WO2022038710A1 - Watching method, watching program, and watching device - Google Patents

Watching method, watching program, and watching device Download PDF

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Publication number
WO2022038710A1
WO2022038710A1 PCT/JP2020/031261 JP2020031261W WO2022038710A1 WO 2022038710 A1 WO2022038710 A1 WO 2022038710A1 JP 2020031261 W JP2020031261 W JP 2020031261W WO 2022038710 A1 WO2022038710 A1 WO 2022038710A1
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Prior art keywords
living body
unit
body temperature
activity
temperature
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PCT/JP2020/031261
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French (fr)
Japanese (ja)
Inventor
卓郎 田島
雄次郎 田中
大地 松永
倫子 瀬山
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日本電信電話株式会社
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Priority to PCT/JP2020/031261 priority Critical patent/WO2022038710A1/en
Priority to JP2022543871A priority patent/JP7420264B2/en
Publication of WO2022038710A1 publication Critical patent/WO2022038710A1/en

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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
    • A61B5/01—Measuring temperature of body parts ; Diagnostic temperature sensing, e.g. for malignant or inflamed tissue
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/08—Detecting, measuring or recording devices for evaluating the respiratory organs
    • 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
    • A61B5/16—Devices for psychotechnics; Testing reaction times ; Devices for evaluating the psychological state

Definitions

  • the present invention relates to a watching method, a watching program, and a watching device for measuring the core body temperature of a living body and grasping the state of the living body.
  • the body temperature of the living body is roughly divided into the core body temperature and the body surface temperature. Since the core body temperature is not easily affected by the environmental temperature, it is possible to accurately grasp the state of the living body, and it is expected to be developed as a method for grasping the timing of dosing during the treatment of the living body.
  • a measuring device disclosed in Patent Document 1 is known.
  • the measuring device disclosed in Patent Document 1 has a problem that the sleeping state of a living body cannot be grasped. Since the core body temperature differs between the awake state and the sleep state, it is desirable to be able to grasp not only the core body temperature but also the sleep state of the living body at the same time in order to grasp the state of the living body more accurately.
  • the present invention has been made to solve the above problems, and an object of the present invention is to provide a watching method, a watching program, and a watching device capable of grasping the sleeping state of a living body at the same time as the core body temperature of the living body.
  • the monitoring method of the present invention includes a first step of measuring the core body temperature of the living body, a second step of measuring the respiration frequency of the living body, a third step of measuring the activity amount of the living body, and the core body temperature.
  • the watching program of the present invention is characterized in that each of the above steps is executed by a computer.
  • the monitoring device of the present invention has a deep body temperature measuring unit configured to measure the deep body temperature of the living body, a respiratory frequency measuring unit configured to measure the respiratory frequency of the living body, and an activity amount of the living body.
  • An activity measuring unit configured to measure, an analysis unit configured to determine whether the living body can sleep or not based on the deep body temperature, the breathing frequency, and the activity, and the above-mentioned It is characterized by including an output unit configured to output the measurement result of the core body temperature and the determination result of the analysis unit.
  • the deep body temperature, the respiratory frequency, and the amount of activity of the living body are measured, and it is determined whether the living body can sleep or cannot sleep based on the deep body temperature, the respiratory frequency, and the amount of activity.
  • the sleeping state of the living body can be grasped at the same time as the core body temperature of the living body, and the state of the living body can be accurately grasped.
  • FIG. 1 is a block diagram showing a configuration of a watching device according to an embodiment of the present invention.
  • FIG. 2 is a block diagram showing a configuration of a deep body temperature measuring unit according to an embodiment of the present invention.
  • FIG. 3 is a cross-sectional view showing the configuration of a temperature measuring device of the deep body temperature measuring unit according to the embodiment of the present invention.
  • FIG. 4 is a block diagram showing a configuration of a respiratory frequency measuring unit according to an embodiment of the present invention.
  • FIG. 5 is a flowchart illustrating the operation of the respiratory frequency measuring unit according to the embodiment of the present invention.
  • FIG. 6 is a block diagram showing a configuration of a feature amount extraction unit of the respiratory frequency measuring unit according to the embodiment of the present invention.
  • FIG. 7A-7C are diagrams showing signal waveforms of each part of the feature amount extraction part of the respiratory frequency measuring part according to the embodiment of the present invention.
  • FIG. 8 is a diagram illustrating the processing of the Kalman filter of the respiratory frequency measuring unit according to the embodiment of the present invention.
  • FIG. 9 is a block diagram of the Kalman filter of the respiratory frequency measuring unit according to the embodiment of the present invention.
  • FIG. 10 is a block diagram showing a configuration of an integrated processing unit of the respiratory frequency measuring unit according to the embodiment of the present invention.
  • FIG. 11 is a block diagram showing a configuration of an activity amount measuring unit according to an embodiment of the present invention.
  • FIG. 12 is a flowchart illustrating the operation of the watching device according to the embodiment of the present invention.
  • FIG. 13 is a block diagram showing a configuration example of a computer that realizes the monitoring device according to the embodiment of the present invention.
  • FIG. 1 is a block diagram showing a configuration of a monitoring device according to an embodiment of the present invention.
  • the monitoring device includes a deep body temperature measuring unit 1 that measures the deep body temperature of a living body (human body), a breathing frequency measuring unit 2 that measures the breathing frequency of the living body, an activity measuring unit 3 that measures the activity amount of the living body, and a deep part.
  • An analysis unit 4 that determines whether the living body can sleep or cannot sleep based on the body temperature, respiratory frequency, and activity amount, an output unit 5 that outputs the measurement result of the deep body temperature and the determination result of the analysis unit 4, and watching over.
  • the output unit 5 includes a communication unit 8 for communication with the outside and a display unit 9 for displaying information.
  • FIG. 2 is a block diagram showing the configuration of the core body temperature measuring unit 1.
  • the deep body temperature measuring unit 1 is a deep part of the living body based on the measurement results of the temperature measuring device 10 for measuring the skin temperature of the living body and the temperature at a position away from the living body, the measuring unit 11 for measuring the time, and the temperature measuring device 10. It is composed of an estimation unit 12 for calculating body temperature.
  • the temperature measuring instrument 10 is provided on the sheet-shaped base material 13 as shown in FIG.
  • the temperature measuring instrument 10 includes three heat flux sensors 101, 102, 103.
  • the heat flux sensors 101, 102, and 103 are devices that measure heat transfer per unit time and unit area.
  • the heat flux sensors 101, 102, 103 include heat resistors 101r, 102r, 103r, temperature sensors 101u, 102u, 103u provided at both ends of the thermal resistance bodies 101r, 102r, 103r, and temperature sensors 101s, 102s, 10. It is equipped with 103s.
  • the thermal resistance bodies 101r, 102r, 103r included in the heat flux sensors 101, 102, 103, respectively, are made of heat insulating materials having different thicknesses and materials, and have different thermal resistance values R S1 , R S2 , and R S 3.
  • the temperature sensors 101s, 102s, 103s are provided on the living body 100 side of the thermal resistors 101r, 102r, 103r and measure the skin temperature TS of the living body 100.
  • the temperature sensors 101u, 102u, 103u are provided on the opposite side of the thermal resistors 101r, 102r, 103r from the living body 100, and measure the top surface temperature TU at a position away from the living body 100.
  • a known thermistor, a thermopile using a thermocouple, or the like can be used as the temperature sensors 101s to 103s and 101u to 103u.
  • the estimation unit 12 includes the skin temperatures T S1 , T S2 , and T S3 measured by the temperature sensors 101s to 103s at the time t of the time measuring unit 11, and the top surface temperatures T U1 and T U2 measured by the temperature sensors 101u to 103u. , T U3 is substituted into the following estimation model formula of the core body temperature to estimate the core body temperature TC of the living body 100.
  • R S1 , R S2 , and R S3 are known thermal resistance values of the thermal resistors 101r, 102r, and 103r as described above, and are stored in advance in the estimation unit 12.
  • the sheet-shaped base material 13 there is a deformable flexible substrate.
  • the base material 13 is attached so as to be attached to the skin of the living body 100, particularly the head, chest or armpit of the living body 100 (human body).
  • An opening is provided in a part of the base material 13, and the heat flux sensors 101, 102, and 103 are provided in the base material 13 so as to be in contact with the epidermis of the living body 100 from the opening.
  • Patent Document 1 The configuration of the deep body temperature measuring unit 1 as described above is disclosed in Patent Document 1.
  • FIG. 4 is a block diagram showing the configuration of the respiratory frequency measuring unit 2.
