A sleep monitoring method using FMCW millimeter wave radar

Abstract

The present invention discloses a FMCW millimeter wave radar sleep monitoring method, step S1: the millimeter wave radar continuously sends a frequency modulated continuous wave to the target, so that the millimeter wave radar performs a first processing on the received echo signal, thereby obtaining first signal data, step S2: after receiving the first signal data, the processing module performs a second processing on the first signal data to obtain target information including activity status, distance and intensity of the target. The present invention discloses a FMCW millimeter wave radar sleep monitoring method, which can accurately distinguish the target human activity, such as the presence of the human body and keeping still, subtle body activities, large body movements, fast movements, slow movements, etc. The above detailed body movement information can be used to analyze the sleep state, which has the advantages of accurate monitoring, good real-time performance, and the ability to obtain sleep state information without contact, over a long distance, and accurately.

Description

Technical Field

[0001] The present invention belongs to the technical field of millimeter wave radar sleep monitoring, and in particular relates to a FMCW (frequency modulated continuous wave) millimeter wave radar sleep monitoring method. Background Art

[0002] Sleep monitoring can help users record their sleep status. By analyzing sleep data, it can evaluate the user's sleep quality and physical condition, and provide targeted improvement suggestions.

[0003] Common sleep monitoring equipment usually needs to fit a part of the subject's body or be very close to it. The most widely used in daily life are mobile phone sleep monitoring apps and bracelet monitoring, and a few are implemented through multiple cameras, smart mattresses, chest / waist belts, etc. Mobile phone sleep monitoring apps usually use the microphone and body movement meter built into the phone to record the sound and body movement of the whole night. Generally, the phone needs to be placed near the pillow. This application may increase the power consumption of the phone and affect daytime use. In addition, recording sound may cause privacy leakage. Bracelet monitoring is currently the most widely used sleep detection method in daily life. The bracelet can record body movement, heart rate, blood oxygen and other information throughout the night, and the above information can be used to obtain sleep data for the whole night. Bracelet monitoring requires the bracelet to fit the tester's wrist, otherwise it will affect the accuracy of the test. This wearing method may cause discomfort to the tester and affect sleep. Some bracelet monitoring is based on optical principles and will emit light periodically. In addition, some hand movements may activate the dial, generate light, and affect sleep.

[0004] Millimeter-wave radar can provide a non-contact sleep monitoring method. Millimeter-wave radar obtains target information through the emission, reception and information demodulation of high-frequency electromagnetic waves. It does not need to contact the subject, which can ensure that the subject is in a more natural sleep state, is not affected by the test equipment, and does not have any risk of privacy infringement. Millimeter-wave radar can monitor human movements and vital signs (including breathing and heart rate), and the above information can be used to analyze the sleep state. At present, millimeter-wave radar sleep monitoring is mostly based on body movement state detection combined with vital sign detection (including heartbeat and breathing), and the sleep state is obtained through comprehensive analysis of body movement and vital sign information. At present, millimeter-wave radar body movement monitoring mostly detects whether the human body has obvious activities, and cannot make detailed distinctions between the human body's activity status, such as continuous human movement, human existence and stillness, subtle body movement, movement, etc. In addition, the accuracy of millimeter-wave radar for vital sign detection is greatly affected by the detection distance, the body part irradiated by the radar, and the body movement. For example, when the radar is irradiating the chest cavity, the detection accuracy is good, and when the radar is irradiated to the radar, the detection accuracy is poor. In particular, the heart rate signal is usually very weak, and the detection conditions are relatively high. The human body state during sleep is usually random and uncontrollable, and varies from person to person, so it is difficult to ensure the accuracy of sleep monitoring using this method.

[0005] Therefore, further improvements are made to the above problems. Summary of the invention

[0006] The main purpose of the present invention is to provide a FMCW millimeter wave radar sleep monitoring method, which can accurately distinguish the target human body activities, such as the presence of the human body and remaining still, subtle body movements, large body movements, fast movements, slow movements, etc. The sleep state can be analyzed using the above detailed body movement information. It has the advantages of accurate monitoring, good real-time performance, and the ability to obtain sleep state information non-contact, long-distance, and accurately.

