CN117612717A - Multifunctional health monitoring method based on millimeter wave radar - Google Patents

Multifunctional health monitoring method based on millimeter wave radar Download PDF

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Publication number
CN117612717A
CN117612717A CN202311580515.9A CN202311580515A CN117612717A CN 117612717 A CN117612717 A CN 117612717A CN 202311580515 A CN202311580515 A CN 202311580515A CN 117612717 A CN117612717 A CN 117612717A
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user
data
wave radar
millimeter wave
event
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黄东红
董媛
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Qinglan Technology Shenzhen Co ltd
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Qinglan Technology Shenzhen Co ltd
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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
    • A61B5/0205—Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
    • A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
    • A61B5/1116—Determining posture transitions
    • A61B5/1117—Fall detection
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7225—Details of analog processing, e.g. isolation amplifier, gain or sensitivity adjustment, filtering, baseline or drift compensation
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235—Details of waveform analysis
    • A61B5/7253—Details of waveform analysis characterised by using transforms
    • A61B5/7257—Details of waveform analysis characterised by using transforms using Fourier transforms
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/74—Details of notification to user or communication with user or patient ; user input means
    • A61B5/746—Alarms related to a physiological condition, e.g. details of setting alarm thresholds or avoiding false alarms
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/02—Systems using reflection of radio waves, e.g. primary radar systems; Analogous systems
    • G01S13/06—Systems determining position data of a target
    • G01S13/42—Simultaneous measurement of distance and other co-ordinates
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/02—Systems using reflection of radio waves, e.g. primary radar systems; Analogous systems
    • G01S13/50—Systems of measurement based on relative movement of target
    • G01S13/58—Velocity or trajectory determination systems; Sense-of-movement determination systems
    • G01S13/581—Velocity or trajectory determination systems; Sense-of-movement determination systems using transmission of interrupted pulse modulated waves and based upon the Doppler effect resulting from movement of targets
    • G01S13/582—Velocity or trajectory determination systems; Sense-of-movement determination systems using transmission of interrupted pulse modulated waves and based upon the Doppler effect resulting from movement of targets adapted for simultaneous range and velocity measurements
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/88—Radar or analogous systems specially adapted for specific applications
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
    • G01S7/41—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
    • G01S7/415—Identification of targets based on measurements of movement associated with the target
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/10—Pre-processing; Data cleansing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/213—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods
    • G06F18/2131—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods based on a transform domain processing, e.g. wavelet transform
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/23—Clustering techniques
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
    • G16H10/60—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/70—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients

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  • Engineering & Computer Science (AREA)
  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Physics & Mathematics (AREA)
  • Medical Informatics (AREA)
  • Public Health (AREA)
  • Radar, Positioning & Navigation (AREA)
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Abstract

The invention discloses a multifunctional health monitoring method and system based on millimeter wave radar and a computer readable storage medium, wherein the method comprises the following steps: receiving echo signals in a space, and calculating point cloud data of a user and phase change data caused by respiration and heartbeat of the user according to the echo signals; calculating the behavior of the user according to the point cloud data; calculating vital signs of the user according to the phase change data, wherein the vital signs comprise respiratory frequency and heart rate; monitoring whether a user triggers a set alarm event according to the behaviors and/or vital signs; if yes, executing corresponding alarm operation according to the triggered alarm event type. The multifunctional health monitoring method based on the millimeter wave radar has the advantages of multifunctional monitoring, low cost, non-contact all-weather monitoring, privacy protection, two-way call and the like.

Description

Multifunctional health monitoring method based on millimeter wave radar
Technical Field
The invention relates to the technical field of radar monitoring, in particular to a multifunctional health monitoring method, system and computer readable storage medium based on millimeter wave radar.
Background
Along with the trend of aging population, the health and daily care of the old are more and more important, and the conditions of health monitoring, accidental falling of the old, independent outgoing of the old and the like need a large amount of manpower and material resources to support, so that many wearable and non-wearable devices are emerging on the market to monitor the health, falling and outgoing of the old.
Wearable devices are usually based on wrist bands and hand rings. These wearable devices often suffer from uncomfortable wearing experience and are not monitored all-weather.
Whereas non-wearable devices are typically camera, radar-based. The problem of the camera scheme is that user activities need to be shot in real time during monitoring, and privacy invasion is easy to occur. The radar scheme has the problem that only one of a plurality of monitoring functions such as fall detection, people number detection and the like can be performed, so that the radar scheme has more limitations in actual daily care.
Disclosure of Invention
The embodiment of the application aims to realize all-weather, non-contact and privacy-infringement-free multifunctional monitoring by providing the millimeter wave radar-based multifunctional health monitoring method.
To achieve the above object, an embodiment of the present application provides a multifunctional health monitoring method based on millimeter wave radar, including:
Receiving echo signals in a space, and calculating point cloud data of a user and phase change data caused by respiration and heartbeat of the user according to the echo signals;
calculating the behavior of the user according to the point cloud data;
calculating vital signs of the user according to the phase change data, wherein the vital signs comprise respiratory frequency and heart rate;
monitoring whether a user triggers a set alarm event according to the behaviors and/or vital signs;
if yes, executing corresponding alarm operation according to the triggered alarm event type.
In some embodiments, calculating the point cloud data of the user from the echo signals includes:
converting the echo signal into a digital signal;
performing distance-dimension fast Fourier transform and Doppler-dimension Fourier transform on the digital signals to obtain distance-Doppler spectrums of all target units in the space;
performing incoherent accumulation on the distance-Doppler spectrum, and performing two-dimensional constant false alarm detection on an accumulation result by adopting a preset threshold parameter to extract a target unit corresponding to the user on the distance-Doppler spectrum;
performing azimuth-pitch combined angle measurement on a target unit corresponding to the user to obtain an azimuth angle and a pitch angle of the target unit;
Obtaining point data corresponding to the target unit in the space according to the azimuth angle, the pitch angle and the corresponding distance unit on the distance-Doppler spectrum;
and generating point cloud data of the user according to all the point data of the user.
In some embodiments, computing the behavior of the user from the point cloud data includes:
calculating distribution state data of all monitoring points in the point cloud data;
clustering and fusing the point cloud data of the user to obtain a fitting center of the point cloud data;
carrying out Kalman filtering tracking on the fitting center to obtain motion data of the fitting center, wherein the motion data comprises motion track data and motion speed data;
and generating the behavior of the user according to the distribution state data and the motion data.