  • the respiratory frequency measuring unit 2 includes an electrocardiograph 20 that measures the electrocardiographic waveform of the living body, a 3-axis acceleration sensor 21 that detects 3-axis acceleration due to the respiratory movement of the living body, and an electrocardiograph 20 and a 3-axis acceleration sensor 21.
  • the storage units 22 and 23 for storing the obtained data, the R wave amplitude detection unit 24 for detecting the R wave amplitude from the electrocardiographic waveform, and the RR interval detection unit 25 for detecting the RR interval from the electrocardiographic waveform.
  • the acceleration displacement detection unit 26 that detects the angular displacement of the acceleration vector from the 3-axis acceleration signal, and the sampling unit 27 that samples each of the time-series signal of R wave amplitude, the time-series signal of RR interval, and the time-series signal of angular displacement.
  • the bandpass filter 28 that band-limits each of the time-series signal of R-wave amplitude, the time-series signal of RR interval, and the time-series signal of angular displacement, and the time-series signal of R-wave amplitude obtained by the bandpass filter 28.
  • the feature quantity extraction unit 29 that extracts phase information from each of the time series signal of RR interval and the time series signal of angular displacement, the phase information of R wave amplitude obtained by the feature quantity extraction unit 29, and the phase information of RR interval.
  • the Kalman filter 30 that estimates the phase information obtained by filtering out the noise for each of the phase information of the angular displacement, the estimated phase value of the R wave amplitude obtained by the Kalman filter 30, the estimated phase value of the RR interval, and the estimated phase value of the angular displacement.
  • an integrated processing unit 31 that integrates data by weighted averaging
  • a respiratory frequency conversion unit 32 that converts the phase value integrated by the integrated processing unit 31 into a frequency and converts the result into a living body's respiratory frequency. ..
  • the frequency conversion unit 32 constitutes a respiratory frequency calculation unit 33.
  • FIG. 5 is a flowchart illustrating the operation of the respiratory frequency measuring unit 2.
  • the electrocardiograph 20 measures the electrocardiographic waveform of the living body and outputs a time-series signal sequence of the electrocardiographic waveform (step S100 in FIG. 5).
  • the storage unit 22 stores a time-series signal sequence of the electrocardiographic waveform output from the electrocardiograph 20.
  • the electrocardiographic waveform consists of continuous heartbeat waveforms, and one heartbeat waveform consists of components such as P wave, Q wave, R wave, S wave, and T wave that reflect the activity of the atrium and ventricle.
  • the R wave amplitude detection unit 24 detects the amplitude of the R wave from the signal of the electrocardiographic waveform stored in the storage unit 22 (step S101 in FIG. 5).
  • the R wave amplitude detection unit 24 detects the amplitude of each R wave of the electrocardiographic waveform.
  • the RR interval detection unit 25 detects the RR interval, which is the interval between the R wave and the previous R wave, from the signal of the electrocardiographic waveform stored in the storage unit 22 (step S102 in FIG. 5).
  • the RR interval detection unit 25 detects the RR interval for each R wave of the electrocardiographic waveform.
  • a method for detecting the R wave and the RR interval for example, there is a technique disclosed in Japanese Patent Application Laid-Open No. 2015-21060.
  • the 3-axis acceleration sensor 21 is attached to the chest of the living body, detects the 3-axis acceleration due to the respiratory movement of the living body, and outputs a time-series signal sequence of the 3-axis acceleration (FIG. 5, step S103).
  • the storage unit 23 stores a time-series signal sequence of the 3-axis acceleration output from the 3-axis acceleration sensor 21.
  • the acceleration / displacement detection unit 26 detects the angular displacement of the acceleration vector from the 3-axis acceleration signal stored in the storage unit 23 (step S104 in FIG. 5). To detect this angular displacement, after defining the change plane of the acceleration from the average of the acceleration displacements in the three axes of the X, Y, and Z directions, the acceleration data of the three axes of the X, Y, and Z directions.
  • the angle of the projection vector when the acceleration vector composed of the above composites is projected onto the changing surface may be calculated as the angular displacement.
  • the acceleration / displacement detection unit 26 detects such an angular displacement for each acceleration sampling cycle.
  • the sampling unit 27 outputs the R wave amplitude time-series signal output from the R-wave amplitude detection unit 24, the RR interval time-series signal output from the RR interval detection unit 25, and the acceleration / displacement detection unit 26.
  • Each of the time-series signals of the angular displacement to be performed is sampled at a sampling frequency (for example, 1 Hz interval) slower than the sampling frequency of the electrocardiograph 20 and the sampling frequency of the 3-axis acceleration sensor 21 (FIG. 5, step S105).
  • the bandpass filter 28 band-limits each of the R wave amplitude time-series signal, the RR interval time-series signal, and the angular displacement time-series signal acquired by the sampling unit 27 (FIG. 5, step S106).
  • the reason for using the bandpass filter 28 is that the human breathing frequency is limited to low frequencies.
  • the pass band of the bandpass filter 28 is, for example, 0.15 to 0.4 Hz.
  • the feature amount extraction unit 29 instantaneously linearizes phase information and instantaneously from each of the R wave amplitude time-series signal, the RR interval time-series signal, and the angular displacement time-series signal obtained by the bandpass filter 28. Amplitude information and instantaneous frequency information are extracted (FIG. 5, step S108).
  • Amplitude information and instantaneous frequency information are extracted (FIG. 5, step S108).
  • the change in respiratory rate in a short time is negligibly small and the movement of breathing becomes a sinusoidal wave in the ideal model of the respiratory curve.
  • instantaneous phase information is extracted by Hilbert conversion.
  • FIG. 6 is a block diagram showing the configuration of the feature amount extraction unit 29.
  • the feature amount extraction unit 29 includes a Hilbert transform unit 290, an angle calculation unit 291 and an unwrap processing unit 292.
  • the Hilbert transform unit 290 Hilbert transforms the time-series signal of the R wave amplitude band-limited by the bandpass filter 28, the time-series signal of the RR interval, and the time-series signal of the angular displacement, and the phases are different by ⁇ / 2. Generates two signals (real part and imaginary part).
  • the signal input to the Hilbert converter 290 is expressed by a sine wave of Aexp (-i ⁇ ) (FIG. 7A)
  • the generated real part signal is a sine wave expressed by Acos ⁇
  • the imaginary part signal is iAsin ⁇ . It is a sine wave signal expressed by.
  • A is an amplitude
  • ⁇ is an angle
  • i is an imaginary unit.
  • the angle calculation unit 291 calculates the angle ⁇ (from ⁇ to + ⁇ ) from the real part Acos ⁇ and the imaginary part iAsin ⁇ of the output signal of the Hilbert transform unit 290.
  • the unwrap processing unit 292 performs phase unwrap in which the angle ⁇ calculated by the angle calculation unit 291 is linearized into continuous phase values. Since the angle ⁇ calculated by the angle calculation unit 291 is a value between ⁇ and + ⁇ , a phase skip of 2 ⁇ may occur at adjacent points as shown in FIG. 7B. Therefore, the unwrap processing unit 292 connects the phases by, for example, adding or subtracting 2 ⁇ . As a result, a continuous phase as shown in FIG. 7C is obtained.
  • the feature amount extraction unit 29 performs the above processing for each of the time-series signal of the R wave amplitude band-limited by the bandpass filter 28, the time-series signal of the RR interval, and the time-series signal of the angular displacement. That is, three sets of the Hilbert transform unit 290, the angle calculation unit 291 and the unwrap processing unit 292 may be provided, and the three time-series signals of R wave amplitude, RR interval, and angle displacement may be processed in parallel at the same time. ..
  • the Kalman filter 30 estimates the phase information obtained by filtering out the noise for each of the phase information of the R wave amplitude, the phase information of the RR interval, and the phase information of the angular displacement (FIG. 5, step S109).
  • the Kalman filter 30 is provided for each of the R wave amplitude, the RR interval, and the angular displacement, and the phase information of the R wave amplitude, the phase information of the RR interval, and the angular displacement obtained by the feature amount extraction unit 29 are provided.
  • the phase information of is input to the corresponding Kalman filters 30-1, 30-2, 30-3, respectively.
  • FIG. 9 is a block diagram of the Kalman filter 30.
  • a block diagram is shown for one of the three Kalman filters 30-1, 30-2, and 30-3.
  • the Kalman filter is based on Bayesian estimation and automatic recursive estimation.
  • the Kalman gain K is obtained by the minimum square approximation on the assumption that the input to the system has Gaussian noise.