[0007] To achieve the above objectives, the present invention provides a FMCW millimeter wave radar sleep monitoring method for monitoring the sleep state of a person, comprising the following steps:

[0008] Step S1: the millimeter wave radar continuously sends a frequency modulated continuous wave to the target (person), so that the millimeter wave radar performs a first processing on the received echo signal, thereby obtaining first signal data;

[0009] Step S2: after receiving the first signal data, the processing module performs a second processing on the first signal data to obtain target information including activity state, distance and intensity of the target;

[0010] Step S3: the processing module performs a third process on the target information within the unit time, thereby classifying and recording the target's motion features;

[0011] Step S4: The processing module performs a fourth processing on the motion features within the monitoring period (the whole night), thereby obtaining the target's sleep state information within the monitoring period.

[0012] As a further preferred 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 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 that improves the quality of the original signal (relative to the echo signal).

[0013] As a further preferred technical solution of the above technical solution, step S2 is specifically implemented as the following steps (performing range_FFT or range_doppler_FFT processing on the first signal data, assisting various static clutter filtering technologies and phase monitoring technologies, and monitoring target information in the area to be measured):

[0014] Step S2.1: Obtain the target's activity status: status = 0 means there is no target in the monitoring area, status = 1 means the target activity is detected, and status = 2 means the target is in the monitoring area and remains motionless;

[0015] Step S2.2: Obtain the distance dis between the target and the millimeter wave radar;

[0016] Step S2.3: Obtain the target signal strength str, and reflect the magnitude of the target action strength through the magnitude of the signal strength.

[0017] As a further preferred technical solution of the above technical solution, step S3 is specifically implemented as the following steps:

[0018] Step S3.1: Obtain the target information of the target in unit time: {status1, status2, ..., statusN}, {dis1, dis2, ..., disN}, {str1, str2, ..., strN}, and then classify and record the action features according to the target information;

[0019] Step S3.1.1: Screen out the monitoring points whose distance dis is greater than a certain threshold dis_th, and clear the activity state ststus, distance dis and signal strength str corresponding to the monitoring point to zero;

[0020] Step S3.1.2: Count the number n1 of activities with status = 0 during this unit time period. If n1 is equal to N, that is, all activities have status = 0 during this unit time period, it is determined that there is no target in the monitored area during this time period, and the target is in the out-of-bed state. Define this state as the first state;

[0021] Step S3.1.3: Count the number n2 of status = 2 during this unit time period. If n2 is greater than or equal to a certain threshold th1, it is determined that there are relatively many actions of the target during this unit time period. Otherwise (0 < n2 < th1), it is determined that there are only slight movements of the body (such as slight movements of the head or hands and feet) during this unit time period. Define this state as the second state;

[0022] When n2 >= th1, further count the number n3 of activity status str greater than the threshold th2 during this unit time. If n3 >= th3, it is determined that there are large-amplitude movements of the body (such as turning over) during this time period. Define this state as the third state. Otherwise, it is determined that there are only slight movements of the body, and this state is classified into the second state;

[0023] Step S3.1.4: If the number of status = 2 during this 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 an effective monitored target in the monitored area during this unit time period, and the target body has (almost) no movement. This state is the fourth state. Otherwise, it is determined as the fifth state of ineffective monitoring.

[0024] As a further preferred technical solution of the above technical solution, step S4 is specifically implemented as the following steps:

[0025] Step S4.1: For the judgment of going-to-bed time, within a period of time T1 starting from the current time, the target action status is only the second state, the third state, or the fourth state;

[0026] Step S4.2: For the judgment of the sleep start time, it has been confirmed that the target is within the monitoring range (in bed). When the proportion t1 / T2 of the duration t1 of the third state in the total duration within a period of time T2 starting from the current time is lower than a certain threshold pth1, it is determined that the target enters the sleep state;

[0027] Step S4.3: For the judgment of the sleep end time, it has been confirmed to enter the sleep state. When the proportion t1 / T2 of the duration t1 of the third state in the total duration within a period of time T2 starting from the current time point is higher than a certain threshold pth2, or there is only the first state within a period of time T2 starting from the current time point, it is determined that the target's sleep ends;

[0028] Step S4.4: For the judgment of deep sleep or light sleep, if it is confirmed that the sleep state has been entered, and there is only the fourth state for a period of time and the maintenance time is greater than or equal to pth3, it is identified as the target deep sleep (otherwise it is light sleep).