In some embodiments, the set alarm events include fall events and bed fall events;
monitoring whether a user triggers a set alarm event according to the behavior, including:
calculating the ratio of monitoring points lower than or equal to a set height to all monitoring points in the point cloud data according to the distribution state data;
calculating a speed change value of a user according to the movement speed data;
When the speed change value is larger than or equal to a set value and the ratio is larger than or equal to a set ratio, confirming the action state of the user according to the movement track data, wherein the action state comprises a bedridden state and an out-of-bed state;
if the user is in a bedridden state, judging that the user triggers a falling event;
and if the user is in the out-of-bed state, judging that the user triggers a falling event.
In some embodiments, the set alarm events include an individual out event and an unmanned event;
monitoring whether the user triggers a set alarm event according to the behavior, and further comprising:
when the fact that the user leaves the space and does not return to the space after a first set time interval is monitored according to the motion trail data, determining that the user triggers an independent outgoing event; and
and when the time length of single stay of the user in the space exceeds the second set time length according to the movement track data, determining that the user triggers an unmanned activity event.
In some embodiments, the millimeter wave radar has multiple antennas;
calculating phase change data caused by respiration and heartbeat of a user according to the echo signals, wherein the phase change data comprises:
after the distance-Doppler graphs of all target units in the space are obtained, extracting phase data with Doppler of 0 from the distance-Doppler graph of any antenna;
Performing multi-frame data accumulation on the phase data to obtain slow Doppler phase data with Doppler of 0;
performing static filtering on the slow Doppler phase data to screen out slow Doppler phase data of a user in a static state in space;
screening peak values from the statically filtered slow Doppler phase data based on a preset peak value threshold value, and determining a target distance unit where a static user is located according to the peak values;
and extracting the phase on the target distance unit of the slow Doppler phase data to obtain the phase change data.
In some embodiments, calculating vital signs of the user from the phase change data comprises:
filtering the phase change data by adopting a 6-order Butterworth filter designed according to the frequency band of the respiratory frequency, and extracting respiratory waveform data;
performing fast Fourier transform on the respiration waveform data to extract a respiration period so as to calculate the respiration frequency of a user;
filtering the phase change data by adopting a 6-order Butterworth filter designed according to the frequency band of the heart rate, and extracting heart rate waveform data;
and performing wavelet transformation on the heart rate waveform data, and then performing fast Fourier transformation to extract a heart rate period so as to calculate the heart rate of the user.
In some embodiments, the set alarm events further include respiratory abnormalities and heart rate abnormalities;
monitoring whether a user triggers a set alarm event according to the vital sign, and further comprising:
if the heart rate of the user is monitored to be lower than the set heart rate threshold value, judging that the user triggers a heart rate abnormal event;
if the respiration rate of the user is monitored to be lower than the set respiration threshold value, the user is judged to trigger a respiration abnormal event.
To achieve the above objective, an embodiment of the present application further provides a millimeter wave radar-based multifunctional health monitoring system, which includes a memory, a processor, and a millimeter wave radar-based multifunctional health monitoring program stored in the memory and capable of running on the processor, wherein the processor implements the millimeter wave radar-based multifunctional health monitoring method according to any one of the above when executing the millimeter wave radar-based multifunctional health monitoring program.
To achieve the above object, an embodiment of the present application further provides a computer readable storage medium, where a millimeter wave radar-based multifunctional health monitoring program is stored on the computer readable storage medium, where the millimeter wave radar-based multifunctional health monitoring program is executed by a processor to implement the millimeter wave radar-based multifunctional health monitoring method according to any one of the above.
It can be appreciated that according to the multifunctional health monitoring method based on millimeter wave radar in the technical scheme, point cloud data and phase change data of a monitored user can be obtained through echo signals in a space, and then behavior and vital signs of the monitored user are obtained through calculation based on the point cloud data and the phase change data, so that multiple monitoring functions such as functions of people detection, fall detection, heart rate detection, breath detection, outgoing detection and the like can be realized simultaneously based on the behavior and the vital signs, and the monitoring functions are all-weather and noninductive. Thus, compared with the existing monitoring scheme, the scheme has the following advantages:
1. multifunctional monitoring: according to the technical scheme, based on the millimeter wave radar technology, a plurality of detection functions such as people number detection, fall detection, heart rate detection, breath detection, outgoing detection and the like can be realized at the same time, and a user can autonomously select one or more functions according to requirements;
2. low cost: according to the technical scheme, multiple monitoring functions can be realized by only one radar, and compared with a system for realizing multiple function monitoring by combining multiple non-wearable devices, the system is lower in cost and higher in cost performance;
3. Non-contact all-weather monitoring: the monitoring method of the technical scheme can realize 24-hour all-weather monitoring without any contact, and realize real noninductive daemon;
4. protection of privacy: unlike cameras, millimeter wave radars do not generate images during the monitoring process, but only capture simple trajectory and point cloud data. This means that the privacy of the user is effectively protected, and personal privacy problems are not touched;
5. and (3) two-way call: when the monitored user triggers an alarm event, the system can dial a telephone to the binding terminal by the cloud server platform, so that the monitored user contacts with the terminal user at the first time.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to the structures shown in these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a block diagram of one embodiment of a millimeter wave radar-based multifunctional health monitoring system of the present invention;
Fig. 2 is a flow chart of an embodiment of a method for multifunctional health monitoring based on millimeter wave radar according to the present invention.
The achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
In order that the above-described aspects may be better understood, exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
It should be noted that 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 other than those listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention 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. And the use of "first," "second," and "third," etc. do not denote any order, and the terms may be construed as names.
As shown in fig. 1, fig. 1 is a schematic structural diagram of a multifunctional health monitoring system based on millimeter wave radar according to an embodiment of the present invention.
As shown in fig. 1, the server 1 includes: millimeter wave radar 15, memory 11, processor 12, and network interface 13.
In this embodiment, the radar of the present invention adopts a TD-MIMO (i.e. multiple access multiple input multiple output) signal transmission mode, a chirp continuous wave signal is transmitted, the radar signal is transmitted by a transmitting antenna, the transmitted electromagnetic wave signal encounters an obstacle and is reflected back, after a time τ, the receiving antenna receives the echo signal, passes through a low noise amplifier, filters out the noise influence, mixes with one of the transmitting signals, passes through a low pass filter, and obtains an intermediate frequency signal (IF signal), and digitally samples the intermediate frequency signal to obtain an adc signal, and the frequency f of the intermediate frequency signal IF The method comprises the following steps: f (f) IF K×τ, where k is the signal chirp rate, τ is the target delay, and delay τ and target distance d have the following relationship: τ=2d/c, where d is the target distance and c is the speed of light. Therefore, the distance between the target and the radar can be obtained by the frequency of the intermediate frequency signal: f (f) IF ＝k*τ＝k2d/c。
Furthermore, the millimeter wave radar adopts a design of 3-transmission and 4-reception antennas, the 3 transmitting antennas transmit signals in a time division multiplexing mode, and the 4 receiving antennas simultaneously receive the signals. Each transmitting antenna transmits 32 chips in a frame time, the number of the adc sampling points of each chip is 512, and the data volume of the adc data received in one frame is 12×32×512×2 by adopting I/Q complex sampling.