  • the physical quantity x (k) that describes the biological system is recursively determined and is expressed by the following equation.
  • u (k) is a system input
  • x (k) is a noise-free physical quantity
  • m is the number of measurements
  • n is the number of signals from the biological system
  • A is the n ⁇ m matrix indicating the system model
  • H is the m ⁇ n matrix indicating the measurement system model.
  • the physical quantity x (k + 1) includes the physical quantity Ax (k) at the previous time and the biological system noise of ws (k).
  • the system input u (k) includes the measured value Hx (k) and the measurement system noise of the wm (k).
  • the measured value Hx (k) is a phase value input from the feature amount extraction unit 29.
  • K (k) is an n ⁇ m matrix indicating the Kalman gain
  • hat x (k) is an estimated value of the physical quantity x (k) (in this embodiment, an estimated value of the phase).
  • ⁇ attached on the character is also called a hat.
  • the Kalman gain K (k) can be obtained by the following equations (5) to (7).
  • R is a covariance matrix related to sensor noise
  • Q is a covariance matrix related to biological system noise
  • P is a covariance matrix related to estimated values.
  • HT and AT are transposed matrices of matrices H and A, respectively.
  • the Kalman gain K (k) is recursively determined so as to minimize the measurement system noise wm (k).
  • k) is the squared estimation error minimized by the filtering process.
  • the phase information of the R wave amplitude and the RR interval obtained by the feature amount extraction unit 29 are used in the time range where the respiratory rate of the living body is stable.
  • the standard deviation ⁇ in the relevant time range is calculated.
  • the covariance matrix R is a diagonal matrix on the assumption that the inputs of the R wave amplitude, the RR interval, and the angular displacement are independent of each other. Therefore, the average value of the standard deviation ⁇ obtained for the phase information of the R wave amplitude is set in advance as the diagonal component of the covariance matrix R of the Kalman filter 30-1 for the R wave amplitude, and the phase information of the RR interval is obtained.
  • the average value of the obtained standard deviation ⁇ is set in advance as the diagonal component of the covariance matrix R of the Kalman filter 30-2 for the RR interval, and the average value of the standard deviation ⁇ obtained for the phase information of the angular displacement is set to the angle. It is preset as a diagonal component of the covariance matrix R of the Kalman filter 30-3 for displacement.
  • the covariance matrix Q regarding the biological system noise is the biological system noise, and individual differences are reflected.
  • This covariance matrix Q is a diagonal matrix.
  • the optimum value of the diagonal component of the covariance matrix Q is numerically tested for each living body, and the value of the diagonal component according to the living body is set in advance. You can leave it.
  • the phase estimation accuracy can be improved by measuring the data while breathing the living body in advance and determining the covariance matrices R and Q based on the measured data.
  • x (k) can be expressed as a vector of the phase ⁇ k and the dot ⁇ k.
  • the dot ⁇ k is a derivative of the phase ⁇ k.
  • the " ⁇ " attached to the character is called a dot.
  • the Kalman filter 30 (30-1 to 30-3) performs the filter processing every sampling cycle of the sampling unit 27 (every second in the example of this embodiment).
  • the integrated processing unit 31 applies the estimated phase value of the R wave amplitude obtained by the Kalman filter 30 (30-1 to 30-3), the estimated phase value of the RR interval, and the estimated phase value of the angular displacement to the Kalman filter 30 (30-1 to 30-3).
  • weighting averaging processing using weights based on the squared estimation error of 30-1 to 30-3), data of estimated phase values of R wave amplitude, RR interval, and angular displacement are integrated (FIG. 5 step S110). ..
  • FIG. 10 is a block diagram showing the configuration of the integrated processing unit 31.
  • the integrated processing unit 31 includes a weighting constant generation processing unit 310 and a weighting averaging processing unit 311.
  • the estimated phase value is obtained every time (every second in the example of this embodiment), and the squared estimation error is updated.
  • the Kalman filter processing self-estimation error in the Kalman filter 30-1 for R wave amplitude is ⁇ 1
  • the Kalman filter processing self-estimation error in the RR interval Kalman filter 30-2 is ⁇ 2
  • the Kalman filter 30-3 for angular displacement Let ⁇ 3 be the self-estimation error of the Kalman filter processing in.
  • the weighting constant generation processing unit 310 calculates the weighting constant ⁇ i from the squared estimation errors ⁇ i of the R wave amplitude, the RR interval, and the angular displacement by the equation (9).
  • the integrated output value hat xf (k) which is a phase value obtained by weighting and averaging the value hat x2 and the estimated phase value hat x3 of the angular displacement, is calculated by the equation (10).
  • the integrated processing unit 31 performs the above integrated processing every sampling cycle of the sampling unit 27 (every second in the example of this embodiment).
  • the respiratory frequency conversion unit 32 converts the phase value integrated by the integrated processing unit 31 into a frequency and outputs a respiratory frequency signal (step S111 in FIG. 5). Since the instantaneous angular frequency can be obtained by time-differentiating the phase value output from the integrated processing unit 31, the respiratory frequency f can be obtained by dividing this instantaneous angular frequency by 2 ⁇ . In this way, the respiratory frequency conversion unit 32 outputs the data of the respiratory frequency f.
  • the configuration of the above-mentioned respiratory frequency measuring unit 2 is disclosed in the international publication WO2017 / 090732.
  • FIG. 11 is a block diagram showing the configuration of the activity amount measuring unit 3.
  • the activity amount measuring unit 3 is composed of a 3-axis acceleration sensor 34 that measures the 3-axis acceleration of the living body and an activity amount calculating unit 35 that calculates the activity amount of the living body based on the measurement result of the 3-axis acceleration sensor 34. ..
  • the activity amount calculation unit 35 uses the following formula.
  • the standard deviation is calculated by the calculation shown in (11), and the value is taken as the activity amount ⁇ of the living body.
  • n is the number of samples of the acquired acceleration measured values.
  • ai is a composite value of the measured values of the accelerations of the three axes, and is represented by the following equation (12).
  • a AV is the average value of ai.
  • the 3-axis accelerometer 34 is attached to the head of the living body, and the activity amount calculation unit 35 calculates the activity amount ⁇ of the head of the living body.
  • the configuration of the above activity amount measuring unit 3 is disclosed in Japanese Patent Application Laid-Open No. 2017-38839.
  • FIG. 12 is a flowchart illustrating the operation of the watching device of this embodiment.
  • the power supply control unit 7 supplies a power supply voltage from the battery 6 to the deep body temperature measuring unit 1, the respiratory frequency measuring unit 2, and the activity measuring unit 3 when measuring the deep body temperature, the respiratory frequency, and the activity amount (FIG. 12 step S1). ).
  • the deep body temperature measuring unit 1 measures the deep body temperature TC of the head of the living body (step S2 in FIG. 12).
  • the respiratory frequency measuring unit 2 measures the respiratory frequency f of the living body (step S3 in FIG. 12).
  • the activity amount measuring unit 3 measures the activity amount ⁇ of the head of the living body (FIG. 12, step S4).
  • the analysis unit 4 determines whether the living body can sleep or cannot sleep based on the measurement results of the deep body temperature measuring unit 1, the respiratory frequency measuring unit 2, and the activity measuring unit 3 (step S5 in FIG. 12). Specifically, the analysis unit 4 keeps the condition that the core body temperature T C is less than the core body temperature threshold T th (for example, 37.5 ° C.) and the respiratory frequency f is constant below the respiratory frequency threshold value f th (for example, 10 Hz). When all the conditions that the activity amount ⁇ is equal to or more than the activity amount threshold value ⁇ th and the duration that the activity amount ⁇ is less than the time threshold value t th (for example, 10 minutes) are satisfied, it is determined to be in a sleep state.
  • the core body temperature threshold T th for example, 37.5 ° C.
  • the respiratory frequency f is constant below the respiratory frequency threshold value f th (for example, 10 Hz).
  • the analysis unit 4 has a condition that the deep body temperature T C is equal to or higher than the deep body temperature threshold T th , a condition that the respiratory frequency f is not constant at the respiratory frequency threshold f th or higher, and the activity amount ⁇ is the activity threshold ⁇ th or higher.
  • the conditions that the duration is t th or more it is determined that the person cannot sleep.
  • the analysis unit 4 uses, for example, the first breathing frequency f that is less than the breathing frequency threshold fth after the start of measurement as a reference value, and the breathing frequency f obtained in the subsequent measurements is less than the breathing frequency threshold fth . If the absolute value of the change in the breathing frequency f with respect to the reference value is less than the change amount threshold ⁇ th , it may be determined that the breathing frequency f is constant below the breathing frequency threshold fth .
  • the analysis unit 4 uses, for example, the first respiration frequency f that becomes the respiration frequency threshold f th or more after the start of measurement as a reference value, and the respiration frequency f obtained in the subsequent measurements is the respiration frequency threshold f th or more. If the absolute value of the amount of change in the respiratory frequency f with respect to the reference value is equal to or greater than the change amount threshold ⁇ th , it may be determined that the respiratory frequency f is equal to or greater than the respiratory frequency threshold f th and is not constant.
  • the thresholds such as the core body temperature threshold T th , the respiratory frequency threshold f th , the activity threshold ⁇ th , and the time threshold t th can be changed from an external device such as a smartphone.
  • the communication unit 8 receives the threshold value wirelessly transmitted from the external device, and passes the received threshold value to the analysis unit 4.
  • the analysis unit 4 updates the set threshold value to the threshold value received from the communication unit 8.
  • the output unit 5 outputs the measurement result of the core body temperature TC and the determination result of the analysis unit 4 (FIG. 12, step S6).
  • the display unit 9 may display the measurement result of the core body temperature TC and the determination result of the analysis unit 4.
  • the display of the determination result of the analysis unit 4 for example, when the determination result that the living body can sleep is obtained, the green LED is turned on, and when the determination result that the living body cannot sleep is obtained, the red LED is displayed. You may do it by turning on.
  • the communication unit 8 may wirelessly transmit the measurement result of the core body temperature TC and the determination result of the analysis unit 4 to an external device such as a smartphone.
  • the analysis unit 4 may cause the output unit 5 to output an insomnia alarm when the determination result that the living body cannot sleep is obtained (YES in step S7 of FIG. 12).
  • a method of outputting the insomnia alarm for example, there is a method of wirelessly transmitting a signal notifying the occurrence of the insomnia alarm to an external device.
  • the analysis unit 4 may cause the output unit 5 to output a high temperature alarm when the core body temperature TC is equal to or higher than a predetermined high temperature threshold value T high (38.5 ° C.) (YES in step S9 in FIG. 12). Good (step S10 in FIG. 12).
  • a method of outputting the high temperature alarm for example, there is a method of wirelessly transmitting a signal notifying the occurrence of the high temperature alarm to an external device.
  • the analysis unit 4 determines whether the sleep state of the living body is REM sleep or non-REM sleep based on the core body temperature TC , the respiratory frequency f, and the activity amount ⁇ (step S11 in FIG. 12), and outputs the determination result to the output unit 5. May be output to (FIG. 12, step S12).
  • the analysis unit 4 may perform REM sleep when the core body temperature TC is equal to or higher than the average body temperature from the start of measurement, the rate of change in respiratory frequency f is less than 30%, and the rate of change in activity ⁇ is less than 30%.
  • the rate of change in respiratory frequency f is 30% or more, and the rate of change in activity ⁇ is 30% or more, it is determined to be non-REM sleep. ..
  • the watching device periodically performs the process shown in FIG.
  • the sleeping state (sleeping state or sleepless state) of the living body can be grasped at the same time as the deep body temperature TC of the living body, and the state of the living body can be accurately grasped.
  • the timing suitable for dosing For example, when a person has a fever due to influenza or a cold, it is more appropriate to wake up the person and give an antipyretic agent when he / she sleeps lightly for the subsequent sleep onset. According to this embodiment, it is possible to grasp the timing of such dosing.
  • the analysis unit 4 described in this embodiment can be realized by a computer provided with a CPU (Central Processing Unit), a storage device, and an interface, and a program for controlling these hardware resources.
  • a computer provided with a CPU (Central Processing Unit), a storage device, and an interface, and a program for controlling these hardware resources.
  • CPU Central Processing Unit
  • the computer includes a CPU 200, a storage device 201, and an interface device (I / F) 202.
  • the hardware of the deep body temperature measuring unit 1, the hardware of the breathing frequency measuring unit 2, the hardware of the activity measuring unit 3, the hardware of the output unit 5, and the like are connected to the I / F 202.
  • the watching program for realizing the watching method of the present invention is stored in the storage device 201.
  • the CPU 200 executes the process described in this embodiment according to the program stored in the storage device 201.
  • the estimation unit 12 of the deep body temperature measurement unit 1, the respiration frequency calculation unit 33 of the respiration frequency measurement unit 2, and the activity amount calculation unit 35 of the activity amount measurement unit 3 can also be realized by a computer as shown in FIG.
  • the analysis unit 4, the estimation unit 12, the respiratory frequency calculation unit 33, and the activity amount calculation unit 35 may be realized by separate computers, or may be realized by one computer.
  • the present invention can be applied to a technique for grasping the state of a living body.

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Abstract

This watching device comprises: a deep body temperature measurement unit (1) for measuring the deep body temperature of a live body; a respiration frequency measurement unit (2) for measuring the respiration frequency of the live body; an activity amount measurement unit (3) for measuring the activity amount of the live body; an analysis unit 4 for determining, on the basis of the deep body temperature, the respiration frequency, and the activity amount, whether the live body is in a state of sleepiness or in a state of sleeplessness; and an output unit (5) for outputting a result of the deep body temperature measurement and a result of the determination by the analysis unit (4).

Description

見守り方法、見守りプログラムおよび見守り装置Watching method, watching program and watching device
　本発明は、生体の深部体温を測定して生体の状態を把握する見守り方法、見守りプログラムおよび見守り装置に関するものである。 The present invention relates to a watching method, a watching program, and a watching device for measuring the core body temperature of a living body and grasping the state of the living body.
　生体の体温は、深部体温と体表面温に大別される。深部体温は、環境温度の影響を受けにくいため、生体の状態を正確に把握することが可能であり、生体の治療時における投薬のタイミングを把握する手法としての展開が期待されている。従来、深部体温を測定する装置として、特許文献１に開示された測定装置が知られている。 The body temperature of the living body is roughly divided into the core body temperature and the body surface temperature. Since the core body temperature is not easily affected by the environmental temperature, it is possible to accurately grasp the state of the living body, and it is expected to be developed as a method for grasping the timing of dosing during the treatment of the living body. Conventionally, as a device for measuring core body temperature, a measuring device disclosed in Patent Document 1 is known.
　しかしながら、特許文献１に開示された測定装置では、生体の睡眠状態を把握することができないという課題があった。深部体温は、覚醒状態と睡眠状態では温度が異なるため、生体の状態をより正確に把握するためには、深部体温だけでなく生体の睡眠状態も同時に把握できることが望ましい。 However, the measuring device disclosed in Patent Document 1 has a problem that the sleeping state of a living body cannot be grasped. Since the core body temperature differs between the awake state and the sleep state, it is desirable to be able to grasp not only the core body temperature but also the sleep state of the living body at the same time in order to grasp the state of the living body more accurately.
特開２０２０－００３２９１号公報Japanese Unexamined Patent Publication No. 2020-003291
　本発明は、上記課題を解決するためになされたもので、生体の深部体温と同時に生体の睡眠状態を把握することができる見守り方法、見守りプログラムおよび見守り装置を提供することを目的とする。 The present invention has been made to solve the above problems, and an object of the present invention is to provide a watching method, a watching program, and a watching device capable of grasping the sleeping state of a living body at the same time as the core body temperature of the living body.
　本発明の見守り方法は、生体の深部体温を測定する第１のステップと、生体の呼吸周波数を測定する第２のステップと、生体の活動量を測定する第３のステップと、前記深部体温と前記呼吸周波数と前記活動量とに基づいて生体が眠れる状態か眠れない状態かを判定する第４のステップと、前記深部体温の測定結果と前記第４のステップの判定結果とを出力する第５のステップとを含むことを特徴とするものである。
　また、本発明の見守りプログラムは、前記の各ステップをコンピュータに実行させることを特徴とするものである。 The monitoring method of the present invention includes a first step of measuring the core body temperature of the living body, a second step of measuring the respiration frequency of the living body, a third step of measuring the activity amount of the living body, and the core body temperature. A fifth step of determining whether the living body can sleep or not based on the breathing frequency and the amount of activity, and a fifth step of outputting the measurement result of the core body temperature and the determination result of the fourth step. It is characterized by including the steps of.
Further, the watching program of the present invention is characterized in that each of the above steps is executed by a computer.
　また、本発明の見守り装置は、生体の深部体温を測定するように構成された深部体温測定部と、生体の呼吸周波数を測定するように構成された呼吸周波数測定部と、生体の活動量を測定するように構成された活動量測定部と、前記深部体温と前記呼吸周波数と前記活動量とに基づいて生体が眠れる状態か眠れない状態かを判定するように構成された解析部と、前記深部体温の測定結果と前記解析部の判定結果とを出力するように構成された出力部とを備えることを特徴とするものである。 Further, the monitoring device of the present invention has a deep body temperature measuring unit configured to measure the deep body temperature of the living body, a respiratory frequency measuring unit configured to measure the respiratory frequency of the living body, and an activity amount of the living body. An activity measuring unit configured to measure, an analysis unit configured to determine whether the living body can sleep or not based on the deep body temperature, the breathing frequency, and the activity, and the above-mentioned It is characterized by including an output unit configured to output the measurement result of the core body temperature and the determination result of the analysis unit.
　本発明によれば、生体の深部体温と呼吸周波数と活動量とを測定し、深部体温と呼吸周波数と活動量とに基づいて生体が眠れる状態か眠れない状態かを判定する。本発明では、生体の深部体温と同時に生体の睡眠状態を把握することができ、生体の状態を正確に把握することができる。その結果、本発明では、例えば投薬の要否や投薬に適したタイミングなどを把握することが可能となる。 According to the present invention, the deep body temperature, the respiratory frequency, and the amount of activity of the living body are measured, and it is determined whether the living body can sleep or cannot sleep based on the deep body temperature, the respiratory frequency, and the amount of activity. In the present invention, the sleeping state of the living body can be grasped at the same time as the core body temperature of the living body, and the state of the living body can be accurately grasped. As a result, in the present invention, for example, it becomes possible to grasp the necessity of dosing and the timing suitable for dosing.
図１は、本発明の実施例に係る見守り装置の構成を示すブロック図である。FIG. 1 is a block diagram showing a configuration of a watching device according to an embodiment of the present invention. 図２は、本発明の実施例に係る深部体温測定部の構成を示すブロック図である。FIG. 2 is a block diagram showing a configuration of a deep body temperature measuring unit according to an embodiment of the present invention. 図３は、本発明の実施例に係る深部体温測定部の温度測定器の構成を示す断面図である。FIG. 3 is a cross-sectional view showing the configuration of a temperature measuring device of the deep body temperature measuring unit according to the embodiment of the present invention. 図４は、本発明の実施例に係る呼吸周波数測定部の構成を示すブロック図である。FIG. 4 is a block diagram showing a configuration of a respiratory frequency measuring unit according to an embodiment of the present invention. 図５は、本発明の実施例に係る呼吸周波数測定部の動作を説明するフローチャートである。FIG. 5 is a flowchart illustrating the operation of the respiratory frequency measuring unit according to the embodiment of the present invention. 図６は、本発明の実施例に係る呼吸周波数測定部の特徴量抽出部の構成を示すブロック図である。FIG. 6 is a block diagram showing a configuration of a feature amount extraction unit of the respiratory frequency measuring unit according to the embodiment of the present invention. 図７Ａ－図７Ｃは、本発明の実施例に係る呼吸周波数測定部の特徴量抽出部の各部の信号波形を示す図である。7A-7C are diagrams showing signal waveforms of each part of the feature amount extraction part of the respiratory frequency measuring part according to the embodiment of the present invention. 図８は、本発明の実施例に係る呼吸周波数測定部のカルマンフィルタの処理を説明する図である。FIG. 8 is a diagram illustrating the processing of the Kalman filter of the respiratory frequency measuring unit according to the embodiment of the present invention. 図９は、本発明の実施例に係る呼吸周波数測定部のカルマンフィルタのブロック線図である。FIG. 9 is a block diagram of the Kalman filter of the respiratory frequency measuring unit according to the embodiment of the present invention. 図１０は、本発明の実施例に係る呼吸周波数測定部の統合処理部の構成を示すブロック図である。FIG. 10 is a block diagram showing a configuration of an integrated processing unit of the respiratory frequency measuring unit according to the embodiment of the present invention. 図１１は、本発明の実施例に係る活動量測定部の構成を示すブロック図である。FIG. 11 is a block diagram showing a configuration of an activity amount measuring unit according to an embodiment of the present invention. 図１２は、本発明の実施例に係る見守り装置の動作を説明するフローチャートである。FIG. 12 is a flowchart illustrating the operation of the watching device according to the embodiment of the present invention. 図１３は、本発明の実施例に係る見守り装置を実現するコンピュータの構成例を示すブロック図である。FIG. 13 is a block diagram showing a configuration example of a computer that realizes the monitoring device according to the embodiment of the present invention.
　以下、本発明の実施例について図面を参照して説明する。図１は本発明の実施例に係る見守り装置の構成を示すブロック図である。見守り装置は、生体（人体）の深部体温を測定する深部体温測定部１と、生体の呼吸周波数を測定する呼吸周波数測定部２と、生体の活動量を測定する活動量測定部３と、深部体温と呼吸周波数と活動量とに基づいて生体が眠れる状態か眠れない状態かを判定する解析部４と、深部体温の測定結果と解析部４の判定結果とを出力する出力部５と、見守り装置の各部に電源電圧を供給する電池６と、深部体温測定部１と呼吸周波数測定部２と活動量測定部３への電源電圧の供給を制御する電源制御部７とを備えている。出力部５は、外部との通信のための通信部８と、情報表示のための表示部９とから構成される。 Hereinafter, examples of the present invention will be described with reference to the drawings. FIG. 1 is a block diagram showing a configuration of a monitoring device according to an embodiment of the present invention. The monitoring device includes a deep body temperature measuring unit 1 that measures the deep body temperature of a living body (human body), a breathing frequency measuring unit 2 that measures the breathing frequency of the living body, an activity measuring unit 3 that measures the activity amount of the living body, and a deep part. An analysis unit 4 that determines whether the living body can sleep or cannot sleep based on the body temperature, respiratory frequency, and activity amount, an output unit 5 that outputs the measurement result of the deep body temperature and the determination result of the analysis unit 4, and watching over. It includes a battery 6 that supplies power supply voltage to each part of the device, and a power supply control unit 7 that controls supply of power supply voltage to the deep body temperature measuring unit 1, the breathing frequency measuring unit 2, and the activity amount measuring unit 3. The output unit 5 includes a communication unit 8 for communication with the outside and a display unit 9 for displaying information.
　図２は深部体温測定部１の構成を示すブロック図である。深部体温測定部１は、生体の表皮温度と生体から遠ざかる位置の温度とを測定する温度測定器１０と、時間を計測する計時部１１と、温度測定器１０の測定結果に基づいて生体の深部体温を算出する推定部１２とから構成される。 FIG. 2 is a block diagram showing the configuration of the core body temperature measuring unit 1. The deep body temperature measuring unit 1 is a deep part of the living body based on the measurement results of the temperature measuring device 10 for measuring the skin temperature of the living body and the temperature at a position away from the living body, the measuring unit 11 for measuring the time, and the temperature measuring device 10. It is composed of an estimation unit 12 for calculating body temperature.
　温度測定器１０は、図３に示すようにシート状の基材１３の上に設けられる。温度測定器１０は、３つの熱流束センサ１０１，１０２，１０３を備える。熱流束センサ１０１，１０２，１０３は、単位時間、単位面積当たりの熱の移動を測定するデバイスである。 The temperature measuring instrument 10 is provided on the sheet-shaped base material 13 as shown in FIG. The temperature measuring instrument 10 includes three heat flux sensors 101, 102, 103. The heat flux sensors 101, 102, and 103 are devices that measure heat transfer per unit time and unit area.
　熱流束センサ１０１，１０２，１０３は、熱抵抗体１０１ｒ，１０２ｒ，１０３ｒと、各熱抵抗体１０１ｒ，１０２ｒ，１０３ｒの両端に設けられた温度センサ１０１ｕ，１０２ｕ，１０３ｕと、温度センサ１０１ｓ，１０２ｓ，１０３ｓとを備えている。 The heat flux sensors 101, 102, 103 include heat resistors 101r, 102r, 103r, temperature sensors 101u, 102u, 103u provided at both ends of the thermal resistance bodies 101r, 102r, 103r, and temperature sensors 101s, 102s, 10. It is equipped with 103s.
　熱流束センサ１０１，１０２，１０３がそれぞれ備える熱抵抗体１０１ｒ，１０２ｒ，１０３ｒは、厚みや材質が異なる断熱材で構成され、互いに異なる熱抵抗値ＲS1，ＲS2，ＲS3を有する。 The thermal resistance bodies 101r, 102r, 103r included in the heat flux sensors 101, 102, 103, respectively, are made of heat insulating materials having different thicknesses and materials, and have different thermal resistance values R S1 , R S2 , and R S 3.
