CN115153487A - FMCW millimeter wave radar sleep monitoring method - Google Patents
FMCW millimeter wave radar sleep monitoring method Download PDFInfo
- Publication number
- CN115153487A CN115153487A CN202210896908.XA CN202210896908A CN115153487A CN 115153487 A CN115153487 A CN 115153487A CN 202210896908 A CN202210896908 A CN 202210896908A CN 115153487 A CN115153487 A CN 115153487A
- Authority
- CN
- China
- Prior art keywords
- state
- target
- sleep
- millimeter wave
- wave radar
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/48—Other medical applications
- A61B5/4806—Sleep evaluation
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/05—Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves
Landscapes
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Medical Informatics (AREA)
- Biophysics (AREA)
- Pathology (AREA)
- Engineering & Computer Science (AREA)
- Biomedical Technology (AREA)
- Heart & Thoracic Surgery (AREA)
- Physics & Mathematics (AREA)
- Molecular Biology (AREA)
- Surgery (AREA)
- Animal Behavior & Ethology (AREA)
- General Health & Medical Sciences (AREA)
- Public Health (AREA)
- Veterinary Medicine (AREA)
- Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
- Radiology & Medical Imaging (AREA)
- Radar Systems Or Details Thereof (AREA)
Abstract
The invention discloses a FMCW millimeter wave radar sleep monitoring method, which comprises the following steps of S1: the millimeter wave radar continuously sends frequency modulation continuous waves to the target, so that the millimeter wave radar performs first processing on the received echo signals to obtain first signal data, and the step S2: and the processing module receives the first signal data and then carries out second processing on the first signal data so as to obtain target information including the activity state, the distance and the strength of the target. The FMCW millimeter wave radar sleep monitoring method can accurately distinguish the target human body activity conditions, such as the existence and the keeping of a human body to be static, the fine activity of the human body, the large motion of the human body, the fast motion, the slow motion and the like, can analyze the sleep state by utilizing the detailed body motion information, and has the advantages of accurate monitoring, good real-time performance, no contact, long-distance and accurate acquisition of the sleep state information and the like.
Description
Technical Field
The invention belongs to the technical field of millimeter wave radar sleep monitoring, and particularly relates to a FMCW (frequency modulated continuous wave) millimeter wave radar sleep monitoring method.
Background
The sleep monitoring can help the user to record the sleep state, and through analyzing the sleep data, the sleep quality and the physical condition of the user can be evaluated, and improvement opinions can be made in a targeted manner.
Conventional sleep monitoring devices are typically required to be attached to or in close proximity to a portion of the subject's body. The most extensive of daily use has cell-phone sleep monitoring APP and bracelet monitoring, and mode realization such as few logical many cameras, intelligent mattress, chest/waistband in addition. The mobile phone sleep monitoring APP usually records sound and body movement conditions all night by using a microphone and a body movement instrument of the mobile phone. The mobile phone is generally required to be placed near the pillow edge, and the application may increase the power consumption of the mobile phone to affect the daytime use. Furthermore, recording sound may cause a problem of privacy disclosure. Bracelet monitoring is the most widely used sleep detection method in daily use at present. The bracelet can record the information such as body movement, rhythm of the heart, blood oxygen overnight, utilizes above information can obtain the sleep data overnight. Bracelet monitoring needs bracelet laminating tester wrist, otherwise can influence the test accuracy. Such a wearing manner may cause discomfort to the tester and affect sleep. Partial bracelet monitoring is based on optical principle, can periodic luminous, and some actions of hand probably activate the dial plate in addition, produce the light, cause the influence to sleep.
Millimeter wave radar may provide a non-contact sleep monitoring method. The millimeter wave radar acquires target information through the emission and the reception of high-frequency electromagnetic waves and information demodulation, does not need to be in contact with a tested person, can ensure that the tested person is in a more natural sleep state, is not influenced by test equipment, and does not have any risk of invading privacy. The millimeter wave radar can monitor the actions and vital signs (including respiration and heart rate) of a human body, and the sleep state can be analyzed by utilizing the information. The existing millimeter wave radar sleep monitoring method combines body movement state detection and vital sign detection (including heartbeat and respiration), and obtains the sleep condition through comprehensive analysis of body movement and vital sign information. The existing millimeter wave radar body movement monitoring mostly detects whether a human body has obvious movement, and can not carry out detailed distinction on the movement state of the human body, such as continuous movement of the human body, existence and keeping of the human body still, fine movement of the human body, movement and the like. In addition, the accuracy of the millimeter wave radar for detecting the vital signs is greatly influenced by the detection distance, the body part irradiated by the radar and the body action. For example, when Lei Dazheng is irradiated to the chest, the detection accuracy is good, and when radar is irradiated to the radar, the detection accuracy is poor. Especially, the heart rate signal is usually very weak, and the requirement on the detection condition is relatively high. The state of the human body is usually random and uncontrollable during the sleep process and varies from person to person, so that the accuracy of sleep monitoring is difficult to ensure by using the method.
