CN215078369U - Non-contact sleep state monitoring system based on biological microwave radar - Google Patents
Non-contact sleep state monitoring system based on biological microwave radar Download PDFInfo
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Abstract
The utility model relates to a non-contact sleep state monitoring system based on biological microwave radar, including microwave radar sensor terminal, marginal service unit, high in the clouds service unit and user equipment end, high in the clouds service unit respectively with microwave radar sensor terminal, marginal service unit and user equipment end communication connection, microwave radar sensor terminal place around the individual sleep position that awaits measuring, and be connected with marginal service unit, the user equipment end include the human-computer interaction panel for compare with prior art with user real-time interaction, the utility model has the advantages of convenient and reliable.
Description
Technical Field
The utility model belongs to the technical field of intelligence mode identification and physiological signal processing technique and specifically relates to a non-contact sleep state monitoring system based on biological microwave radar is related to.
Background
With the development of social economy, people pay more and more attention to their health, and with the coming of the 5G and everything interconnected times, sensor devices in the aspects of smart homes, human health monitoring, medical care and the like are in a lot of progress, and the real-time human health monitoring becomes a research hotspot of intelligent wearable devices, for example, the real-time monitoring of vital signs of human breathing, heartbeat, blood pressure and the like is performed, wherein the monitoring of human sleep states is a great hotspot of research in the field.
Currently, in the monitoring of the sleep state of a human body, the respiratory rate and the heart rate of the human body during sleeping are mostly monitored by wearing a contact type sensor device, so as to estimate the sleep state of the human body. In addition, the traditional visual image method has the defects of high calculation cost, high requirement on illumination conditions, incapability of penetrating or bypassing barriers, poor interference resistance, risk of revealing privacy of users and the like.
SUMMERY OF THE UTILITY MODEL
The utility model aims at providing a convenient and reliable non-contact sleep state monitoring system based on biological microwave radar in order to overcome the defect that above-mentioned prior art exists.
The purpose of the utility model can be realized through the following technical scheme:
the utility model provides a non-contact sleep state monitoring system based on biological microwave radar, includes microwave radar sensor terminal, edge service unit, high in the clouds service unit and user equipment end, high in the clouds service unit respectively with microwave radar sensor terminal, edge service unit and user equipment end communication connection, microwave radar sensor terminal place around the individual sleep position that awaits measuring, and be connected with edge service unit, the user equipment end include the human-computer interaction panel for with user real-time interaction.
Furthermore, the cloud service unit is a cloud server and is in communication connection with the user equipment end through a wired or wireless communication link.
Furthermore, the cloud server comprises a visual human-computer interaction interface, so that a background manager can analyze and control data conveniently.
Furthermore, the microwave radar sensor terminal comprises a transceiving antenna module, a radar chip module, a first microprocessor module, a first communication module, a power supply module and a peripheral circuit module which are connected with each other.
Furthermore, the transceiving antenna module adopts a multi-input multi-output antenna array for transmitting and receiving microwave signals, and the first communication module is in communication connection with the edge service unit for transmitting original radar data.
Furthermore, the first microprocessor module is manufactured by Intel corporation of America
And the radar chip module adopts an IWR6843 radar chip manufactured by Texas instruments of America.
Further, the edge service unit comprises a DSP module, a second microprocessor module and a second communication module, which are connected to each other.
Furthermore, the second communication module is in communication connection with the first communication module and used for receiving original radar data, the DSP module and the second microprocessor module are in communication connection with each other and used for processing signals of the original radar data, and the second communication module is also in communication connection with the cloud service unit.
Furthermore, the DSP module is a 1C67x @600MHz DSP chip manufactured by Texas instruments, USA, and the second microprocessor module is a 1C67x @600MHz DSP chip manufactured by Intel, USA
A microprocessor chip.
And furthermore, the microwave radar sensor terminal and the edge service unit are integrated in the same embedded hardware terminal to realize high-speed data transmission and processing of the original radar data.
Compared with the prior art, the utility model has the advantages of it is following:
the utility model discloses utilize microwave radar sensor terminal to obtain radar raw data to carry out radar signal processing to radar raw data through marginal service unit and high in the clouds service unit, carry out the sleep state monitoring to the individual that awaits measuring, the individual that awaits measuring need not to wear wearable equipment and just can realize the sleep state monitoring, has realized that non-contact, real-time, anti shelters from, does not rely on illumination, the invasion of non-privacy, the monitoring of convenient noninductive to human sleep health state, convenient reliable.