Alternatively, the radar of the present invention has 2 mounting modes, namely top mounting and side mounting, wherein the top mounted radar is mounted on a ceiling or a suspended ceiling right above a bed, and the side mounted radar is mounted above the middle position of the bed head in a range of 1.5m to 1.8m from the ground.
Further, the memory 11 may also include an internal storage unit of the server 1 as well as an external storage device. The memory 11 may be used not only for storing application software installed in the server 1 and various types of data, such as codes of the millimeter wave radar-based multifunctional health monitoring program 10, but also for temporarily storing data that has been output or is to be output.
Processor 12 may in some embodiments be a central processing unit (Central Processing Unit, CPU), controller, microcontroller, microprocessor or other data processing chip for running program code or processing data stored in memory 11, such as executing millimeter wave radar-based multifunctional health monitoring program 10, etc.
The network interface 13 may optionally comprise a standard wired interface, a wireless interface (e.g. WI-FI interface), typically used to establish a communication connection between the server 1 and other electronic devices.
The network may be the internet, a cloud network, a wireless fidelity (Wi-Fi) network, a Personal Area Network (PAN), a Local Area Network (LAN), and/or a Metropolitan Area Network (MAN). Various devices in a network environment may be configured to connect to a communication network according to various wired and wireless communication protocols. Examples of such wired and wireless communication protocols may include, but are not limited to, at least one of the following: transmission control protocol and internet protocol (TCP/IP), user Datagram Protocol (UDP), hypertext transfer protocol (HTTP), file Transfer Protocol (FTP), zigBee, EDGE, IEEE 802.11, light fidelity (Li-Fi), 802.16, IEEE 802.11s, IEEE 802.11g, multi-hop communications, wireless Access Points (APs), device-to-device communications, cellular communication protocol and/or bluetooth (bluetooth) communication protocol, or combinations thereof.
Optionally, the server may further comprise a user interface, which may comprise a Display (Display), an input unit such as a Keyboard (Keyboard), and optionally a standard wired interface, a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch, or the like. The display may also be referred to as a display screen or a display unit, for displaying information processed in the server 1 and for displaying a visual user interface.
Fig. 1 shows only a server 1 having components 11-13 and a millimeter wave radar based multifunctional health monitoring program 10, it will be understood by those skilled in the art that the structure shown in fig. 1 does not constitute a limitation of the server 1, and may include fewer or more components than shown, or may combine certain components, or a different arrangement of components.
In this embodiment, the processor 12 may be configured to call the millimeter wave radar-based multifunctional health monitoring program stored in the memory 11, and perform the following operations:
receiving echo signals in a space, and calculating point cloud data of a user and phase change data caused by respiration and heartbeat of the user according to the echo signals;
calculating the behavior of the user according to the point cloud data;
calculating vital signs of the user according to the phase change data, wherein the vital signs comprise respiratory frequency and heart rate;
monitoring whether a user triggers a set alarm event according to the behaviors and/or vital signs;
if yes, executing corresponding alarm operation according to the triggered alarm event type.
In one embodiment, processor 12 may be configured to invoke the millimeter wave radar-based multifunctional health monitoring program stored in memory 11 and perform the following operations:
Converting the echo signal into a digital signal;
performing distance-dimension fast Fourier transform and Doppler-dimension Fourier transform on the digital signals to obtain distance-Doppler spectrums of all target units in the space;
performing incoherent accumulation on the distance-Doppler spectrum, and performing two-dimensional constant false alarm detection on an accumulation result by adopting a preset threshold parameter to extract a target unit corresponding to the user on the distance-Doppler spectrum;
performing azimuth-pitch combined angle measurement on a target unit corresponding to the user to obtain an azimuth angle and a pitch angle of the target unit;
obtaining point data corresponding to the target unit in the space according to the azimuth angle, the pitch angle and the corresponding distance unit on the distance-Doppler spectrum;
and generating point cloud data of the user according to all the point data of the user.
In one embodiment, processor 12 may be configured to invoke the millimeter wave radar-based multifunctional health monitoring program stored in memory 11 and perform the following operations:
calculating distribution state data of all monitoring points in the point cloud data;
clustering and fusing the point cloud data of the user to obtain a fitting center of the point cloud data;
Carrying out Kalman filtering tracking on the fitting center to obtain motion data of the fitting center, wherein the motion data comprises motion track data and motion speed data;
and generating the behavior of the user according to the distribution state data and the motion data.
In one embodiment, processor 12 may be configured to invoke the millimeter wave radar-based multifunctional health monitoring program stored in memory 11 and perform the following operations:
calculating the ratio of monitoring points lower than or equal to a set height to all monitoring points in the point cloud data according to the distribution state data;
calculating a speed change value of a user according to the movement speed data;
when the speed change value is larger than or equal to a set value and the ratio is larger than or equal to a set ratio, confirming the action state of the user according to the movement track data, wherein the action state comprises a bedridden state and an out-of-bed state;
if the user is in a bedridden state, judging that the user triggers a falling event;
and if the user is in the out-of-bed state, judging that the user triggers a falling event.
In one embodiment, processor 12 may be configured to invoke the millimeter wave radar-based multifunctional health monitoring program stored in memory 11 and perform the following operations:
When the fact that the user leaves the space and does not return to the space after a first set time interval is monitored according to the motion trail data, determining that the user triggers an independent outgoing event; and
and when the time length of single stay of the user in the space exceeds the second set time length according to the movement track data, determining that the user triggers an unmanned activity event.
In one embodiment, processor 12 may be configured to invoke the millimeter wave radar-based multifunctional health monitoring program stored in memory 11 and perform the following operations:
after the distance-Doppler graphs of all target units in the space are obtained, extracting phase data with Doppler of 0 from the distance-Doppler graph of any antenna;
performing multi-frame data accumulation on the phase data to obtain slow Doppler phase data with Doppler of 0;
performing static filtering on the slow Doppler phase data to screen out slow Doppler phase data of a user in a static state in space;
screening peak values from the statically filtered slow Doppler phase data based on a preset peak value threshold value, and determining a target distance unit where a static user is located according to the peak values;
and extracting the phase on the target distance unit of the slow Doppler phase data to obtain the phase change data.