Therefore, the above problems are further improved.
Disclosure of Invention
The invention mainly aims to provide an FMCW millimeter wave radar sleep monitoring method, which can accurately distinguish target human body activity conditions, such as the existence and the keeping of a human body still, the fine activity of the human body, the large motion of the human body, the fast motion, the slow motion and the like, can analyze the sleep state by utilizing the detailed body motion information, and has the advantages of accurate monitoring, good real-time performance, capability of acquiring the sleep state information in a non-contact, long-distance and accurate manner and the like.
In order to achieve the above purpose, the invention provides a FMCW millimeter wave radar sleep monitoring method, which is used for monitoring the sleep state of a person and comprises the following steps:
step S1: the millimeter wave radar continuously sends frequency modulation continuous waves to a target (personnel), so that the millimeter wave radar performs first processing on received echo signals to obtain first signal data;
step S2: the processing module receives the first signal data and then carries out second processing on the first signal data so as to obtain target information including an activity state, a distance and strength of a target;
and step S3: the processing module carries out third processing on the target information in unit time so as to classify and record the action characteristics of the target;
and step S4: and the processing module performs fourth processing on the action characteristics in the monitoring period (all night), so as to obtain the sleep state information of the target in the monitoring period.
As a further preferable technical solution of the above technical solution, for the first processing in step S1, the millimeter wave radar performs low-noise amplification, down-conversion, and filtering processing on the received echo signal to obtain an intermediate frequency signal, then samples the intermediate frequency signal and performs a processing manner including smoothing processing, filtering processing, and windowing processing, thereby obtaining first signal data that improves the quality of the original signal (with respect to the echo signal).
As a further preferred technical solution of the above technical solution, the step S2 is specifically implemented as the following steps (performing range _ FFT or range _ doppler _ FFT on the first signal data, assisting various static clutter filtering techniques and phase monitoring techniques, and monitoring target information in the region to be detected):
step S2.1: active state status of the target: status =0 indicates no object in the monitored area, status =1 indicates that object activity is monitored, and status =2 indicates that the object is in the monitored area and the body remains motionless;
step S2.2: obtaining the distance dis between the target and the millimeter wave radar;
step S2.3: and obtaining the signal intensity str of the target, and reflecting the action intensity of the target according to the signal intensity.
As a further preferred embodiment of the above technical solution, the step S3 is specifically implemented as the following steps:
step S3.1: obtaining target information of a target in unit time: { status1, status2,. Gtoreq, status n }, { dis1, dis2,. Gtoreq, dis }, { str1, str2,. Gtoreq, strN }, and further classify and record the action characteristics according to the target information;
step S3.1.1: screening out monitoring points with the distance dis larger than a certain threshold dis _ th, and clearing the active state ststus, the distance dis and the signal strength str corresponding to the monitoring points;
step S3.1.2: counting the number N1 of the active states status =0 in the unit time period, if N1 is equal to N, namely all the active states status =0 in the unit time period, determining that the monitoring area in the unit time period has no target and the target is in a bed leaving state, and defining the state as a first state;
step S3.1.3: counting the number n2 of status =2 in the unit time period, if n2 is greater than or equal to a certain threshold th1, considering that more actions exist in the target in the unit time period, otherwise (0 and n2 and t 1) considering that only slight actions (such as slight movement of head or hands and feet) occur in the body in the unit time period, and defining the state as a second state;
when n2 > = th1, further counting the number n3 that the activity state str in the unit time is greater than a threshold th2, if n3 > = th3, determining that the body has a motion with a larger amplitude (such as turning over) in the time period, and defining the state as a third state, otherwise, determining that the body only has slight motion, and classifying the state as a second state;
step S3.1.4: if the number of status =2 in the unit time period is 0 and the number of status =1 is greater than or equal to the threshold th4, it is determined that there is a valid monitoring target in the monitoring area in the unit time period and the target body (almost) does not move, and the state is a fourth state, otherwise, it is determined as a fifth state of invalid monitoring.
