CN113827216A

CN113827216A - Method and system for sensorless heart rate monitoring based on micro-motion algorithm

Info

Publication number: CN113827216A
Application number: CN202111101698.2A
Authority: CN
Inventors: 杨天元
Original assignee: Terminus Technology Group Co Ltd
Current assignee: Terminus Technology Group Co Ltd
Priority date: 2021-09-18
Filing date: 2021-09-18
Publication date: 2021-12-24

Abstract

The embodiment of the application provides a method and a system for sensorless heart rate monitoring based on a micromotion algorithm. The method comprises the following steps: providing a millimeter wave radar which can receive and transmit detection signals; the millimeter wave radar is used for sending a plurality of detection signals to target personnel and correspondingly receiving a plurality of reflection signals; processing according to the reflected signal to obtain a micro-motion signal of the target person; screening out heartbeat signals according to the inching signals, and drawing a heart rate curve graph; extracting the characteristic flow of the heart rate curve graph, inputting the characteristic flow into a trained convolutional neural network, classifying the heart rate type of the target person, and judging the body state of the target person; and recommending and displaying the health exercise method of the target person according to the classification result and the physical state of the target person. The invention can accurately monitor the heart rate condition of personnel, accurately classify the body state of the user through the neural network technology according to the heart rate condition, further provide an exercise scheme and improve the user experience.

Description

Method and system for sensorless heart rate monitoring based on micro-motion algorithm

Technical Field

The application relates to the technical field of artificial intelligence training, in particular to a method and a system for sensorless heart rate monitoring based on a micro-motion algorithm.

Background

With the improvement of living standard, people are more concerned about their health condition, the requirements of monitoring technology of vital signs are higher and higher, and the technology of non-contact vital sign monitoring is concerned by a plurality of scholars. Ultrasonic waves, WIFI, cameras, radars and the like are all applied to non-contact vital sign monitoring, wherein the ultrasonic waves, the WIFI and the radars are all based on Doppler effect. Compared with the defects of high power, large noise, inconvenient signal processing of WIFI and the like of ultrasonic equipment, the radar is favored by broad scholars in non-contact vital sign monitoring.

Although the existing radar technology can monitor the heart rate condition of a person, the technology for judging whether the person is in a fatigue state or not according to the heart rate condition does not exist, the technology for analyzing according to the characteristics of the heart rate conditions of different people does not exist, and scientific and effective suggestions for physical exercise and cardiopulmonary function exercise cannot be provided in a targeted manner.

Disclosure of Invention

In view of this, an object of the present application is to provide a method and a system for sensorless heart rate monitoring based on a micro-motion algorithm, which can accurately monitor the heart rate condition of a person, accurately classify the body state of a user through a neural network technology according to the heart rate condition, further provide an exercise scheme, and improve user experience.

Based on the above purpose, the present application provides a method for sensorless heart rate monitoring based on a micromotion algorithm, which includes:

providing a millimeter wave radar which can receive and transmit detection signals; the millimeter wave radar is used for sending a plurality of detection signals to target personnel and correspondingly receiving a plurality of reflection signals;

processing according to the reflected signal to obtain a micro-motion signal of the target person;

screening out heartbeat signals according to the inching signals, and drawing a heart rate curve graph;

extracting the characteristic flow of the heart rate curve graph, inputting the characteristic flow into a trained convolutional neural network, classifying the heart rate type of the target person, and judging the body state of the target person;

and recommending and displaying the health exercise method of the target person according to the classification result and the physical state of the target person.

In some embodiments, the processing according to the reflected signal to obtain the micro-motion signal of the target person includes:

mixing each reflection signal with a detection signal corresponding to each reflection signal to obtain a plurality of intermediate frequency signals to form an original data matrix;

carrying out Fourier transform on the original data matrix to obtain a distance matrix;

obtaining subscripts of the target personnel in the distance matrix;

acquiring an original phase signal of the target person according to the subscript of the target person in the distance matrix;

and acquiring a micro-motion signal of the target person according to the original phase signal.

In some embodiments, an N × M matrix of raw data is constructed, where N is the number of detected signals, and where M is the number of sample points at which each detected signal is sampled.

In some embodiments, a fast time dimension fourier transform is performed on the raw data matrix to obtain the distance matrix.

In some embodiments, before acquiring the jogging signal of the target person according to the original phase signal, the method further comprises the following steps: and correcting the phase jump of the original phase signal of the target person.

In some embodiments, screening out the heartbeat signals according to the inching signals and plotting a heart rate graph comprises:

using a PE-based MEEMD filter to screen the micro-motion signal to obtain a heartbeat signal;

obtaining a heart rate estimation value through a peak detection algorithm;

and drawing a heart rate curve graph according to the heart rate estimation value.

The extracting the feature stream of the heart rate curve graph and inputting the feature stream into a trained convolutional neural network so as to classify the heart rate type of the target person and judge the body state of the target person, and the method comprises the following steps:

firstly, carrying out target positioning on the heart rate curve graph, intercepting detected targets and respectively extracting features to obtain target features; comparing the difference degree of the target characteristic and the exception characteristic; if the difference degree is smaller than the threshold value, the heart rate curve graph is rejected, and subsequent steps are not carried out; if the difference degree is larger than the threshold value, carrying out the subsequent steps;

then, extracting a real-time feature stream of the heart rate graph, including: extracting the characteristics of the heart rate curve graph to obtain a real-time characteristic stream; performing feature transformation on the real-time feature stream;

then, inputting the feature stream into a trained convolutional neural network, thereby classifying the heart rate type of the target person and judging the physical state of the target person, and the method comprises the following processes:

leading the characteristic flow of the heart rate curve graph of a large number of known people into a convolutional neural network to obtain the heart rate type of each person; taking a feature vector formed by the feature stream and the heart rate type of the heart rate curve graph of the known person as a training sample, and constructing a training sample set;

training an AKC model consisting of an automatic encoder model and a K-means model based on a fully-connected neural network by using a training data set;

inputting the real-time characteristic stream of the heart rate curve graph of the target person to be classified into the trained AKC model to obtain the classification of the target person, and judging the body state of the target person.

Based on the above object, the present application further provides a system for sensorless heart rate monitoring based on a jiggle algorithm, comprising:

the radar module is used for providing a millimeter wave radar which can receive and transmit detection signals; the millimeter wave radar is used for sending a plurality of detection signals to target personnel and correspondingly receiving a plurality of reflection signals;

the micro-motion signal module is used for processing according to the reflection signal to obtain a micro-motion signal of the target person;

the heart rate curve module is used for screening out heartbeat signals according to the micro-motion signals and drawing a heart rate curve graph;

the classification and judgment module is used for extracting the characteristic flow of the heart rate curve graph and inputting the characteristic flow into a trained convolutional neural network so as to classify the heart rate type of the target person and judge the body state of the target person;

and the recommending and displaying module is used for recommending and displaying the health exercise method of the target person according to the classification result and the physical state of the target person.

In general, the advantages of the present application and the experience brought to the user are: the invention can accurately monitor the heart rate condition of personnel, accurately classify the body state of the user through the neural network technology according to the heart rate condition, further provide an exercise scheme and improve the user experience.

Drawings

In the drawings, like reference numerals refer to the same or similar parts or elements throughout the several views unless otherwise specified. The figures are not necessarily to scale. It is appreciated that these drawings depict only some embodiments in accordance with the disclosure and are therefore not to be considered limiting of its scope.

Fig. 1 shows a schematic diagram of the system architecture of the present invention.

FIG. 2 shows a flow chart of a method of sensorless heart rate monitoring based on a micromotion algorithm, according to an embodiment of the invention.

FIG. 3 shows a block diagram of a system for sensorless heart rate monitoring based on a micromotion algorithm, in accordance with an embodiment of the invention.

Fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present application;

fig. 5 is a schematic diagram of a storage medium provided in an embodiment of the present application.

Detailed Description

The present application will be described in further detail with reference to the following drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the relevant invention and not restrictive of the invention. It should be noted that, for convenience of description, only the portions related to the related invention are shown in the drawings.

It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the embodiments with reference to the attached drawings.

Fig. 1 shows a schematic diagram of the system architecture of the present invention. In the embodiment of the invention, the equipment comprises a millimeter wave radar, a processor, a display screen, a voice broadcaster and the like. Firstly, providing a millimeter wave radar which can receive and transmit detection signals; the millimeter wave radar is used for sending a plurality of detection signals to target personnel and correspondingly receiving a plurality of reflection signals; processing according to the reflected signal to obtain a micro-motion signal of the target person; screening out heartbeat signals according to the inching signals, and drawing a heart rate curve graph; extracting the characteristic flow of the heart rate curve graph, inputting the characteristic flow into a trained convolutional neural network, classifying the heart rate type of the target person, and judging the body state of the target person; and recommending and displaying the health exercise method of the target person according to the classification result and the physical state of the target person. For example, according to the classification result, the running training method of the person is recommended and displayed through a display screen. Furthermore, the intelligent recommendation result can be played through a voice broadcaster.

FIG. 2 shows a flow chart of a method of sensorless heart rate monitoring based on a micromotion algorithm, according to an embodiment of the invention. As shown in fig. 2, the method for sensorless heart rate monitoring based on the jiggle algorithm includes:

step 101: providing a millimeter wave radar which can receive and transmit detection signals; the millimeter wave radar is used for sending a plurality of detection signals to target personnel and correspondingly receiving a plurality of reflection signals;

in the specific embodiment, the method of millimeter wave radar combined with heartbeat screening is used for realizing human body identification and heartbeat detection. The millimeter wave radar can measure the distance based on the phase to obtain a more accurate distance measurement result, has better distance detection precision, and can obtain more precise distance resolution along with the increase of the bandwidth. In addition, because the millimeter wave radar transmits continuous waves, more energy can be transmitted, the signal-to-noise ratio is also improved, and the distance precision is further improved.

In this embodiment, the millimeter wave radar is a large-bandwidth millimeter wave radar, works in the 77GHz frequency band, and the transmitted detection signal is a millimeter wave, and can acquire high-distance resolution through designing appropriate parameters, so that clutter around detected target personnel can be isolated, micro-motion of the target personnel is detected, and a more accurate heart rate estimation effect is achieved.

Step 102: processing according to the reflected signal to obtain a micro-motion signal of the target person, wherein the processing comprises the following steps:

mixing each reflection signal with a detection signal corresponding to each reflection signal to obtain a plurality of intermediate frequency signals to form an original data matrix; an N x M matrix of raw data is constructed, where N is the number of detected signals, and where M is the number of samples taken for each detected signal.

In one embodiment, the detection signal is a chirp signal (chirp) that may modulate a carrier frequency to increase the transmission bandwidth of the signal and achieve pulse compression upon reception. Because the linear frequency modulation signal has higher distance resolution, when multiple targets cannot be distinguished in speed, the multi-target resolution problem can be solved by increasing the target distance test. Meanwhile, in the aspect of anti-interference, the chirp signals can distinguish interference and targets in distance, so that dragging type interference can be effectively resisted, and the chirp signals are widely applied to radar waveform design.

Carrying out Fourier transform on the original data matrix to obtain a distance matrix; for example, a fast time dimension fourier transform may be performed on the raw data matrix to obtain the distance matrix.

Obtaining subscripts of the target personnel in the distance matrix;

and acquiring a micro-motion signal of the target person according to the original phase signal. Before this, the phase jump of the original phase signal of the target person can be corrected.

Step 103: according to the fine motion signal, screening out the heartbeat signal to draw a heart rate curve graph, including:

obtaining a heart rate estimation value through a peak detection algorithm;

In one embodiment, the heartbeat signal is filtered from the micromotion signal using a PE (temporal Entropy) based MEEMD (modified empirical mode decomposition) filter. Permutation entropy is a measure of the mean entropy of the complexity of a one-dimensional time series. The smaller noise does not materially alter the complexity of the noisy signal, so it can be considered that any real-world time series calculates the permutation entropy. Due to the fast and robust nature of permutation entropy, it is desirable when there are large data sets and no time for pre-processing and parameter tuning.

In this embodiment, before the selection, the jogging signal needs to be decomposed by a MEEMD algorithm, so that the decomposed signal includes the heartbeat signal, and the heartbeat signal can be selected by arranging the entropy PE.

Step 104: extracting the characteristic flow of the heart rate curve graph, inputting the characteristic flow into a trained convolutional neural network, classifying the heart rate type of the target person, and judging the body state of the target person;

in the embodiment, firstly, the heart rate curve graph is subjected to target positioning, detected targets are intercepted, and characteristics are respectively extracted to obtain target characteristics; comparing the difference degree of the target characteristic and the exception characteristic; if the difference degree is smaller than the threshold value, the heart rate curve graph is rejected, and subsequent steps are not carried out; and if the difference degree is larger than the threshold value, performing the subsequent steps.

Then, extracting a real-time feature stream of the heart rate graph, including: extracting the characteristics of the heart rate curve graph to obtain a real-time characteristic stream; and performing feature transformation on the real-time feature stream. The feature transformation comprises at least one of the following ways: feature scrambling, feature encoding and homomorphic encryption.

Then, inputting the feature stream into a trained convolutional neural network, thereby classifying the heart rate type of the target person, and determining the physical state of the target person, which may specifically include the following processes:

s1, leading the characteristic flow of the heart rate curve graph of a large number of known people into a convolutional neural network to obtain the heart rate type of each person; taking a feature vector formed by the feature stream and the heart rate type of the heart rate curve graph of the known person as a training sample, and constructing a training sample set;

s2, training an AKC model consisting of an automatic encoder model and a K-means model based on a fully-connected neural network by using a training data set;

s3, inputting the real-time characteristic flow of the heart rate curve graph of the target person to be classified into the trained AKC model to obtain the classification of the target person, and judging the body state of the target person.

In medicine, according to the condition of the human heart rate graph, the sex, weight, blood pressure, fat and thin, fatigue, energetic, normal state, tiredness and other information of a person can be accurately obtained and predicted, and the statements, patents and papers about the aspects have already been explained in many aspects and are not repeated herein. The standard heart rate and human body state comparison database adopted by the application can adopt the database and data mentioned in the research and treatise of the cooperative hospital.

The neural network of the present application may be based on, for example, an artificial intelligence reasoning computing device. According to one aspect of the present disclosure, an artificial intelligence reasoning computing device includes a Printed Circuit Board (PCB) and a number of electronic components mounted thereon. The electronic components include a wireless communication module, a controller module, a memory module, a storage module, and at least one Cellular Neural Network (CNN) based Integrated Circuit (IC) configured to perform convolution operations in a deep learning model to extract features from input data. Each CNN-based IC includes a number of CNN processing engines operatively coupled to at least one input/output data bus. The CNN processing engines are connected in a loop using clock skew circuits. The wireless communication module is configured to transmit the pre-trained filter coefficients, the input data, and the classification results of the deep learning model.

A deep convolutional neural network is used for each input stream (spatial input stream and temporal input stream). The original convolution neural network can obtain a characteristic diagram after the hidden layer, and the characteristic diagram is expanded into a vector to carry out subsequent operation on the full connection layer. The method and the device directly use the expanded one-dimensional floating point vector as output, transmit the extracted characteristic data to the cloud end, and use the characteristic data as subsequent analysis calculation processing.

The digital cellular neural network of the present invention is based on a convolutional neural network that processes multi-layered input image data using convolution using a first set of filters or weights. Since the image data is larger than the filter, each corresponding overlapping subregion of the image data is processed. After convolution results are obtained, activation may be performed prior to the first pooling operation. In one embodiment, the activation is achieved by rectification performed in a rectifying linear unit. As a result of the first pooling operation, the image data is reduced to a reduced set of image data. For 2x2 pooling, the set of reduced image datasets was reduced by a factor of 4 from the previous set.

The previous convolution to pooling process is repeated. The reduced set of image data sets is then processed with convolution using a second set of filters. Similarly, each overlapping sub-region is processed. Another activation may be performed prior to the second pooling operation. The convolution to pooling process is repeated multiple layers and eventually connected to a Fully Connected Network (FCN). In image classification, the probability of the respective predefined class may be calculated.

In the present invention, the repeated convolution-to-pooling process is trained using a known data set or database. For image classification, the data set contains predefined categories. Before being used to classify image data, a specific set of filters, activations and pooling may be tuned and obtained, e.g., a specific combination of filter types, number of filters, order of filters, pooling type and/or when to perform the activation. In one embodiment, the convolutional neural network is based on a visual geometry group (VGG16) architecture neural network, which includes 13 convolutional layers and three fully connected network layers.

Step 105: and recommending and displaying the health exercise method of the target person according to the classification result and the physical state of the target person. For example, according to the classification result, the running training method of the person is recommended and displayed through a display screen. Furthermore, the intelligent recommendation result can be played through a voice broadcaster. The following illustrates the recommended effects of the present application (e.g., 8 person-identified case, 4 men and 4 women):

the application embodiment provides a system for sensorless heart rate monitoring based on a jiggle algorithm, which is used for executing the method for sensorless heart rate monitoring based on the jiggle algorithm described in the above embodiment, as shown in fig. 3, the system includes:

a radar module 501, configured to provide a millimeter wave radar, where the millimeter wave radar can receive and transmit detection signals; the millimeter wave radar is used for sending a plurality of detection signals to target personnel and correspondingly receiving a plurality of reflection signals;

a micro-motion signal module 502, configured to perform processing according to the reflected signal to obtain a micro-motion signal of the target person;

the heart rate curve module 503 is used for screening out heartbeat signals according to the inching signals and drawing a heart rate curve graph;

a classification and judgment module 504, configured to extract a feature stream of the heart rate graph, input the feature stream into a trained convolutional neural network, so as to classify the heart rate type of the target person, and judge a physical state of the target person;

and the recommending and displaying module 505 is used for recommending and displaying the health exercise method of the target person according to the classification result and the physical state of the target person.

The system for sensorless heart rate monitoring based on the inching algorithm provided by the embodiment of the application and the method for sensorless heart rate monitoring based on the inching algorithm provided by the embodiment of the application are based on the same inventive concept, and have the same beneficial effects as methods adopted, operated or realized by application programs stored in the system.

The embodiment of the application also provides electronic equipment corresponding to the method for sensorless heart rate monitoring based on the inching algorithm, so as to execute the method for sensorless heart rate monitoring based on the inching algorithm. The embodiments of the present application are not limited.

Referring to fig. 4, a schematic diagram of an electronic device provided in some embodiments of the present application is shown. As shown in fig. 4, the electronic device 2 includes: the system comprises a processor 200, a memory 201, a bus 202 and a communication interface 203, wherein the processor 200, the communication interface 203 and the memory 201 are connected through the bus 202; the memory 201 stores a computer program that can be executed on the processor 200, and the processor 200 executes the computer program to perform the method for sensorless heart rate monitoring based on the inching algorithm provided in any of the previous embodiments of the present application.

The Memory 201 may include a high-speed Random Access Memory (RAM) and may further include a non-volatile Memory (non-volatile Memory), such as at least one disk Memory. The communication connection between the network element of the system and at least one other network element is realized through at least one communication interface 203 (which may be wired or wireless), and the internet, a wide area network, a local network, a metropolitan area network, and the like can be used.

Bus 202 can be an ISA bus, PCI bus, EISA bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. The memory 201 is used for storing a program, the processor 200 executes the program after receiving an execution instruction, and the method for sensorless heart rate monitoring based on a jiggle algorithm disclosed in any of the embodiments of the present application may be applied to the processor 200, or implemented by the processor 200.

The processor 200 may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware or instructions in the form of software in the processor 200. The Processor 200 may be a general-purpose Processor, and includes a Central Processing Unit (CPU), a Network Processor (NP), and the like; but may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components. The various methods, steps, and logic blocks disclosed in the embodiments of the present application may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present application may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software module may be located in ram, flash memory, rom, prom, or eprom, registers, etc. storage media as is well known in the art. The storage medium is located in the memory 201, and the processor 200 reads the information in the memory 201 and completes the steps of the method in combination with the hardware thereof.

The electronic equipment provided by the embodiment of the application and the method for sensorless heart rate monitoring based on the inching algorithm provided by the embodiment of the application have the same beneficial effects as the method adopted, operated or realized by the electronic equipment.

The present embodiment further provides a computer readable storage medium corresponding to the method for sensorless heart rate monitoring based on inching algorithm provided in the foregoing embodiment, please refer to fig. 5, which illustrates the computer readable storage medium as an optical disc 30, on which a computer program (i.e., a program product) is stored, and when the computer program is executed by a processor, the computer program will execute the method for sensorless heart rate monitoring based on inching algorithm provided in any of the foregoing embodiments.

It should be noted that examples of the computer-readable storage medium may also include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory, or other optical and magnetic storage media, which are not described in detail herein.

The computer-readable storage medium provided by the above-mentioned embodiment of the present application and the method for sensorless heart rate monitoring based on the inching algorithm provided by the embodiment of the present application have the same beneficial effects as the method adopted, run or implemented by the application program stored in the computer-readable storage medium.

It should be noted that:

the algorithms and displays presented herein are not inherently related to any particular computer, virtual machine, or other apparatus. Various general purpose devices may be used with the teachings herein. The required structure for constructing such a device will be apparent from the description above. In addition, this application is not directed to any particular programming language. It will be appreciated that a variety of programming languages may be used to implement the teachings of the present application as described herein, and any descriptions of specific languages are provided above to disclose the best modes of the present application.

In the description provided herein, numerous specific details are set forth. However, it is understood that embodiments of the application may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.

Similarly, it should be appreciated that in the foregoing description of exemplary embodiments of the application, various features of the application are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the application and aiding in the understanding of one or more of the various inventive aspects. However, the disclosed method should not be interpreted as reflecting an intention that: this application is intended to cover such departures from the present disclosure as come within known or customary practice in the art to which this invention pertains. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of this application.

Those skilled in the art will appreciate that the modules in the device in an embodiment may be adaptively changed and disposed in one or more devices different from the embodiment. The modules or units or components of the embodiments may be combined into one module or unit or component, and furthermore they may be divided into a plurality of sub-modules or sub-units or sub-components. All of the features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or elements of any method or apparatus so disclosed, may be combined in any combination, except combinations where at least some of such features and/or processes or elements are mutually exclusive. Each feature disclosed in this specification (including any accompanying claims, abstract and drawings) may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.

Furthermore, those skilled in the art will appreciate that while some embodiments described herein include some features included in other embodiments, rather than other features, combinations of features of different embodiments are meant to be within the scope of the application and form different embodiments. For example, in the following claims, any of the claimed embodiments may be used in any combination.

The various component embodiments of the present application may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. Those skilled in the art will appreciate that a microprocessor or Digital Signal Processor (DSP) may be used in practice to implement some or all of the functions of some or all of the components in the creation apparatus of a virtual machine according to embodiments of the present application. The present application may also be embodied as apparatus or device programs (e.g., computer programs and computer program products) for performing a portion or all of the methods described herein. Such programs implementing the present application may be stored on a computer readable medium or may be in the form of one or more signals. Such a signal may be downloaded from an internet website or provided on a carrier signal or in any other form.

It should be noted that the above-mentioned embodiments illustrate rather than limit the application, and that those skilled in the art will be able to design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The application may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The usage of the words first, second and third, etcetera do not indicate any ordering. These words may be interpreted as names.

The above description is only for the specific embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive various changes or substitutions within the technical scope of the present invention, and these should be covered by the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.

Claims

1. A method for sensorless heart rate monitoring based on a micromotion algorithm is characterized by comprising the following steps:

2. The method of claim 1,

the processing according to the reflected signal to obtain the micro-motion signal of the target person comprises the following steps:

obtaining subscripts of the target personnel in the distance matrix;

3. The method of claim 2,

an N x M matrix of raw data is constructed, where N is the number of detected signals, and where M is the number of samples taken for each detected signal.

4. The method of claim 3,

and carrying out fast time dimension Fourier transform on the original data matrix to obtain the distance matrix.

5. The method of claim 2,

before acquiring the micro-motion signal of the target person according to the original phase signal, the method further comprises the following steps: and correcting the phase jump of the original phase signal of the target person.

6. The method of claim 1,

screening out the heartbeat signal according to the fine motion signal to draw the heart rate curve chart, include:

obtaining a heart rate estimation value through a peak detection algorithm;

7. The method of claim 1,

training an AKC model consisting of an automatic encoder model based on a fully-connected neural network and a K-means model by using a training sample set;

8. A system for sensorless heart rate monitoring based on a micromotion algorithm, 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 executes the computer program to implement the method of any one of claims 1-7.

10. A computer-readable storage medium, on which a computer program is stored, characterized in that the program is executed by a processor to implement the method according to any of claims 1-7.