In one embodiment, processor 12 may be configured to invoke the millimeter wave radar-based multifunctional health monitoring program stored in memory 11 and perform the following operations:
filtering the phase change data by adopting a 6-order Butterworth filter designed according to the frequency band of the respiratory frequency, and extracting respiratory waveform data;
performing fast Fourier transform on the respiration waveform data to extract a respiration period so as to calculate the respiration frequency of a user;
filtering the phase change data by adopting a 6-order Butterworth filter designed according to the frequency band of the heart rate, and extracting heart rate waveform data;
and performing wavelet transformation on the heart rate waveform data, and then performing fast Fourier transformation to extract a heart rate period so as to calculate the heart rate of the user.
In one embodiment, processor 12 may be configured to invoke the millimeter wave radar-based multifunctional health monitoring program stored in memory 11 and perform the following operations:
if the heart rate of the user is monitored to be lower than the set heart rate threshold value, judging that the user triggers a heart rate abnormal event;
if the respiration rate of the user is monitored to be lower than the set respiration threshold value, the user is judged to trigger a respiration abnormal event.
Based on the hardware architecture of the multifunctional health monitoring system based on the millimeter wave radar, the embodiment of the multifunctional health monitoring method based on the millimeter wave radar is provided. The invention discloses a multifunctional health monitoring method based on millimeter wave radar, which aims to realize all-weather, non-contact and privacy invasion-free multifunctional monitoring.
Referring to fig. 2, fig. 2 is an embodiment of a millimeter wave radar-based multifunctional health monitoring method according to the present invention, the multifunctional health monitoring method based on millimeter wave radar comprises the following steps:
and S10, receiving echo signals in the space, and calculating point cloud data of a user and phase change data caused by respiration and heartbeat of the user according to the echo signals.
Where the user refers to a subject that needs to be monitored, attended to, and protected. By way of example, the user may be one of the following objects: patients, elderly, infants.
Point cloud data is a collection of three-dimensional coordinate points that describe the shape and location of an object or scene in three-dimensional space. Further, since the respiration and heartbeat of a person cause minute movements of the chest and chest, these movements cause periodic phase changes of the echo signals. Based on the periodic phase change, the radar system can set a certain filtering condition to distinguish users in space from static backgrounds, so that phase change data caused by breathing and heartbeat of the users can be accurately monitored.
In particular, millimeter wave radar systems first transmit millimeter wave signals that interact with objects in space and reflect back. The receiver receives these echo signals and uses their time delay and intensity information to calculate the user's point cloud data and phase change data.
S20, calculating the behavior of the user according to the point cloud data.
Specifically, the behavior refers to information of the user behavior inferred by analyzing point cloud data acquired from the millimeter wave radar. These data include detailed descriptions of the user's activities and actions over the presumed period of time. For example, the behavior may include gesture data (e.g., standing, sitting, walking, running, stretching, etc.) of the user, movement trajectory data (direction, speed, path of movement, etc.), gesture stability data (e.g., monitoring whether the user is in balance, shaking or swaying, etc.), and the like.
Specifically, by analyzing the shape and position in the point cloud, the system can infer the current posture of the user, such as standing, sitting, lying down, and the like. In this way, gesture data of the user can be obtained.
Meanwhile, by comparing the point cloud data between the continuous frames, the system can analyze the moving track, speed and direction of the user, so as to obtain the track data of the user.
The method is also worth supplementing, and the number of people in the space can be detected through the point cloud data so as to obtain the number of people in the current space.
S30, calculating vital signs of the user according to the phase change data, wherein the vital signs comprise respiratory frequency and heart rate.
Specifically, when the human body breathes or the heart beats, the movement of the chest and heart causes the phase of the millimeter wave signal to change. Such variations are typically manifested as phase shifts or rates of change of the signals. The respiration-induced phase change is typically of a different frequency and character than the heartbeat-induced phase change. Breathing typically has a lower frequency (typically between 0.1Hz and 0.8 Hz) and heart beats typically have a higher frequency (typically above 0.8Hz and 2.5 Hz). Based on the principle, the respiration frequency and/or the heart rate of each user can be calculated by monitoring the phase change of echo signals caused by the respiration and the heartbeat of the user and adopting methods such as fast Fourier transform so as to realize non-contact vital sign monitoring.
It is worth noting that in some embodiments, the system may present the detected behavior and vital signs in real time at a terminal, which may be an applet, APP of a mobile device, a web page, etc.
And S40, monitoring whether a user triggers a set alarm event according to the behaviors and/or the vital signs.
In particular, a set alarm event refers to a series of specific conditions or conditions predefined and configured in the radar system, which when met by the user's behaviour or vital signs, the system triggers a corresponding alarm notification or emergency treatment. It is worth noting that these alarm events may be tailored to the health of the user, medical criteria, personal needs or special circumstances.
For example, the set alarm event may include the following events: fall, long-time rest, abnormal breathing, abnormal heart rate, independent outing, etc.
In particular, after obtaining the behavior and vital signs, the radar system may monitor these point cloud data and vital signs in real-time and compare with set alarm thresholds to monitor whether the user triggered a set alarm event.
In some embodiments, the system may monitor whether the user triggered an alarm event by simply comparing the behavior to a set alarm threshold, or comparing the vital sign to a set alarm threshold.
In other embodiments, the system may integrate behavioral and vital signs and by analyzing the data together, the system monitors the health of the user more accurately. For example, if the user has no activity (behavior) for a long period of time and the heart rate is abnormally low (vital signs), the system may monitor that the user may be in a dangerous state.
S50, if yes, executing corresponding alarm operation according to the triggered alarm event type.
Specifically, after determining that the user triggered the set alarm event, the system may perform a corresponding alarm operation according to the type of the triggered alarm event. Depending on the degree of emergency and the situation, the system may perform different operations to ensure the safety of the user and to provide appropriate assistance.
For example, depending on the type of alarm event triggered by the user, the system may send different types of alarm information to the pre-bound terminals. For example, when the monitored user triggers a fall event, the system can send alarm information of the user falling to the monitored terminal in a manner of telephone, short message, application push, email, instant message and the like, and remind the terminal user to take appropriate action in time.
The end user may be a relative, guardian, friend, or the like of the user to be monitored, or a family doctor, medical facility, nursing facility, or the like who has a nursing obligation to the user to be monitored.
In some embodiments, based on the alarm operation, the radar system of the technical scheme of the application can also preset a microphone and a loudspeaker, so that the system has a bidirectional voice call function. Therefore, after the monitored user triggers an alarm event and the system performs alarm operation, the system can actively initiate a communication request with the bound terminal, and after the communication is established, bidirectional voice communication is realized. Thus, the user can be timely communicated and helped, and the rapid rescue or support can be provided in emergency.