　温度センサ１０１ｓ，１０２ｓ，１０３ｓは、熱抵抗体１０１ｒ，１０２ｒ，１０３ｒの生体１００側に設けられて生体１００の表皮温度ＴSを測定する。温度センサ１０１ｕ，１０２ｕ，１０３ｕは、熱抵抗体１０１ｒ，１０２ｒ，１０３ｒの生体１００とは反対側に設けられて、生体１００から遠ざかる位置の上面温度ＴUを測定する。温度センサ１０１ｓ～１０３ｓ，１０１ｕ～１０３ｕとしては、例えば公知のサーミスタや、熱電対を用いたサーモパイルなどを用いることができる。 The temperature sensors 101s, 102s, 103s are provided on the living body 100 side of the thermal resistors 101r, 102r, 103r and measure the skin temperature TS of the living body 100. The temperature sensors 101u, 102u, 103u are provided on the opposite side of the thermal resistors 101r, 102r, 103r from the living body 100, and measure the top surface temperature TU at a position away from the living body 100. As the temperature sensors 101s to 103s and 101u to 103u, for example, a known thermistor, a thermopile using a thermocouple, or the like can be used.
　推定部１２は、計時部１１の計時時刻ｔにおいて温度センサ１０１ｓ～１０３ｓによって測定された表皮温度ＴS1，ＴS2，ＴS3と、温度センサ１０１ｕ～１０３ｕによって測定された上面温度ＴU1，ＴU2，ＴU3とを、以下の深部体温の推定モデル式に代入して生体１００の深部体温ＴCを推定する。 The estimation unit 12 includes the skin temperatures T S1 , T S2 , and T S3 measured by the temperature sensors 101s to 103s at the time t of the time measuring unit 11, and the top surface temperatures T U1 and T U2 measured by the temperature sensors 101u to 103u. , T U3 is substituted into the following estimation model formula of the core body temperature to estimate the core body temperature TC of the living body 100.
Figure JPOXMLDOC01-appb-M000001
Figure JPOXMLDOC01-appb-M000001
　式（１）において、Ｔi＝（ＴSi（ｔ）－ＴUi（ｔ））、ＴSi’＝ｄＴSi（ｔ）／ｄｔ、（ｉ＝１，２，３）である。ＲS1，ＲS2，ＲS3は、上記のとおり熱抵抗体１０１ｒ，１０２ｒ，１０３ｒの既知の熱抵抗値であり、推定部１２に予め記憶されている。 In the formula (1), T i = (T Si (t) −T Ui (t)), T Si '= dT Si (t) / dt, (i = 1, 2, 3). R S1 , R S2 , and R S3 are known thermal resistance values of the thermal resistors 101r, 102r, and 103r as described above, and are stored in advance in the estimation unit 12.
　シート状の基材１３としては、変形可能なフレキシブル基板がある。基材１３は、生体１００の皮膚、特に生体１００（人体）の頭部または胸部または脇の下に貼り付けるようにして装着される。基材１３の一部には開口が設けられ、熱流束センサ１０１，１０２，１０３は開口から生体１００の表皮に接するように基材１３に設けられる。
　以上のような深部体温測定部１の構成は特許文献１に開示されている。 As the sheet-shaped base material 13, there is a deformable flexible substrate. The base material 13 is attached so as to be attached to the skin of the living body 100, particularly the head, chest or armpit of the living body 100 (human body). An opening is provided in a part of the base material 13, and the heat flux sensors 101, 102, and 103 are provided in the base material 13 so as to be in contact with the epidermis of the living body 100 from the opening.
The configuration of the deep body temperature measuring unit 1 as described above is disclosed in Patent Document 1.
　図４は呼吸周波数測定部２の構成を示すブロック図である。呼吸周波数測定部２は、生体の心電位波形を測定する心電計２０と、生体の呼吸運動による３軸加速度を検出する３軸加速度センサ２１と、心電計２０と３軸加速度センサ２１で得られたデータを記憶するための記憶部２２，２３と、心電位波形からＲ波振幅を検出するＲ波振幅検出部２４と、心電位波形からＲＲ間隔を検出するＲＲ間隔検出部２５と、３軸加速度信号から加速度ベクトルの角度変位を検出する加速度変位検出部２６と、Ｒ波振幅の時系列信号、ＲＲ間隔の時系列信号、角度変位の時系列信号の各々をサンプリングする標本化部２７と、Ｒ波振幅の時系列信号、ＲＲ間隔の時系列信号、角度変位の時系列信号の各々を帯域制限するバンドパスフィルタ２８と、バンドパスフィルタ２８で得られたＲ波振幅の時系列信号、ＲＲ間隔の時系列信号、角度変位の時系列信号の各々から位相情報を抽出する特徴量抽出部２９と、特徴量抽出部２９で得られたＲ波振幅の位相情報、ＲＲ間隔の位相情報、角度変位の位相情報の各々についてノイズを濾した位相情報を推定するカルマンフィルタ３０と、カルマンフィルタ３０で得られたＲ波振幅の推定位相値、ＲＲ間隔の推定位相値、角度変位の推定位相値を重み付け平均化することによりデータを統合する統合処理部３１と、統合処理部３１で統合された位相値を周波数に変換した結果を、生体の呼吸周波数とする呼吸周波数変換部３２とを備えている。 FIG. 4 is a block diagram showing the configuration of the respiratory frequency measuring unit 2. The respiratory frequency measuring unit 2 includes an electrocardiograph 20 that measures the electrocardiographic waveform of the living body, a 3-axis acceleration sensor 21 that detects 3-axis acceleration due to the respiratory movement of the living body, and an electrocardiograph 20 and a 3-axis acceleration sensor 21. The storage units 22 and 23 for storing the obtained data, the R wave amplitude detection unit 24 for detecting the R wave amplitude from the electrocardiographic waveform, and the RR interval detection unit 25 for detecting the RR interval from the electrocardiographic waveform. The acceleration displacement detection unit 26 that detects the angular displacement of the acceleration vector from the 3-axis acceleration signal, and the sampling unit 27 that samples each of the time-series signal of R wave amplitude, the time-series signal of RR interval, and the time-series signal of angular displacement. And the bandpass filter 28 that band-limits each of the time-series signal of R-wave amplitude, the time-series signal of RR interval, and the time-series signal of angular displacement, and the time-series signal of R-wave amplitude obtained by the bandpass filter 28. , The feature quantity extraction unit 29 that extracts phase information from each of the time series signal of RR interval and the time series signal of angular displacement, the phase information of R wave amplitude obtained by the feature quantity extraction unit 29, and the phase information of RR interval. , The Kalman filter 30 that estimates the phase information obtained by filtering out the noise for each of the phase information of the angular displacement, the estimated phase value of the R wave amplitude obtained by the Kalman filter 30, the estimated phase value of the RR interval, and the estimated phase value of the angular displacement. It is provided with an integrated processing unit 31 that integrates data by weighted averaging, and a respiratory frequency conversion unit 32 that converts the phase value integrated by the integrated processing unit 31 into a frequency and converts the result into a living body's respiratory frequency. ..
　記憶部２２，２３とＲ波振幅検出部２４とＲＲ間隔検出部２５と加速度変位検出部２６と標本化部２７とバンドパスフィルタ２８と特徴量抽出部２９とカルマンフィルタ３０と統合処理部３１と呼吸周波数変換部３２とは、呼吸周波数算出部３３を構成している。 Storage units 22, 23, R wave amplitude detection unit 24, RR interval detection unit 25, acceleration displacement detection unit 26, sampling unit 27, bandpass filter 28, feature quantity extraction unit 29, Kalman filter 30, integrated processing unit 31, and breathing. The frequency conversion unit 32 constitutes a respiratory frequency calculation unit 33.
　図５は呼吸周波数測定部２の動作を説明するフローチャートである。心電計２０は、生体の心電位波形を測定し、心電位波形の時系列信号列を出力する（図５ステップＳ１００）。記憶部２２は、心電計２０から出力された心電位波形の時系列信号列を記憶する。 FIG. 5 is a flowchart illustrating the operation of the respiratory frequency measuring unit 2. The electrocardiograph 20 measures the electrocardiographic waveform of the living body and outputs a time-series signal sequence of the electrocardiographic waveform (step S100 in FIG. 5). The storage unit 22 stores a time-series signal sequence of the electrocardiographic waveform output from the electrocardiograph 20.
