WO2019174027A1 - 一种生理信息监测方法及生理信息监测垫、一种床垫 - Google Patents

一种生理信息监测方法及生理信息监测垫、一种床垫 Download PDF

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Publication number
WO2019174027A1
WO2019174027A1 PCT/CN2018/079263 CN2018079263W WO2019174027A1 WO 2019174027 A1 WO2019174027 A1 WO 2019174027A1 CN 2018079263 W CN2018079263 W CN 2018079263W WO 2019174027 A1 WO2019174027 A1 WO 2019174027A1
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Prior art keywords
signal
physiological
respiratory
physiological information
preset
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PCT/CN2018/079263
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English (en)
French (fr)
Inventor
冯澍婷
刘洪涛
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深圳和而泰数据资源与云技术有限公司
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Priority to CN201880000905.5A priority Critical patent/CN108697348A/zh
Priority to PCT/CN2018/079263 priority patent/WO2019174027A1/zh
Publication of WO2019174027A1 publication Critical patent/WO2019174027A1/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
    • A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
    • A61B5/0205—Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
    • A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
    • A61B5/113—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
    • A61B5/103—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
    • A61B5/48—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
    • A61B5/68—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient
    • A61B5/6887—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient mounted on external non-worn devices, e.g. non-medical devices
    • A61B5/6892—Mats
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/74—Details of notification to user or communication with user or patient ; user input means
    • A61B5/746—Alarms related to a physiological condition, e.g. details of setting alarm thresholds or avoiding false alarms

Definitions

  • the present application relates to the field of sleep monitoring technology, and in particular to a physiological information monitoring method, a physiological information monitoring pad, and a mattress.
  • the household portable sleep detecting device analyzes the user's sleep quality by detecting the movement, breathing and heartbeat during the sleep process of the user, and the sleep detecting device has the functions of assessing the user's work stress, fatigue and mental state. Broad market prospects.
  • the existing sleep monitoring devices are mostly wearable devices, which are easy to restrain the user and affect the normal sleep quality, and the sensors in the sleep monitoring device are in the process of detecting. There is also a risk of shedding; on the other hand, the existing sleep monitoring device is limited to detecting a single person's sleep state, and when applied to multi-person detection, the test result is inaccurate.
  • the embodiments of the present application aim to solve the technical problem that the existing physiological information monitoring is not suitable for detecting sleep conditions of two or more people.
  • a technical solution adopted by the embodiment of the present application is to provide a physiological information monitoring method, including: receiving a first to pth physiological signal; detecting a respiratory signal in each physiological signal; In the first to the p-th physiological signals, if the respiratory signals of the adjacent m-path physiological signals meet the preset judgment conditions, it is determined that the m-way adjacent physiological signals correspond to a monitored object and the monitoring is performed.
  • the number of objects; the physiological information corresponding to the monitored object is extracted from the adjacent m-path physiological signals by a preset signal processing algorithm; wherein, p and m are positive integers, m ⁇ p; The number of monitored objects and the physiological information corresponding to each of the monitored objects.
  • the detecting the respiratory signal in each physiological signal comprises: determining, by using a preset breathing detection algorithm, whether there is a respiratory waveform corresponding to the respiratory motion in the physiological signal; and the breathing is present in the physiological signal In the case of the respiratory waveform corresponding to the motion, the frequency of the respiratory waveform and the amplitude of the waveform are detected.
  • the preset determining condition is: the respiratory waveform is present in the m-way physiological signal, and at least one physiological signal has a waveform amplitude greater than a preset number in the m-way physiological signal.
  • the physiological information includes a heart rate, a respiratory frequency, and a physical motion number
  • the physiological information corresponding to the monitoring object is extracted from the adjacent m physiological signals by a preset signal processing algorithm
  • the method includes: separating, by using a preset signal separation algorithm, a target cardiac signal, a target respiratory signal, and a target body motion signal from the adjacent m-path physiological signals; and calculating a heart rate of the monitored object according to the target cardiac signal, Calculating a respiratory frequency of the monitored object according to the target respiratory signal and calculating a number of physical motions of the monitored object according to the target body motion signal.
  • the calculating the heart rate of the monitoring object according to the target cardiac signal specifically includes: removing a baseline drift of the target cardiac signal to obtain a standard cardiac signal; and detecting a peak point and a valley point of the standard cardiac signal And selecting the peak point and the valley point according to the dynamic threshold to obtain a target peak point and a target valley point; determining a distance between the target peak point and the target valley point as a heart rate of the monitored object.
  • the calculating, according to the target breathing signal, the respiratory frequency of the monitoring object specifically comprising: performing a Fourier transform on the target respiratory signal; determining that the target respiratory signal after the Fourier transform exceeds a spectral peak of the preset energy threshold; calculating a respiratory frequency corresponding to the spectral peak as a candidate respiratory frequency; and determining a respiratory frequency of the monitored object from the candidate respiratory frequency in combination with historical data of the monitored object.
  • the method further includes determining a sleep state of the monitored object according to the heart rate of the monitored object, the frequency of the breathing, and the number of physical motions.
  • the sleep state includes: falling asleep and awakening; determining the sleep state of the monitored object according to the heart rate, the respiratory frequency, and the number of physical motions of the monitored object, specifically: determining Whether the monitored object has body motion within a predetermined time, and whether the heart rate and the change frequency of the respiratory frequency exceed a preset second amplitude threshold; if the monitored object does not have body motion within a predetermined time, and The change amplitude does not exceed the second amplitude threshold, and determines that the sleep state of the monitored object is falling asleep; if the monitored object occurs body motion within a predetermined time, determining that the monitored object occurs within the predetermined time Whether the number of body movements exceeds a preset number of times threshold or whether the time of body motion exceeds a preset duration; if the number of body movements of the monitored object that occurs during the predetermined time exceeds the threshold number or occurs The time of the body movement exceeds the preset duration, and it is determined that the sleep state of the monitored object is converted into an awakening.
  • the method further includes: issuing a corresponding alarm signal if the heart rate of the monitored object exceeds a preset heart rate threshold and/or the respiratory frequency exceeds a preset respiratory frequency threshold.
  • a physiological information monitoring pad including: a pad body, a plurality of micro-motion signal sensors, and a processor; and the plurality of micro-motion signal sensors are distributed.
  • the pad body for collecting a physiological signal; the plurality of micro-motion signal sensors are connected to the processor, and the plurality of micro-motion signal sensors transmit the plurality of the physiological signals to the processor;
  • the processor executes the physiological information monitoring method described above, and calculates the number of monitoring objects on the physiological information monitoring pad and the sleep parameters of each monitoring object.
  • the physiological information monitoring pad further includes: a signal amplifying circuit, a filter, and an analog-to-digital converting circuit;
  • the signal amplifying circuit is connected to the micro-motion signal sensor for amplifying the micro-motion signal sensor to collect a weak voltage signal, forming an amplified signal;
  • the filter being coupled to the signal amplifying circuit for filtering out interference of a 50 Hz power frequency and low frequency noise and high frequency noise in the amplified signal;
  • the analog to digital conversion circuit And connected to the filter, configured to convert an analog signal output by the filter into the digital physiological signal, and output the physiological signal to the processor.
  • the micro-motion signal sensor is distributed on the pad body in an array of a row by b column; wherein a and b are positive integers.
  • the set number of the micro signal sensors is greater than or equal to 6.
  • the micro signal sensor is one or more of a friction generator, a PVDF piezoelectric film material, an acceleration sensor, a fiber sensor or a gyro sensor.
  • the physiological information monitoring pad further includes a wireless communication module; the wireless communication module is communicatively coupled to the processor, and configured to output the monitored object quantity and physiological information of each monitored object.
  • another technical solution adopted by the embodiment of the present application is to provide a mattress including a mattress body and physiological information monitoring as described above; the physiological information monitoring pad is fixed on the mattress body s surface.
  • another technical solution adopted by the embodiments of the present application is to provide a non-transitory computer readable storage medium storing computer executable instructions, the computer executable The instructions are executed by one or more processors to enable the at least one processor to perform the physiological information monitoring method described above.
  • the physiological information monitoring method provided by the present application is suitable for monitoring the sleep state of a plurality of people, and realizing accurate judgment of each monitored object, so as to promote mutual understanding between the human body and the health state of the related monitoring object when falling asleep, and sleep health for all monitored objects. provide assurance.
  • 1a is a schematic structural diagram of a physiological information monitoring pad provided by an embodiment of the present application.
  • FIG. 1b is a schematic structural diagram of a physiological information monitoring pad according to another embodiment of the present application.