As a further preferable embodiment of the above technical solution, the step S4 is specifically implemented as the following steps:
step S4.1: for the judgment of the bed-entering time, in a period of time T1 from the current time, the target action state is only the second state, the third state or the fourth state;
step S4.2: judging the sleep starting time, confirming that the target is positioned in a monitoring range (on a bed), and when the proportion T1/T2 of the time T1 of the third state to the total time is lower than a certain threshold pth1 in a period of time T2 from the current time, determining that the target enters a sleep state;
step S4.3: judging the sleep ending time, confirming to enter a sleep state, and determining that the target sleep is ended when the proportion T1/T2 of the time T1 of the third state to the total time is higher than a certain threshold pth2 in a period of time T2 from the current time point or only the first state exists in a period of time T2 from the current time point;
step S4.4: and for judging deep sleep and light sleep, confirming to enter a sleep state, only setting the fourth state for a period of time and setting the maintaining time to be greater than or equal to pth3, and determining to be target deep sleep (otherwise, light sleep).
The invention has the beneficial effects that:
1. and the device is free of contact, so that the influence on sleep is avoided.
2. The FMCW millimeter wave radar has the advantages of high response speed for detecting a moving target, good real-time property and more detailed and accurate detection data.
3. The FMCW millimeter wave laser is used for subdividing the activity state and the action type of the human body, such as continuous activity, existence and still keeping of the human body, fine physical activity, fast large action, slow large action and the like, and the detailed physical movement information is used for analyzing and monitoring the sleep state.
4. Sign signals such as respiration and heart rate are not used as main factors of sleep analysis, the installation place value is more random, and the detection is more accurate.
Drawings
Fig. 1 is a schematic diagram of a sleep monitoring method of an FMCW millimeter wave radar of the present invention.
Fig. 2 is a schematic diagram of a sleep monitoring method of an FMCW millimeter wave radar of the present invention.
Detailed Description
The following description is presented to disclose the invention so as to enable any person skilled in the art to practice the invention. The preferred embodiments in the following description are given by way of example only, and other obvious variations will occur to those skilled in the art. The basic principles of the invention, as defined in the following description, may be applied to other embodiments, variations, modifications, equivalents, and other technical solutions without departing from the spirit and scope of the invention.
In the preferred embodiments of the present invention, those skilled in the art should note that the objects, persons, etc. to which the present invention relates may be viewed as prior art.
Preferred embodiments.
The invention discloses a FMCW millimeter wave radar sleep monitoring method, which is used for monitoring the sleep state of a person and comprises the following steps:
step S1: the millimeter wave radar continuously sends frequency modulation continuous waves to a target (personnel), so that the millimeter wave radar performs first processing (preprocessing) on received echo signals to obtain first signal data;
step S2: the processing module receives the first signal data and then carries out second processing on the first signal data so as to obtain target information including an activity state, a distance and strength of a target;
and step S3: the processing module carries out third processing on the target information in unit time so as to classify and record the action characteristics of the target;
and step S4: and the processing module performs fourth processing on the action characteristics in the monitoring period (all night), so as to obtain the sleep state information of the target in the monitoring period.
Specifically, for the first processing in step S1, the millimeter wave radar performs low-noise amplification, down-conversion, and filtering processing on the received echo signal to obtain an intermediate frequency signal, then samples the intermediate frequency signal and performs processing modes including smoothing processing, filtering processing, and windowing processing on the intermediate frequency signal, thereby obtaining first signal data (relative to the echo signal) that improves the quality of the original signal.
More specifically, the step S2 is specifically implemented as the following steps (performing range _ FFT or range _ doppler _ FFT on the first signal data, assisting various static clutter filtering techniques and phase monitoring techniques, and monitoring target information in the region to be detected):
step S2.1: get target active status: status =0 indicates no object in the monitored area, status =1 indicates that object activity is monitored, and status =2 indicates that the object is in the monitored area and the body remains motionless;
step S2.2: obtaining the distance dis between the target and the millimeter wave radar;
step S2.3: and obtaining the signal intensity str of the target, and reflecting the action intensity of the target according to the signal intensity.
