly describes the method for finding the main peak and the echo peak position. As shown in the second sub-diagram in fig. 10, peaks (findpeaks) present in RSS data are first found. The peak is defined here as: if one point has a higher signal strength than the adjacent points on both the left and right sides, it is marked as a peak, as shown by the point on the second sub-graph in FIG. 10. The peak points found at this time include the main peak and the echo peak. The next step is to screen out the main peak. Considering that the peak value of the main peak is significantly higher than the echo peak, performing findpeaks process on the found peak point sequence once can eliminate the echo peak and only leave the main peak, as shown by the point in the third sub-graph in fig. 10. After the main peaks are found, as mentioned above, the Interval between the main peaks is counted, and the pulse period can be calculated by combining the signal sampling rate SampleRate. The specific calculation formula is as follows:

units are times/minute. Similarly, when extracting RWTT information, only the interval between the main peak and the first echo peak in each period needs to be counted. The amplitudes of the main peak and the echo peak can be further counted to be used as the basis for calculating the blood pressure value in the next step, and the operation of extracting the characteristics is very simple after the positions of the main peak and the echo peak are found.

(3) And (5) calculating blood pressure. Features related to blood pressure values have been extracted from the millimeter wave signals and the last step is to use these features to derive blood pressure values. A fully-connected neural network model is designed for this purpose, multiple times of measurement data of a testee are used for training, a feature vector formed by pulse frequency, RWT value, main peak amplitude, echo peak amplitude and the like is input, and the low pressure value and the high pressure value of the blood pressure of the testee are output through derivation.

In summary, the application example of the application provides a novel non-contact human body pulse and blood pressure detection method, the deployment is simple, and the sensing result with high accuracy can be robustly realized at a distance of 5-40 cm without being directly connected to a human body. The scheme can be deployed and used in the following scenes: 1) the pulse or blood pressure measuring instrument is applied to the scenes such as hospitals, and the like, and can be used for conveniently and hygienically measuring the pulse or blood pressure on an intelligent watch and a mobile phone; 2) the millimeter wave chip is embedded into furniture in the scenes of smart home and the like, so that the pulse and the blood pressure can be simply measured in life, and early warning can be brought to diseases; 3) and other scenes requiring non-contact pulse detection and blood pressure detection.

The application example of the application discloses the relation between the periodic change of the signal intensity of the millimeter wave radar and the periodic change of the blood volume in the blood vessel, and accordingly provides an algorithm for calculating the blood pressure and the pulse by using the change of the signal intensity.

The application example of the application provides a method for sensing periodic pulse waves in a non-contact mode based on a millimeter wave radar, the position of the pulse of a hand arm is sensed by means of the millimeter wave radar, and the periodic characteristics of pulse vibration are extracted through a customized filtering algorithm and a multi-antenna signal data processing algorithm. A pulse wave feature extraction algorithm is designed, a neural network for further processing features and outputting blood pressure values is designed, and pulse and blood pressure measurement is achieved with high accuracy.

The application example of the application uses the millimeter wave radar of 60GHz to construct a system prototype, and designs a data collection flow aiming at the system prototype. The system is small in size (only the size of a coin), simple to deploy, capable of measuring pulse and blood pressure in a high-precision non-contact mode, capable of being integrated in smart homes, smart wearable watches, smart phones and other devices in future and convenient for long-term continuous monitoring of vital signs such as pulse and blood pressure.

Based on the above, the present application further provides a human pulse wave sensing device for implementing the human pulse wave sensing method provided in one or more embodiments of the present application, where the human pulse wave sensing device may be implemented as a processor, a controller, or a server, and in a specific example, referring to fig. 11, the human pulse wave sensing device specifically includes the following contents:

the blood volume change information acquisition module 11 is used for acquiring the blood volume change information of the target human body monitoring point based on the signal intensity of the millimeter waves reflected by the target human body monitoring point;

the acceleration signal acquisition module 12 is configured to calculate a second derivative of the blood volume change information with respect to time, and obtain an acceleration signal used for representing the change of the blood vessel volume at the target human body monitoring point;

and a fine-grained pulse wave signal acquisition module 13, configured to filter the acceleration signal to obtain a fine-grained pulse wave signal of the pulse counterpulsation wave, which can be extracted from the target human body within the current monitoring time.

The embodiment of the human pulse wave sensing device provided in the present application may be specifically configured to execute the processing procedure of the embodiment of the human pulse wave sensing method in the foregoing embodiment, and the functions of the embodiment are not described herein again, and reference may be made to the detailed description of the embodiment of the human pulse wave sensing method.

According to the above description, the human pulse wave sensing device provided by the embodiment of the application can effectively improve the sensing capability of human pulse waves on the basis of realizing non-contact human pulse wave sensing, and further can effectively improve the monitoring accuracy of human heart rate and blood pressure data.

Based on the foregoing, the present application further provides a heart rate monitoring apparatus for implementing the heart rate monitoring method provided in one or more embodiments of the present application, where the heart rate monitoring apparatus may be implemented as a processor, a controller, or a server, and in a specific example, referring to fig. 12, the heart rate monitoring apparatus specifically includes the following contents:

and a main peak interval calculation module 21, configured to calculate intervals of main peaks of fine-grained pulse wave signals, where the fine-grained pulse wave signals are obtained by applying the human pulse wave sensing method in advance.

And the heart rate determining module 22 is configured to perform histogram statistics on each main peak interval, and determine the heart rate of the target human body within the current monitoring time according to a corresponding statistical result.

The embodiment of the heart rate monitoring apparatus provided in the present application may be specifically configured to execute the processing procedure of the embodiment of the heart rate monitoring method in the above embodiment, and its functions are not described herein again, and reference may be made to the detailed description of the embodiment of the heart rate monitoring method.

According to the above description, the heart rate monitoring device provided by the embodiment of the application can effectively improve the monitoring capability of the heart rate on the basis of realizing non-contact heart rate monitoring, and further can effectively improve the monitoring accuracy of the heart rate of the human body.

Based on the above, the present application further provides a blood pressure monitoring device for implementing the blood pressure monitoring method provided in one or more embodiments of the present application, where the blood pressure monitoring device may be implemented by a processor, a controller, or a server, and in a specific example, referring to fig. 13, the blood pressure monitoring device specifically includes the following contents:

the pulse feature number obtaining module 31 is configured to obtain at least two pulse feature data of the target human body in the current monitoring time based on a fine-grained pulse wave signal, where the pulse feature data includes pulse dicrotic wave arrival time and other feature data, and the fine-grained pulse wave signal is obtained by applying the human body pulse wave sensing method in advance.

And the blood pressure result acquisition module 32 is configured to input each item of pulse characteristic data into a preset neural network model, so that the neural network model outputs blood pressure monitoring result data of the target human body within the current monitoring time.

The embodiment of the blood pressure monitoring device provided in the present application may be specifically configured to execute the processing procedure of the embodiment of the blood pressure monitoring method in the foregoing embodiment, and the functions of the embodiment of the blood pressure monitoring device are not described herein again, and reference may be made to the detailed description of the embodiment of the blood pressure monitoring method.

According to the blood pressure monitoring device, the blood pressure monitoring result is obtained by obtaining and adopting at least two pulse characteristic data, the accuracy and the intelligent degree of blood pressure monitoring can be effectively improved on the basis of realizing non-contact blood pressure monitoring and improving the convenience of blood pressure monitoring, and the user experience of a tested person can be effectively improved.

Embodiments of the present invention also provide a computer device, which may include a processor, a memory, a receiver, and a transmitter, where the processor and the memory may be connected by a bus or in other manners, for example, connected by a bus. The receiver can be connected with the processor and the memory in a wired or wireless mode.

The processor may be a Central Processing Unit (CPU). The Processor may also be other general purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components, or a combination thereof.

The memory, which is a non-transitory computer-readable storage medium, may be used to store non-transitory software programs, non-transitory computer-executable programs, and modules, such as program instructions/modules corresponding to the various methods in the embodiments of the present invention. The processor executes various functional applications and data processing of the processor by executing non-transitory software programs, instructions and modules stored in the memory, that is, the method in the above-described method embodiment is implemented.

The memory may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created by the processor, and the like. Further, the memory may include high speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory optionally includes memory located remotely from the processor, and such remote memory may be coupled to the processor via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.

The one or more modules are stored in the memory and, when executed by the processor, perform the methods of the embodiments.

In some embodiments of the present disclosure, the user equipment may include a processor, a memory, and a transceiving unit, the transceiving unit may include a receiver and a transmitter, the processor, the memory, the receiver, and the transmitter may be connected through a bus system, the memory to store computer instructions, the processor to execute the computer instructions stored in the memory to control the transceiving unit to transceive signals.

As an implementation manner, the functions of the receiver and the transmitter in the present invention may be implemented by a transceiver circuit or a dedicated chip for transceiving, and the processor may be implemented by a dedicated processing chip, a processing circuit or a general-purpose chip.

As another implementation manner, a server provided by the embodiment of the present invention may be implemented by using a general-purpose computer. That is, program code that implements the functions of the processor, receiver, and transmitter is stored in the memory, and a general-purpose processor implements the functions of the processor, receiver, and transmitter by executing the code in the memory.

Embodiments of the present application also provide a computer-readable storage medium, on which a computer program is stored, where the computer program is executed by a processor to implement the steps of the foregoing methods. The computer readable storage medium may be a tangible storage medium such as Random Access Memory (RAM), memory, Read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, floppy disks, hard disks, removable storage disks, CD-ROMs, or any other form of storage medium known in the art.

Those of ordinary skill in the art will appreciate that the various illustrative components, systems, and methods described in connection with the embodiments disclosed herein may be implemented as hardware, software, or combinations of both. Whether this is done in hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application. When implemented in hardware, it may be, for example, an electronic circuit, an Application Specific Integrated Circuit (ASIC), suitable firmware, plug-in, function card, or the like. When implemented in software, the elements of the present application are the programs or code segments used to perform the required tasks. The program or code segments may be stored in a machine-readable medium or transmitted by a data signal carried in a carrier wave over a transmission medium or a communication link.

It is to be understood that the present application is not limited to the particular arrangements and instrumentality described above and shown in the attached drawings. A detailed description of known methods is omitted herein for the sake of brevity. In the above embodiments, several specific steps are described and shown as examples. However, the method processes of the present application are not limited to the specific steps described and illustrated, and those skilled in the art can make various changes, modifications, and additions or change the order between the steps after comprehending the spirit of the present application.

Features that are described and/or illustrated with respect to one embodiment may be used in the same way or in a similar way in one or more other embodiments and/or in combination with or instead of the features of the other embodiments.

The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made to the embodiment of the present invention by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A human pulse wave sensing method is characterized by comprising the following steps:

2. The human pulse wave sensing method of claim 1, wherein the target human monitoring point comprises: the wrist of the target human body.

3. The human pulse wave sensing method according to claim 1, further comprising, before obtaining the blood volume change information of the target human monitoring point based on the signal intensity of the millimeter waves reflected by the target human monitoring point:

4. A method of heart rate monitoring, comprising:

calculating each main peak interval of a fine-grained pulse wave signal, wherein the fine-grained pulse wave signal is obtained by applying the human pulse wave sensing method of any one of claims 1 to 3 in advance;

5. A method of monitoring blood pressure, comprising:

acquiring at least two pulse feature data of the target human body in the current monitoring time based on a fine-grained pulse wave signal, wherein the pulse feature data comprise pulse dicrotic wave arrival time and other feature data, and the fine-grained pulse wave signal is acquired by applying the human pulse wave sensing method of any one of claims 1 to 3 in advance;

6. The method for monitoring blood pressure according to claim 5, wherein the obtaining at least two pulse feature data of the target human body at the current monitoring time based on the fine-grained pulse wave signals comprises: calculating the interval of each main peak and the interval of each echo peak of the fine-grained pulse wave signals;

and performing histogram statistics on the main peak intervals and the echo peak intervals, and respectively determining the pulse dicrotic wave arrival time and other characteristic data of the target human body within the current monitoring time according to corresponding statistical results.

7. The method of claim 6, further comprising, after said histogram statistics of each of said main peak intervals and said echo peak intervals:

8. A human pulse wave sensing device, comprising:

9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the human pulse wave sensing method of any one of claims 1 to 3, the heart rate monitoring method of claim 4 or the blood pressure monitoring method of any one of claims 5 to 7 when executing the computer program; the electronic equipment is in communication connection with the millimeter wave radar so as to receive millimeter wave original monitoring signals of a target human body, which are acquired by the millimeter wave radar in real time.

10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, implements the human pulse wave sensing method of any one of claims 1 to 3, the heart rate monitoring method of claim 4 or the blood pressure monitoring method of any one of claims 5 to 7.