  • FIG. 2 is a circuit block diagram of a physiological information monitoring pad provided by an embodiment of the present application.
  • FIG. 3 is a schematic flow chart of a physiological information monitoring method provided by an embodiment of the present application.
  • FIG. 4 is a schematic flow chart of a physiological information monitoring method according to another embodiment of the present application.
  • FIG. 5 is a schematic structural diagram of a physiological information monitoring apparatus according to an embodiment of the present application.
  • FIG. 1a is a schematic structural diagram of a physiological information monitoring pad 100 according to an embodiment of the present application
  • FIG. 2 is a circuit block diagram of a physiological information monitoring pad provided by an embodiment of the present application.
  • the physiological information monitoring pad 100 includes a pad body 110, a plurality of micro-motion signal sensors 120, and a processor 130.
  • the pad body 110 can be placed on a mattress, a blanket, or in contact with human skin or embedded in a mattress.
  • the specific placement may be, for example, where a mattress or bed sheet is placed in contact with a fixing device, such as a Velcro, and the pad body 110 is fixed to the mattress, blanket or bed sheet by a fixing device to optimally monitor the position of the physiological signal (such as the location of the user's chest and abdomen).
  • the pad body 110 may be selected from a skin-friendly soft fabric.
  • the package thickness of the cushion body 110 may be less than 0.2 mm to reduce the foreign body sensation generated by the user sleeping on the cushion body 110.
  • a plurality of micro-motion signal sensors 120 are distributed on the pad body 110, for example, distributed on a plurality of locations such as a surface, an inner portion or a bottom portion of the pad body 110 for collecting physiological signals of the user.
  • a plurality of micro-motion signal sensors 120 may be equally spaced or unequally distributed on the pad body in any suitable array form, for example, the pad body is rectangular, and the plurality of micro-motion signal sensors 120 are in a matrix array.
  • the form is equally distributed on the pad body; for example, the pad body is circular, and a plurality of micro-motion signal sensors 120 are distributed on the pad body in a circular array.
  • micro-motion signal sensors 120 are equally spaced on the pad body 110 in the form of a matrix array.
  • FIG. 1b Several micro-motion signal sensors 120 are multiplied by a row of b columns, and the like. The spacing is laid on the rectangular pad body 110.
  • at least six micro-motion signal sensors 120 are disposed on each pad body 110, and each of the micro-motion signal sensors 120 can be spaced apart by a suitable distance, such as each micro-motion signal sensor 120.
  • the pitch is less than 20 cm to ensure that the detection range of the micro-motion signal sensor 120 can cover all areas of the pad body 10.
  • the micro-motion signal sensor 110 may specifically use a friction generator, a PVDF piezoelectric film material, an acceleration sensor, a fiber-optic sensor or a gyro sensor to acquire a physiological signal of the user.
  • the processor 130 is coupled to the plurality of micro-motion signal sensors 120 for receiving physiological signals output from the micro-motion signal sensor 120, the physiological signals being acquired by the micro-motion signal sensor 120 for fine movements of the human body (eg, chest undulation during breathing)
  • the physiological signal is a mixed waveform signal, which can separate biological waveform signals (such as respiratory signals, cardiac signals, body motion signals, etc.) related to the human body through a predetermined algorithm, the voltage waveform
  • the signal can reflect the physiological information of the human body when falling asleep.
  • different physiological information of the user can be determined. For example, according to the separated respiratory signal, the respiratory frequency of the human body can be determined, and according to the separated cardiac signal, The heart rate of the user is determined, and the number of body movements of the human body can be determined according to the separated body motion signals.
  • the processor 130 may perform a corresponding operation according to the different bio-waveform signals obtained separately, for example, the physiological information monitoring method in the following embodiments to monitor the sleep state of the user when going to sleep.
  • the processor 130 is a control core of the physiological information monitoring pad 100, which can be implemented using any suitable type of control chip in the prior art.
  • the physiological information monitoring pad 100 may further be provided with a circuit for receiving physiological signals and pre-processing the physiological signals, as shown in FIG. 2, physiologically.
  • the information monitoring pad 100 further includes a signal amplifying circuit 140, a filter 150, and an analog to digital converting circuit 160.
  • the signal amplifying circuit 140 is connected to the micro-motion signal sensor 120 for amplifying the weak voltage signal collected by the micro-motion signal sensor to form an amplified signal.
  • the filter 150 is coupled to the signal amplifying circuit 140 for filtering out 50 Hz power frequency and low frequency and high frequency noise interference in the amplified signal.
  • the analog to digital conversion circuit 160 is coupled to the filter 150 for converting the analog signal output by the filter into a digital physiological signal and outputting the digital physiological signal to the processor 130 for processing.
  • the processor 130 can perform logical operations on the input digital physiological signals or execute corresponding algorithms to implement corresponding functions.
  • the physiological information monitoring pad 100 can also include a wireless communication module 170.
  • the wireless communication module 170 is in communication with the processor 130, and can establish a corresponding communication channel with the outside world to transmit data or instructions.
  • the processor 130 may output the number of monitored objects calculated by the processor 130 and the physiological information of each monitored object to the corresponding terminal device (such as a smart phone, a server) through the wireless communication module 170.
  • the wireless communication module 170 can specifically employ any type of communication module, including a wifi, Bluetooth, infrared or near field communication module or a combination of multiple wireless communication modules.
  • the above circuit may be specifically carried by a Printed Circuit Board (PCB) or other similar structure (such as a chip), and establishes a communication connection with the processor through a wire or other wireless connection.
  • PCB Printed Circuit Board
  • the physiological information monitoring pad can be considered as a part of the mattress, and the embodiment of the present application further provides a mattress including the physiological information monitoring pad 100 and the mattress body as described above, considering The mattress body is used for the user to fall asleep.
  • the mattress body can be made of a skin-friendly soft fabric.
  • the physiological information monitoring pad is fixed on the surface of the mattress body, and when the user falls asleep, the physiological information monitoring pad contacts the user for realizing the corresponding function.
  • the physiological information monitoring pad may also be part of a bedding such as a blanket, a massage bed, an air bed, etc., and the physiological information monitoring pad may be fixed on the surface of the above-mentioned bedding product, and the physiological information monitoring pad is used when the user falls asleep. User contact for implementing the corresponding function.
  • FIG. 3 is a schematic flowchart of a physiological information monitoring method 300 according to an embodiment of the present application. The method may be performed by the processor 130 to calculate the number of monitored objects and physiological information of each monitored object. As shown in FIG. 3, the method includes:
  • Step 31 Receive the first to pth physiological signals, and detect the respiratory signals in each physiological signal.
  • the first to p-th physiological signals refer to the processor 130 receiving the physiological signals acquired by the p sensors. That is, p micro-motion signal sensors are disposed on any physiological information monitoring pad, and p micro-motion signal sensors can collect physiological signals of the human body, and transmit the collected physiological signals to the processor 130. Each physiological signal corresponds to the set position of the micro-motion signal sensor, and is determined by the relative positional relationship of the micro-motion signal sensor.
  • the first physiological signal may be a physiological signal collected by the first micro-motion signal sensor from the left
  • the second physiological signal is a physiological signal collected by the second micro-motion signal sensor from the left, and so on.
  • the processor 130 sequentially extracts the respiratory signals in the p-way physiological signals in a suitable manner, that is, separates the respiratory signals from the physiological signals collected by each of the micro-motion signal sensors.
  • the specific separation method may be a respiratory signal separation processing method commonly used in the prior art, which is well known to those skilled in the art and will not be described herein.
  • the detection of the respiratory signal may specifically include the following parameters: First, whether there is a respiratory waveform corresponding to the respiratory motion in the physiological signal is detected. Then, for the extracted respiratory waveform, the waveform frequency and the waveform amplitude of the respiratory waveform are calculated for characterizing the respiratory signal of the monitored object.
  • the signal collected by the micro-motion signal sensor does not contain the components of the human physiological signal, and accordingly, there is no breathing in the physiological signal of the path. Waveform.
  • Step 32 Determine whether the respiratory signals of the adjacent m-path physiological signals meet the preset determination condition in the physiological signals of the first to the p-th channels. If not, go to step 33, and if yes, go to step 34.
  • Step 34 Determine that the m-way adjacent physiological signal corresponds to a monitored object, and count the number of monitored objects.
  • the number of sensors that need to be contacted in the actual human body lying on the micro-motion signal sensor area is denoted as m.
  • the m value can be determined according to specific factors such as the average body size of the monitored human body, the size of the detection area of the sensor, and the like. For example, when the human body normally falls asleep, when the number of sensors that are generally in contact is four, the value of m can be four.