Drawings
FIG. 1 is a schematic diagram of the system of the present invention;
FIG. 2 is a flow chart of a process of the system of the present invention;
FIG. 3 is a top view of a microwave radar sensor terminal in accordance with the present invention;
FIG. 4 is a front view of a microwave radar sensor terminal in accordance with the present invention;
FIG. 5 is a hardware configuration diagram of a microwave radar sensor terminal;
fig. 6 is a hardware configuration diagram of an edge service unit.
Wherein: 1. microwave radar sensor terminal, 2, marginal service unit, 3, high in the clouds service unit, 4, user equipment end, 5, the individual that awaits measuring.
Detailed Description
The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.
Example 1
As shown in fig. 1, the utility model provides a non-contact sleep state monitoring system based on biological microwave radar, including microwave radar sensor terminal 1, marginal service unit 2, high in the clouds service unit 3 and user equipment end 4, high in the clouds service unit 3 respectively with microwave radar sensor terminal 1, marginal service unit 2 and user equipment end 4 communication connection, microwave radar sensor terminal 1 places around the individual 5 sleep positions that await measuring, and is connected with marginal service unit 2, and user equipment end 4 includes the human-computer interaction panel for with user real-time interaction.
The cloud service unit 3 is a cloud server, and has the capabilities of real-time communication control, high-speed reading and writing, data storage, data forwarding, high-speed calculation, big data analysis, deep learning operation, processing and the like. The cloud service unit 3 controls the starting, the closing, the parameter configuration and the signal receiving and sending of the microwave radar sensor terminals by carrying out command control on the plurality of microwave radar sensor terminals 1, and realizes unified scheduling. In addition, the cloud server is provided with a visual human-computer interaction interface, so that background management personnel can analyze and control data conveniently, and is in communication connection with the user equipment terminal 4 through a wired or wireless communication link, so that the processed and analyzed human sleep state result is transmitted to the user equipment terminal.
As shown in fig. 5, the microwave radar sensor terminal 1 includes a transceiving antenna module, a radar chip module, a first microprocessor module, a first communication module, a power supply module, and a peripheral circuit module, which are connected to each other. The receiving and transmitting antenna module adopts a multi-input multi-output antenna array and is used for sending and receiving microwave signals, and the first communication module is in communication connection with the edge service unit 2 and is used for sending original radar data. In this embodiment, the first microprocessor module is manufactured by Intel corporation of America, model number
The microprocessor chip and the s-radar chip module adopt an IWR6843 radar chip manufactured by Texas instruments of America.
The microwave radar sensor terminal 1 has main functional devices such as signal generation, signal reception, frequency multiplication, frequency mixing, filtering, analog-to-digital conversion (ADC), data caching, communication interface and the like; the first microprocessor module can control the radar, configure parameters and process data; the first communication module can communicate with the edge service unit and transmit data; the power supply module can provide a power supply for the whole microwave radar sensor terminal; and the peripheral circuit module is used for connecting other sub-modules of the sensor terminal, other sensor expansion interfaces and an external system unit.
As shown in fig. 6, the edge service unit 2 includes a DSP module, a second microprocessor module and a second communication module connected to each other. The second communication module is in communication connection with the first communication module and used for receiving original radar data, the DSP module is in communication connection with the second microprocessor module and used for processing signals of the original radar data, and the second communication module is also in communication connection with the cloud service unit 3. In this embodiment, the DSP module is a 1C67x @600MHz DSP chip manufactured by Texas instruments, USA, and the second microprocessor module is a model manufactured by Intel, USA
A microprocessor chip.
The DSP module is used for signal processing of radar original data, data collected by the microwave radar sensor terminal 1 are transmitted to the second microprocessor module and the DSP module in a wired or wireless mode through a communication link, mutual data transmission and data processing are carried out between the second microprocessor module and the DSP module, a series of radar signal processing algorithms mainly comprise data arrangement, basic mathematical operation, Fast Fourier Transform (FFT), Constant False Alarm Rate (CFAR) detection, arrival angle calculation, filter processing and the like, physiological characteristic data (respiration rate, heart rate, blood pressure and the like) and sleep movement characteristic data of an individual to be detected can be obtained, and the physiological characteristic data and the sleep movement characteristic data are transmitted to the cloud service unit through the communication link.
As shown in fig. 2, the data processing process of each unit in the system of the present invention is specifically as follows:
(1) microwave radar sensor terminal: sending a microwave signal to an individual to be tested, receiving the microwave signal reflected by the individual to be tested, preprocessing the microwave signal, obtaining radar original data, and sending the radar original data to an edge service unit, wherein the method specifically comprises the following steps: sending microwave signals to an individual to be tested, receiving the microwave signals reflected by the individual to be tested, performing preprocessing such as frequency mixing, low-pass filtering, analog-digital conversion and the like to obtain intermediate-frequency data signals containing sleep sign information of the individual to be tested, namely radar original data, wherein the processing process comprises the following steps:
s101: the method comprises the following steps that a system is initialized, and a plurality of microwave radar sensor terminals, an edge service unit and a cloud service unit are started;
s102: the cloud service unit configures configuration parameters of a plurality of microwave radar sensor terminals in a wired or wireless mode through a communication link;
s103: the method comprises the following steps that a plurality of microwave radar sensor terminals respectively send radar signals to an individual to be detected and receive radar echo signals;
s104: radar echo signals obtained by a plurality of microwave radar sensor terminals are subjected to frequency mixing, filtering and analog-to-digital conversion (ADC) sampling, and then intermediate-frequency original data are output.
(2) An edge service unit: radar signal processing is carried out on radar original data to obtain a sleep state classification data set and a sleep movement characteristic data set, and the sleep state classification data set and the sleep movement characteristic data set are forwarded to a cloud service unit, and the method specifically comprises the following steps: the radar signal processing and forwarding method comprises the following steps of performing radar signal processing and forwarding on radar original data to obtain physiological sign information and body movement information of an individual to be detected, and forwarding the physiological sign information and the body movement information to a cloud service unit in a wired or wireless mode through a communication link, wherein the processing process comprises the following steps:
s201, performing distance FFT on the acquired original radar data, then performing ROI extraction, performing phase extraction and phase expansion on the ROI extracted data to obtain a phase difference, performing band-pass filtering with different applicable bandwidths to obtain frequency spectrum estimation of a respiratory rate and a heart rate, and further calculating the respiratory rate and the heart rate;
s202, after ROI extraction, performing Doppler FFT, performing channel averaging to obtain a range Doppler spectrum, detecting a single target point by using CFAR, and obtaining the distance, speed, azimuth angle, elevation angle and other physical and dynamic characteristic data;
s203, extracting and selecting several types of body motion characteristic data obtained by extraction, then performing multi-channel characteristic fusion to form a sleep body motion characteristic data set which needs to be sent into an artificial intelligence classification model, and respectively generating a sleep stage classification data set and an OSAHS classification data set after performing characteristic extraction and processing on the respiratory rate and the heart rate;
(3) cloud service unit: based on big data and artificial intelligence algorithm, combining the sleep physical movement characteristic data set and the sleep state classification data set to obtain the sleep state result of the individual to be tested, and sending the sleep state result to the user equipment, specifically: the method comprises the following steps of collecting physiological characteristic data and body movement data of a plurality of individuals to be detected, realizing analysis and identification of various sleep states based on big data and artificial intelligence classification, and feeding back a processing result to a user equipment end through a communication link, wherein the processing process comprises the following steps:
s301, respectively sending the sleep physical movement characteristic data set, the sleep stage classification data set and the OSAHS classification data set into corresponding artificial intelligence classification models, and outputting the respective classification models after corresponding artificial neural network training if no classification model exists;
s302, if a trained classification model exists, predicting the sleep movement data, the respiration rate and the heart rate characteristic data which are obtained from the edge service unit and subjected to characteristic selection and processing through the classification model to obtain a corresponding prediction result, and realizing the recognition and judgment of the sleep movement information, the prediction of the stage of the sleep state and the prediction of obstructive sleep apnea in the sleep process;
s303, screening out actions causing respiratory rate and heart rate jump of the predicted sleep body movement information, feeding back the actions to the characteristic processing process of the edge service unit through a communication link, removing noise signals of the respiratory rate and the heart rate caused by the actions to obtain accurate and sustainable respiratory rate and heart rate signals of the person in a quiet sleep state, and sending the accurate and sustainable respiratory rate and heart rate signals to respective classification models again to realize more accurate prediction;
s304, downloading respective classification models obtained after training, updating and optimizing the cloud service units to the edge service units regularly through communication links in a wired or wireless mode, so that the edge service units and the cloud service units can realize sleep state monitoring, and the edge service units are in a local mode, so that the basic functions of sleep state monitoring are guaranteed;
s305, the cloud service unit analyzes the body movement information of the human body during sleeping, the stage staging results of the sleeping state and the prediction results of obstructive sleep apnea output by each classification model through big data, and then judges and analyzes the sleeping health state of the individual to be detected in a previous period;
s306, the result predicted by the classification model and the sleep health state result of the individual to be tested after big data analysis are sent to the user terminal in a wired and wireless mode through the communication vertical link, so that the user can know the sleep health state of the user in a previous period.