　周知のとおり、心電位波形は、連続した心拍波形からなり、１つの心拍波形は、心房や心室の活動を反映したＰ波、Ｑ波、Ｒ波、Ｓ波、Ｔ波等の成分からなっている。Ｒ波振幅検出部２４は、記憶部２２に格納された心電位波形の信号からＲ波の振幅を検出する（図５ステップＳ１０１）。Ｒ波振幅検出部２４は、心電位波形のＲ波ごとに振幅検出を行う。 As is well known, the electrocardiographic waveform consists of continuous heartbeat waveforms, and one heartbeat waveform consists of components such as P wave, Q wave, R wave, S wave, and T wave that reflect the activity of the atrium and ventricle. There is. The R wave amplitude detection unit 24 detects the amplitude of the R wave from the signal of the electrocardiographic waveform stored in the storage unit 22 (step S101 in FIG. 5). The R wave amplitude detection unit 24 detects the amplitude of each R wave of the electrocardiographic waveform.
　ＲＲ間隔検出部２５は、記憶部２２に格納された心電位波形の信号から、Ｒ波と１つ前のＲ波の間隔であるＲＲ間隔を検出する（図５ステップＳ１０２）。ＲＲ間隔検出部２５は、心電位波形のＲ波ごとにＲＲ間隔検出を行う。なお、Ｒ波とＲＲ間隔を検出する方法としては、例えば特開２０１５－２１７０６０号公報に開示された技術がある。 The RR interval detection unit 25 detects the RR interval, which is the interval between the R wave and the previous R wave, from the signal of the electrocardiographic waveform stored in the storage unit 22 (step S102 in FIG. 5). The RR interval detection unit 25 detects the RR interval for each R wave of the electrocardiographic waveform. As a method for detecting the R wave and the RR interval, for example, there is a technique disclosed in Japanese Patent Application Laid-Open No. 2015-21060.
　一方、３軸加速度センサ２１は、生体の胸部に装着され、生体の呼吸運動による３軸加速度を検出して、３軸加速度の時系列信号列を出力する（図５ステップＳ１０３）。記憶部２３は、３軸加速度センサ２１から出力された３軸加速度の時系列信号列を記憶する。 On the other hand, the 3-axis acceleration sensor 21 is attached to the chest of the living body, detects the 3-axis acceleration due to the respiratory movement of the living body, and outputs a time-series signal sequence of the 3-axis acceleration (FIG. 5, step S103). The storage unit 23 stores a time-series signal sequence of the 3-axis acceleration output from the 3-axis acceleration sensor 21.
　加速度変位検出部２６は、記憶部２３に格納された３軸加速度の信号から加速度ベクトルの角度変位を検出する（図５ステップＳ１０４）。この角度変位を検出するには、Ｘ方向、Ｙ方向、Ｚ方向の３軸方向の加速度変位の平均から加速度の変化面を規定した後に、Ｘ方向、Ｙ方向、Ｚ方向の３軸の加速度データの合成からなる加速度ベクトルを前記変化面に射影したときの射影ベクトルの角度を角度変位として算出すればよい。 The acceleration / displacement detection unit 26 detects the angular displacement of the acceleration vector from the 3-axis acceleration signal stored in the storage unit 23 (step S104 in FIG. 5). To detect this angular displacement, after defining the change plane of the acceleration from the average of the acceleration displacements in the three axes of the X, Y, and Z directions, the acceleration data of the three axes of the X, Y, and Z directions. The angle of the projection vector when the acceleration vector composed of the above composites is projected onto the changing surface may be calculated as the angular displacement.
　このような方法は、例えば文献「A.Bates，M.J.Ling，J.Mann and D.K.Arvind，”Respiratory rate and flow waveform estimation from tri-axial accelerometer data”，International Conference on Body Sensor Network，pp.144-150，June 2010」に開示されている。加速度変位検出部２６は、このような角度変位の検出を加速度のサンプリング周期ごとに行う。 Such a method can be described, for example, in the literature "A.Bates, MJLing, J.Mann and DKArvind," Respiratory rate and flow waveform estimation from tri-axial accelerometer data ", International Conference on Body Sensor Network, pp.144-150. , June 2010 ”. The acceleration / displacement detection unit 26 detects such an angular displacement for each acceleration sampling cycle.
　続いて、標本化部２７は、Ｒ波振幅検出部２４から出力されるＲ波振幅の時系列信号、ＲＲ間隔検出部２５から出力されるＲＲ間隔の時系列信号、加速度変位検出部２６から出力される角度変位の時系列信号の各々を、心電計２０のサンプリング周波数および３軸加速度センサ２１のサンプリング周波数よりも遅いサンプリング周波数（例えば１Ｈｚ間隔）でサンプリングする（図５ステップＳ１０５）。 Subsequently, the sampling unit 27 outputs the R wave amplitude time-series signal output from the R-wave amplitude detection unit 24, the RR interval time-series signal output from the RR interval detection unit 25, and the acceleration / displacement detection unit 26. Each of the time-series signals of the angular displacement to be performed is sampled at a sampling frequency (for example, 1 Hz interval) slower than the sampling frequency of the electrocardiograph 20 and the sampling frequency of the 3-axis acceleration sensor 21 (FIG. 5, step S105).
　バンドパスフィルタ２８は、標本化部２７が取得したＲ波振幅の時系列信号、ＲＲ間隔の時系列信号、角度変位の時系列信号の各々を帯域制限する（図５ステップＳ１０６）。バンドパスフィルタ２８を用いる理由は、人の呼吸周波数が低周波のみに限られるためである。このバンドパスフィルタ２８の通過帯域は、例えば０．１５～０．４Ｈｚである。 The bandpass filter 28 band-limits each of the R wave amplitude time-series signal, the RR interval time-series signal, and the angular displacement time-series signal acquired by the sampling unit 27 (FIG. 5, step S106). The reason for using the bandpass filter 28 is that the human breathing frequency is limited to low frequencies. The pass band of the bandpass filter 28 is, for example, 0.15 to 0.4 Hz.
　特徴量抽出部２９は、バンドパスフィルタ２８で得られたＲ波振幅の時系列信号、ＲＲ間隔の時系列信号、角度変位の時系列信号の各々から瞬時的な線形化された位相情報及び瞬時的な振幅情報及び瞬時的な周波数情報を抽出する（図５ステップＳ１０８）。本実施例では、人の呼吸の動きを波形で表したときに、呼吸曲線の理想モデルにおいては、短時間での呼吸数の変化が無視できるほど小さいことと呼吸の動きが正弦波になると仮定し、ヒルベルト変換により瞬時的な位相情報を抽出する。 The feature amount extraction unit 29 instantaneously linearizes phase information and instantaneously from each of the R wave amplitude time-series signal, the RR interval time-series signal, and the angular displacement time-series signal obtained by the bandpass filter 28. Amplitude information and instantaneous frequency information are extracted (FIG. 5, step S108). In this example, when the movement of human breathing is represented by a waveform, it is assumed that the change in respiratory rate in a short time is negligibly small and the movement of breathing becomes a sinusoidal wave in the ideal model of the respiratory curve. Then, instantaneous phase information is extracted by Hilbert conversion.
　図６は特徴量抽出部２９の構成を示すブロック図である。特徴量抽出部２９は、ヒルベルト変換部２９０と、角度算出部２９１と、アンラップ処理部２９２とから構成される。 FIG. 6 is a block diagram showing the configuration of the feature amount extraction unit 29. The feature amount extraction unit 29 includes a Hilbert transform unit 290, an angle calculation unit 291 and an unwrap processing unit 292.
　まず、ヒルベルト変換部２９０は、バンドパスフィルタ２８によって帯域制限されたＲ波振幅の時系列信号、ＲＲ間隔の時系列信号、角度変位の時系列信号をヒルベルト変換して、位相がπ／２異なる２つの信号（実部及び虚部）を生成する。ヒルベルト変換部２９０に入力される信号をＡｅｘｐ（－ｉθ）の正弦波で表現すると（図７Ａ）、生成された実部の信号はＡｃｏｓθで表現される正弦波であり、虚部の信号はｉＡｓｉｎθで表現される正弦波の信号である。Ａは振幅、θは角度、ｉは虚数単位である。 First, the Hilbert transform unit 290 Hilbert transforms the time-series signal of the R wave amplitude band-limited by the bandpass filter 28, the time-series signal of the RR interval, and the time-series signal of the angular displacement, and the phases are different by π / 2. Generates two signals (real part and imaginary part). When the signal input to the Hilbert converter 290 is expressed by a sine wave of Aexp (-iθ) (FIG. 7A), the generated real part signal is a sine wave expressed by Acosθ, and the imaginary part signal is iAsinθ. It is a sine wave signal expressed by. A is an amplitude, θ is an angle, and i is an imaginary unit.