Further, step S3 is specifically implemented as the following steps:
step S3.1: obtaining target information of a target in unit time: { status1, status2,. Gtoreq, status n }, { dis1, dis2,. Gtoreq, dis }, { str1, str2,. Gtoreq, strN }, and further classify and record the action characteristics according to the target information;
step S3.1.1: screening out monitoring points with the distance dis larger than a certain threshold dis _ th, and clearing the active state ststus, the distance dis and the signal strength str corresponding to the monitoring points;
step S3.1.2: counting the number N1 of the active states status =0 in the unit time period, if N1 is equal to N, namely all the active states status =0 in the unit time period, determining that the monitoring area in the unit time period has no target and the target is in a bed leaving state, and defining the state as a first state;
step S3.1.3: counting the number n2 of status =2 in the unit time period, if n2 is greater than or equal to a certain threshold th1, determining that more actions exist in the target in the unit time period, otherwise (0-n 2-t-1) determining that only slight actions (such as slight movement of head or hands and feet) occur in the body in the unit time period, and defining the state as a second state;
when n2 > = th1, further counting the number n3 that the activity state str in the unit time is greater than a threshold th2, if n3 > = th3, determining that the body has a motion with a larger amplitude (such as turning over) in the time period, and defining the state as a third state, otherwise, determining that the body only has slight motion, and classifying the state as a second state;
step S3.1.4: if the number of status =2 in the unit time period is 0 and the number of status =1 is greater than or equal to the threshold th4, it is determined that there is a valid monitoring target in the monitoring area in the unit time period and the target body (almost) does not move, and this state is a fourth state, otherwise, it is determined that there is a fifth state of invalid monitoring (it is likely that the radar installation position is far away from the subject (target)).
Further, step S4 is specifically implemented as the following steps:
while sleep monitoring typically requires longer data acquisition and storage, FMCW target detection is typically a relatively fast detection, thus producing a large amount of detection data. And comprehensively analyzing the target information in real time according to a fixed time interval to obtain the action classification information of the time period, so that the data storage capacity can be reduced on one hand, and the obtained action characteristic classification can be directly used for subsequent sleep state analysis on the other hand. The unit may often be several tens of seconds to several minutes, in this example 1 minute, in view of real-time monitoring and reduced data storage.
Step S4.1: for the judgment of the bed-entering time, in a period of time T1 from the current time, the target action state is only the second state, the third state or the fourth state;
step S4.2: judging the sleep starting time, determining that the target is located in a monitoring range (on a bed), and determining that the target enters a sleep state when the proportion T1/T2 of the time T1 of the third state to the total time is lower than a certain threshold pth1 in a period of time T2 from the current time;
step S4.3: judging the sleep ending time, confirming to enter a sleep state, and determining that the target sleep is ended when the proportion T1/T2 of the time T1 of the third state to the total time is higher than a certain threshold pth2 in a period of time T2 from the current time point or only the first state exists in a period of time T2 from the current time point;
step S4.4: and for judging deep sleep and light sleep, confirming to enter a sleep state, only setting the fourth state for a period of time and setting the maintaining time to be greater than or equal to pth3, and determining to be target deep sleep (otherwise, light sleep).
Further analysis of sleep may be made from the above information, for example: deep sleep proportion, turnover frequency, waking frequency, sleep quality grading and the like.
In addition, the detailed body movement data can be used in advanced data processing algorithms such as machine learning and AI to obtain sleep analysis results.
Preferably, the FMCW millimeter wave radar can accurately detect the distance and the speed of the target, and for the multi-transmitting multi-receiving radar, the FMCW millimeter wave radar can also detect the angle of the target. The position and motion information of the target can be judged by integrating the distance, speed and angle information of the target. By utilizing various static clutter filtering technologies and phase detection technologies, static targets in the environment can be effectively filtered, and the motion states of the targets, such as general motion, micromotion and human body existence, can be accurately distinguished.
When a person is in a sleeping state, the whole body of the person is in a relatively static state, and due to the breathing heartbeat, small-amplitude and slow actions such as thoracic cavity fluctuation and slight fluctuation of the body caused by the thoracic cavity fluctuation still exist in the body of the person, which can be detected by the FMCW radar. Typical roll-over, limb movements are of faster speed and greater amplitude than body movements produced by breathing, heartbeat. Generally, when a person is in a waking state, the body movement is more frequent and the movement amplitude is large, the frequency and the amplitude of the body movement are reduced as the person falls asleep, and when the person enters deep sleep, the body hardly moves, and the breathing and the heartbeat become slower and lighter. The FMCW radar can accurately detect the activity of the human body in real time and can divide the activity of the human body more finely, for example, the human body exists and keeps still, the human body has fine activity, the human body has large motion, fast motion, slow motion, etc. The detailed body movement situation of the target can be obtained by recording the detection data all night, and then the body movement situation is comprehensively analyzed, so that the sleep situation all night, such as the time to fall asleep, the wake-up time, the deep sleep proportion and the like, can be obtained.