It can be appreciated that according to the multifunctional health monitoring method based on millimeter wave radar in the technical scheme, point cloud data and phase change data of a monitored user can be obtained through echo signals in a space, and then behavior and vital signs of the monitored user are obtained through calculation based on the point cloud data and the phase change data, so that multiple monitoring functions such as functions of people detection, fall detection, heart rate detection, breath detection, outgoing detection and the like can be realized simultaneously based on the behavior and the vital signs, and the monitoring functions are all-weather and noninductive. Thus, compared with the existing monitoring scheme, the scheme has the following advantages:
1. multifunctional monitoring: according to the technical scheme, based on the millimeter wave radar technology, a plurality of detection functions such as people number detection, fall detection, heart rate detection, breath detection, outgoing detection and the like can be realized at the same time, and a user can autonomously select one or more functions according to requirements;
2. low cost: according to the technical scheme, multiple monitoring functions can be realized by only one radar, and compared with a system for realizing multiple function monitoring by combining multiple non-wearable devices, the system is lower in cost and higher in cost performance;
3. Non-contact all-weather monitoring: the monitoring method of the technical scheme can realize 24-hour all-weather monitoring without any contact, and realize real noninductive daemon;
4. protection of privacy: unlike cameras, millimeter wave radars do not generate images during the monitoring process, but only capture simple trajectory and point cloud data. This means that the privacy of the user is effectively protected, and personal privacy problems are not touched;
5. and (3) two-way call: when the monitored user triggers an alarm event, the system can dial a telephone to the binding terminal by the cloud server platform, so that the monitored user contacts with the terminal user at the first time.
In some embodiments, calculating the point cloud data of the user from the echo signals includes:
s11, converting the echo signals into digital signals.
Specifically, the echo signals returned by the objects in space are analog signals, which need to be converted to digital signals by an analog-to-digital conversion module (ADC) before subsequent processing.
S12, performing distance dimension fast Fourier transform (Range FFT) and Doppler dimension Fourier transform (Doppler FFT) on the digital signals to obtain a distance-Doppler map (RD-map) of all target units in the space.
In radar signal processing, the distance dimension is used to measure the distance between the target object and the radar, and the doppler dimension is used to measure the speed of the target object.
Further, the Range FFT is a method of measuring a target distance using a relationship between a frequency variation of a chirp signal and the target distance. The doppler FFT is a method of measuring the target velocity using the relationship between the frequency change of the echo signal and the target velocity caused by the target motion.
In this embodiment, the system first performs Range FFT on each antenna of the radar to obtain distance information of each target unit in space. And performing doppler FFT on the Range FFT result to obtain Doppler data of each target unit in the space. Combining the Range FFT result and the doppler FFT result, the RD-map can be obtained.
S13, performing incoherent accumulation on the distance-Doppler spectrum, and performing two-dimensional constant false alarm detection (2D Constant False Alarm Rate Detector,2DCFAR) on the accumulation result by adopting a preset threshold parameter to extract a target unit corresponding to the user on the distance-Doppler spectrum.
After obtaining the RD-map of each antenna, incoherent accumulation can be performed on the RD-maps of all antennas, so as to obtain the accumulated RD-maps. Incoherent accumulation is a method of adding RD-maps of different antennas or different frames in terms of amplitude without taking into account phase differences. The signal to noise ratio of the target unit can be improved through incoherent accumulation, so that the target unit can be detected more easily when two-dimensional constant false alarm detection is carried out in the subsequent step.
Further, 2D CFAR detection is a method of constant false alarm detection in both the range and doppler dimensions, which can determine whether a target cell is present based on the average of each cell in the RD-map and surrounding cells.
Specifically, the preset threshold parameter is a false alarm rate threshold value required by 2D CFAR detection, two-dimensional threshold judgment is performed on the accumulated distance-doppler data, and a target corresponding to a data point exceeding the threshold value is regarded as a present target. Therefore, the target units in the space can be screened out after the 2D CFAR detection is completed.
S14, carrying out azimuth-elevation combined angle measurement on the target unit corresponding to the user to obtain the azimuth angle and the elevation angle of the target unit.
Where azimuth and pitch are parameters describing the position of the target unit within the radar detection space, are angular measurements in a polar coordinate system for determining the direction and elevation of the target object relative to the radar. The azimuth describes the horizontal direction of the target unit relative to the radar position. The pitch angle describes the vertical direction of the target unit relative to the radar position.
The azimuth-elevation joint goniometer algorithm is a method for calculating the azimuth and elevation angles of a target object in the radar detection space. The core principle of the combined azimuth-elevation angle measurement algorithm is to measure the signals of a target object using a plurality of antennas or sensors, and calculate azimuth and elevation angles by combining the phase and amplitude information of the signals.
Specifically, after screening out the target unit, all antenna data for the target unit may be extracted from the RD-map. Then, a combined azimuth-elevation angle measurement algorithm is used to calculate the azimuth and elevation angles of the target unit.
And S15, obtaining point data corresponding to the target unit in space according to the azimuth angle, the pitch angle and the corresponding distance unit and Doppler unit on the distance-Doppler spectrum.
Specifically, after the azimuth angle and the pitch angle of the target unit are calculated, the three-dimensional position of the target unit in the radar detection space can be determined by combining the distance unit and the Doppler unit recorded in the RD-map, so as to obtain point data of the target unit.
S16, generating point cloud data of the user in the space according to all the point data of the user.
Specifically, point cloud data of the user can be obtained through point data of all monitoring points of the user.
In this way, through the steps S11 to S16, the point cloud data of the user can be extracted from the echo signals, so as to monitor the movement and the position of the user.
In some embodiments, computing the behavior of the user from the point cloud data includes:
s21, calculating distribution state data of all monitoring points in the point cloud data.
Specifically, the system may analyze the user's point cloud data, including the location of the monitoring points in space, the density of the monitoring points, and so forth. These distribution status data can help the system understand which areas in space have monitoring points, and the distribution density of these monitoring points. By analyzing these features in the point cloud data, the system can derive the current posture of the user, such as standing, lying, etc.
S22, carrying out cluster fusion on the point cloud data of the user to obtain a fitting center of the point cloud data.
Specifically, the point cloud data of the user can be clustered and fused, and then the least square method is adopted for spherical fitting. And obtaining the spherical center coordinates of the clustering result, and taking the spherical center coordinates as fitting centers of users in space.
Among these, spherical fitting is a mathematical method for finding parameters of a sphere that best fits a set of data points, including the center coordinates and radius.
S23, carrying out Kalman filtering tracking on the fitting center to obtain motion data of the fitting center, wherein the motion data comprise motion track data and motion speed data.
Among these, kalman filtering (Kalman filtering) is an algorithm for optimally estimating the state of a system by inputting and outputting observation data through the system using a linear system state equation. The change of the coordinate of the fitting center along with time can be obtained by carrying out Kalman filtering tracking on the coordinate of the fitting center.