The cloud service unit acquires body motion data (including but not limited to distance, speed, acceleration, azimuth angle, elevation angle, height, energy, statistical characteristics and the like) in the body motion classification data set from the edge service unit, and the body motion data is sent to the body motion classification model after data preprocessing and data enhancement, so that the classification and judgment of the motion of an individual to be detected are realized, the recording of the human body sleep state motion is realized, certain auxiliary functions such as falling detection on a bed, turning detection and the like are realized, the motion influencing physiological sign data is screened out and fed back to the edge service unit.
The cloud service unit acquires physiological sign data in the sleep state data set from the edge service unit, monitors sleep stage staging and Obstructive Sleep Apnea Hypopnea Syndrome (OSAHS) through a traditional machine learning method (comprising principal component analysis, a decision tree model, a support vector machine and the like) or a deep learning model (comprising an artificial neural network, an RNN and the like), and specifically sends the physiological sign data to a trained sleep stage classification model and an OSAHS classification model respectively to monitor the sleep stage staging and the Obstructive Sleep Apnea Hypopnea Syndrome (OSAHS). For the sleep stage, the input physiological sign data is extracted by feature extraction (such as Hidden Markov Model (HMM), FFT, autocorrelation, peak detection, etc.) to extract features such as respiration rate, heart rate, etc. in a certain time range, and feature results corresponding to the corresponding sleep stage are calculated; for obstructive sleep apnea, input physiological sign data are divided into respiration rate, heart rate and blood pressure signals related to apnea and respiration rate, heart rate and blood pressure signals related to non-apnea, the two types of data are sent to an OSAHS classification model, connection and difference between related features are obtained, and prediction and identification of obstructive sleep apnea are achieved.
The cloud service unit can utilize big data to combine artificial intelligence algorithm to carry out sleep analysis to the physiological sign data of a plurality of individuals to be detected who acquire from a plurality of terminals, obtain the sustainable time of different sleep states, realize the real-time supervision of sleep state, sleep state body movement information combines artificial intelligence classification model to realize the analysis and the judgement of body movement characteristics simultaneously, the two are as mutual complementation, body movement data during sleep belongs to interference information, influence the accuracy and the continuity of individual physiological characteristic data to be detected, utilize the analysis and the judgement to body movement information, can be at the noise signal of feature processing in-process filtering interference, thereby realize accurate analysis and the calculation of physiological sign data, further obtain improving to the sleep health monitoring degree of accuracy.
In addition, the artificial intelligence classification model trained by the cloud service unit can be updated, optimized and improved in real time through analysis of big data, so that the artificial intelligence classification model is suitable for different individuals to be tested, the model trained by the cloud service unit can be backed up by the cloud service unit after being trained and updated by the cloud service unit, meanwhile, the edge service unit can be cached, calling of the model is realized locally, the classification model trained, optimized and updated by the cloud service unit is downloaded and backed up by the edge service unit in a wired or wireless mode through a communication link, the limitation that the access of the cloud service unit cannot be realized after the communication of the edge service unit and the cloud service unit is broken is avoided, the individuals to be tested can still be monitored and analyzed in the sleep health state after being off-line, and normal use of basic functions of equipment by local users is ensured, the robustness of the system is improved.