It should be noted that technical features related to the objects, persons and the like in the present patent application should be regarded as the prior art, and specific structures, operation principles, control modes and spatial arrangement modes of the technical features may be selected conventionally in the field, and should not be regarded as the points of the invention of the present patent, and the present patent is not further specifically described in detail.
It will be apparent to those skilled in the art that modifications and equivalents can be made to the embodiments described above, or some features of the embodiments described above, and any modifications, equivalents, improvements, and the like, which fall within the spirit and principle of the present invention, are intended to be included within the scope of the present invention.
Claims (5)
1. A FMCW millimeter wave radar sleep monitoring method is used for monitoring the sleep state of a person and is characterized by comprising the following steps:
step S1: the millimeter wave radar continuously sends frequency modulation continuous waves to a target, so that the millimeter wave radar performs first processing on received echo signals to obtain first signal data;
step S2: the processing module receives the first signal data and then carries out second processing on the first signal data so as to obtain target information including an activity state, a distance and strength of a target;
and step S3: the processing module carries out third processing on the target information in unit time so as to classify and record the action characteristics of the target;
and step S4: and the processing module carries out fourth processing on the action characteristics in the monitoring period so as to obtain the sleep state information of the target in the monitoring period.
2. The sleep monitoring method of the FMCW millimeter wave radar as set forth in claim 1, wherein for the first process of step S1, the millimeter wave radar performs low noise amplification, down conversion and filtering on the received echo signal to obtain an intermediate frequency signal, and then samples the intermediate frequency signal and performs a processing method including smoothing, filtering and windowing to obtain the first signal data for improving the quality of the original signal.
3. The FMCW millimeter wave radar sleep monitoring method as set forth in claim 2, wherein the step S2 is embodied as the steps of:
step S2.1: get target active status: status =0 indicates no object in the monitored area, status =1 indicates that object activity is monitored, and status =2 indicates that the object is in the monitored area and the body remains motionless;
step S2.2: obtaining the distance dis between the target and the millimeter wave radar;
step S2.3: and obtaining the signal intensity str of the target, and reflecting the action intensity of the target according to the signal intensity.
4. The FMCW millimeter wave radar sleep monitoring method as set forth in claim 3, wherein the step S3 is embodied as the steps of:
step S3.1: obtaining target information of a target in unit time: { status1, status2,. Gtoreq, status n }, { dis1, dis2,. Gtoreq, dis }, { str1, str2,. Gtoreq, strN }, and further classify and record the action characteristics according to the target information;
step S3.1.1: screening out monitoring points with the distance dis larger than a certain threshold dis _ th, and clearing the active state ststus, the distance dis and the signal strength str corresponding to the monitoring points;
step S3.1.2: counting the number N1 of the active states status =0 in the unit time period, if N1 is equal to N, namely all the active states status =0 in the unit time period, determining that the monitoring area in the unit time period has no target and the target is in a bed leaving state, and defining the state as a first state;
step S3.1.3: counting the number n2 of status =2 in the unit time period, if n2 is greater than or equal to a certain threshold th1, determining that the target has more actions in the unit time period, otherwise, determining that the body only has slight actions in the unit time period, and defining the state as a second state;
when n2 > = th1, further counting the number n3 of the activity states str in the unit time which are larger than a threshold th2, if n3 > = th3, determining that the body has a motion with larger amplitude in the time period, and defining the state as a third state, otherwise, determining that the body only has slight motion, and classifying the state as a second state;
step S3.1.4: if the number of status =2 in the unit time period is 0 and the number of status =1 is greater than or equal to the threshold th4, it is determined that a valid monitoring target exists in the monitoring area in the unit time period and the target body does not act, and the state is a fourth state, otherwise, it is determined as a fifth state of invalid monitoring.