[0029] The beneficial effects of the present invention are:

[0030] 1. Non-contact, avoiding affecting sleep.

[0031] 2. FMCW millimeter wave radar has a fast response speed and good real-time performance in detecting moving targets, and the detection data is more detailed and accurate.

[0032] 3. Use FMCW millimeter wave radar to subdivide the human body's activity status and movement types, such as continuous activity, human presence and stillness, subtle body movement, fast and large movements, slow and large movements, etc. Use the above detailed body movement information to analyze and monitor the sleep state.

[0033] 4. Respiration, heart rate and other vital signs are not used as the main factors in sleep analysis, the installation position value is more arbitrary, and the detection is more accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 It is a schematic diagram of a FMCW millimeter wave radar sleep monitoring method of the present invention.

[0035] Figure 2 It is a schematic diagram of a FMCW millimeter wave radar sleep monitoring method of the present invention. DETAILED DESCRIPTION

[0036] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are only examples, and those skilled in the art can think of other obvious variations. The basic principles of the present invention defined in the following description can be applied to other embodiments, variations, improvements, equivalents, and other technical solutions that do not deviate from the spirit and scope of the present invention.

[0037] In the preferred embodiments of the present invention, those skilled in the art should note that the objects, persons, etc. involved in the present invention may be regarded as prior art.

[0038] Preferred embodiments.

[0039] The present invention discloses a FMCW millimeter wave radar sleep monitoring method for monitoring the sleep state of a person, comprising the following steps:

[0040] Step S1: the millimeter wave radar continuously sends a frequency modulated continuous wave to the target (person), so that the millimeter wave radar performs a first processing (preprocessing) on ​​the received echo signal, thereby obtaining first signal data;

[0041] Step S2: after receiving the first signal data, the processing module performs a second processing on the first signal data to obtain target information including activity state, distance and intensity of the target;

[0042] Step S3: the processing module performs a third process on the target information within the unit time, thereby classifying and recording the target's motion features;

[0043] Step S4: The processing module performs a fourth processing on the motion features within the monitoring period (the whole night), thereby obtaining the target's sleep state information within the monitoring period.

[0044] Specifically, for the first processing in 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 that improves the quality of the original signal (relative to the echo signal).

[0045] More specifically, step S2 is specifically implemented as the following steps (performing range_FFT or range_doppler_FFT processing on the first signal data, assisting various static clutter filtering techniques and phase monitoring techniques, and monitoring target information in the area to be measured):

[0046] Step S2.1: Obtain the target's activity status: status = 0 means there is no target in the monitoring area, status = 1 means the target activity is detected, and status = 2 means the target is in the monitoring area and remains motionless;

[0047] Step S2.2: Obtain the distance dis between the target and the millimeter wave radar;

[0048] Step S2.3: Obtain the target signal strength str, and reflect the magnitude of the target action strength through the magnitude of the signal strength.

[0049] Furthermore, step S3 is specifically implemented as the following steps:

[0050] Step S3.1: Obtain the target information of the target in unit time: {status1, status2, ..., statusN}, {dis1, dis2, ..., disN}, {str1, str2, ..., strN}, and then classify and record the action features according to the target information;

[0051] Step S3.1.1: Screen out the monitoring points whose distance dis is greater than a certain threshold dis_th, and clear the activity state ststus, distance dis and signal strength str corresponding to the monitoring point to zero;

[0052] Step S3.1.2: Count the number n1 of the activity status status = 0 within this unit time period. If n1 is equal to N, that is, all the activity statuses status = 0 within this unit time period, it is determined that there is no target in the monitored area during this time period, and the target is in the out-of-bed state. Define this state as the first state;

[0053] Step S3.1.3: Count the number n2 of status = 2 within this unit time period. If n2 is greater than or equal to a certain threshold th1, it is determined that there are relatively many actions of the target within this unit time period. Otherwise (0 < n2 < th1), it is determined that only slight movements (such as slight movements of the head or hands and feet) occur to the body within this unit time period. Define this state as the second state;

[0054] When n2 >= th1, further count the number n3 of the activity status str greater than the threshold th2 within this unit time. If n3 >= th3, it is determined that there are large-amplitude movements (such as turning over) of the body within this time period. Define this state as the third state. Otherwise, it is determined that only slight movements occur to the body, and this state is classified into the second state;

[0055] Step S3.1.4: If the number of status = 2 within this 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 an effective monitored target in the monitored area within this unit time period, and the body of the target has (almost) no movement. This state is the fourth state. Otherwise, it is determined as the fifth state of ineffective monitoring (most likely caused by a relatively large deviation between the radar installation position and the person to be measured (target)).

[0056] Furthermore, step S4 is specifically implemented as the following steps:

[0057] Sleep monitoring usually requires long-time data acquisition and storage, while FMCW target detection is usually relatively fast detection. Therefore, a large amount of detection data will be generated. Comprehensively analyze the target information in real time at fixed time intervals to obtain the action classification information of this time period. On the one hand, it can reduce the data storage amount. On the other hand, the obtained action feature classification can be directly used for subsequent sleep state analysis. Considering the real-time nature of monitoring and reducing the data storage amount, the unit time can be from dozens of seconds to several minutes. In this example, 1 minute is selected.

[0058] Step S4.1: For the judgment of going to bed time, within a period of time T1 starting from the current time, the target action status is only the second state, the third state or the fourth state;

[0059] Step S4.2: For sleep start time judgment, it is confirmed that the target is within the monitoring range (on the bed), and within a period of time T2 from the current time, when the ratio t1 of the duration of the third state to the total duration t1 / T2 is lower than a certain threshold pth1, the target is deemed to have entered the sleep state;

[0060] Step S4.3: For the judgment of the end time of sleep, it is confirmed that the sleep state has been entered. If the ratio t1 / T2 of the duration t1 of the third state to the total duration in a period T2 after the current time point is higher than a certain threshold pth2, or there is only the first state in a period T2 after the current time point, then the target sleep is deemed to be over;

[0061] Step S4.4: For the judgment of deep sleep or light sleep, if it is confirmed that the sleep state has been entered, and there is only the fourth state for a period of time and the maintenance time is greater than or equal to pth3, it is identified as the target deep sleep (otherwise it is light sleep).

[0062] Based on the above information, further analysis of sleep conditions can be done, such as: proportion of deep sleep, number of tossing and turning, number of waking up, sleep quality score, etc.

[0063] The above are some methods for directly using motion feature data to determine sleep status. In addition, detailed body movement data can also be used in advanced data processing algorithms such as machine learning and AI to obtain sleep analysis results.

[0064] Preferably, FMCW millimeter wave radar can accurately detect the distance and speed of the target, and for multi-transmitter multi-receiver radar, it can also detect the angle of the target. By combining the distance, speed and angle information of the target, the position and motion information of the target can be judged. Using various static clutter filtering technologies and phase detection technologies, it is possible to effectively filter out stationary targets in the environment, and accurately distinguish the target's motion state, such as general motion, micro-motion, and the presence of a human body and remaining still.

[0065] When a person is asleep, the whole body is in a relatively static state. Due to the effects of breathing and heartbeat, the body still has small and slow movements, such as chest rise and fall and the slight rise and fall of the body driven by chest rise and fall, which can be detected by FMCW radar. Compared with the body movements caused by breathing and heartbeat, general turning over and limb movements have faster speed and larger amplitude. Usually, when a person is awake, the body movements are more frequent and large in amplitude. As the person falls asleep, the frequency and amplitude of body movements decrease. When a person enters deep sleep, the body is almost motionless, and the breathing and heartbeat become slower and lighter. FMCW radar can accurately and real-time detect human activities and divide body movements more finely, such as the presence of the human body and keeping still, subtle body movements, large body movements, fast movements, slow movements, etc. Recording the detection data for the whole night can obtain the detailed body movement of the target throughout the night, and then a comprehensive analysis of the body movement can be performed to obtain the sleep situation for the whole night, such as the time of falling asleep, the time of waking up, the proportion of deep sleep, etc.

[0066] It is worth mentioning that the technical features such as the targets and personnel involved in the patent application of this invention should be regarded as the prior art. The specific structure, working principle and possible control method and spatial layout method of these technical features can be selected according to the conventional selection in the field, and should not be regarded as the inventive point of this patent. This patent will not be further elaborated.

[0067] For those skilled in the art, it is still possible to modify the technical solutions described in the aforementioned embodiments, or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A FMCW millimeter wave radar sleep monitoring method for monitoring the sleep state of a person, characterized in that: The following steps are involved: Step S1: the millimeter wave radar continuously sends a frequency modulated continuous wave to the target, so that the millimeter wave radar performs a first processing on the received echo signal to obtain first signal data; For the first processing in 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 first signal data with improved original signal quality; Step S2: after receiving the first signal data, the processing module performs a second processing on the first signal data to obtain target information including activity state, distance and intensity of the target; Step S2 is specifically implemented as follows: Step S2.1: Obtain the target's activity status: status = 0 means there is no target in the monitoring area, status = 1 means the target activity is detected, and status = 2 means the target is in the monitoring area and remains motionless; Step S2.2: Obtain the distance dis between the target and the millimeter wave radar; Step S2.3: Obtain the signal strength str of the target, and reflect the magnitude of the target action strength through the magnitude of the signal strength; Step S3: the processing module performs a third process on the target information within the unit time, thereby classifying and recording the target's motion features; Step S3 is specifically implemented as the following steps: Step S3.1: Obtain the target information of the target in unit time: {status1, status2, ..., statusN}, {dis1, dis2, ..., disN}, {str1, str2, ..., strN}, and then classify and record the action features according to the target information; Step S3.1.1: Screen out the monitoring points whose distance dis is greater than a certain threshold dis_th, and clear the activity state ststus, distance dis and signal strength str corresponding to the monitoring point to zero; Step S3.1.2: Count the number n1 of activity status=0 in the unit time period. If n1 is equal to N, that is, all activity statuses in the unit time period are 0, it is determined that there is no target in the monitoring area in the time period, and the target is in the state of leaving the bed, and this state is defined as the first state; Step S3.1.3: Count the number n2 of status=2 in the unit time period. If n2 is greater than or equal to a certain threshold th1, it is determined that the target has more movements in the unit time period. Otherwise, it is determined that the body has only slightly moved in the unit time period, and the state is defined as the second state. When n2>=th1, further count the number n3 of activity states str greater than the threshold th2 in the unit time. If n3>=th3, it is considered that the body has a large movement in the time period, and the state is defined as the third state. Otherwise, it is considered that the body has only a slight movement, and the state is classified as the 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 does not move, and this state is the fourth state; otherwise, it is determined to be the fifth state of invalid monitoring; Step S4: the processing module performs a fourth processing on the motion features within the monitoring period, thereby obtaining the target's sleep state information within the monitoring period; Step S4 is specifically implemented as the following steps: Step S4.1: For the judgment of the bedtime, within 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: For sleep start time judgment, it is confirmed that the target is within the monitoring range. If the ratio t1 / T2 of the duration t1 of the third state to the total duration in a period T2 from the current time is lower than a certain threshold pth1, the target is deemed to have entered the sleep state; Step S4.3: For the judgment of the end time of sleep, it is confirmed that the sleep state has been entered. If the ratio t1 / T2 of the duration t1 of the third state to the total duration in a period T2 after the current time point is higher than a certain threshold pth2, or there is only the first state in a period T2 after the current time point, then the target sleep is deemed to be over; Step S4.4: For the judgment of deep sleep or light sleep, if the sleep state has been confirmed, and there is only the fourth state for a period of time and the maintenance time is greater than or equal to pth3, it is identified as the target deep sleep.

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