  • the physiological signal of each path corresponds to a micro-motion signal sensor. Therefore, the physiological signal collected by the m sensors adjacent to any of the p sensors is detected to obtain a respiratory signal, and when the adjacent m respiratory signals meet the preset determination condition, the The m-way physiological signal corresponds to a human body (ie, a monitoring object). Further, after determining that the m-way adjacent physiological signal corresponds to one monitoring object, the processor 130 may count the number of monitoring objects on the physiological information monitoring pad 100.
  • the preset judgment condition here refers to a judgment condition for judging whether there is a significant difference between different physiological signals. Through the preset judgment condition, it can be determined whether there is a significant difference between the different physiological signals, and if the different physiological signals have significant differences, it is considered that the same monitoring object does not belong to the same monitoring object, and the judgment is ended; Different physiological signals do not have significant differences, and it is determined that the human body in contact with the adjacent micro-motion signal sensors belongs to the same monitoring object.
  • the preset condition may be specifically determined according to actual conditions or the need for detection accuracy. In some embodiments, the preset determination condition may include the following two:
  • the respiratory waveform is present in the m-path physiological signal, and at least one physiological signal has a waveform amplitude greater than a preset first amplitude threshold in the m-path physiological signal.
  • the waveform frequency of each physiological signal is within a preset frequency range, and the waveform frequency cumulative deviation value is within a preset deviation range.
  • the "first amplitude threshold" in the condition 1 is a preset value as a criterion for judging whether the waveform signal belongs to a normal human respiratory motion, and the first amplitude threshold can be obtained by counting the respiratory waveform corresponding to the breathing motion of a normal person. .
  • the cumulative deviation value of the waveform frequency in the condition 2 is the fluctuation amplitude of the waveform frequency.
  • the statistic indicating the magnitude of the variation of the waveform frequency such as a variance or a standard deviation, can be used.
  • the threshold of the standard deviation of the waveform frequency can be determined by multiple trials or big data analysis. Then, the threshold value is used to determine whether the adjacent m-way physiological signal satisfies the judgment condition 2.
  • the judgment condition 1 is for clarifying that there is indeed a human body (ie, a monitoring object) that is breathing on the m micro-motion signal sensors; the judgment condition 2 is for specifying the m-motion signal sensors in the m On the same person is breathing (ie the same monitoring object). Therefore, by the above two determination conditions, it can be determined whether a certain physiological signal of the m path has a corresponding monitoring object. After all the p-way physiological signals are sequentially detected and judged, the number of corresponding monitoring objects can be calculated.
  • Step 35 Extract physiological information corresponding to the monitored object from the adjacent m physiological signals by using a preset signal processing algorithm.
  • the physiological signals of the adjacent m-path, the signal integration and the processing operation, etc. may be used to determine the physiological rate of the corresponding monitored subject, the respiratory rate and the number of physical movements. information.
  • the adjacent m-path physiological signals can be processed by a preset signal processing algorithm to obtain different physiological information.
  • the signal processing algorithm may specifically be any suitable algorithm capable of separating different physiological information. For example, it may be a direct filtering of the adjacent m-path physiological signals, separation of the cardiac signal, the respiratory signal and the body motion signal, or a signal separation method using blind source separation, independent component analysis (ICA), or the like. Separate the corresponding breathing, heartbeat, and body motion signals from the m-channel signal.
  • ICA independent component analysis
  • cardiac, respiratory, and body motion signals it is also possible to first separate the cardiac, respiratory, and body motion signals from the respective signals by using a wavelet decomposition method for each of the m physiological signals. Then, the cardiac signal, the respiratory signal and the body motion signal separated from the m-channel signal are superimposed to obtain the final cardiac signal, respiratory signal and body motion signal of the monitoring object.
  • Step 36 Record the number of monitored objects and the physiological information corresponding to each monitored object.
  • the processor 130 may record it in a specific memory, or may output it to the corresponding terminal device through the wireless communication module 170 described above.
  • the underlying data is provided to subsequent applications.
  • the record is merely used to indicate that the processor 130 obtains the data, and does not limit the processor 130 to perform a corresponding recording operation.
  • the processor 130 can also transmit the corresponding physiological data to the other device in real time through the wireless communication module to implement the detection function for the monitoring object.
  • Example 1 A respiratory signal is separated from a physiological signal, and the respiratory rate of the monitored subject is calculated based on the respiratory signal.
  • the target breathing signal of the voltage waveform collected by the micro-motion signal sensor is Fourier transformed. Then, the peak of the target respiratory signal after the Fourier transform exceeds the preset energy threshold, that is, the first few peaks with the largest energy are found.
  • the respiratory frequency corresponding to the peaks is calculated as the candidate respiratory frequency.
  • the most reasonable respiratory frequency is selected from the candidate respiratory frequencies as the respiratory frequency output of the monitored subject.
  • the historical data refers to the case of monitoring the respiratory frequency before the subject. It can be represented by a corresponding statistical item, for example by the median of the historical respiratory rate.
  • the most reasonable respiratory rate is the candidate respiratory rate that is closest to historical data or most consistent with historical data changes.
  • the undulation is more obvious, and the respiratory signal collected by the micro-motion signal sensor is stronger.
  • the respiratory signal period is long, the probability of multiple peaks and valleys in a single-cycle respiratory waveform is high. Therefore, the above method can well avoid the problem that the waveform method is easy to misdetect the peaks and valleys, resulting in inaccurate calculation of the respiratory rate.
  • Example 2 Separating the cardiac signal from the physiological signal, and determining the heart rate of the monitored object according to this:
  • the baseline drift of the target cardiac signal is removed to obtain a standard cardiac signal.
  • the time domain removal baseline drift method can be used to achieve filtering of low frequency signals.
  • other suitable algorithms can also be used to filter the effects of low frequency signals in the cardiac signal, such as wavelet decomposition, empirical mode decomposition (EMD), and the like.
  • the peak points and valley points of the standard cardiac signal are detected.
  • the peak point and the valley point are screened according to the dynamic threshold to obtain the target peak point and the target valley point.
  • the distance between the target peak point and the target valley point is determined as the heart rate of the monitored object.
  • the filtering operation of the low frequency signal can improve the quality of the cardiac signal and ensure the accuracy of the heart rate calculation.
  • Example 3 The body motion signal was separated from the physiological signal and the number of body motions was calculated.
  • the micro-motion signal sensor Since the micro-motion signal sensor is very sensitive to pressure and vibration signals, when the body motion occurs, the voltage waveform output by the micro-motion signal sensor will be abrupt or even saturated. Therefore, in the present embodiment, the sudden change and saturation of the voltage waveform are used as the body motion signal.
  • the threshold and the number of times the repetition is repeated may be set according to actual conditions. For example, it may be set to determine that body motion occurs when the waveform amplitude repeatedly exceeds the threshold value by three times.
  • body motion can also be determined when the voltage waveform is saturated (ie, remains high) and continues for a period of time.
  • the condition for performing the body motion counting is that the voltage waveform is in a saturated state for at least 3 s, and the interval at which the body motion occurs is at least 20 s.
  • FIG. 4 is a physiological information monitoring method 400 according to another embodiment of the present application. The explanation of each step in the foregoing embodiment is also applicable in the embodiment, and the method includes:
  • Step 41 Receive a first to pth physiological signal, and detect a respiratory signal in each physiological signal.
  • Step 42 Determine whether the respiratory signals of the adjacent m-path physiological signals meet the preset determination condition in the physiological signals of the first to the p-th channels. If not, go to step 33, and if yes, go to step 34.
  • Step 44 Determine that the m-way adjacent physiological signal corresponds to a monitored object, and count the number of monitored objects.
  • Step 45 Extract physiological information corresponding to the monitored object from the adjacent m physiological signals by using a preset signal processing algorithm.
  • Step 46 Record the number of monitored objects and the physiological information corresponding to each monitored object.
  • Step 47 Determine a sleep state of the monitored object according to the heart rate, the respiratory frequency, and the number of body movements of the monitored object.
  • the sleep state specifically refers to different states of the human body (ie, the monitoring object) on the mattress.
  • the sleep state changes, the corresponding heart rate, respiratory rate, and number of body movements of the monitored subject change accordingly.
  • the sleep state of the human body can be determined according to the monitored heart rate, respiratory rate, and number of body movements of the human body.
  • the sleep state in which the monitoring object is located may be determined by determining whether the monitoring object has body motion within a predetermined time and whether the heart rate and the change frequency of the respiratory frequency exceed a preset second amplitude threshold.
  • the "predetermined time” herein refers to a specific length of time, such as 1 min or 10 min, and the like.
  • the predetermined time can be determined by counting the time rule of the interval between the body movements when the normal human body falls asleep.