5. The FMCW millimeter wave radar sleep monitoring method as in claim 4, wherein the step S4 is embodied as the steps of:
step S4.1: for the judgment of the bed-entering time, in a period of time T1 from the current time, the target action state is only the second state, the third state or the fourth state;
step S4.2: judging the sleep starting time, determining that the target is located in the monitoring range, and when the proportion T1/T2 of the time T1 of the third state to the total time is lower than a certain threshold pth1 in a period of time T2 from the current time, determining that the target enters the sleep state;
step S4.3: judging the sleep ending time, confirming to enter a sleep state, and determining that the target sleep is ended when the proportion T1/T2 of the time T1 of the third state to the total time is higher than a certain threshold pth2 in a period of time T2 from the current time point or only the first state exists in a period of time T2 from the current time point;
step S4.4: and for judging deep sleep and light sleep, confirming to enter a sleep state, only setting the fourth state within a period of time and setting the maintaining time to be greater than or equal to pth3, and determining as the target deep sleep.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210896908.XA CN115153487A (en) | 2022-07-28 | 2022-07-28 | FMCW millimeter wave radar sleep monitoring method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210896908.XA CN115153487A (en) | 2022-07-28 | 2022-07-28 | FMCW millimeter wave radar sleep monitoring method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115153487A true CN115153487A (en) | 2022-10-11 |
Family
ID=83477041
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210896908.XA Pending CN115153487A (en) | 2022-07-28 | 2022-07-28 | FMCW millimeter wave radar sleep monitoring method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115153487A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116449353A (en) * | 2023-06-20 | 2023-07-18 | 精华隆智慧感知科技（深圳）股份有限公司 | Human body existence detection method, device, equipment and storage medium in sleep process |
-
2022
- 2022-07-28 CN CN202210896908.XA patent/CN115153487A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116449353A (en) * | 2023-06-20 | 2023-07-18 | 精华隆智慧感知科技（深圳）股份有限公司 | Human body existence detection method, device, equipment and storage medium in sleep process |
| CN116449353B (en) * | 2023-06-20 | 2023-08-15 | 精华隆智慧感知科技（深圳）股份有限公司 | Human body existence detection method, device, equipment and storage medium in sleep process |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109480787B (en) | Non-contact sleep monitoring equipment based on ultra-wideband radar and sleep staging method | |
| Liu et al. | TagSheet: Sleeping posture recognition with an unobtrusive passive tag matrix | |
| CN101489478B (en) | Apparatus, system, and method for monitoring physiological signs | |
| CN112716474B (en) | Non-contact sleep state monitoring method and system based on biological microwave radar | |
| Reddy et al. | On-device integrated PPG quality assessment and sensor disconnection/saturation detection system for IoT health monitoring | |
| Liu et al. | WiPhone: Smartphone-based respiration monitoring using ambient reflected WiFi signals | |
| CN115153487A (en) | FMCW millimeter wave radar sleep monitoring method | |
| CN112244794B (en) | Vital sign detection method and device based on periodic characteristics and storage medium | |
| US11690563B2 (en) | Methods and systems for remote sleep monitoring | |
| Liu et al. | Human biometric signals monitoring based on wifi channel state information using deep learning | |
| US20220047209A1 (en) | Contactless sleep detection and disturbance attribution for multiple users | |
| JP2023539060A (en) | Contactless sleep detection and fault attribution | |
| CN109620177A (en) | A kind of contactless Biont information detection alarm device and method | |
| CN114305355B (en) | Breathing heartbeat detection method, system and device based on millimeter wave radar | |
| US10537253B2 (en) | Detecting live tissues using signal analysis | |
| US20200367784A1 (en) | Method and device for diagnosing arrhythmia using uwb radar | |
| US11832961B2 (en) | Contactless sleep detection and disturbance attribution | |
| US20220050175A1 (en) | Precision Sleep Tracking Using a Contactless Sleep Tracking Device | |
| Giordano et al. | Survey, analysis and comparison of radar technologies for embedded vital sign monitoring | |
| CN110946555A (en) | Sudden stop of respiration detection alarm system | |
| CN109316174A (en) | A kind of vital sign wireless monitoring method based on Waveform Design and back wave processing | |
| Zhuang et al. | SleepSense: Non-invasive sleep event recognition using an electromagnetic probe | |
| Zhou et al. | A modified two-dimension EEMD method for breathing and heart rates monitoring via through-wall IR-UWB radar | |
| US11808839B2 (en) | Initializing sleep tracking on a contactless health tracking device | |
| Zhang et al. | Radar-Beat: Contactless beat-by-beat heart rate monitoring for life scenes |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |