WO2020173374A1 - 人体姿态测量方法、装置和基于此方法工作的装置 - Google Patents

人体姿态测量方法、装置和基于此方法工作的装置 Download PDF

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Publication number
WO2020173374A1
WO2020173374A1 PCT/CN2020/075978 CN2020075978W WO2020173374A1 WO 2020173374 A1 WO2020173374 A1 WO 2020173374A1 CN 2020075978 W CN2020075978 W CN 2020075978W WO 2020173374 A1 WO2020173374 A1 WO 2020173374A1
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Prior art keywords
radar
posture
module
feature data
waveform
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PCT/CN2020/075978
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English (en)
French (fr)
Inventor
曹可瀚
曹乃承
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曹可瀚
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Publication of WO2020173374A1 publication Critical patent/WO2020173374A1/zh

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    • 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
    • A—HUMAN NECESSITIES
    • A47—FURNITURE; DOMESTIC ARTICLES OR APPLIANCES; COFFEE MILLS; SPICE MILLS; SUCTION CLEANERS IN GENERAL
    • A47G—HOUSEHOLD OR TABLE EQUIPMENT
    • A47G9/00—Bed-covers; Counterpanes; Travelling rugs; Sleeping rugs; Sleeping bags; Pillows
    • A47G9/10—Pillows
    • 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
    • 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
    • A61B5/113—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb occurring during breathing
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316—Modalities, i.e. specific diagnostic methods
    • A61B5/318—Heart-related electrical modalities, e.g. electrocardiography [ECG]
    • 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
    • A61B5/4809—Sleep detection, i.e. determining whether a subject is asleep or not
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61M—DEVICES FOR INTRODUCING MEDIA INTO, OR ONTO, THE BODY; DEVICES FOR TRANSDUCING BODY MEDIA OR FOR TAKING MEDIA FROM THE BODY; DEVICES FOR PRODUCING OR ENDING SLEEP OR STUPOR
    • A61M21/00—Other devices or methods to cause a change in the state of consciousness; Devices for producing or ending sleep by mechanical, optical, or acoustical means, e.g. for hypnosis
    • 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
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D3/00—Control of position or direction
    • G05D3/12—Control of position or direction using feedback
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
    • G06F3/14—Digital output to display device ; Cooperation and interconnection of the display device with other functional units
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61M—DEVICES FOR INTRODUCING MEDIA INTO, OR ONTO, THE BODY; DEVICES FOR TRANSDUCING BODY MEDIA OR FOR TAKING MEDIA FROM THE BODY; DEVICES FOR PRODUCING OR ENDING SLEEP OR STUPOR
    • A61M21/00—Other devices or methods to cause a change in the state of consciousness; Devices for producing or ending sleep by mechanical, optical, or acoustical means, e.g. for hypnosis
    • A61M2021/0005—Other devices or methods to cause a change in the state of consciousness; Devices for producing or ending sleep by mechanical, optical, or acoustical means, e.g. for hypnosis by the use of a particular sense, or stimulus
    • A61M2021/0022—Other devices or methods to cause a change in the state of consciousness; Devices for producing or ending sleep by mechanical, optical, or acoustical means, e.g. for hypnosis by the use of a particular sense, or stimulus by the tactile sense, e.g. vibrations
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61M—DEVICES FOR INTRODUCING MEDIA INTO, OR ONTO, THE BODY; DEVICES FOR TRANSDUCING BODY MEDIA OR FOR TAKING MEDIA FROM THE BODY; DEVICES FOR PRODUCING OR ENDING SLEEP OR STUPOR
    • A61M21/00—Other devices or methods to cause a change in the state of consciousness; Devices for producing or ending sleep by mechanical, optical, or acoustical means, e.g. for hypnosis
    • A61M2021/0005—Other devices or methods to cause a change in the state of consciousness; Devices for producing or ending sleep by mechanical, optical, or acoustical means, e.g. for hypnosis by the use of a particular sense, or stimulus
    • A61M2021/0027—Other devices or methods to cause a change in the state of consciousness; Devices for producing or ending sleep by mechanical, optical, or acoustical means, e.g. for hypnosis by the use of a particular sense, or stimulus by the hearing sense
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61M—DEVICES FOR INTRODUCING MEDIA INTO, OR ONTO, THE BODY; DEVICES FOR TRANSDUCING BODY MEDIA OR FOR TAKING MEDIA FROM THE BODY; DEVICES FOR PRODUCING OR ENDING SLEEP OR STUPOR
    • A61M21/00—Other devices or methods to cause a change in the state of consciousness; Devices for producing or ending sleep by mechanical, optical, or acoustical means, e.g. for hypnosis
    • A61M2021/0005—Other devices or methods to cause a change in the state of consciousness; Devices for producing or ending sleep by mechanical, optical, or acoustical means, e.g. for hypnosis by the use of a particular sense, or stimulus
    • A61M2021/0083—Other devices or methods to cause a change in the state of consciousness; Devices for producing or ending sleep by mechanical, optical, or acoustical means, e.g. for hypnosis by the use of a particular sense, or stimulus especially for waking up

Definitions

  • the present invention relates to a human body posture measurement method, device, and a device that uses this method, and in particular to a non-contact human body posture measurement method, device, and a device that uses this method.
  • the existing method for determining the posture of the human body includes laying a pressure sensor array on the bed and determining the posture based on the data measured by the array. This method cannot distinguish between supine and prone. There is also the use of face recognition technology to recognize the orientation of the face and determine the sleeping posture based on this. This method cannot distinguish between side lying and prone side head posture.
  • the object of the present invention is to provide a method and device for determining body posture, especially torso orientation, using radar, and a device that works based on this method.
  • the method for determining body posture using radar of the present invention includes the following steps: S1, receiving a radar wave signal reflected by the body; S2, extracting one or more signal characteristic data from the radar signal; S3, according to a characteristic Data waveform/characteristics/numerical value/trajectory or combination of two/multiple characteristic data waveforms/characteristics/numerical value/trajectory to judge the body posture After obtaining the posture information of the human body, the subsequent modules can be controlled to perform operations corresponding to the posture of the human body.
  • An apparatus for determining body posture by using radar includes: at least one radar module capable of emitting radar waves to illuminate the body and receiving radar signals reflected by the body; at least one characteristic data processing module capable of generating at least one radar signal based on the received radar signal A type of radar characteristic data, capable of outputting/displaying radar characteristic data, capable of analyzing the characteristics and/or different points of at least one characteristic data, capable of determining characteristic waveforms/values/trajectories indicating body posture; at least one body posture judgment module capable of Determine the body posture according to the characteristic waveform/value/trajectory indicating the posture of the human body.
  • a device for determining body posture based on radar includes: at least one radar module capable of emitting radar waves to illuminate the body, and capable of receiving radar signals reflected by the body; at least one characteristic data processing module capable of generating according to received radar signals At least one type of radar feature data, able to analyze the characteristics/different points of at least one feature data, and able to determine the indicator The characteristic waveform/value of the body posture; at least one body posture judgment module, which can determine the body posture according to the characteristic waveform/value indicating the body posture; at least one execution module, which can perform operations/outputs corresponding to the body posture according to different body postures .
  • the execution module can include the control module in the host computer.
  • the present invention uses the characteristics of the radar signal reflected by the human body to determine the body orientation with high accuracy and is not affected by obstructions.
  • the device working with this method can correctly judge the body orientation.
  • FIG. 1 is a schematic diagram of the device of the present invention
  • Figure 2 is the Envelope diagram with the back facing the radar antenna
  • Figure 3 is an Envelope diagram with the side facing the antenna
  • Figure 4 is an Envelope diagram with the front chest facing the antenna
  • Figure 5 is the IQ at Peak diagram with the back facing the antenna
  • Figure 6 is the IQ at Peak diagram with the side facing the antenna
  • Figure 7 is the IQ at Peak diagram with the front chest facing the antenna
  • Figure 8 is the Breathing Movement diagram with the back facing the antenna
  • Figure 9 is the Breathing Movement diagram with the side facing the antenna
  • Figure 10 is the Breathing Movement diagram with the front chest facing the antenna
  • Figure 11 is the Relative Movement diagram with the back facing the antenna
  • Figure 12 is the Relative Movement diagram with the side facing the antenna
  • Figure 13 is the Relative Movement diagram with the front chest facing the antenna
  • Figure 14 is a flowchart of a method for determining the posture of a human body by using radar
  • Figure 15 is a schematic diagram of the device based on the method of using radar to determine the posture of the human body
  • Figure 16 is a schematic diagram of a pillow with height adjustment based on the method of using radar to determine the posture of the human body;
  • Figure 17 is a schematic diagram of a pillow with height adjustment based on the method of using radar to determine the posture of the human body.
  • the upper computer 3 includes at least one computer and/or a Mini computer and/or a single-chip computer and/or an intelligent terminal device.
  • the upper computer 3 is connected with the radar module 2, and the radar module can generate radar waves to irradiate the human body 1, and receive the reflected radar waves. It is preferable to irradiate the body's trunk, chest and abdomen.
  • Radar module 2 includes radio frequency chip/radar chip, PCB, antenna/antenna board, microcontroller, peripheral circuit, and can include breathing detection module, presence sensor module, heartbeat detection module, motion detection module, these components can be integrated on a PCB .
  • Preferred radars include but are not limited to PCR radar, Doppler radar, UWB radar, and ultrasonic radar.
  • Preferred radar modules include but are not limited to based on X4 radar chip X4M03, radar system based on Acconeer A1 chip.
  • the radar module can generate 5GHz ⁇ 100GHz electromagnetic waves, which are directionally emitted through the antenna. After the electromagnetic wave irradiates the human body, it is reflected back to the radar module and received by the antenna.
  • the radar module can process the signal received by the antenna, including but not limited to detection, filtering, and signal feature extraction.
  • the respiration detection module can detect the respiration of the human body and can obtain baseband data.
  • the presence detection module can detect the presence of a human body.
  • the heartbeat detection module can detect the human heartbeat and measure the amplitude and waveform of the heartbeat.
  • the motion detection module can measure the motion of the human body's trunk and limbs, including turning, arm and leg movements.
  • the radar module can obtain but not limited to the number of breaths, single breath waveform, physical activity, heartbeat frequency, heartbeat waveform, thoracic motion, abdominal motion, relative motion and other data, and can obtain Envelope data, IQ at Peak, Data such as the movement range of human body parts. These data are passed to the characteristic data processing module in the upper computer 3.
  • the host computer 3 includes at least one characteristic data processing module 31 and at least one human body posture judgment module 32.
  • the characteristic data processing module 31 is capable of processing and analyzing various characteristic data transmitted from the radar module, extracting indications that can indicate the posture of the human body, and passing it to the human posture judgment module 32.
  • the human body posture judging module 32 can judge the posture of the human body according to the indication, especially the orientation of the torso relative to the radar module/antenna.
  • the indication characterization is the value/waveform/trajectory, etc. in the radar signal/characteristic data that can indicate/discriminate different human postures.
  • FIGS. 2-13 are graphs of characteristic data measured when a person is lying on a bed, the radar antenna is arranged at the same height as the person, facing the torso, and 0.5-2 meters from the person's chest. When lying on the back, the antenna is located on the left side of the person.
  • the posture of the human body can be judged by analyzing the data characteristics at different positions in the same feature data graph; the posture of the human can be judged by comprehensive comparison/combined analysis of data features in different feature data graphs.
  • the ordinate in each figure is the Y axis, and the abscissa is the X axis.
  • the Y-axis unit in the figure can be the absolute value of distance/amplitude/voltage/echo power, or the normalized relative value
  • the X-axis can be the absolute value of time/distance/amplitude, or it can be normalized.
  • the X axis can be the number of radar scans, and the radar module will scan every time it completes a transmit/receive cycle. The number of scans completed per second is called the scan frequency, also called the refresh rate.
  • a number of scan times can be set as the sampling period, such as 1700 scans as a sampling period. The radar module can automatically set the sampling period.
  • the characteristic data processing module receives Envelope data transmitted by the radar module, and analyzes the characteristic value/waveform of the data.
  • Envelope is envelope data, which reflects the heartbeat and the movement of the internal organs and thorax caused by the heartbeat.
  • the sequence, amplitude, and direction of the contractions of the atria and ventricles are different, which causes the vibrations of the internal organs and thorax at different positions of the human body to be different in time, position/area, amplitude, and direction.
  • the envelope waveform is different.
  • Figure 2 shows the human body lying on the bed with the back facing the antenna
  • Figure 3 shows the human lying on the back with the left side of the body facing the antenna
  • Figure 4 shows the human lying on the side with the front chest facing the antenna.
  • the X axis in Figures 2, 3, 4 can be the number of scans in a sampling period.
  • the abscissa of the feature area 601 is between 400 and 700
  • the abscissa of the feature area 603 is between 700 and 950
  • the abscissa of the feature area 605 is between 950 and 1100
  • the abscissa of the feature area 607 is between 1100 and 1100.
  • 1,350 the abscissa of feature area 609 is between 1350 and 1800.
  • the feature area can be divided into equidistant divisions, such as dividing each given number of X-axis into one area, such as one feature area per 300 units, or dividing a period into multiple feature areas, such as dividing into 8 Regions.
  • the feature area can be divided into unequal distances. According to the waveform/characteristics of the feature data, select the interval with obvious characteristics, the interval with the waveform significantly different from other body postures, the interval with the minimum value less than the given threshold, and the interval with the waveform in a V shape as the feature area.
  • the waveform can select the minimum value/multiple minimum values of the waveform, determine the abscissa of these points, and then use the abscissa of these points as the base point to extend the specified units along the positive and negative directions of the X axis, such as 100 units, as the characteristic area.
  • the number of scans included in the sampling period can be different, and the start point and end point of the characteristic region can be adjusted accordingly.
  • 603 has a minimum value between 750 and 900, the minimum value is less than 10 and close to 0, the waveform is V-shaped, and the waveform on both sides of the lowest point is steep and straight.
  • the minimum value of position 601 in Figure 2 is between 10 and 30, and the lowest point of the waveform is obviously greater than 0, that is, greater than the minimum value of 603, and the waveform at 603 is flat.
  • the minimum value of 603 in Fig. 3 is 20-50, which is obviously greater than 0, and the waveform is not a steep V shape.
  • the minimum value of 601 appears between 400 and 500, the minimum value is between 10 and 20, and the minimum value at 601 is less than the minimum value at 603.
  • the minimum value of 603 in Figure 4 is 10-40, the waveform is V-shaped, and the lines on both sides of the lowest point are straight but not steep.
  • the minimum value at 601 is 10-40, which is close to the minimum value at 603. Because the minimum value at 603 in Figure 2 is close to 0 and the V shape is obvious, the data/waveform characteristics at 603 can clearly distinguish the situation where the back faces the radar antenna from the side facing the antenna and the front chest facing the antenna. When the minimum value at 603 is less than 10 and the waveform is V-shaped, the back faces the antenna. The value and waveform at 603 are indicative.
  • the minimum value at 605 is 10-20, which appears on the abscissa 950-1000, and the waveform is relatively flat.
  • the minimum value at 605 appears on the abscissa from 1000 to 1100, the waveform fluctuates greatly, and the minimum value is 15-25.
  • the minimum value at 605 appears in 980 ⁇ 1050, the minimum value is close to 0, the waveform is V-shaped, and the waveform on both sides of the lowest point is steep and straight.
  • the minimum value at 605 is close to 0 and the V-shaped feature is distinct.
  • the data feature at 605 can clearly distinguish the situation where the front chest faces the antenna from the back facing the antenna and the side facing the antenna.
  • the value/waveform at 605 is indicative.
  • the minimum value at 605 is less than 10 and the waveform is V-shaped, the front chest faces the antenna. Combining the data/waveform characteristics at 603 and 605, it can distinguish the situation where the back, side, and front of the human body are facing the radar antenna.
  • the waveform at position 607 in FIG. 2 changes drastically, and the curve is an asymmetric V-shaped.
  • the waveform at 607 in Figure 4 is flat.
  • the undulation degree of the waveform at 607 in Figure 3 is between Figure 2 and Figure 4.
  • the following methods can be used to determine the shape of the data waveform: record the maximum value of each wave peak and the minimum value of each wave trough in the characteristic area, calculate the average value of each point in the characteristic area, and calculate multiple (more than 2) poles Maximum value/minimum value and average value
  • a threshold g is set. When Sa is greater than the threshold g, it is considered that the waveform fluctuates greatly in the characteristic area. It is also possible to record only the maximum/minimum value in the characteristic area, and then compare the absolute value a of the difference between the maximum/minimum value and the average value with the threshold g. If a is greater than g, the fluctuation is large.
  • One or more threshold intervals can be set, such as gl (20, 50), and the waveform fluctuation situation corresponding to the threshold interval, for example, gl corresponds to large fluctuations. Judging the threshold range in which a/Sa falls, that is, the fluctuation situation can be determined. For example, if a is in the gl interval, the region fluctuates greatly.
  • the following methods can be used to determine the shape of the waveform: Find the lowest point in the feature area, take multiple points (more than 2) on the left and right sides of the lowest point, and fit the lowest point with a straight line. Multiple points on the side and multiple points on the right, find the fit between the left/right points and the straight line. Set the threshold g. When the goodness of fit, for example, the variance is less than g, the fit is good, and the waveform of this segment is straight/small, otherwise the wave of this segment is large and not straight. If the fit of the fitted straight line to the left and right of the lowest point is good, the waveform at that point is V-shaped. Calculate the angle between the left and right fitted straight lines, and set a threshold.
  • the formed V-shape will be steep on both sides, otherwise flat. Calculate the angle between the fitted straight line and the Y axis (take the acute angle) and set a threshold. If the angle is less than the threshold, the curve is steep, otherwise it is gentle.
  • the minimum value of the waveform at 609 in FIG. 2 appears between 1450 and 1500 on the X axis, and the value is between 0 and 20, which is close to zero.
  • the minimum value of the waveform at 609 appears between 1500 and 1550, and the value is between 0 and 20, which is close to zero.
  • the waveform at 609 is smooth and straight, the minimum position is not obvious, and the value is greater than 20.
  • the 609 waveforms in Figure 4 are obviously different from the 609 waveforms in Figures 2 and 3.
  • the waveform at 609 is V-shaped as a whole, with an overall upward trend from the lowest point to the right.
  • the waveform at 609 in Figure 2 rises first to the right and then falls at the lowest point, and a peak appears.
  • the absolute value of the difference between the maximum and minimum values of the peaks and valleys of the data at 609 and the average value at 609 is very small, less than the angle between the two straight lines fitted on both sides of the minimum at 40,609 It is greater than 150 degrees and close to 180 degrees, and the fit of the two straight lines is good.
  • each waveform can be distinguished, and then the corresponding human posture can be determined according to the waveform. If the measured waveform 609 has a smooth and straight curve, the minimum value is greater than 20, and the angle between the fitting straight line on both sides of the minimum value is greater than 1507, and the angle between the fitting straight line and the Y axis is greater than 75°, then the corresponding posture of the human body is the front chest Towards the antenna. If the minimum value at 609 is less than 10, and the fit on both sides of the minimum value is good, then the side faces the antenna. If the minimum value at 609 is less than 10, and the fit on one side of the minimum value is not good, then the back is facing the antenna.
  • the minimum values of 603 and 605 are greater than 10 because the side faces the antenna.
  • the minimum value at 603 is less than 10, and the minimum value at 605 is greater than 10, then the back faces the antenna.
  • the indicating value/waveform of each feature area is passed to the human body posture judgment module, and the human body posture judgment module can determine the orientation/posture of the human body based on these characteristics.
  • the X axis is the I channel
  • the Y axis is the Q channel.
  • the points in the figure represent the peak value of the IQ component amplitude at a certain time.
  • the figure includes the current time point (the largest point in the figure) and the data of multiple points in the previous time period, so the figure shows the drift/offset trajectory of IQ at Peak over a period of time.
  • the method of judging the characteristics of IQ at Peak data includes firstly selecting multiple data points in a given time period, and fitting these data points with circles to obtain the best fitting circle.
  • the diameter of the circle can indicate the drift/offset of IQ at Peak.
  • Set a threshold a such as 20 units.
  • a threshold b such as 10 units
  • compare the goodness of fit index with b For example, compare the variance with b.
  • the variance is less than b and the diameter of the fitted circle is greater than a
  • the drift is large and the trajectory is an arc
  • the variance is greater than b and the diameter of the circle is greater than a
  • the drifting trajectory is not circular.
  • Different human postures can be distinguished according to the characteristics of the IQ at Peak data.
  • the Stddev is less than 0.5mm
  • the person's side faces the antenna.
  • the Stddev is less than 0.5mm
  • the track is not arc-shaped
  • the back of the person faces the antenna.
  • the deviation of IQ at Peak is large, the Stddev is greater than 1.5mm, and the trajectory is a circular arc, the front chest of the person faces the antenna.
  • the drift amount/track/Stddev of IQ at Peak is indicative.
  • the posture of the human body can be determined. For example, the data drift of IQ at Peak is used to determine whether the side is facing the radar. If the drift of IQ at Peak is small, the side of the body faces the antenna. If the IQ at Peak drift is large, the other two cases are distinguished based on the Envelope data: If the waveform at 609 of the Envelope data is flat and greater than 15, then the front chest is facing the antenna. If the waveform at 609 fluctuates greatly and the minimum value is less than 15, then the back is facing the antenna. Combining two or more feature data to recognize body posture can bring greater flexibility in selecting feature values/waveforms/trajectories, and several indicator features/characterizations mutually confirm each other, which can improve the accuracy of judgment.
  • the Y axis in FIG. 8, FIG. 9, and FIG. 10 is the Breathing movement amplitude.
  • the amplitude is larger, more than 3mm, with the front chest facing the antenna. Because at this time, the thorax and abdomen are undulating greatly when breathing, and the movement relative to the antenna is large.
  • the radar The measured Breathing movement value is large.
  • Figure 8, Figure 9 the human body moves relatively small relative to the antenna, and the Breathing movement amplitude is less than 3mm. According to the magnitude of the Breathing movement data, the front chest facing the antenna can be distinguished from the other two cases. Set a threshold. When the amplitude of the Breathing movement exceeds the threshold, the front chest faces the antenna.
  • the posture of the human body can be determined.
  • IQ at Peak's drift size can be used to identify the side of the body facing the antenna, the amplitude of the Breathing movement to identify the front chest facing the antenna, and in other cases, the back facing the antenna. The amplitude of the Breathing movement is indicative.
  • the Y axis in Figure 11, Figure 12 and Figure 13 is the relative movement amplitude of the Relative movement.
  • the characteristic area 611 in Figures 11, 12, and 13 is the peak of the waveform, and the characteristic area 613 is the valley of the waveform.
  • the waveform with the largest amplitude in each figure is the relative motion waveform caused by breathing.
  • Fig. 12 when the person’s side faces the antenna, the troughs at 613 are in a single sharp V shape.
  • the back of the person in Fig. 11 is facing the antenna and the front of the person in Fig. 13 is facing the antenna, there are multiple small high-frequency oscillations in the trough.
  • the width of the trough is larger than the trough at 613 in FIG. 12.
  • the relative movement amplitude is greater than 4mm, and when the back/side faces the antenna, the relative movement amplitude is less than 4mm. Based on the waveform of the trough at 613, the situation where the side faces the antenna can be distinguished from the other two situations. According to the amplitude, the front chest facing the antenna can be distinguished from the other two cases.
  • the following method can be used: sort the representations, and take the result determined by the character with the highest priority as the human posture If the character with the highest priority cannot determine the posture, the result determined by the character with the second priority is taken, and so on. For example, take the drift of IQ at Peak as the first characterization, and the relative movement amplitude as the second characterization.
  • the body posture if not, judge whether the front chest is facing the antenna by the relative movement amplitude, if yes, determine the body posture, such as No, the back faces the antenna.
  • step 501 the radar module receives the radar signal reflected from the body, and the radar signal is subjected to subsequent processing, including but not limited to known radar signal processing operations such as filtering, denoising, and amplification.
  • Step 503 Extract IQ at Peak, Envelope > Relative Movement, Movement data and other radar data from the radar signal, and perform well-known processing such as normalization of the data to generate a waveform diagram of each data.
  • Step 505 Determine the characteristic area of data such as IQ at Peak, Envelope > Relative Movement, Movement, etc., and determine the characteristic/feature number of the above data Value/characteristic waveform/characteristic trace.
  • Step 507 Judging the posture of the body according to one of the characteristics/characteristic value/characteristic waveform/characteristic track of the data, or a combination of multiple types.
  • At least one radar module 2 is included, which is arranged near the human body and can emit radar waves to illuminate the human body 1, and receive reflected signals.
  • the radar module processes the signal and extracts various information/characteristic data.
  • It includes at least one host computer 3, and the host computer 3 includes at least one characteristic data processing module 31.
  • the information/data is transferred to the module 31.
  • the module 31 can identify, extract and analyze the characteristic regions/waveforms/feature values/trajectories in the data. , Obtain the indication characterization that characterizes the posture of the human body.
  • the host computer 3 includes at least one human body posture judgment module 32.
  • the indication characterization is passed to the module 32, and the module 32 can determine the posture of the human body according to one indication characterization or a combination of multiple indication characterizations.
  • the upper computer includes at least one control module 35, which can receive human posture information, can issue control commands corresponding to the posture according to the posture information, and transmit the control commands to the execution module 4.
  • the execution module implements corresponding operations or actions, such as generating mechanical movements or adjusting equipment parameters.
  • the control module can be arranged in the execution module.
  • the radar module or the radar antenna can be arranged on one side of the bed, at the same height as the human body, preferably facing the torso, 0-4 meters away from the human body, and can also be arranged above or above the human body.
  • the beam from the radar antenna can illuminate the torso.
  • an upward pole is arranged on the side of the pillow/bed, and the radar module/antenna is arranged on the pole.
  • the radar module/antenna can be arranged on the bedside or on the bedside wall.
  • the radar module/antenna/host computer can be arranged in the pillow.
  • the radar module 2 measures the human body 1, and transmits the measurement results to the upper computer 3.
  • the characteristic data processing module in the host computer can extract and analyze characteristic waveforms/characteristic values/trajectories in one/multiple types of radar signals, and determine one/multiple indications indicating body posture.
  • the body posture judgment module in the host computer can determine the human torso posture according to the indications, and can determine the person's sleeping posture according to the torso posture.
  • the upper computer includes a control module, which can record preset pillow heights corresponding to different sleeping positions.
  • the control module can receive the sleeping posture information from the body posture judgment module, and then determine the pillow height corresponding to the sleeping posture according to the sleeping posture information.
  • the control module can record the current pillow height, compare the current pillow height with the measured pillow height corresponding to the sleeping position, and get the direction and amplitude of the pillow surface adjustment.
  • the control module transmits the pillow adjustment command to at least one driving module 43 in the pillow 41.
  • the driving module can drive at least one action module 45, and the action module can be raised and lowered to adjust the height of the pillow/tilt surface inclination/jitter.
  • the driving module drives the action module to adjust the pillow surface according to the adjustment command transmitted from the control module.
  • the control module can adjust the pillow to the preset supine height according to the set pillow height corresponding to the supine position.
  • the control module can adjust the pillow to the height of the side lying. Adjust the pillow to prone height.
  • the pillow in Figure 16 includes at least one pillow height adjustment module, which can include a control module, a drive module, and an action module in the host computer, can receive sleeping posture information, and can adjust the height of the pillow according to the sleeping posture.
  • the radar module includes at least one human body motion measurement module, which can measure the motion of the human trunk and limbs.
  • the host computer 3 includes at least one sleep state judging module, which can be based on the person’s body movement/frequency of movement and breathing rate/ Human body parameters such as amplitude, respiratory stability/respiratory frequency change rate determine a person's sleep state, such as deep sleep, light sleep, and rapid eye movement periods.
  • the host computer includes a wake-up module, which can receive sleep state information, and can set the sleep state and wake-up time during wake-up operations. When the user sets the wake-up time, the wake-up operation is performed only when the person is in the set sleep state at that moment, otherwise the wake-up operation is not performed until the user enters the set sleep state.
  • the user can set the sleep state during the wake-up operation as light sleep, and the wake-up time is 6 o'clock.
  • the wake-up module judges whether the person is in a light sleep state, if it is, the wake-up module performs the wake-up operation, otherwise the wake-up module does not perform the wake-up operation, until the user enters the light sleep posture, the wake-up operation is performed .
  • Wake-up operations include repeated lifting, shaking, and sounding of the pillow surface.
  • the second measurement module 71 can measure the posture/orientation of the human head.
  • the second measurement module includes, but is not limited to, a face recognition module, a three-dimensional measurement recognition module, a pressure sensor array arranged on a pillow, Deformation sensor array on pillow surface.
  • the three-dimensional measurement recognition module is preferably a three-dimensional laser measurement and body part recognition module.
  • the face recognition module can determine the orientation of the head according to the orientation of the face
  • the three-dimensional measurement recognition module can determine the head posture according to the spatial position of the head and head organs
  • the pressure sensor array can determine the head according to the pressure distribution of each point Which side of the head is in contact with the pillow determines the head posture
  • the deformation sensor array can determine which side of the head is in contact with the pillow according to the deformation of the pillow surface to determine the head posture.
  • the measurement result of the second measurement module is transmitted to the upper computer 3, and the upper computer includes a head posture judgment module, which determines the head posture according to the measurement data of the second measurement module.
  • the upper computer includes a body posture judgment module, which determines the posture of the torso according to the data transmitted by the radar module 2.
  • the body posture judgment module determines the person's posture/sleeping posture according to the head posture and torso posture.
  • the torso has the chest up and the head face up, in a supine sleeping position.
  • the chest is upwards and the face is facing one side, which is a supine sleeping position. Sleep sideways with the front chest/back facing one side and the face facing the side. Sleeping prone position with back facing up and face to side.
  • the control module of the host computer executes operations corresponding to the sleeping position according to the sleeping position control execution module 4, such as raising the pillow when the person is lying on his side, and lowering the pillow when he is lying prone.
  • the present invention can use the following method to work: Set at least one movement amplitude threshold, such as 5cm.
  • Set at least one first scanning frequency that is, the refresh rate, the number of times the radar completes the transmission/reception measurement process per second
  • 100 Hz preferably 20 Hz.
  • At least one second scanning frequency is set, such as 200 Hz, preferably 800 Hz.
  • the radar module works at a frequency lower than the first scanning frequency, and the body posture judgment module/subsequent modules (including but not limited to the control module and the execution module) can be in a low power consumption/off state.
  • the motion measurement module measures the motion amplitude of the body/limb according to the received signal, and compares it with the threshold value of the motion amplitude. If the motion amplitude is less than the threshold value, it is considered that the human body has no movement and keeps working below the first scanning frequency; if the threshold value is exceeded, then The human body is considered to be in motion, and the human body is in motion, such as turning over.
  • the motion measurement module continuously measures the body motion, and when it detects that the body motion amplitude is lower than the threshold again, it is considered that the person has returned to rest. Within a period of time (such as 3 seconds) after the human body starts to move to when it returns to rest, set a selected time arbitrarily, such as 2 seconds after starting exercise/1 second after rest.
  • the body posture judgment module/follow-up module starts to work.
  • the body posture judgment module judges the body posture according to the information transferred from the characteristic data processing module, and passes the body posture information to the subsequent modules to perform operations corresponding to the body posture.
  • Set the one-time threshold, and the time for a person to return to static exceeds the time threshold then reduce the operating frequency of the radar module to be lower than the first scanning frequency again, and the body posture judgment module/subsequent module becomes a low power consumption/off state.
  • the value/waveform of the characteristic data of each characteristic signal of each person measured by the radar will be different.
  • the values/waveforms of each characteristic data measured by the radar are also different.
  • the characteristics/values/waveforms/trajectories of the radar characteristic data of different people in different body postures, radar antennas and the human body are in different relative positions, etc. can be determined in advance.
  • the steps include first determining the relative positional relationship between the radar antenna and the body; then determining a human body posture, measuring the value/waveform of one/multiple radar characteristic data when the body is in that posture; then, analyzing the characteristics of the value/waveform, Determine the characteristic waveform/numerical value/trajectory that can indicate the human body posture. Then, establish the corresponding relationship between each indicator and different body postures and store the records. These indications can be used to indicate the posture of the human body. During the measurement, analyze the waveform/value/trajectory of the received radar signal, find out the indication and determine the posture of the human body according to the aforementioned correspondence.
  • the specific embodiments of the present invention are not limited to the foregoing, and the feature data/signal types and indications used to determine the posture of the human body based on the radar signal feature data are not limited to the foregoing, and the radar signal includes one or more
  • the feature data alone or in combination can be used to extract feature regions, analyze data/waveform features, and determine human posture.

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Abstract

一种利用雷达确定身体(1)姿态的方法，包括以下步骤：接收身体(1)反射的雷达波信号(501)；从雷达信号中提取特征数据(503)；根据特征数据的波形/特点/数值/轨迹判断身体(1)的姿态(507)。获得身体(1)姿态信息后，能够控制后续模块执行与身体(1)姿态对应的操作。利用雷达确定身体(1)姿态的装置包括雷达模块(2)，能接收身体(1)反射的雷达信号；特征数据处理模块(31)，能根据雷达信号生成雷达特征数据，能测定指示身体(1)姿态的特征波形/数值/轨迹；身体姿态判断模块(32)，能够根据指示身体(1)姿态的特征波形/数值/轨迹确定身体(1)姿态。基于雷达确定身体(1)姿态工作的装置，包括执行模块(4)，能根据不同身体(1)姿态执行与身体(1)姿态对应的操作/输出。

Description

人体姿态测量方法、 装置和基于此方法工作的装置 技术领域
[0001] 本发明涉及一种人体姿态测量方法、 装置和应用此方法工作的装置， 尤其是一种非 接触人体姿态测量方法、 装置和应用此方法工作的装置。
背景技术
[0002] 在很多领域中， 需要确定人体的姿态， 例如智能枕头， 需要确定人的睡姿， 然后调 节枕头高度。 现有的确定人体姿态的方法包括在床面上铺设压力传感阵列， 根据阵列测得的 数据确定姿态， 该方法无法区分仰卧和俯卧。 还有采用人脸识别技术识别出脸的朝向， 据此 判断睡姿， 该方法无法区分侧卧和俯卧侧头姿势。
[0003] 上述方法都无法准确测量躯干的朝向。
发明内容
[0004] 本发明的目的是提供一种利用雷达确定身体姿态尤其是躯干朝向的方法和装置， 以 及基于此方法工作的装置。
[0005] 本发明的利用雷达确定身体姿态方法包括以下步骤： S1， 接收身体反射的雷达波信 号； S2, 从所述雷达信号中提取一种 /多种信号特征数据； S3 , 根据一种特征数据的波形 /特 点 /数值 /轨迹或结合二种 /多种特征数据的波形 /特点 /数值 /轨迹判断身体的姿态。 获得人体姿 态信息后， 能够控制后续模块执行与人体姿态对应的操作。
[0006] 为了提高测量的准确性， 需要预先确定不同人在不同姿态时、 雷达天线与人体处于 不同位置时雷达数据的指示特征。 包括步骤： S1， 确定雷达天线和身体之间的相对位置关 系； S2, 测量人体在不同姿态时的一种 /多种雷达特征数据的数值 /波形； S3 , 分析人体在不 同姿态时特征数据的特点 /不同点， 确定能够指示人体姿态的特征波形 /特征数值 /特征轨迹。
[0007] 一种利用雷达确定身体姿态的装置， 包括： 至少一雷达模块， 能发射雷达波照射身 体， 能接收身体反射的雷达信号； 至少一特征数据处理模块， 能根据接收的雷达信号生成至 少一种雷达特征数据， 能输出 /显示雷达特征数据， 能分析至少一种特征数据的特点和 /或不 同点， 能测定指示身体姿态的特征波形 /数值 /轨迹； 至少一身体姿态判断模块， 能够根据指 示人体姿态的特征波形 /数值 /轨迹确定身体姿态。
[0008] 一种基于雷达确定身体姿态工作的装置， 包括： 至少一雷达模块， 能够发射雷达波 照射身体， 能够接收身体反射的雷达信号； 至少一特征数据处理模块， 能够根据接收的雷达 信号生成至少一种雷达特征数据， 能分析至少一种特征数据的特点 /不同点， 能确定指示人 体姿态的特征波形 /数值； 至少一身体姿态判断模块， 能根据指示人体姿态的特征波形 /数值 确定身体的姿态； 至少一执行模块， 能根据不同人体姿态执行与该人体姿态对应的操作 /输 出。 执行模块能包括上位机中的控制模块。
[0009] 本发明通过人体反射的雷达信号的特征来确定身体朝向， 准确度高， 不受遮挡物影 响。 利用此方法工作的装置能够正确判断身体朝向。
附图说明
[0010] 图 1是本发明装置的原理图；
图 2是后背朝向雷达天线的 Envelope图；
图 3是侧面朝向天线的 Envelope图；
图 4是前胸朝向天线的 Envelope图；
图 5是后背朝向天线的 IQ at Peak图；
图 6是侧面朝向天线的 IQ at Peak图；
图 7是前胸朝向天线的 IQ at Peak图；
图 8是后背朝向天线的 Breathing Movement图；
图 9是侧面朝向天线的 Breathing Movement图；
图 10是前胸朝向天线的 Breathing Movement图；
图 11是后背朝向天线的 Relative Movement图；
图 12是侧面朝向天线的 Relative Movement图；
图 13是前胸朝向天线的 Relative Movement图；
图 14是利用雷达确定人体姿态的方法的流程图；
图 15是基于用雷达确定人体姿态方法工作的装置原理图；
图 16是基于用雷达确定人体姿态方法工作的调节高度的枕头原理图；
图 17是基于用雷达确定人体姿态方法工作的调节高度的枕头原理图。
具体实施方式
[0011] 图 1 中的， 上位机 3包括至少一计算机和 /或 Mini计算机和 /或单片机和 /或智能终端 设备。 上位机 3与雷达模块 2连接， 雷达模块能够发生雷达波照射人体 1， 并接收反射回来 的雷达波。 优选照射人体躯干、 胸腹位置。 雷达模块 2 包括射频芯片 /雷达芯片、 PCB、 天 线 /天线板、 微控制器、 外围电路， 能够包括呼吸检测模块、 存在感应模块、 心跳检测模 块、 运动检测模块， 这些部件能够集成在一块 PCB上。 优选的雷达能包括但不限于 PCR雷 达、 Doppler雷达、 UWB雷达、 超声波雷达。 优选雷达模块包括但不限于基于 X4雷达芯片 的 X4M03、 基于 Acconeer A1 芯片的雷达系统。 雷达模块能产生 5GHz〜 100GHz 的电磁 波， 通过天线定向发射。 电磁波照射人体后， 被反射回雷达模块， 被天线接收。 雷达模块能 够对天线接收到的信号进行处理， 包括但不限于检波、 滤波、 信号特征提取。 呼吸检测模块 能够检测到人体的呼吸， 能够获取基带数据。 存在检测模块可检测人体的存在。 心跳检测模 块能检测到人的心脏跳动， 测量心跳的幅度和波形。 运动检测模块能测量人体躯干和四肢的 运动， 包括转身、 胳膊和腿运动。 信号经过处理后， 雷达模块能够得到但不限于呼吸次数、 单次呼吸波形、 身体活动情况、 心跳次数、 心跳波形、 胸廓运动、 腹部运动、 相对运动等数 据， 能够得到 Envelope数据、 IQ at Peak、 人体部位运动幅度等数据。 这些数据被传递给上 位机 3中的特征数据处理模块。 上位机 3包括至少一特征数据处理模块 31和至少一人体姿 态判断模块 32。 特征数据处理模块 31 能够对雷达模块传递来的各种特征数据进行处理分 析， 提取出能指示人体姿态的指示表征， 并传递给人体姿态判断模块 32。 人体姿态判断模 块 32能根据指示表征判断人体的姿态， 尤其是躯干相对于雷达模块 /天线的朝向。 指示表征 是雷达信号 /特征数据中能指示 /区分不同人体姿态的具有指示 /标识作用的数值 /波形 /轨迹 等。
[0012] 图 2〜 13 是人躺在床上、 雷达天线布置在与人同高度、 朝向躯干、 距人胸部 0.5-2 米时测得的特征数据图， 仰卧时， 天线位于人体左侧。 通过分析同一特征数据图中不同位置 处的数据特点， 能够判断人体的姿势； 通过综合比较 /结合分析不同特征数据图中的数据特 点， 能够判断人的姿态。 各图中纵坐标即 Y轴， 横坐标即 X轴。 图中 Y轴单位能是距离 /幅 度 /电压 /回波功率的绝对值， 也能是归一化后的相对数值， X 轴能是时间 /距离 /幅度的绝对 值， 也能是归一化后的相对数值。 X 轴能是雷达扫描次数， 雷达模块每完成一次发射 /接受 循环即扫描一次。 每秒完成的扫描次数叫扫描频率， 也叫刷新率。 能设定一个扫描次数的数 值作为采样周期， 如设定 1700次扫描为一采样周期。 雷达模块能自动设定采样周期。
[0013] 图 2、 图 3、 图 4中， 特征数据处理模块收雷达模块传递的 Envelope数据， 并对数据 的特征值 /波形进行分析。 Envelope 是包络数据， 数据反映了心跳和由心跳引起的内脏、 胸 廓运动情况。 心脏跳动时各心房、 心室收缩的先后次序、 幅度、 方向不同， 进而引起人体不 同位置处的内脏、 胸廓的振动在时间、 位置 /区域、 幅度、 方向上也不同， 当人体的不同位 置朝向天线时， Envelope波形有差异。 图 2 是人体侧卧在床、 后背朝向天线， 图 3 是人仰 卧、 身体左侧朝向天线， 图 4是人侧卧、 前胸朝向天线。 图 2、 3、 4 中 X轴能是一个采样 周期中的扫描次数。 特征区域 601 横坐标位于 400〜 700之间， 特征区域 603 横坐标位于 700〜 950之间， 特征区域 605横坐标位于 950〜 1100间， 特征区域 607横坐标位于 1100〜 1350间， 特征区域 609横坐标位于 1350〜 1800间。
[0014] 特征区域能采取等距划分， 如 X轴每一给定数量划分为一个区域， 如每 300单位为 一特征区域， 或把一个周期等距划分为多个特征区域， 如划分为 8个区域。 特征区域能采取 不等距划分， 根据特征数据的波形 /特点， 选择特点明显的区间、 波形显著区别其它身体姿 态的区间、 最小值小于给定闕值的区间、 波形呈 V 形的区间作为特征区域。 能选取波形的 最小值 /多个极小值， 确定这些点的横坐标， 然后以这些点的横坐标为基点沿 X 轴向正负方 向各延伸指定单位， 如 100单位， 作为特征区域。
[0015] 在测量的不同时刻， 采样周期中包括的扫描次数能够不同， 特征区域的起点、 终点 能相应调整。
[0016] 图 2中 603在 750〜 900间数据出现最小值， 最小值小于 10， 接近 0， 波形呈 V形， 最低点两侧的波形陡峭且直。 图 2中 601位置的最小值在 10〜 30， 波形最低点明显大于 0， 即大于 603的最小值， 603处波形平缓。 图 3中 603的最小值在 20〜 50， 明显大于 0， 波形 不是陡峭 V形。 图 3中 601的最小值出现在 400〜 500间， 最小值在 10〜 20， 且 601处最小 值小于 603处最小值。 图 4中 603的最小值在 10〜 40， 波形呈 V形， 最低点两侧线条直但 不陡峭。 图 4中 601处最小值在 10〜 40， 与 603处最小值接近。 因为图 2中 603处的最小 值接近 0且 V形形状明显， 从 603处的数据 /波形特征能够把后背朝向雷达天线的情况与侧 面朝向天线、 前胸朝向天线明显区分开来。 当 603处最小值小于 10, 且波形呈 V形， 则后 背朝向天线。 603处的数值、 波形是指示表征。
[0017] 图 2中 605处最小值在 10〜 20， 出现在横坐标 950〜 1000， 波形比较平缓。 图 3 中 605处最小值出现在横坐标 1000〜 1100， 波形起伏大， 最小值在 15〜 25。 图 4中 605处最 小值出现在 980〜 1050, 最小值数值接近 0, 波形呈 V形， 最低点两侧的波形陡峭且直。 图 4中， 605处的最小值接近 0且 V形特征鲜明， 从 605处的数据特征能把前胸朝向天线的情 况与后背朝向天线、 侧面朝向天线明显区分开来。 605处数值 /波形是指示表征。 当 605处最 小值小于 10且波形呈 V形， 则前胸朝向天线。 结合 603、 605处的数据 /波形特征， 能区分 人体后背、 侧面、 前胸朝向雷达天线的情况。
[0018] 图 2中 607位置处波形变化剧烈， 曲线呈不对称 V型。 图 4中 607处波形平缓。 图 3 中 607处波形起伏程度介于图 2、 图 4之间。
[0019] 确定一个区间中最小值、 各极大值、 极小值的方法是公知技术。
[0020] 能够采用下述方法判断数据波形形态： 记录特征区域内各波峰的极大值、 各波谷的 极小值， 计算特征区域内各点的平均值， 计算多个 （大于 2 个） 极大值 /极小值与平均值之 差的绝对值之和 Sa， 设置一闕值 g， 当 Sa大于闕值 g时， 即认为该特征区域内波形波动较 大。 也能只记录特征区域内的最大值 /最小值， 然后把最大值 /最小值与平均值之差的绝对值 a 与闕值 g 比较， 如果 a 大于 g , 即波动大。 能够设定一个 /多个闕值区间， 例如 gl （20,50）, 和该闕值区间所对应的波形波动情况， 例如 gl对应波动大。 判断 a/Sa落入的闕 值区间， 即能确定波动情况。 例如 a在 gl区间内， 则该区域波动大。
[0021] 判断波形的形态能采用下述方法： 找到特征区域中的最低点， 分别在最低点的左侧 和右侧取多个点 （多于 2个）， 分别用直线拟合最低点左侧的多点和右侧多点， 求左侧 /右侧 各点与直线的拟合度。 设定闕值 g， 当拟合优度例如方差小于 g时， 则拟合度好， 该段波形 直 /起伏小， 否则该段起伏大， 不直。 若最低点左侧和右侧拟合直线的拟合度好， 则该处波 形为 V 型。 计算左侧和右侧拟合直线的夹角， 设定一闕值， 如果夹角角度小于闕值， 则形 成的 V形两侧陡峭， 否则平缓。 计算拟合直线与 Y轴的夹角 （取锐角）， 设定一闕值， 如果 夹角小于闕值， 则曲线陡峭， 否则平缓。
[0022] 图 2中 609处波形最小值出现在 X轴 1450〜 1500间， 数值在 0〜 20， 接近于 0。 图 3 中 609处波形最小值出现在 1500〜 1550间， 数值在 0〜 20， 接近于 0。 图 4中 609处波形平 缓平直， 最小值位置不明显， 数值大于 20。 图 4的 609处波形与图 2、 3中 609处波形区别 明显。 图 3中 609处波形整体呈 V形， 从最低点向右整体呈上升趋势。 图 2中 609处波形 在最低点向右先上升后下降， 出现一个波峰。 图 4中 609处数据的各波峰波谷的极大值、 极 小值与 609处的平均值之差的绝对值很小， 小于 40, 609处最小值两侧拟合的两条直线的夹 角大于 150度， 接近 180度， 且两条直线的拟合度好。 图 3中 609处数据的各波峰波谷的极 大值、 极小值与 609 处的平均值之差的绝对值大， 大于 40, 最小值左右两侧的拟合直线夹 角介于 70〜 110度， 左右两侧直线拟合度好。 图 2中 609处数据的各波峰波谷的极大值、 极 小值与 609处的平均值之差的绝对值大， 大于 40, 最小值左右两侧拟合直线夹角介于 110〜 130度， 左侧直线拟合度好， 右侧拟合度不好。 根据图 2、 3、 4中 609处波形的特点能够区 分出各波形， 进而根据波形确定对应的人体姿态。 如果测得的波形 609处曲线平缓且平直， 最小值大于 20, 最小值两侧拟合直线的夹角大于 1507拟合直线与 Y轴夹角大于 75 ° , 则对 应的人体姿势为前胸朝向天线。 如果 609 处最小值小于 10, 且最小值两侧拟合度好， 则为 侧面朝向天线。 如果 609处最小值小于 10, 最小值某侧拟合度不会， 则为后背朝向天线。
[0023] 综合分析 Envelope 图中 601、 603、 605、 607、 609处的数据特征和波形， 能准确地 确定人体相对于天线的不同朝向， 即确定人体姿态。 例如， 603、 609处出现小于 10的极小 值， 则可确定为人体后背朝向天线。 603、 609处的最小值在 10以上且 609处波形平缓， 则 为人体前胸朝向天线。 603处最小值在 10以上， 609处最小值小于 10则为人体侧面朝向雷 达。 605处出现小于 10的极小值且波形呈 V形， 609处波形平缓且最小值大于 10, 则为前 胸朝向天线。 603、 605处最小值都大于 10是侧面朝向天线的特征。 603处最小值小于 10， 605处最小值大于 10则后背朝向天线。
[0024] 把各特征区域的指示数值 /波形等标识性信息传递给人体姿态判断模块， 人体姿态判 断模块能够根据这些特征确定人体的朝向 /姿态。
[0025] 图 5、 图 6、 图 7中 X轴是 I通道， Y轴是 Q通道， 图中点代表某时刻 IQ分量幅度 峰值。 图中包括了当前时点 （图中最大的点） 和之前一个时间段内的多个点的数据， 因此图 中呈现了 IQ at Peak一段时间内的漂移 /偏移轨迹。 判断 IQ at Peak数据特征的方法包括， 首 先选取一个给定时间段内的多个数据点， 用圆拟合这些数据点， 得到最优拟合圆。 圆的直径 能指示 IQ at Peak的漂移量 /偏移量。 设定一闕值 a， 如 20单位， 当圆直径小于 a时， 则认为 IQ at Peak漂移小， 各点接近重合。 设定一闕值 b， 如 10单位， 把拟合优度指标与 b进行比 较， 如把方差与 b比较， 当方差小于 b且拟合圆直径大于 a时， 则漂移大且轨迹为圆弧， 当 方差大于 b且圆直径大于 a时， 则漂移大胆轨迹不呈圆形。
[0026] 图 6中， 当人体侧面朝向雷达天线时， IQ at Peak漂移小， 各点几乎重合。 图 7中， 当人前胸朝向天线时， IQ at Peak漂移大， 漂移轨迹呈圆弧形， 直径超过 20。 图 6 中， Stddev （标准差） 数值小于 0.5mm, 图 7中 Stddev大于 1.5mm。 图 5中， 人后背朝向天线， IQ at Peak点漂移大， 各点轨迹不呈圆形， Stddev小于 0.5mm。
[0027] 根据 IQ at Peak数据的特征能区分不同人体姿态。 当 IQ at Peak偏移小且 Stddev小于 0.5mm时， 则人侧面朝向天线。 IQ at Peak漂移大、 Stddev小于 0.5mm、 轨迹不呈圆弧形， 则人后背朝向天线。 IQ at Peak偏移大、 Stddev大于 1.5mm， 轨迹为圆弧， 则人前胸朝向天 线。 IQ at Peak的漂移量 /轨迹 /Stddev是指示表征。
[0028] 结合 Envelope、 IQ at Peak数据， 能确定人体姿态。 例如通过 IQ at Peak数据漂移情 况确定是否为侧面朝向雷达， IQ at Peak漂移小， 则身体侧面朝向天线。 如果 IQ at Peak漂 移大， 则根据 Envelope数据区分另两种情况： 如 Envelope数据的 609处波形平缓且大于 15 则为前胸朝向天线。 如果 609 处波形起伏大且最小值小于 15 , 则为后背朝向天线。 结合二 种及以上特征数据来识别身体姿态能够给选择特征数值 /波形 /轨迹带来更大灵活性， 并且几 种指示特征 /表征相互印证， 能提高判断准确性。
[0029] 图 8、 图 9、 图 10 中 Y轴是 Breathing movement幅度。 图 10 中振幅较大， 超过 3mm, 是前胸朝向天线。 因为此时人在呼吸时胸廓和腹部起伏大， 相对天线的运动大， 雷达 测得的 Breathing movement数值大。 图 8、 图 9中人体相对天线运动小， Breathing movement 幅度小于 3mm。 根据 Breathing movement数据幅度， 能把前胸朝向天线同另两种情况区分 开。 设定一闕值， 当 Breathing movement振幅超过闕值时， 则为前胸朝向天线。
[0030] 结合 Breathing movement、 IQ at Peak, 能确定人体姿态。 通过 IQ at Peak的漂移大小 识别出身体侧面朝向天线， 通过 Breathing movement的幅度识别出前胸朝向天线， 其它情况 为后背朝向天线。 Breathing movement的幅度是指示表征。
[0031] 图 11、 图 12、 图 13中 Y轴是 Relative movement相对运动幅度。 图 11、 12、 13中特 征区域 611是波形的波峰， 特征区域 613是波形的波谷。 各图中振幅最大的波形是呼吸引起 的相对运动波形。 图 12中， 当人侧面朝向天线时， 613处波谷呈单一尖角 V形， 而图 11中 人后背朝向天线和图 13 中人前胸朝向天线时 613处波谷有多个高频小幅振荡， 波谷宽度大 于图 12中 613处的波谷。 前胸朝向天线时， Relative movement振幅大于 4mm, 后背/侧面朝 向天线时 Relative movement振幅小于 4mm。 根据 613处波谷的波形， 能把侧面朝向天线的 情况与另两种情况区分开来。 根据振幅， 能把前胸朝向天线同另两种情况区分开来。
[0032] 当采用多个指示表征来确定人体姿态时， 为避免各指示表征的判断结果出现矛盾， 能够采用如下方法： 对各表征进行排序， 取优先级最高的表征所确定的结果作为人体姿态， 如果优先级最高的表征不能确定姿态， 则取优先级第二的表征所确定的结果， 依此类推。 如 取 IQ at Peak的漂移量做第一表征， Relative movement振幅做第二表征。 测量时， 首先通过 IQ at Peak 的漂移量判断是否为侧面朝向天线， 如果是， 则确定身体姿态， 如否， 则通过 Relative movement振幅判断是否前胸朝向天线， 如果是， 则确定身体姿态， 如否， 则后背朝 向天线。
[0033] 或给各表征赋予相同 /不同的权重， 然后对指示不同姿态的各个表征的权重进行求 和， 取和最大的结果作为人体姿态。 如取 5个不同的指示表征， 设每个表征的权重为 1， 分 别把指示仰卧、 侧卧、 俯卧的各表征的权重求和， 和最大的结果作为人体姿态， 如 5个指示 表征中有 4个指示侧面朝向天线， 权重之和为 4, 而指示其它姿态的表征的权重之和为 1， 则测量结果为人侧面朝向天线， 仰卧。
[0034] 图 14 中， 步骤 501， 雷达模块接收身体反射回来的雷达信号， 雷达信号被进行后续 处理， 包括但不限于滤波、 去噪、 放大等公知的雷达信号处理操作。 步骤 503 , 从雷达信号 中提取出 IQ at Peak、 Envelope > Relative Movement、 Movement数据和其它雷达数据， 能对 数据进行归一化等公知的处理， 生成各数据的波形图。 步骤 505， 确定 IQ at Peak、 Envelope > Relative Movement、 Movement等数据的特征区域， 确定上述数据的特点 /特征数 值 /特征波形 /特征轨迹。 步骤 507, 根据数据的特点 /特征数值 /特征波形 /特征轨迹中的一种， 或多种相结合判断身体的姿态。
[0035] 图 15 中， 包括至少一雷达模块 2, 布置在人体附近， 能够发射雷达波照射人体 1， 接收反射信号。 雷达模块对信号进行处理， 提取各种信息 /特征数据。 包括至少一上位机 3 , 上位机 3包括至少一特征数据处理模块 31， 所述信息 /数据被传递给模块 31， 模块 31 能对 数据中的特征区域 /波形 /特征值 /轨迹进行识别提取分析， 得到表征人体姿态的指示表征。 上 位机 3包括至少一人体姿态判断模块 32。 指示表征被传递给模块 32, 模块 32能根据一个指 示表征， 或多个指示表征的组合确定人体姿态。 上位机包括至少一控制模块 35 , 能接收人 体姿态信息， 能根据姿态信息发出与姿态相对应的控制命令， 并把控制命令传给执行模块 4。 执行模块实施相应的操作或动作， 例如产生机械运动或调整设备参数。 控制模块能布置 在执行模块中。
[0036] 图 16 中， 雷达模块或雷达天线能布置在床的一侧， 与人体同高， 优选正对躯干， 距 人体 0〜 4 米， 也能布置在人的上方或侧上方。 雷达天线发出的波束能照射躯干。 优选在枕 头 /床的侧边布置一向上的杆， 雷达模块 /天线布置在杆上。 雷达模块 /天线能布置在床头上或 床头的墙上。 雷达模块 /天线 /上位机能布置在枕头中。 雷达模块 2 测量人体 1， 并把测量结 果传递到上位机 3。 上位机中的特征数据处理模块能对一种 /多种雷达信号中的特征波形 /特 征值 /轨迹进行提取、 分析， 确定指示身体姿态的一个 /多个指示表征。 上位机中的身体姿态 判断模块能根据指示表征确定人体躯干姿态， 能够根据躯干姿态确定人的睡姿。 上位机包括 一控制模块， 控制模块能记录预设的不同睡姿对应的枕头高度。 控制模块能接收身体姿态判 断模块传递来的睡姿信息， 然后根据睡姿信息确定与该睡姿对应的枕头高度。 控制模块能记 录当前枕头高度， 能比较当前枕头高度和测量得到的与睡姿对应的枕头高度， 能得到调整枕 面的方向和幅度。 控制模块把枕头调节命令传递给枕头 41 中的至少一驱动模块 43。 驱动模 块能驱动至少一动作模块 45, 动作模块能够升降以调节枕头高度 /枕面倾斜度 /抖动。 驱动模 块根据控制模块传递来的调节命令驱动动作模块以调节枕面。 当人前胸朝上、 仰卧时， 控制 模块能根据设定的与仰卧姿势对应的枕头高度把枕头调节到预设仰卧高度， 侧卧时， 控制模 块能把枕头调节到侧卧高度， 俯卧时能把枕头调节到俯卧高度。 图 16 中的枕头包括至少一 枕头高度调节模块， 该模块能包括上位机中的控制模块、 驱动模块、 动作模块， 能接收睡姿 信息， 能根据睡姿调节枕头的高度。
[0037] 雷达模块包括至少一人体运动测量模块， 能够测量人体躯干、 四肢的运动。 上位机 3 中包括至少一睡眠状态判断模块， 能根据人在睡眠中的身体动作情况 /动作频次、 呼吸频率 / 幅度、 呼吸稳定性 /呼吸频率变化率等人体参数确定人的睡眠状态， 如深度睡眠、 浅睡眠、 快速眼动期。 上位机中包括一叫醒模块， 能接收睡眠状态信息， 能设定执行叫醒操作时的睡 眠状态和叫醒时间。 当用户设置了叫醒时刻， 只有该时刻人处于设定的睡眠状态时， 才执行 叫醒操作， 否则不执行叫醒操作直到用户进入设定的睡眠状态。 如， 用户能设定叫醒操作时 的睡眠状态为浅睡眠， 叫醒时刻为 6点。 当 6点时， 叫醒模块判断人是否处于浅睡眠状态， 如果是， 叫醒模块执行叫醒操作， 否则叫醒模块不执行叫醒操作， 直到用户进入浅睡眠姿态 时， 才执行叫醒操作。 叫醒操作包括枕面反复升降、 抖动、 发出声音。
[0038] 图 17, 第二测量模块 71 能够测量人头部的姿态 /朝向， 第二测量模块包括但不限于 人脸识别模块、 三维测量识别模块、 布置在枕头上的压力传感器阵列、 布置在枕面上的变形 传感器阵列。 三维测量识别模块优选三维激光测量、 人体部位识别模块。 人脸识别模块能根 据人脸的朝向判断头部的朝向， 三维测量识别模块能根据头部、 头部器官的空间位置确定头 部的姿态， 压力传感器阵列能根据各点的压力分布判断头部的哪侧与枕头接触而确定头的姿 态， 变形传感器阵列能根据枕面各处的变形情况确定头部的哪侧与枕头接触而确定头的姿 态。 第二测量模块的测量结果被传递给上位机 3 , 上位机包括一头部姿态判断模块， 该模块 根据第二测量模块的测量数据确定头部的姿态。 上位机包括身体姿态判断模块， 该模块根据 雷达模块 2传递来的数据确定躯干的姿态。 身体姿态判断模块根据头部姿态和躯干姿态确定 人的姿态 /睡姿。 躯干前胸向上且头部脸朝上， 为仰卧睡姿。 前胸向上而脸朝向一侧为仰卧 侧头睡姿。 前胸 /后背朝向一侧、 脸朝侧面为侧睡。 后背朝上脸朝侧面为俯卧睡姿。 上位机 的控制模块根据睡姿控制执行模块 4执行与该睡姿对应的操作， 如在人侧卧时升高枕头， 在 俯卧时降低枕头。
[0039] 为节能， 本发明能采用下述方法工作： 设置至少一运动幅度闕值， 如 5cm。 设置至少 一第一扫描频率 （即刷新率， 雷达每秒完成发射 /接收测量过程的次数）， 如 100Hz， 优选 20Hz。 设置至少一第二扫描频率， 如 200Hz, 优选 800Hz。 初始时， 雷达模块以低于第一扫 描频率工作， 身体姿态判断模块 /后续模块 （包括但不限于控制模块、 执行模块） 能处于低 功耗 /关闭状态。 运动测量模块根据接收的信号测量身体 /肢体的运动幅度， 与运动幅度闕值 比较， 如果运动幅度小于闕值， 则认为人体没有动作， 保持低于第一扫描频率工作； 如超过 闕值， 则认为人体有动作， 人体处于运动中， 如翻身。 运动测量模块持续测量身体运动， 当 检测到身体运动幅度重新小于闕值时， 则认为人已恢复静止。 从人体开始运动到重新恢复静 止后的一个时间段内 （如 3秒）， 任意设定一选定时刻， 如开始运动后 2秒 /静止后 1秒。 在 选定时刻， 调整雷达模块的扫描频率到高于第二扫描频率， 这样雷达模块就能测量到身体 / 肢体运动 /心跳 /呼吸的细节。 当人恢复静止后， 身体姿态判断模块 /后续模块开始工作。 身体 姿态判断模块根据特征数据处理模块传递来的信息判断身体姿态， 并把身体姿态信息传递给 后续模块， 执行与身体姿态对应的操作。 设置一时长闕值， 人恢复静止的时间超过时长闕 值， 则重新降低雷达模块工作频率到低于第一扫描频率， 身体姿态判断模块 /后续模块变成 低功耗 /关闭状态。
[0040] 因为不同人的心跳幅度、 呼吸时胸廓 /腹部起伏幅度有差异， 雷达测量的各人的各特 征信号的特征数据的数值 /波形会有差异。 当雷达天线与人体处于不同的相对位置时， 雷达 测量的各特征数据的数值 /波形也有差异。 为了准确判断身体姿态， 能预先确定不同人在不 同身体姿态、 雷达天线与人体处于不同相对位置等各种情况下的雷达特征数据的特点 /数值 / 波形 /轨迹。 首先确定雷达天线和人体之间的相对位置关系， 然后测量人体在不同姿态时的 各雷达特征数据， 确定雷达数据 /波形的特征区域， 找出人体在不同姿态时各特征区域的指 示表征， 找出能指示人体姿态的特征波形 /数值。 测量时， 在确定了雷达天线与人体的相对 位置后， 从雷达各特征数据 /信号中找出指示表征并据此确定人体姿态。 步骤包括首先确定 雷达天线和身体之间的相对位置关系； 然后确定一人体姿态， 测量人体在该姿态时的一种 / 多种雷达特征数据的数值 /波形； 然后， 分析数值 /波形的特点， 确定能够指示人体姿态的特 征波形 /数值 /轨迹等指示表征。 然后， 建立各指示表征与不同人体姿态的对应关系并存储记 录。 这些指示表征能被用于指示人体姿态。 在测量时， 分析接收到的雷达信号的波形 /数值 / 轨迹， 找出指示表征并根据前述对应关系确定人体姿态。
[0041] 本发明的具体实施方式并不只限于前述， 根据雷达信号特征数据判断人体姿态所依 据的特征数据 /信号种类、 指示表征也不限于前述各种， 雷达信号所包含的一种或多种特征 数据单独或相结合都能够用于提取特征区域、 分析数据 /波形特征、 判断人体姿态。

Claims

权 利 要 求 书
1. 一种利用雷达确定身体姿态的方法， 包括：
si , 接收身体反射的雷达波信号；
52, 从所述雷达信号中提取一种或多种特征数据；
53 , 根据所述一种特征数据的波形 /特点 /数值 /轨迹或结合二种 /多种特征数据的波形 /特点 /数 值 /轨迹判断身体的姿态。
2. 一种利用雷达确定身体姿态时确定指示表征的方法， 包括：
51 , 确定雷达天线与身体之间的相对位置；
52, 测量人体在不同姿态时的一种 /多种雷达特征数据的数值 /波形；
53 , 分析人体在不同姿态时各特征数据的特点 /不同点， 找出能指示人体姿态的特征波形 /数 值 /轨迹作为指示表征；
54, 建立指示表征与不同人体姿态的对应关系。
3. 一种利用雷达确定身体姿态的装置， 包括：
至少一雷达模块， 能够发射雷达波照射身体， 能够接收身体反射的雷达信号；
至少一特征数据处理模块， 能够根据接收的雷达信号生成至少一种雷达特征数据， 能够输出 /显示雷达特征数据， 能够确定特征数据的特征区域， 能够分析 /确认 /判断特征数据的波形 /数 值 /轨迹的特点或不同点， 能够确定指示身体姿态的指示表征；
至少一身体姿态判断模块， 能够接收指示表征信息， 能够根据一种或多种指示表征相结合确 定身体姿态。
4. 一种基于雷达确定身体姿态工作的装置， 包括：
至少一雷达模块， 能够发射雷达波照射身体， 能够接收身体反射的雷达信号；
至少一特征数据处理模块， 能够根据接收的雷达信号生成至少一种雷达特征数据， 能够输出 /显示雷达特征数据， 能够确定特征数据的特征区域， 能够分析 /确认 /判断特征数据的波形 /数 值 /轨迹的特点或不同点， 能够确定指示身体姿态的指示表征；
至少一身体姿态判断模块， 能够接收指示表征信息， 能够根据一种或多种指示表征相结合确 定身体姿态；
至少一执行模块， 能够根据身体姿态判断模块所确定的身体姿态执行与所述姿态相对应的操 作 /输出 /动作。
5. 一种基于雷达确定身体姿态工作的装置的工作方法， 包括：
51 , 接收身体反射的雷达波信号；
52, 从所述雷达信号中提取一种或多种特征数据； 53 , 根据所述一种特征数据的波形 /特点 /数值 /轨迹或结合二种 /多种特征数据的波形 /特点 /数 值 /轨迹判断身体的姿态；
54, 根据所确定的身体姿态执行与所述身体姿态对应的操作 /输出 /动作。
6. 一种基于雷达确定身体姿态工作的枕头， 包括：
至少一雷达模块， 能够发射雷达波照射身体， 能够接收身体反射的雷达信号；
至少一特征数据处理模块， 能够根据接收的雷达信号生成至少一种雷达特征数据， 能够输出 /显示雷达特征数据， 能够确定特征数据的特征区域， 能够分析 /确定 /判断至少一种特征数据 的波形 /数值 /轨迹的特点或不同点， 能够确定指示人体姿态的特征波形 /数值 /轨迹； 至少一身体姿态判断模块， 能够根据指示人体姿态的特征波形 /数值 /轨迹信息确定人的躯干 姿态， 能够根据躯干姿态确定人的睡姿；
至少一枕头高度调节模块， 能够接收所述睡姿信息， 能够根据睡姿调节枕头的高度。
7. 如权利要求 6所述的枕头， 包括：
至少一第二测量模块， 所述第二测量模块能够测量头部的姿态 /朝向；
所述身体姿态判断模块能够结合头部姿态 /朝向信息和躯干姿态信息确定睡姿。
8. 一种基于雷达确定身体姿态工作的信息展示装置， 包括：
至少一雷达模块， 能够发射雷达波照射身体， 能够接收身体反射的雷达信号；
至少一特征数据处理模块， 能够根据接收的雷达信号生成至少一种雷达特征数据， 能够输出 /显示雷达特征数据， 能够确定特征数据的特征区域， 能够分析 /确认 /判断特征数据的波形 /数 值 /轨迹的特点或不同点， 能够确定指示身体姿态的指示表征；
至少一身体姿态判断模块， 能够根据指示表征确定人体姿态；
至少一输出模块， 能够根据人体姿态调节信息展示装置的开关 /显示内容 /声音 /图像特征。
9. 一种利用雷达确定身体姿态的方法， 包括以下步骤：
S1， 雷达模块接收身体反射回来的雷达信号；
52, 从雷达信号中提取出 IQ at Peak、 Envelope > Relative Movement、 Movement数据；
53 , 确定 IQ at Peak、 Envelope > Relative Movement、 Movement数据中能作为指示表征的特 征数值 /波形 /轨迹；
54, 根据 IQ at Peak、 Envelope > Relative Movement、 Movement数据中一种， 或多种指示表 征相结合判断身体的姿态。
10. 一种测量身体姿态雷达的工作方法， 包括至少一第一扫描频率， 至少一第二扫描频率， 至少一运动幅度闕值， 至少一时长闕值， 初始时， 雷达模块以低于第一扫描频率工作； 运动测量模块根据接收的信号持续测量身体 /肢体的运动幅度， 并与运动幅度闕值比较； 如果运动幅度小于闕值， 则认为人体没有动作， 雷达保持低于第一扫描频率工作； 如果超过闕值， 则认为人体处于运动状态；
持续测量运动幅度， 当检测到身体运动幅度重新小于闕值时， 则认为人已恢复静止； 在人体开始运动到重新恢复静止后期间的选定时刻， 调整雷达模块的扫描频率到高于第二扫 描频率；
在人恢复静止后， 身体姿态判断模块开始工作， 身体姿态判断模块根据特征数据处理模块传 递来的信息判断身体姿态， 并把身体姿态信息传递给后续模块， 执行与身体姿态对应的操 作；
人恢复静止的时间超过时长闕值， 则重新降低雷达模块工作频率到低于第一扫描频率。
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