  • the "second amplitude threshold” here is a criterion for measuring the heart rate and the magnitude of the change of the respiratory signal, and the second amplitude threshold can be obtained by counting the range of the heart rate and the range of the respiratory signal change of the average person's sleep, or according to the user. Personalized adjustments to the actual situation.
  • the monitored object does not have body motion within a predetermined time, and the amplitude of the heart rate and the respiratory rate does not exceed the second amplitude threshold, it indicates that the activity of the monitored object is not active at this time, and the physiological information is in a very stable state. It can be considered that the monitoring object is already in a state of falling asleep.
  • the monitoring object has a body motion within a predetermined time, it is determined whether the number of body movements of the monitoring object within a predetermined time exceeds a preset number of times threshold or whether the time of body motion exceeds a preset duration.
  • the preset number of times threshold and the preset duration can be obtained by multiple trials or big data analysis. If the number of body movements of the monitoring object within a predetermined time exceeds the threshold of the number of times or the time when the body motion occurs exceeds the preset duration, the monitored object may be considered to be in an awake state. When the body of the monitoring object occurs within a predetermined time, the number of body movements is further determined, and the disturbance of the occasional body motion may occur in the monitored subject during sleep. At the same time, the above two body motion judgments are satisfied, that is, the monitoring object is in a relatively active state at this time, and the sleep state of the monitored object is considered to be awake.
  • the foregoing method may further include:
  • Step 48 When the heart rate of the monitoring object exceeds a preset heart rate threshold and/or the respiratory frequency exceeds a preset respiratory frequency threshold, a corresponding alarm signal is issued.
  • the calculation of the physiological information (such as heart rate, respiratory rate or number of body motions) of the monitoring object by the processor 130 is a continuous process. In this process, if the user has an unexpected situation, it will be reflected in the corresponding physiological information. At this time, the processor 130 can issue a corresponding alarm signal according to whether the physiological information exceeds a normal value, thereby ensuring the safety of the user.
  • the heart rate threshold and/or respiratory rate threshold is determined by the normal heart rate or respiratory threshold of the human body.
  • the alert signal can be implemented in any type, such as an audible alarm signal, a light alarm signal, or a combination thereof.
  • the processor 130 can also send the alarm signal to the corresponding terminal device through the wireless communication module, and the terminal device sends a corresponding light or sound alarm to prompt the user.
  • the processor 130 may also establish a connection with the server through the wireless communication module, and transmit the physiological information of the user to a hospital or other medical institution, so as to make a better evaluation of the physical state of the user, and perform targeted. Treatment.
  • the embodiment of the present application further provides a physiological information monitoring device.
  • the physiological information monitoring device 500 includes a receiving module 51, a detecting module 52, a first determining module 53, an extracting module 54, and a recording module 55.
  • the receiving module 51 is configured to receive the first to the p-th physiological signals; the detecting module 52 is configured to detect the respiratory signals in each of the physiological signals; and the first determining module 53 is configured to use the first to the p-th channels In the physiological signal, when the respiratory signal of the adjacent m-path physiological signal meets the preset determination condition, it is determined that the m-way adjacent physiological signal corresponds to a monitoring object; and the extraction module 54 is configured to adopt a preset signal processing algorithm. Extracting physiological information corresponding to the monitored object from the adjacent m-path physiological signals; wherein, p and m are positive integers, m ⁇ p; and a recording module 55 for recording the determined number of monitored objects and Physiological information corresponding to each of the monitored objects.