Specifically, the coordinates of the fitting center at each sampling time can be obtained by performing kalman filter tracking on the coordinates of the fitting center, so that the change of the coordinates of the fitting center along with time can be obtained, and the movement track data and the calculation speed data of the user can be generated.
S24, generating the behavior of the user according to the distribution state data and the motion data.
Specifically, in this step, the system will integrate the distribution state data and the motion data to generate the user's behavior. These behaviors may include gestures, motion trajectories, motion speeds, dwell times, etc. of the user. By comprehensively analyzing these data, the system can identify patterns of behavior of the user, such as when the user is walking, lying in bed, standing, sitting, etc.
It can be understood that the motion data obtained by tracking the fitting center by adopting Kalman filtering and other algorithms has higher accuracy. Meanwhile, the point cloud data usually contains a certain degree of noise, and the influence of the noise on the calculation of the motion data can be reduced through the calculation of the clustering and fitting centers. In addition, the distribution state of the cloud data and the motion data of the fitting center are combined, and meanwhile, the behavior of the user is generated, and the data dimension is increased, so that the accuracy of judging the behavior of the user is further improved.
In some embodiments, the set alarm event includes a fall event and a bed fall event;
monitoring whether a user triggers a set alarm event according to the behavior, including:
s41, calculating the ratio of monitoring points lower than or equal to a set height to all monitoring points in the point cloud data according to the distribution state data.
Specifically, according to the point cloud data of the user, the system analyzes the number of monitoring points lower than or equal to the set height in the point cloud data, and calculates the ratio of the monitoring points to all the monitoring points. This ratio reflects the degree of contact of the user's body with these beds, floors and other furniture.
For example, if the set height is below the height of the bed, the higher the duty cycle of the monitoring points in the point cloud data that are below or equal to the set height, the closer the user is to the ground.
S42, calculating a speed change value of the user according to the movement speed data.
In particular, based on the motion data, the system may calculate a user's velocity change value between adjacent frames, the velocity value representing the degree of change in the user's motion velocity over a set time. This speed change value may be used to monitor the user's movement status, for example, whether there is sudden rapid movement or a stop.
S43, confirming the action state of the user according to the movement track data, wherein the action state comprises a bedridden state and an out-of-bed state.
Specifically, the system can confirm the action state of the user according to the motion trail data of the user. For example, when the movement track of the user is always kept within the range of the bed, it may be determined that the user is in a bedridden state; when the movement track of the user remains out of the range of the bed, it can be determined that the user is in an out-of-bed state.
S44, if the user is in a bedridden state, and the speed change value is greater than or equal to a set value, and when the ratio is greater than or equal to the set ratio, judging that the user triggers a falling event.
Specifically, if the system detects that the user is in a bedridden state, the speed change value of the system is greater than or equal to a set threshold value, and the proportion of monitoring points lower than or equal to a set height is greater than or equal to a set ratio, the system will determine that the user has triggered a falling event.
S45, if the user is in an off-bed state, and the speed change value is greater than or equal to a set value, and the ratio is greater than or equal to the set ratio, judging that the user triggers a falling event.
Specifically, if the system detects that the user is in an out-of-bed state, the speed change value of the system is greater than or equal to a set threshold value, and the proportion of the monitoring points lower than or equal to a set height is greater than or equal to a set ratio, the system will determine that the user has triggered a fall event.
Through the steps, the multidimensional data such as the height information, the movement speed, the movement track and the like in the point cloud data can be combined, whether a user triggers a falling event or a falling event can be accurately monitored, and reliable basis is provided for finding and alarming of emergency situations.
In some embodiments, the set alarm event comprises an individual egress event.
Specifically, an individual out event refers to the act of a monitored user individually leaving a home, medical facility, or other secure space without the accompaniment of other people. Such events may often present risks or safety issues in certain situations, particularly for people requiring special attention or supervision, such as elderly people, children, people suffering from cognitive impairment or other diseases (e.g. autism, epilepsy, etc.), and the like.
In some embodiments, monitoring whether the user triggered the set alarm event based on the behavior further comprises:
And when the fact that the user leaves the space and does not return to the space after the first set time interval is monitored according to the movement track data, determining that the user triggers an independent outgoing event.
In particular, the zone boundaries of the monitoring space may be set in the system in the face of the need to have independent outbound monitoring. Thus, when the user is monitored to cross the boundaries through the user's motion profile, the system can determine that the user has left the space.
Further, when it is monitored that the user leaves the room, the system records the moment when the user leaves the space, and monitors whether the user returns to the space again within a set time. If the user leaving the space does not return again within the set period of time, the system will determine that the user triggered an individual egress event.
Once the system determines that the user triggered an individual out event, it immediately triggers a corresponding alarm operation. This may include sending alert notifications to guardians, family members or related institutions so that they can take appropriate action, such as contacting the user, seeking the user's location or notifying the police, etc.
Through the steps, the system can timely find and alarm when the user goes out alone, so that the safety of the monitored user is ensured.
In some embodiments, the set alarm event further comprises an unmanned event.
Specifically, an unmanned event refers to an action in which a monitored user alone stays in a home, medical facility, or other secure space for a period of time exceeding a set period of time without being accompanied by other people. Such events may often present risks or safety issues in certain situations, particularly for people requiring special attention or supervision, such as elderly people, children, people suffering from cognitive impairment or other diseases (e.g. autism, epilepsy, etc.), and the like.
In some embodiments, monitoring whether the user triggered the set alarm event based on the behavior further comprises:
and when the time length of single stay of the user in the space is monitored to be longer than the second set time length according to the movement track data, judging that the user triggers an unmanned activity event.
In particular, the zone boundaries of the monitoring space may be set in the system in the face of the need for long-lived monitoring. Thus, when the monitored user does not cross the boundaries for a long time (more than a second set period of time, such as more than 24 hours, 36 hours, etc.) through the motion trail of the monitored user, the system can determine that the user stays in the space for too long. Further, the system will determine that the monitored user triggered an unmanned event.
Once the system determines that the monitored user triggered an inactivity event, it immediately triggers a corresponding alert operation. This may include sending alert notifications to guardians, family members or related institutions so that they can take appropriate action, such as contacting the user, seeking the user's location or notifying the police, etc.
Through the steps, the system can timely find and alarm when the monitored user stays in the space for a long time, so that the safety of the monitored user is ensured.
In some embodiments, calculating phase change data caused by respiration and heartbeat of the user from the echo signals includes:
s110, after the distance-Doppler graphs of all target units in the space are obtained, extracting phase data with Doppler of 0 from the distance-Doppler graph of any antenna.
In particular, the range-doppler profiles of all target units in the space may be acquired in the step of acquiring point cloud data of the user. The processing can reduce the calculation flow of the system and save calculation power and energy.