(4) The user equipment end: and providing real-time interaction of the sleep state result for the user, such as the user checking the sleep state detection and analysis result in real time.
An application scenario given in this embodiment is shown in fig. 3 and fig. 4, the placeable positions of the microwave radar sensor terminal are 6 positions as shown in the figure, fig. 3 is a top view, which is 4 modes respectively in front, back, left and right (A, B, C, D four positions in the figure), fig. 4 is a front view, which is 2 modes respectively in up and down (E, F two positions in the figure). The placement mode of a microwave radar sensor terminal can be selected respectively according to the scene, but is not limited to these 6 placement position modes for the monitoring of the sleep health state of the individual to be measured, can suitably adjust according to the actual scene, and what adopted in this embodiment is the radar placement mode shown in position A.
The utility model discloses the whole work flow of system does:
s1: the microwave radar sensor sends microwave signals to an individual to be detected, receives radar echo signals and carries out preprocessing to obtain radar original data, and the method specifically comprises the following steps: the method comprises the following steps of carrying out frequency mixing and filtering on radar echo signals to obtain intermediate frequency signals, and then carrying out analog-to-digital conversion sampling on the intermediate frequency signals to obtain radar original data:
the microwave radar sensor sends a microwave signal S to the individual to be measuredTThe expression of (a) is:
wherein, tfIs a fast time index within a frequency modulation period, ATTo transmit signal amplitude, fcThe central frequency of the signal, K is the frequency modulation slope of the signal, and K tau is the signal emission frequency at the moment of tau;
radar echo signal SRThe expression of (a) is:
wherein A isRFor receiving the signal amplitude, Δ tfThe time interval between the round-trip transmission and reception of a signal, K (tau-deltat)f) For the frequency of the received signal at time τ, Δ fdIs a Doppler shift;
intermediate frequency signal SIFThe expression of (a) is:
SIF(tf)＝ST(tf)SR(tf)＝ATARexp{j2π[fcΔtf+(fIF-Δfd)tf]}
fIF＝KΔtf
wherein f isIFIs tfThe frequency of the intermediate frequency signal at the moment.
S2: radar signal processing is carried out on radar original data to obtain a sleep physical and dynamic characteristic data set and a sleep state classification data set comprising physiological sign data, and the method specifically comprises the following steps:
s21: performing distance FFT on original radar data and then performing ROI extraction;
s22: obtaining physiological sign data of the individual to be detected by using the data extracted from the ROI, specifically comprising the following steps:
s221: phase extraction and phase expansion are carried out on the data extracted by the ROI to obtain a phase difference;
s222: obtaining the frequency spectrum estimation of the respiration rate and the heart rate through band-pass filtering with different applicable bandwidths;
s223: calculating physiological sign data including a respiratory rate and a heart rate;
s23: reprocessing the data after ROI extraction to obtain body motion data and forming a body motion characteristic data set, which specifically comprises the following steps:
s231: performing Doppler FFT on the data after ROI extraction;
s232: carrying out channel averaging to obtain a range Doppler spectrum;
s233: detecting a single target point by using the CFAR to obtain body motion data comprising distance, speed, azimuth angle and elevation angle;
s234: sequentially extracting, selecting and fusing multi-channel features of the body motion data to obtain a body motion feature data set;
s24: eliminating noise signals caused by feedback actions in the step S33 in the physiological sign data, and performing feature processing on the physiological sign data to obtain a sleep state classification data set, wherein the sleep state classification data set comprises a sleep stage classification data set and an OSAHS classification data set, and the feature processing specifically comprises:
according to the time range, carrying out feature extraction on the physiological sign data in a segmented manner to form a sleep stage classification data set; and respectively extracting apnea related features and non-apnea related features in the physiological sign data to form an OSAHS classification data set.
S3: based on big data and artificial intelligence algorithm, combine the characteristic data set of the physical movement of sleep and the categorised data set of sleep state, obtain the individual's sleep state result that awaits measuring, accomplish sleep state monitoring, specifically include:
s31: constructing and training a physical movement classification model and a sleep state classification model, wherein the sleep state classification model comprises a sleep stage classification model and an OSAHS classification model;
s32: inputting the sleep body movement data set into a trained body movement classification model, identifying and classifying the sleep movement of the individual to be tested, and screening out the movement influencing physiological signs, wherein the physiological sign data comprise respiration rate and heart rate, and the movement influencing the physiological signs is specifically the movement causing the respiration rate and the heart rate to jump;
s33: feeding back the screened operation to step S2;
s34: inputting the sleep state classification data set into a trained sleep state classification model to obtain a sleep state classification result of the individual to be tested;
s35: and (5) performing big data analysis on the recognition result and the sleep state classification result of the sleep movement to obtain the sleep state result of the individual to be detected, and finishing the sleep state monitoring.
In this embodiment, the microwave radar sensor terminal 1 and the edge service unit 2 are separately separated, the microwave radar sensor terminal 1 is only used for collecting raw data, and the edge service unit 2 is used for processing the raw data, extracting features, and the like, and is used for simultaneously processing the raw data collected by a plurality of microwave sensor terminals.
Example 2
In this embodiment, in order to meet the requirement of better real-time performance, the microwave radar sensor terminal 1 (including the radio frequency front-end module, the DSP module, the power supply module, and the like) and the edge service unit 2 are integrated into the same embedded hardware terminal to implement high-speed data transmission and processing of the original data, and the extraction and selection of the features are directly implemented in the embedded hardware terminal, and the rest are the same as those in embodiment 1.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily think of various equivalent modifications or replacements within the technical scope of the present invention, and these modifications or replacements should be covered within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. The utility model provides a non-contact sleep state monitoring system based on biological microwave radar, its characterized in that, includes microwave radar sensor terminal (1), edge service unit (2), high in the clouds service unit (3) and user equipment end (4), high in the clouds service unit (3) respectively with microwave radar sensor terminal (1), edge service unit (2) and user equipment end (4) communication connection, microwave radar sensor terminal (1) place around individual (5) sleep position that awaits measuring, and be connected with edge service unit (2), user equipment end (4) include the human-computer interaction panel for with user real-time interaction.
2. The system for monitoring the non-contact sleep state based on the bio-microwave radar as claimed in claim 1, wherein the cloud service unit (3) is a cloud server and is in communication connection with the user equipment (4) through a wired or wireless communication link.
3. The system according to claim 2, wherein the cloud server comprises a visual human-computer interface.
4. The system for monitoring the non-contact sleep state based on the biological microwave radar is characterized in that the microwave radar sensor terminal (1) comprises a transceiving antenna module, a radar chip module, a first microprocessor module, a first communication module, a power supply module and a peripheral circuit module which are connected with each other.
5. The system for monitoring the sleep state of the living beings microwave radar-based non-contact type according to claim 4, characterized in that the transceiving antenna module adopts a multi-input multi-output antenna array for transmitting and receiving microwave signals, and the first communication module is in communication connection with the edge service unit (2) for transmitting raw radar data.
6. The system according to claim 4, wherein the first microprocessor module is manufactured by Intel corporation of America as model number
And the radar chip module adopts an IWR6843 radar chip manufactured by Texas instruments of America.
7. The system for monitoring the non-contact sleep state based on the biological microwave radar is characterized in that the edge service unit (2) comprises a DSP module, a second microprocessor module and a second communication module which are connected with each other.
8. The system for monitoring the non-contact sleep state based on the bio-microwave radar as claimed in claim 7, wherein the second communication module is communicatively connected with the first communication module for receiving the raw radar data, the DSP module and the second microprocessor module are communicatively connected with each other for processing the signals of the raw radar data, and the second communication module is further communicatively connected with the cloud service unit (3).
9. According to claim 7The non-contact sleep state monitoring system based on the biological microwave radar is characterized in that the DSP module adopts a DSP chip which is manufactured by Texas instruments of America and has the model number of 1C67x @600MHz, and the second microprocessor module adopts a DSP chip which is manufactured by Intel of America and has the model number of 1C67x @600MHz
A microprocessor chip.
10. The system for monitoring the sleep state of the living beings microwave radar-based non-contact type is characterized in that the microwave radar sensor terminal (1) and the edge service unit (2) are integrated in the same embedded hardware terminal.
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