  • the physiological information monitoring device provided by the present application is suitable for monitoring the sleep state of a plurality of people, and realizing accurate judgment of each monitoring object, so as to promote mutual understanding between the human body and the health state of the related monitoring object when falling asleep, and sleep health for all monitoring objects. provide assurance.
  • the detecting module 52 specifically includes: a first determining unit and a detecting unit.
  • a first determining unit configured to determine, by using a preset breathing detection algorithm, whether there is a respiratory waveform corresponding to the breathing motion in the physiological signal; and a detecting unit, configured to: when the physiological signal has a respiratory waveform corresponding to the respiratory motion Next, the frequency of the respiratory waveform and the amplitude of the waveform are detected.
  • the predetermined determining condition is that the m-channel physiological signal has the respiratory waveform, and at least one physiological signal has a waveform amplitude greater than a preset number in the m-way physiological signal. a magnitude threshold; and,
  • the waveform frequency of each physiological signal is within a preset frequency range, and the waveform frequency cumulative deviation value of each physiological signal is within a preset deviation range.
  • the physiological information includes a heart rate, a respiratory rate, and a number of body motions
  • the extraction module includes: a separation unit, a first calculation unit, a second calculation unit, and a third calculation unit.
  • a separating unit configured to separately obtain a target cardiac signal, a target respiratory signal, and a target body motion signal from the adjacent m-path physiological signals by using a preset signal separation algorithm; and a first calculating unit, configured to perform, according to the target Calculating a heart rate of the monitoring object; a second calculating unit, configured to calculate a respiratory frequency of the monitoring object according to the target breathing signal; and a third calculating unit, configured to calculate the monitoring according to the target body motion signal The number of body movements of the object.
  • the physiological information monitoring device further includes a second determining module.
  • the second determining module is configured to determine a sleep state of the monitored object according to the heart rate, the respiratory frequency, and the number of physical motions of the monitored object.
  • the physiological information monitoring device further includes a reminder module.
  • the reminding module is configured to issue a corresponding alarm signal when the heart rate or the respiratory frequency of the monitored object exceeds a preset frequency threshold.
  • the embodiment of the present application further provides a non-volatile computer storage medium storing computer-executable instructions executed by one or more processors, such as one processor in FIG. 130.
  • the one or more processors may be configured to perform the physiological information monitoring method in any of the foregoing method embodiments, for example, to perform the steps shown in FIG. 3 and FIG. 4 described above; The function of each module.

Abstract

一种生理信息监测方法、装置及生理信息监测垫，其中生理信息监测方法包括：接收第1路至第p路生理信号；检测每一路生理信号中的呼吸信号；当第1路至第p路生理信号中，相邻m路生理信号的呼吸信号符合预设的判断条件的情况下，确定m路相邻生理信号与一个监测对象对应并统计监测对象的数量；通过预设的信号处理算法，从相邻m路生理信号中提取与监测对象对应的生理信息；记录监测对象的数量以及每个监测对象对应的生理信息。该生理信息监测方法适用于监测多个人的睡眠状况，实现对每个监测对象的准确判断，为所有监测对象睡眠健康提供保障。

Description

一种生理信息监测方法及生理信息监测垫、一种床垫 技术领域
本申请涉及睡眠监测技术领域，特别是涉及一种生理信息监测方法、及生理信息监测垫、一种床垫。
背景技术
随着社会和经济的发展，人们开始越来越多的关注个人健康问题。睡眠质量与个人健康息息相关，因此也引起了人们的高度重视。家用便携式睡眠检测设备通过检测使用者睡眠过程中的运动，呼吸与心跳建立睡眠检测算法，分析使用者的睡眠质量，这类睡眠检测设备对评估使用者的工作压力、疲劳度和精神状况等具有广阔的市场前景。
申请人在实现本申请的过程中发现：一方面，现有睡眠监测设备多为可穿戴式设备，容易对使用者造成束缚，影响正常睡眠质量，并且，睡眠监测设中的传感器在检测过程中也存在脱落风险；另一方面，现有睡眠监测设备仅限于检测单人睡眠状态，而在应用于多人检测时，该检测结果不准确。
发明内容
本申请实施方式旨在解决现有生理信息监测不适合检测两人甚至多人睡眠状况的技术问题。
为解决上述技术问题，本申请实施方式采用的一个技术方案是：提供一种生理信息监测方法，包括：接收第1路至第p路生理信号；检测每一路生理信号中的呼吸信号；在所述第1路至第p路生理信号中，相邻m路生理信号的呼吸信号符合预设的判断条件的情况下，确定所述m路相邻生理信号与一个监测对象对应并统计所述监测对象的数量；通过预设的信号处理算法，从所述相邻m路生理信号中提取与所述监测对象 对应的生理信息；其中，p和m均为正整数，m＜p；记录所述监测对象的数量以及每个所述监测对象对应的生理信息。
可选地，所述检测每一路生理信号中的呼吸信号，具体包括：通过预设呼吸检测算法，判断所述生理信号中是否存在呼吸动作对应的呼吸波形；在所述生理信号存在所述呼吸动作对应的呼吸波形的情况下，检测所述呼吸波形的频率以及波形幅度。
可选地，所述预设的判断条件为：在所述m路生理信号中均存在所述呼吸波形，并且在所述m路生理信号中至少存在一路生理信号的波形幅度大于预设的第一幅度阈值；以及，在所述m路生理信号中，每一路生理信号的波形频率均在预设的频率范围内，并且，所述每一路生理信号的波形频率累计偏差值在预设的偏差范围内。
可选地，所述生理信息包括心率、呼吸频率和体动次数；所述通过预设的信号处理算法，从所述相邻m路生理信号中提取与所述监测对象对应的生理信息，具体包括：从所述相邻m路生理信号中，通过预设的信号分离算法，分离获得目标心动信号、目标呼吸信号以及目标体动信号；根据所述目标心动信号计算所述监测对象的心率，根据所述目标呼吸信号计算所述监测对象的呼吸频率并根据所述目标体动信号计算所述监测对象的体动次数。
可选地，所述根据所述目标心动信号计算所述监测对象的心率，具体包括：去除所述目标心动信号的基线漂移，获得标准心动信号；检测所述标准心动信号的波峰点和波谷点；根据动态阈值筛选所述波峰点和所述波谷点，获得目标波峰点和目标波谷点；确定所述目标波峰点和所述目标波谷点的间距为所述监测对象的心率。
可选地，所述根据所述目标呼吸信号计算所述监测对象的呼吸频率，具体包括：对所述目标呼吸信号进行傅里叶变换；确定傅里叶变换后的所述目标呼吸信号中超过预设的能量阈值的谱峰；计算所述谱峰对应的呼吸频率，作为候选呼吸频率；结合所述监测对象的历史数据，从所述候选呼吸频率中确定所述监测对象的呼吸频率。
可选地，所述方法还包括：根据所述监测对象的所述心率、所述呼 吸频率以及所述体动次数，确定所述监测对象的睡眠状态。
可选地，所述睡眠状态包括：入睡以及觉醒；所述根据所述监测对象的所述心率、所述呼吸频率以及所述体动次数，确定所述监测对象的睡眠状态，具体包括：判断所述监测对象在预定时间内是否发生体动，并且所述心率和所述呼吸频率的变化幅度是否超出预设的第二幅度阈值；若所述监测对象在预定时间内未发生体动，且所述变化幅度没有超出所述第二幅度阈值,确定所述监测对象的睡眠状态为入睡；若所述监测对象在预定时间内发生体动，判断所述监测对象在所述预定时间内发生的体动次数是否超出预设的次数阈值或者发生体动的时间是否超出预设的持续时间；若所述监测对象在所述预定时间内发生体动的的体动次数超过所述次数阈值或者发生体动的时间超出预设的持续时间，确定所述监测对象的睡眠状态转换为觉醒。
可选地，所述方法还包括：在所述监测对象的所述心率超出预设的心率阈值和/或所述呼吸频率超出预设的呼吸频率阈值的情况下，发出对应的警报信号。
为解决上述技术问题，本申请实施方式采用的另一个技术方案是：提供一种生理信息监测垫，包括：垫体、若干个微动信号传感器以及处理器；所述若干个微动信号传感器分布于所述垫体上，用于采集生理信号；所述若干个微动信号传感器与所述处理器连接，所述若干个微动信号传感器向所述处理器传输若干路所述生理信号；所述处理器执行上述的生理信息监测方法，计算所述生理信息监测垫上的监测对象数量及每个监测对象的睡眠参数。
可选地，所述的生理信息监测垫还包括:信号放大电路、滤波器以及模数转换电路；所述信号放大电路与所述微动信号传感器连接，用于放大所述微动信号传感器采集的弱电压信号，形成放大信号；所述滤波器与所述信号放大电路连接，用于滤除所述放大信号中的50Hz工频和低频噪声与高频噪声的干扰；所述模数转换电路与所述滤波器连接，用于将所述滤波器输出的模拟信号转换为数字的所述生理信号，并将所述生理信号输出至所述处理器。
可选地，所述微动信号传感器以a行乘b列的阵列形式分布于所述垫体上；其中，a、b均为正整数。
可选地，所述微动信号传感器的设置数量大于或等于6。
可选地，所述微动信号传感器为摩擦发电机，PVDF压电薄膜材料，加速度传感器，光纤传感器或陀螺仪传感器中的一种或者多种。
可选地，所述的生理信息监测垫还包括无线通信模块；所述无线通信模块与所述处理器通信连接，用于输出所述监测对象数量及每个监测对象的生理信息。
为解决上述技术问题，本申请实施方式采用的另一个技术方案是：提供一种床垫，包括床垫本体以及如上所述的生理信息监测；所述生理信息监测垫固定在所述床垫本体的表面。
为解决上述技术问题，本申请实施方式采用的另一个技术方案是：提供一种非易失性计算机可读存储介质，所述计算机可读存储介质存储有计算机可执行指令，所述计算机可执行指令被一个或多个处理器执行，以使所述至少一个处理器能够执行上述生理信息监测方法。
本申请提供的生理信息监测方法适用于监测多个人的睡眠状况，实现对每个监测对象的准确判断，以促进人体在入睡时，与相关监测对象健康状态的相互了解，为所有监测对象睡眠健康提供保障。
附图说明
图1a是本申请实施例提供的一种生理信息监测垫的结构示意图；
图1b是本申请另一实施例提供的一种生理信息监测垫的结构示意图；
图2是本申请实施例提供的一种生理信息监测垫的电路框图；
图3是本申请实施例提供的一种生理信息监测方法的流程示意图；
图4是本申请另一实施例提供的一种生理信息监测方法的流程示意图；
图5是本申请实施例提供的一种生理信息监测装置的结构示意图。
具体实施方式
为了使本申请的目的、方案及优点更加清楚明白，以下结合实施例，对本申请进行进一步详细说明。应当理解，此处所描述的具体实施例仅用以解释本申请，并不用于限定本申请。此外，下面所描述的本申请不同实施方式中所涉及的技术特征只要彼此之间未构成冲突就可以相互结合。
请一并参阅图1a和图2,图1a是本申请实施例提供的一种生理信息监测垫100的结构示意图,图2是本申请实施例提供的一种生理信息监测垫的电路框图。生理信息监测垫100包括:垫体110、若干个微动信号传感器120以及处理器130。
垫体110可以放置于床垫、毯子上与人体皮肤接触或者嵌入床垫中使用。具体的放置方式可以是，例如，在床垫或床单接触的地方设置有固定装置，例如魔术贴，将垫体110通过固定装置固定在床垫、毯子或床单上最佳监测生理信号的位置(如用户胸腹部所在的位置)。其中，考虑到垫体110可能会与人体皮肤接触，在本实施例中垫体110可以选择亲肤柔软面料制得。同时，考虑到垫体110的透气性和防潮性，垫体110的封装厚度可以小于0.2mm，以减少用户睡在垫体110上产生的异物感。
若干个微动信号传感器120分布于垫体110上，例如分布于所述垫体110的表面、内部或底部等多个部位，用于采集用户的生理信号。在本实施例中，若干个微动信号传感器120可以以任何合适的阵列形式等间隔或者不等间隔分布于垫体上，例如，垫体为矩形，则若干个微动信号传感器120以矩阵阵列的形式等分布于垫体上；又例如，垫体为圆形，则若干个微动信号传感器120以圆形阵列的方式分布于垫体上。
下面以若干个微动信号传感器120以矩阵阵列形式等间隔分布于垫体110上为例进行说明，具体请参阅图1b，若干个微动信号传感器120以a行乘以b列的阵列形式等间距铺设在长方形垫体110上，优选地，每一垫体110上至少设置6个微动信号传感器120，每一微动信号传感器120可以间隔合适的距离，如每一微动信号传感器120的间距小于20cm，以保证微动信号传感器120的检测范围能覆盖到垫体10的所有 区域。
微动信号传感器110具体可以选用摩擦发电机，PVDF压电薄膜材料，加速度传感器，光纤传感器或陀螺仪传感器来实现采集用户生理信号。
处理器130与若干个微动信号传感器120连接，用于接收从微动信号传感器120输出的生理信号，该生理信号为微动信号传感器120采集获得的人体细微动作(例如呼吸时的胸部起伏动作)对应的电压波形信号，该生理信号是一个混合的波形信号，其可以通过预定的算法，分离出与人体相关的生物波形信号(例如呼吸信号、心动信号和体动信号等)，该电压波形信号可以反映人体的在入睡时的生理信息，根据分离出来的不同波形信号，可以确定用户的不同生理信息，例如，根据分离出来的呼吸信号可以确定人体的呼吸频率、根据分离出来的心动信号可以确定用户的心率，根据分离出来的体动信号可以确定人体的体动次数。
处理器130可以根据分离获得的不同的生物波形信号，执行相应的操作，例如执行下述实施例中的生理信息监测方法，以监测用户在入睡时的睡眠状态。处理器130为生理信息监测垫100的控制核心，其可以采用现有技术中任何合适类型的控制芯片来实现。
在一些实施例中，为了提高检测的准确性，排除不必要的干扰，生理信息监测垫100上还可以设置有接收生理信号，并且对生理信号进行预处理的电路，如图2所示，生理信息监测垫100还包括：信号放大电路140、滤波器150以及模数转换电路160。
其中，信号放大电路140与微动信号传感器120连接，用于放大微动信号传感器采集的弱电压信号，形成放大信号。滤波器150与信号放大电路140连接，用于滤除放大信号中的50Hz工频和低频与高频的噪声干扰。模数转换电路160与滤波器150连接，用于将滤波器输出的模拟信号转换为数字的生理信号，并将该数字的生理信号输出至处理器130中进行处理。处理器130可对输入的数字的生理信号进行逻辑运算或者执行相应的算法，实现相应的功能。
在一些实施例中，该生理信息监测垫100还可以包括无线通信模块 170。该无线通信模块170与处理器130通信连接，可以与外界建立相应的通信信道，传输数据或者指令。例如，处理器130可以通过该无线通信模块170向相应的终端设备(如智能手机、服务器)输出处理器130计算获得的监测对象数量以及每个监测对象的生理信息。该无线通信模块170具体可以采用任何类型的通信模块，包括wifi、蓝牙、红外或者近场通信模块或者多个无线通信模块的组合。
上述电路具体可以由印刷电路板(Printed Circuit Board,PCB)或者其他类似的结构(如芯片)承载，通过导线或者其他无线连接方式与处理器建立通信连接。
在一些实施例中，可认为生理信息监测垫为床垫的一部分，则本申请实施例还提供一种床垫，该床垫包括如上所述的生理信息监测垫100以及床垫本体，考虑到床垫本体用于用户入睡，在本实施例中床垫本体可以选择亲肤柔软面料制得。该生理信息监测垫固定在床垫本体的表面，在用户入睡时，生理信息监测垫与用户接触，用于实现相应功能。
在其他实施例中，生理信息监测垫还可以为毯子、按摩床、气垫床等床上用品的一部分，该生理信息监测垫可以固定在上述床上用品的表面，在用户入睡时，生理信息监测垫与用户接触，用于实现相应功能。
图3是本申请实施例提供的一种生理信息监测方法300的流程示意图，该方法可由上述处理器130执行，用以计算监测对象的数量以及每个监测对象的生理信息。如图3所示，该方法包括：
步骤31、接收第1路至第p路生理信号，检测每一路生理信号中的呼吸信号。
本实施例中，第1至第p路生理信号是指处理器130接收到来自p个传感器采集获得的生理信号。即，在任一生理信息监测垫上设置有p个微动信号传感器，p个微动信号传感器均可以采集人体的生理信号，并且将采集到的生理信号传输至处理器130。每一路生理信号与微动信号传感器的设置位置对应，由微动信号传感器的相对位置关系所决定。例如，第1路生理信号可以是来自左起第一个微动信号传感器采集的生理信号，第2路生理信号则为左起第二个微动信号传感器采集的生理信 号，依次类推。
处理器130按照合适的方式，依次提取该p路生理信号中的呼吸信号，即从每一微动信号传感器采集到的生理信号分离出呼吸信号。具体的分离方式可以采用现有技术常用的呼吸信号分离处理方法，其为本领域技术人员所熟知，在此不作赘述。
具体的，对呼吸信号的检测具体可以包括如下几个参数：首先，对生理信号中是否存在与呼吸动作对应的呼吸波形进行检测。然后，针对提取出的呼吸波形，计算呼吸波形的波形频率和波形幅度，用于表征监测对象的呼吸信号。
当然，若其中的一个或者多个微动信号传感器并未与人接触时，则微动信号传感器采集到的信号中不包含人体生理信号的成分，相应的则认为该路生理信号中不存在呼吸波形。
步骤32、判断第1路至第p路生理信号中，相邻m路生理信号的呼吸信号是否符合预设的判断条件，若否，执行步骤33，若是，执行步骤34。
步骤33、结束。
步骤34、确定m路相邻生理信号与一个监测对象对应,并统计监测对象的数量。
在本实施例中，将在实际人体躺在微动信号传感器区域上，所需要接触的传感器的个数，记为m。该m值可以根据不同监测人体的平均体型，传感器的检测区域大小等具体因素所确定。例如，当人体正常入睡时，一般接触到的传感器的个数为4个时，则可将m取值为4。
由于每一路的生理信号与一个微动信号传感器相对应。因此，相当于将该p个传感器中的任一相邻的m个传感器采集到的生理信号进行检测获取到呼吸信号，当相邻的m个呼吸信号符合预设的判断条件时，可认为该m路生理信号与一个人体(即监测对象)对应,进一步地,在确定m路相邻生理信号与一个监测对象对应之后,处理器130可以统计生理信息监测垫100上监测对象的数量。
此处的预设判断条件指判断不同的生理信号之间是否存在显著差别 的判断条件。通过该预设的判断条件可以确定多路不同的生理信号之间是否具有显著差别，若多路不同的生理信号具有显著的区别，则认为不属于同一个监测对象，则结束判断；若多路不同的生理信号不具有显著的区别，则确定相邻的微动信号传感器接触的人体属于同一个监测对象。该预设条件具体可以根据实际情况或者检测精度的需要所确定。在一些实施例中，该预设的判断条件可以包括如下两个：
1、m路生理信号中均存在呼吸波形，并且在m路生理信号中至少存在一路生理信号的波形幅度大于预设的第一幅度阈值。
2、m路生理信号中，每一路生理信号的波形频率均在预设的频率范围内，并且波形频率累计偏差值在预设的偏差范围内。
条件1中的“第一幅度阈值”为一个预设的数值，作为判断波形信号是否属于正常人体呼吸动作的标准，该第一幅度阈值可以通过统计正常人的呼吸动作对应的呼吸波形后得出。
条件2中的波形频率的累计偏差值即为波形频率的波动幅度,在本实施例中,可以采用方差或者标准差等用于表示波形频率的变化幅度大小的统计量进行衡量。例如，可以通过多次试验或者大数据分析，确定波形频率的标准差的阈值。然后通过该阈值来判断相邻的m路生理信号是否满足判断条件2。
在本实施例中，判断条件1是用于明确在这m个微动信号传感器上，确实存在正在呼吸的人体(即监测对象)；判断条件2是用于明确在这m个微动信号传感器上，是相同的人在呼吸(即同一监测对象)。由此，通过上述两个判断条件，即可确定某m路生理信号是否存在对应的监测对象。在对全部的p路生理信号依次进行检测判断以后，便可以计算出相应的监测对象的数量。
步骤35、通过预设的信号处理算法，从相邻m路生理信号中提取与监测对象对应的生理信息。
在确定m路相邻生理信号与一个监测对象对应时，可以根据该相邻m路生理信号，经过信号的整合和处理操作等，确定对应的监测对象的心率、呼吸频率和体动次数等生理信息。在实际计算过程中，可以对相 邻m路生理信号通过预设的信号处理算法进行处理，以获得不同的生理信息。
该信号处理算法具体可以是任何合适的，能够分离出不同生理信息的算法。例如，可以是对相邻m路生理信号直接进行滤波，分离得出的心动信号、呼吸信号和体动信号，或者是采用盲源分离、独立分量分析(independent component analysis，ICA)等信号分离方法，从m路信号中分离出相应的呼吸，心动，体动信号。
在另一些实施例中，还可以首先对m路生理信号中的每一路生理信号采用小波分解方法从各路信号中分离出心动、呼吸和体动信号。然后，再将从m路信号分离出的心动信号、呼吸信号和体动信号进行叠加得到监测对象最终的心动信号，呼吸信号和体动信号。
步骤36、记录监测对象的数量以及每个监测对象对应的生理信息。
在处理器130统计监测对象的数量以及各个监测对象的生理信息以后，处理器130可以将其记录在特定的存储器中，也可以通过上述的无线通信模块170，输出到相应的终端设备中，作为基础数据，提供给后续应用。
应当说明的是，该记录只是用于表示处理器130获得这些数据的行为，并不限定处理器130需要执行对应的记录操作。处理器130也可以在计算获得相应的生理数据以后，通过无线通信模块实时传输到其他的设备中，实现对于监测对象的检测功能。
以下结合具体实例，详细描述对心动信号、呼吸信号以及体动信号的分离计算过程。
实施例1：从生理信号中分离出呼吸信号，并根据呼吸信号计算监测对象的呼吸频率。
首先，对微动信号传感器采集到的电压波形的目标呼吸信号进行傅里叶变换。然后，确定傅里叶变换后的目标呼吸信号中超过预设的能量阈值的谱峰，即找到能量最大的前几个谱峰。
在找到以后这些能量较大的谱峰以后，计算所述谱峰对应的呼吸频率，作为候选呼吸频率。最后，结合监测对象的历史数据，从所述候 选呼吸频率中选定最合理的呼吸频率作为监测对象的呼吸频率输出。
在本实施例中，该历史数据是指监测对象之前的呼吸频率的情况。其可以通过相应的统计学项目所表示，例如可以通过历史呼吸频率的中位数所表示。该最合理的呼吸频率是指与历史数据最为接近，或者最为符合历史数据变化情况的候选呼吸频率。
由于人体呼吸伴随着胸廓的起伏，该起伏较为明显，微动信号传感器采集的呼吸信号较强。考虑到呼吸信号周期长，单周期呼吸波形中出现多个峰谷的概率较高。因此，采用上述方式可以很好的避免波形法检测时容易误检峰谷，导致呼吸率计算不准确的问题。
实施例2：从生理信号中分离出心动信号，并据此确定监测对象心率：
首先，去除目标心动信号的基线漂移，获得标准心动信号。具体可以使用时域去除基线漂移法来实现对于低频信号的过滤。当然，也可以使用其他合适的算法实现过滤心动信号中的低频信号的作用，例如小波分解法，经验模态分解法(EMD)等。
然后，检测标准心动信号的波峰点和波谷点。根据动态阈值筛选波峰点和波谷点，获得目标波峰点和目标波谷点。最后，确定目标波峰点和目标波谷点的间距为监测对象的心率。
由于微动信号传感器采集的人体心动信号较弱，又很容易受呼吸信号干扰，提取出来较为困难。其中，电压波形信号中的低频信号对于心动信号影响较为显著。因此，本实施例中，首先对低频信号的滤除操作能够很好的提高心动信号的质量，确保心率计算的准确性。
实施例3：从生理信号中分离出体动信号并计算体动次数。
由于微动信号传感器对压力与振动信号非常敏感，当体动发生时，微动信号传感器输出的电压波形会发生突变甚至饱和。因此，在本实施例中，根据电压波形的突变和饱和作为体动信号。
体动是否发生和体动次数的统计方式具体如下：
首先，当电压波形的波形幅度反复几次超过阈值时，可以确定发生了体动。该阈值和反复超过的次数可以根据实际情况设置，例如可以设 置为当波形幅度反复超过3次阈值时，确定发生体动。其次，在电压波形处于饱和(即持续保持在高位)，并且持续在一段时间内，也可以确定发生体动。
在本实施例中，进行一次体动计数的条件为：电压波形处于饱和状态至少持续3s，并且发生体动的间隔时间至少为20s。由此，通过对体动信号的持续监测，即可确定在一段时间内，监测对象发生体动的次数和体动的频率。
在一些实施中，基于处理器130计算获得的监测对象的心率、呼吸频率以及体动次数这些生理信息，通过相应的逻辑运算以后，还可以进一步的确定监测对象的睡眠状态。具体请参阅图4，图4是本申请另一实施例提供的一种生理信息监测方法400，上述实施例中对各步骤的解释在本实施例中同样适用，该方法包括：
步骤41、接收第1路至第p路生理信号，检测每一路生理信号中的呼吸信号。
步骤42、判断第1路至第p路生理信号中，相邻m路生理信号的呼吸信号是否符合预设的判断条件，若否，执行步骤33，若是，执行步骤34。
步骤43、结束。
步骤44、确定m路相邻生理信号与一个监测对象对应,并统计监测对象的数量。
步骤45、通过预设的信号处理算法，从相邻m路生理信号中提取与监测对象对应的生理信息。
步骤46、记录监测对象的数量以及每个监测对象对应的生理信息。
步骤47、根据监测对象的心率、呼吸频率以及体动次数，确定监测对象的睡眠状态。
一般的，当人体躺卧在床垫上时，会存在从清醒到入睡以及从入睡到清醒的状态变化，该睡眠状态具体是指人体(即监测对象)在床垫上的不同状态。在睡眠状态发生改变时，监测对象相应的心率、呼吸频率以及体动次数均发生相应的改变。例如，人体在觉醒时，会发生翻身， 摆手等动作，则人体的心率和呼吸频率相应增大，人体的体动次数也相应增加。因此，根据监测到的人体的心率、呼吸频率以及体动次数可以确定人体的睡眠状态。具体的，可以通过判断监测对象在预定时间内是否发生体动，并且心率和呼吸频率的变化幅度是否超出预设的第二幅度阈值来确定监测对象所处的睡眠状态。
此处的“预定时间”是指特定长度的时间，例如1min或者10min等。正常人体在睡眠状态时，活动频率较低，通常不会频繁的动作。因此，通过统计正常人体在入睡时，体动之间间隔的时间规律，可以确定该预定时间。此处的“第二幅度阈值”是用于衡量心率和呼吸信号变化幅度的判断标准，第二幅度阈值可以通过统计一般人的睡眠时的心率和呼吸信号变化幅度的范围得出，也可以根据用户的实际情况进行个性化的调整。
一方面，若监测对象在预定时间内未发生体动，且心率和呼吸频率的变化幅度没有超出第二幅度阈值时,则表明此时监测对象的活动不活跃，生理信息处于非常平稳的状态，可以认为监测对象已经处于入睡状态。
另一方面，若监测对象在预定时间内发生体动，则判断监测对象在预定时间内发生的体动次数是否超出预设的次数阈值或者发生体动的时间是否超出预设的持续时间。该预设的次数阈值和预设的持续时间可以多次试验或者大数据分析得到。若监测对象在预定时间内发生的体动次数超过次数阈值或者发生体动的时间超出预设的持续时间，则可以认为监测对象的处于觉醒状态。在监测对象在预定时间内发生体动时，进一步判断体动次数，可以排除监测对象在睡眠时可能会出现偶尔体动的干扰。同时满足上述两次体动判断，即表明此时监测对象处于一个较为活跃的状态，可认为监测对象的睡眠状态为清醒。
在一些实施例中，为了扩展生理信息监测垫的功能，提高其智能化程度。如图4所示，上述方法还可以包括：
步骤48、在监测对象的心率超出预设的心率阈值和/或呼吸频率超出预设的呼吸频率阈值的情况下，发出对应的警报信号。
在确定了m路生理信息对应的监测对象以后，处理器130对于监测对象的生理信息(如心率、呼吸频率或者体动次数)的计算是一个持续的过程。在这个过程中，若用户出现突发状况，会反映在相应的生理信息中。此时，处理器130可以根据生理信息是否超出了正常值来发出相应的警报信号，确保用户的安全。该心率阈值和/或呼吸频率阈值由人体正常的心率或者呼吸阈值所决定。当然，还可以根据实际情况，针对使用者进行个性化的调整。例如，根据用户的历史数据进行自适应调整，避免错误报警。
在一些实施例中，该警报信号具体可以采用任何类型实现，例如声音报警信号、灯光报警信号或者其组合。处理器130还可以通过无线通信模块，向相应的终端设备发送该警报信号，由终端设备发出相应的灯光或者声音报警，以提示用户。
在另一些实施例中，处理器130还可以通过无线通信模块，与服务器建立连接，向医院或者其他医疗机构等传输用户的生理信息，以便于更好的用户的身体状态作出评估，进行针对性的治疗。
本申请实施例还提供一种生理信息监测装置，如图5所示，该生理信息监测装置500包括：接收模块51、检测模块52、第一确定模块53、提取模块54和记录模块55。
接收模块51用于接收第1路至第p路生理信号；检测模块52，用于检测每一路生理信号中的呼吸信号；第一确定模块53，用于当所述第1路至第p路生理信号中，相邻m路生理信号的呼吸信号符合预设的判断条件时，确定所述m路相邻生理信号与一个监测对象对应；提取模块54，用于通过预设的信号处理算法，从所述相邻m路生理信号中提取与所述监测对象对应的生理信息；其中，p和m均为正整数，m＜p；记录模块55，用于记录所确定的监测对象的数量以及每个所述监测对象对应的生理信息。
本申请提供的生理信息监测装置适用于监测多个人的睡眠状况，实现对每个监测对象的准确判断，以促进人体在入睡时，与相关监测对象健康状态的相互了解，为所有监测对象睡眠健康提供保障。
在一些实施例中，检测模块52具体包括：第一判断单元和检测单元。第一判断单元，用于通过预设呼吸检测算法，判断所述生理信号中是否存在呼吸动作对应的呼吸波形；检测单元，用于在所述生理信号存在所述呼吸动作对应的呼吸波形的情况下，检测所述呼吸波形的频率以及波形幅度。
在一些实施例中，所述预设的判断条件为：所述m路生理信号均存在所述呼吸波形，并且在所述m路生理信号中至少存在一路生理信号的波形幅度大于预设的第一幅度阈值；以及，
所述m路生理信号中，每一路生理信号的波形频率均在预设的频率范围内，并且，所述每一路生理信号的波形频率累计偏差值在预设的偏差范围内。
在一些实施例中，所述生理信息包括心率、呼吸频率和体动次数；所述提取模块包括：分离单元、第一计算单元、第二计算单元和第三计算单元。分离单元，用于从所述相邻m路生理信号中，通过预设的信号分离算法，分离获得目标心动信号、目标呼吸信号以及目标体动信号；第一计算单元，用于根据所述目标心动信号计算所述监测对象的心率；第二计算单元，用于根据所述目标呼吸信号计算所述监测对象的呼吸频率；第三计算单元，用于根据所述目标体动信号计算所述监测对象的体动次数。
在一些实施例中，所述生理信息监测装置还包括第二确定模块。所述第二确定模块，用于根据所述监测对象的所述心率、所述呼吸频率以及所述体动次数，确定所述监测对象的睡眠状态。
在一些实施例中，所述生理信息监测装置还包括提醒模块。所述提醒模块，用于在所述监测对象的所述心率或所述呼吸频率超出预设的频率阈值时，发出对应的警报信号。
本申请实施例还提供一种非易失性计算机存储介质，所述计算机存储介质存储有计算机可执行指令，该计算机可执行指令被一个或多个处理器执行，例如图2中的一个处理器130，可使得上述一个或多个处理器可执行上述任意方法实施例中的生理信息监测方法，例如，执行以上 描述的图3和图4所示的各个步骤；也可实现图5所述的各个模块的功能。
以上所述仅为本申请的实施方式，并非因此限制本申请的专利范围，凡是利用本申请说明书及附图内容所作的等效结构或等效流程变换，或直接或间接运用在其他相关的技术领域，均同理包括在本申请的专利保护范围内。

Claims (16)

  1. 一种生理信息监测方法，其特征在于，包括：
    接收第1路至第p路生理信号；
    检测每一路生理信号中的呼吸信号；
    在所述第1路至第p路生理信号中，相邻m路生理信号的呼吸信号符合预设的判断条件的情况下，确定所述m路相邻生理信号与一个监测对象对应并统计所述监测对象的数量；
    通过预设的信号处理算法，从所述相邻m路生理信号中提取与所述监测对象对应的生理信息；其中，p和m均为正整数，m＜p；
    记录所述监测对象的数量以及每个所述监测对象对应的生理信息。
  2. 根据权利要求1所述的方法，其特征在于，所述检测每一路生理信号中的呼吸信号，具体包括：
    通过预设呼吸检测算法，判断所述生理信号中是否存在呼吸动作对应的呼吸波形；
    在所述生理信号存在所述呼吸动作对应的呼吸波形的情况下，检测所述呼吸波形的频率以及波形幅度。
  3. 根据权利要求2所述的方法，其特征在于，所述预设的判断条件为：
    所述m路生理信号中均存在所述呼吸波形，并且在所述m路生理信号中至少存在一路生理信号的波形幅度大于预设的第一幅度阈值；以及，
    所述m路生理信号中，每一路生理信号的波形频率均在预设的频率范围内，并且，所述每一路生理信号的波形频率累计偏差值在预设的偏差范围内。
  4. 根据权利要求1所述的方法，其特征在于，所述生理信息包括心率、呼吸频率和体动次数；
    所述通过预设的信号处理算法，从所述相邻m路生理信号中提取与所述监测对象对应的生理信息，具体包括：
    从所述相邻m路生理信号中，通过预设的信号分离算法，分离获得目标心动信号、目标呼吸信号以及目标体动信号；
    根据所述目标心动信号计算所述监测对象的心率，根据所述目标呼吸信号计算所述监测对象的呼吸频率并根据所述目标体动信号计算所述监测对象的体动次数。
  5. 根据权利要求4所述的方法，其特征在于，所述根据所述目标心动信号计算所述监测对象的心率，具体包括：
    去除所述目标心动信号的基线漂移，获得标准心动信号；
    检测所述标准心动信号的波峰点和波谷点；
    根据动态阈值筛选所述波峰点和所述波谷点，获得目标波峰点和目标波谷点；
    确定所述目标波峰点和所述目标波谷点的间距为所述监测对象的心率。
  6. 根据权利要求4所述的方法，其特征在于，所述根据所述目标呼吸信号计算所述监测对象的呼吸频率，具体包括：
    对所述目标呼吸信号进行傅里叶变换；
    确定傅里叶变换后的所述目标呼吸信号中超过预设的能量阈值的谱峰；
    计算所述谱峰对应的呼吸频率作为候选呼吸频率；
    结合所述监测对象的历史数据，从所述候选呼吸频率中确定所述监测对象的呼吸频率。
  7. 根据权利要求6所述的方法，其特征在于，所述方法还包括：
    根据所述监测对象的所述心率、所述呼吸频率以及所述体动次数，确定所述监测对象的睡眠状态。
  8. 根据权利要求7所述的方法，其特征在于，所述睡眠状态包括：入睡以及觉醒；
    所述根据所述监测对象的所述心率、所述呼吸频率以及所述体动次数，确定所述监测对象的睡眠状态，具体包括：
    判断所述监测对象在预定时间内是否发生体动，并且所述心率和所 述呼吸频率的变化幅度是否超出预设的第二幅度阈值；
    若所述监测对象在预定时间内未发生体动，且所述变化幅度没有超出所述第二幅度阈值,确定所述监测对象的睡眠状态为入睡；
    若所述监测对象在预定时间内发生体动，判断所述监测对象在所述预定时间内发生体动的体动次数是否超出预设的次数阈值或者发生体动的时间是否超出预设的持续时间；
    若所述监测对象在所述预定时间内发生体动的体动次数超过所述次数阈值或者发生体动的时间超出预设的持续时间，确定所述监测对象的睡眠状态转换为觉醒。
  9. 根据权利要求4所述的方法，其特征在于，所述方法还包括：
    在所述监测对象的所述心率超出预设的心率阈值和/或所述呼吸频率超出预设的呼吸频率阈值的情况下，发出对应的警报信号。
  10. 一种生理信息监测垫，其特征在于，包括：垫体、若干个微动信号传感器以及处理器；
    所述若干个微动信号传感器分布于所述垫体上,用于采集生理信号；
    所述若干个微动信号传感器与所述处理器连接，所述若干个微动信号传感器向所述处理器传输若干路所述生理信号；
    所述处理器用于执行如权利要求1-9任一所述的生理信息监测方法。
  11. 根据权利要求10所述的生理信息监测垫，其特征在于，还包括:信号放大电路、滤波器以及模数转换电路；
    所述信号放大电路与所述微动信号传感器连接，用于放大所述微动信号传感器采集的弱电压信号，形成放大信号；
    所述滤波器与所述信号放大电路连接，用于滤除所述放大信号中的50Hz工频和低频噪声与高频噪声的干扰；
    所述模数转换电路与所述滤波器连接，用于将所述滤波器输出的模拟信号转换为数字的所述生理信号，并将所述生理信号输出至所述处理器。
  12. 根据权利要求11所述的生理信息监测垫，其特征在于，所述微动信号传感器以a行乘b列的阵列形式分布于所述垫体上；其中，a、b均为正整数。
  13. 根据权利要求12所述的生理信息监测垫，其特征在于，所述微动信号传感器的设置数量大于或等于6。
  14. 根据权利要求10至13任一所述的生理信息监测垫，其特征在于，所述微动信号传感器为摩擦发电机，PVDF压电薄膜材料，加速度传感器，光纤传感器或陀螺仪传感器中的一种或者多种。
  15. 一种床垫，其特征在于，包括床垫本体以及如权利要求10-14任一所述的生理信息监测垫；所述生理信息监测垫固定在所述床垫本体的表面。
  16. 一种非易失性计算机可读存储介质，所述计算机可读存储介质存储有计算机可执行指令，所述计算机可执行指令被一个或多个处理器执行，以使所述至少一个处理器能够执行如权利要求1至9任一项所述生理信息监测方法。
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