Further, in the multi-channel millimeter wave radar system, the range-doppler spectrum of any one antenna can be selected for data processing. In the selected range-doppler plot, the point with doppler frequency 0 corresponds to a static or slowly moving target object (person or object) whose phase data may vary with small movements of the target object.
Thus, the extracted Doppler frequency is 0 phase data, which can be used for screening out static (usually sleep state) users in space.
S120, multi-frame data accumulation is carried out on the phase data, and slow Doppler phase data with Doppler of 0 is obtained.
Where slow doppler refers to the change in echo signal frequency caused by small movements of the target.
Specifically, the system processes the phase data of multiple frames (for example, 10 continuous seconds), and calculates the difference of the multiple frames of data to obtain the slow doppler phase data of the target object with doppler of 0. In this way, variations in the time dimension of these data can be obtained.
It will be appreciated that by selecting data with doppler 0 and subjecting them to multi-frame data accumulation processing. By superposing multi-frame data, the observation time of a target object can be increased, and the detection reliability is improved.
S130, performing static filtering on the slow Doppler phase data to screen out the slow Doppler phase data of the user in a static state in space.
Specifically, static filtering (subtracting the average value of multiple frames from each frame) is performed on slow doppler data, and the stability of the background signal can be utilized to remove the completely stationary background, so that only the signal with variation is retained. The objects corresponding to these varying signals can be considered as users that are static in space. Accordingly, these varying signals are the slow Doppler phase data of the static user required for the subsequent step calculation.
And S140, screening peak values from the slow Doppler phase data after static filtering based on a preset peak value threshold value, and determining a target distance unit where a static user is located according to the peak values.
Specifically, after performing static filtering, the system screens out peaks in the slow doppler phase data according to a preset peak threshold. These peaks represent the respiration and heartbeat signals of the user. By determining these peaks, the system can determine the target distance cell where the static user is located.
And S150, extracting phases on the target distance unit of the slow Doppler phase data to obtain the phase change data.
And finally, extracting phase data on the determined target distance unit to obtain phase change data caused by respiration and heartbeat of the user.
In some embodiments, calculating vital signs of the user from the phase change data comprises:
and S31, filtering the phase change data by adopting a 6-order Butterworth filter designed according to the frequency range of the respiratory frequency, and extracting respiratory waveform data.
Among these, the butterworth filter is a digital filter commonly used for signal processing, which enhances a signal in a specific frequency band while suppressing unnecessary frequency components. The 6 th order butterworth filter designed by the frequency band of the respiratory frequency can emphasize the respiratory waveform signal and reduce noise.
Specifically, after the phase change data is filtered, the resulting signal may be considered as respiratory waveform data that reflects the respiratory periodicity change of the target subject.
S32, performing fast Fourier transform on the respiration waveform data to extract a respiration period so as to calculate the respiration frequency of the user.
Specifically, the frequency component of the breathing cycle of the target object can be extracted from the waveform by performing Fast Fourier Transform (FFT) on the extracted breathing waveform data, and the breathing frequency of the target object can be calculated based on the periodic variation of the frequency component of the breathing cycle.
In addition, by analyzing the result of the FFT, the amplitude and phase information corresponding to the respiratory frequency in the frequency spectrum can be extracted. Wherein amplitude information of the breathing frequency can be used to represent the intensity of the breath.
And S33, filtering the phase change data by adopting a 6-order Butterworth filter designed according to the frequency range of the heart rate, and extracting heart rate waveform data.
Among these, the butterworth filter is a digital filter commonly used for signal processing, which enhances a signal in a specific frequency band while suppressing unnecessary frequency components. The 6 th order butterworth filter designed by the frequency band of the heart rate can emphasize the heart beat waveform signal and reduce noise.
Specifically, after the phase change data is filtered, the obtained signal may be regarded as heart rate waveform data, which reflects the heart rate periodic change of the target object.
S34, performing wavelet transformation processing on the heart rate waveform data, and then performing fast Fourier transformation to extract a heart rate period so as to calculate the heart rate of the user.
Among these, wavelet transform is a transform that can decompose a signal into basis functions of different scales and frequencies, to extract detailed features of the signal or remove noise.
Specifically, the extracted respiratory waveform data is subjected to wavelet transformation and then subjected to Fast Fourier Transformation (FFT), so that the frequency component of the heart rate period of the target object can be extracted from the waveform, and the heart rate of the target object can be calculated based on the periodic variation of the frequency component of the heart rate period.
In addition, by analyzing the result of the FFT, the amplitude and phase information corresponding to the spectrum center rate can be extracted. Wherein the amplitude information of the heart rate can be used to represent the intensity of the heart beat.
In some embodiments, the set alarm events further include respiratory abnormalities and heart rate abnormalities.
In particular, in addition to fall events, bed fall events, and individual out events, the system can set respiratory and heart rate anomalies as alarm events. Among these, respiratory abnormalities may include respiratory rate abnormalities, apneas, and the like; the heart rate abnormal event may include an abnormal frequency of heart rate, arrhythmia or arrhythmia, or the like.
In some embodiments, monitoring whether a user triggered a set alarm event based on the vital signs further comprises:
s210, if the heart rate of the user is monitored to exceed the set heart rate range, judging that the user triggers a heart rate abnormal event;
and S220, if the respiration frequency of the user is monitored to exceed the set respiration range, judging that the user triggers a respiration abnormal event.
Specifically, the system monitors the vital signs (heart rate and respiratory rate) of the user in real time and sets a specific heart rate range and respiratory rate range. If the heart rate of the user is monitored to be beyond the set heart rate range, the system determines that the user triggered a heart rate abnormality event. Also, if the user's respiratory rate is monitored to be outside of the set respiratory range, the system determines that the user triggered a respiratory abnormality event.
For example, if the user's heart rate exceeds a set upper limit, the system will determine a heart rate abnormality event. An excessively fast heart rate may indicate that the user is in a cardiovascular stress state, requiring vigilance.
For example, if the heart rate of the user is below a set lower limit value, it is also determined as a heart rate abnormality event. An excessively slow heart rate may indicate that the user may have an arrhythmia or other heart problem.
For example, if the user's respiratory rate exceeds a set upper limit, the system will determine a respiratory abnormality event. Excessively rapid breathing may indicate that the user may be in anxiety, respiratory disease, or other emergency conditions.
For example, if the user's respiratory rate is below a set lower limit, it is also determined as a respiratory abnormality event. A too slow breathing may indicate that the user may have respiratory problems or other potential health risks.
It is worth supplementing that when the system monitors that the heart rate or the respiratory rate of the user exceeds the set range, corresponding alarm operation is triggered immediately, so that medical measures can be taken timely or medical personnel, guardianship and the like can be informed timely, and the user can be helped and cured timely when the user is at health risk.
In addition, the embodiment of the invention also provides a computer readable storage medium, which can be any one or any combination of a plurality of hard disk, a multimedia card, an SD card, a flash memory card, an SMC, a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a portable compact disc read-only memory (CD-ROM), a USB memory and the like. The computer readable storage medium includes the millimeter wave radar based multifunctional health monitoring program 10, and the specific embodiment of the computer readable storage medium of the present invention is substantially the same as the above-mentioned millimeter wave radar based multifunctional health monitoring method and the specific embodiment of the server 1, and will not be described herein.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.

Claims (10)

1. The multifunctional health monitoring method based on the millimeter wave radar is characterized by comprising the following steps of:
receiving echo signals in a space, and calculating point cloud data of a user and phase change data caused by respiration and heartbeat of the user according to the echo signals;
calculating the behavior of the user according to the point cloud data;
calculating vital signs of the user according to the phase change data, wherein the vital signs comprise respiratory frequency and heart rate;
monitoring whether a user triggers a set alarm event according to the behaviors and/or vital signs;
if yes, executing corresponding alarm operation according to the triggered alarm event type.
2. The millimeter wave radar-based multifunctional health monitoring method of claim 1, wherein calculating the point cloud data of the user from the echo signals comprises:
Converting the echo signal into a digital signal;
performing distance-dimension fast Fourier transform and Doppler-dimension Fourier transform on the digital signals to obtain distance-Doppler spectrums of all target units in the space;
performing incoherent accumulation on the distance-Doppler spectrum, and performing two-dimensional constant false alarm detection on an accumulation result by adopting a preset threshold parameter to extract a target unit corresponding to the user on the distance-Doppler spectrum;
performing azimuth-pitch combined angle measurement on a target unit corresponding to the user to obtain an azimuth angle and a pitch angle of the target unit;
obtaining point data corresponding to the target unit in the space according to the azimuth angle, the pitch angle and the corresponding distance unit on the distance-Doppler spectrum;
and generating point cloud data of the user according to all the point data of the user.
3. The millimeter wave radar-based multifunctional health monitoring method of claim 2, wherein calculating the behavior of the user from the point cloud data comprises:
calculating distribution state data of all monitoring points in the point cloud data;
clustering and fusing the point cloud data of the user to obtain a fitting center of the point cloud data;
Carrying out Kalman filtering tracking on the fitting center to obtain motion data of the fitting center, wherein the motion data comprises motion track data and motion speed data;
and generating the behavior of the user according to the distribution state data and the motion data.
4. The millimeter wave radar-based multifunctional health monitoring method of claim 3, wherein the set alarm event comprises a fall event and a falling bed event;
monitoring whether a user triggers a set alarm event according to the behavior, including:
calculating the ratio of monitoring points lower than or equal to a set height to all monitoring points in the point cloud data according to the distribution state data;
calculating a speed change value of a user according to the movement speed data;
when the speed change value is larger than or equal to a set value and the ratio is larger than or equal to a set ratio, confirming the action state of the user according to the movement track data, wherein the action state comprises a bedridden state and an out-of-bed state;
if the user is in a bedridden state, judging that the user triggers a falling event;
and if the user is in the out-of-bed state, judging that the user triggers a falling event.
5. The millimeter wave radar-based multifunctional health monitoring method of claim 3, wherein the set alarm event comprises an individual out event and an unmanned event;
monitoring whether the user triggers a set alarm event according to the behavior, and further comprising:
when the fact that the user leaves the space and does not return to the space after a first set time interval is monitored according to the motion trail data, determining that the user triggers an independent outgoing event; and
and when the time length of single stay of the user in the space exceeds the second set time length according to the movement track data, determining that the user triggers an unmanned activity event.
6. The millimeter wave radar-based multifunctional health monitoring method of claim 2, wherein the millimeter wave radar has a plurality of antennas;
calculating phase change data caused by respiration and heartbeat of a user according to the echo signals, wherein the phase change data comprises:
after the distance-Doppler graphs of all target units in the space are obtained, extracting phase data with Doppler of 0 from the distance-Doppler graph of any antenna;
performing multi-frame data accumulation on the phase data to obtain slow Doppler phase data with Doppler of 0;
Performing static filtering on the slow Doppler phase data to screen out slow Doppler phase data of a user in a static state in space;
screening peak values from the statically filtered slow Doppler phase data based on a preset peak value threshold value, and determining a target distance unit where a static user is located according to the peak values;
and extracting the phase on the target distance unit of the slow Doppler phase data to obtain the phase change data.
7. The millimeter wave radar-based multifunctional health monitoring method of claim 1, wherein calculating vital signs of a user from the phase change data comprises:
filtering the phase change data by adopting a 6-order Butterworth filter designed according to the frequency band of the respiratory frequency, and extracting respiratory waveform data;
performing fast Fourier transform on the respiration waveform data to extract a respiration period so as to calculate the respiration frequency of a user;
filtering the phase change data by adopting a 6-order Butterworth filter designed according to the frequency band of the heart rate, and extracting heart rate waveform data;
and performing wavelet transformation on the heart rate waveform data, and then performing fast Fourier transformation to extract a heart rate period so as to calculate the heart rate of the user.
8. The millimeter wave radar-based multifunctional health monitoring method of claim 7, wherein the set alarm event further comprises a respiratory abnormality event and a heart rate abnormality event;
monitoring whether a user triggers a set alarm event according to the vital sign, and further comprising:
if the heart rate of the user is monitored to be lower than the set heart rate threshold value, judging that the user triggers a heart rate abnormal event;
if the respiration rate of the user is monitored to be lower than the set respiration threshold value, the user is judged to trigger a respiration abnormal event.
9. A millimeter wave radar-based multifunctional health monitoring system, comprising a millimeter wave radar, a memory, a processor and a millimeter wave radar-based multifunctional health monitoring program stored on the memory and operable on the processor, wherein the processor implements the millimeter wave radar-based multifunctional health monitoring method according to any one of claims 1-8 when executing the millimeter wave radar-based multifunctional health monitoring program.
10. A computer-readable storage medium, wherein a millimeter-wave radar-based multifunctional health monitoring program is stored on the computer-readable storage medium, which when executed by a processor, implements the millimeter-wave radar-based multifunctional health monitoring method according to any one of claims 1-8.
CN202311580515.9A 2023-11-23 2023-11-23 Multifunctional health monitoring method based on millimeter wave radar Pending CN117612717A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN118072467A (en) * 2024-04-17 2024-05-24 清澜技术(深圳)有限公司 Monitoring alarm method, system, equipment and storage medium

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN118072467A (en) * 2024-04-17 2024-05-24 清澜技术(深圳)有限公司 Monitoring alarm method, system, equipment and storage medium

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