　続いて、角度算出部２９１は、ヒルベルト変換部２９０の出力信号の実部Ａｃｏｓθと虚部ｉＡｓｉｎθとから角度θ（－πから＋π）を算出する。 Subsequently, the angle calculation unit 291 calculates the angle θ (from −π to + π) from the real part Acos θ and the imaginary part iAsin θ of the output signal of the Hilbert transform unit 290.
　最後に、アンラップ処理部２９２は、角度算出部２９１が算出した角度θを連続した位相値に線形化する位相アンラップを行う。角度算出部２９１が算出する角度θは－πから＋πの間の値となるので、図７Ｂに示すように隣り合う点に２πの位相飛びが生じる場合がある。そこで、アンラップ処理部２９２は、例えば２πを足したり引いたりすることで、位相を繋ぎ合わせるようにする。これにより、図７Ｃに示すような連続した位相が得られる。 Finally, the unwrap processing unit 292 performs phase unwrap in which the angle θ calculated by the angle calculation unit 291 is linearized into continuous phase values. Since the angle θ calculated by the angle calculation unit 291 is a value between −π and + π, a phase skip of 2π may occur at adjacent points as shown in FIG. 7B. Therefore, the unwrap processing unit 292 connects the phases by, for example, adding or subtracting 2π. As a result, a continuous phase as shown in FIG. 7C is obtained.
　特徴量抽出部２９は、以上のような処理を、バンドパスフィルタ２８によって帯域制限されたＲ波振幅の時系列信号、ＲＲ間隔の時系列信号、角度変位の時系列信号の各々について行う。すなわち、ヒルベルト変換部２９０と角度算出部２９１とアンラップ処理部２９２の組を３組設け、Ｒ波振幅、ＲＲ間隔、角度変位の３つの時系列信号の処理を同時並行に行うようにすればよい。 The feature amount extraction unit 29 performs the above processing for each of the time-series signal of the R wave amplitude band-limited by the bandpass filter 28, the time-series signal of the RR interval, and the time-series signal of the angular displacement. That is, three sets of the Hilbert transform unit 290, the angle calculation unit 291 and the unwrap processing unit 292 may be provided, and the three time-series signals of R wave amplitude, RR interval, and angle displacement may be processed in parallel at the same time. ..
　次に、カルマンフィルタ３０は、Ｒ波振幅の位相情報、ＲＲ間隔の位相情報、角度変位の位相情報の各々についてノイズを濾した位相情報を推定する（図５ステップＳ１０９）。図８に示すように、カルマンフィルタ３０は、Ｒ波振幅、ＲＲ間隔、角度変位の各々について設けられ、特徴量抽出部２９で得られたＲ波振幅の位相情報、ＲＲ間隔の位相情報、角度変位の位相情報がそれぞれ対応するカルマンフィルタ３０－１，３０－２，３０－３に入力される。 Next, the Kalman filter 30 estimates the phase information obtained by filtering out the noise for each of the phase information of the R wave amplitude, the phase information of the RR interval, and the phase information of the angular displacement (FIG. 5, step S109). As shown in FIG. 8, the Kalman filter 30 is provided for each of the R wave amplitude, the RR interval, and the angular displacement, and the phase information of the R wave amplitude, the phase information of the RR interval, and the angular displacement obtained by the feature amount extraction unit 29 are provided. The phase information of is input to the corresponding Kalman filters 30-1, 30-2, 30-3, respectively.
　図９はカルマンフィルタ３０のブロック線図である。なお、ここでは３つのカルマンフィルタ３０－１，３０－２，３０－３のうちの１つのカルマンフィルタについてブロック線図を示す。カルマンフィルタは、ベイジアン推定と自動再帰推定に基づくものである。システムへの入力はガウシアン雑音を有するという前提の下、カルマン利得Ｋは最小自乗近似により求める。生体システムを記述する物理量ｘ（ｋ）は再帰的に決定されており、次式で表される。 FIG. 9 is a block diagram of the Kalman filter 30. Here, a block diagram is shown for one of the three Kalman filters 30-1, 30-2, and 30-3. The Kalman filter is based on Bayesian estimation and automatic recursive estimation. The Kalman gain K is obtained by the minimum square approximation on the assumption that the input to the system has Gaussian noise. The physical quantity x (k) that describes the biological system is recursively determined and is expressed by the following equation.
Figure JPOXMLDOC01-appb-M000002
Figure JPOXMLDOC01-appb-M000002
　ここで、ｕ（ｋ）はシステム入力、ｘ（ｋ）は雑音無しの物理量である。また、ｍは測定数、ｎは生体システムからの信号数、Ａはシステムモデルを示すｎ×ｍ行列、Ｈは測定系モデルを示すｍ×ｎ行列である。式（２）に示すように、物理量ｘ（ｋ＋１）には、前時刻における物理量Ａｘ（ｋ）と、ｗｓ（ｋ）の生体システム雑音とが含まれる。式（３）に示すように、システム入力ｕ（ｋ）には、測定値Ｈｘ（ｋ）と、ｗｍ（ｋ）の測定系雑音とが含まれる。本実施例の場合、測定値Ｈｘ（ｋ）は特徴量抽出部２９から入力される位相値である。 Here, u (k) is a system input, and x (k) is a noise-free physical quantity. Further, m is the number of measurements, n is the number of signals from the biological system, A is the n × m matrix indicating the system model, and H is the m × n matrix indicating the measurement system model. As shown in the formula (2), the physical quantity x (k + 1) includes the physical quantity Ax (k) at the previous time and the biological system noise of ws (k). As shown in the equation (3), the system input u (k) includes the measured value Hx (k) and the measurement system noise of the wm (k). In the case of this embodiment, the measured value Hx (k) is a phase value input from the feature amount extraction unit 29.
Figure JPOXMLDOC01-appb-M000003
Figure JPOXMLDOC01-appb-M000003
　ここで、Ｋ（ｋ）はカルマン利得を示すｎ×ｍ行列、ハットｘ（ｋ）は物理量ｘ（ｋ）の推定値（本実施例では位相の推定値）である。以下、同様に文字上に付した「∧」をハットと呼ぶ。カルマン利得Ｋ（ｋ）は次の式（５）～式（７）により求めることができる。 Here, K (k) is an n × m matrix indicating the Kalman gain, and hat x (k) is an estimated value of the physical quantity x (k) (in this embodiment, an estimated value of the phase). Hereinafter, the "∧" attached on the character is also called a hat. The Kalman gain K (k) can be obtained by the following equations (5) to (7).
Figure JPOXMLDOC01-appb-M000004
Figure JPOXMLDOC01-appb-M000004
　ここで、Ｒはセンサ雑音に関する共分散マトリクス、Ｑは生体システム雑音に関する共分散マトリクス、Ｐは推定値に関する共分散マトリクスである。ＨＴ，ＡＴはそれぞれ行列Ｈ，Ａの転置行列である。カルマン利得Ｋ（ｋ）は、測定系雑音ｗｍ（ｋ）を最小化するように再帰的に決定される。共分散マトリクスＰ（ｋ｜ｋ）の対角成分はフィルタ処理により最小化される自乗推定誤差である。 Here, R is a covariance matrix related to sensor noise, Q is a covariance matrix related to biological system noise, and P is a covariance matrix related to estimated values. HT and AT are transposed matrices of matrices H and A, respectively. The Kalman gain K (k) is recursively determined so as to minimize the measurement system noise wm (k). The diagonal component of the covariance matrix P (k | k) is the squared estimation error minimized by the filtering process.
　センサ雑音に関する共分散マトリクスＲを予め正確に設定するために、生体の呼吸レートが安定している時刻域を利用し、特徴量抽出部２９で得られたＲ波振幅の位相情報、ＲＲ間隔の位相情報、角度変位の位相情報の各々について、当該時刻域における標準偏差σを計算する。 In order to accurately set the covariance matrix R related to the sensor noise in advance, the phase information of the R wave amplitude and the RR interval obtained by the feature amount extraction unit 29 are used in the time range where the respiratory rate of the living body is stable. For each of the phase information and the phase information of the angular displacement, the standard deviation σ in the relevant time range is calculated.
　Ｒ波振幅、ＲＲ間隔、角度変位のそれぞれの入力が互いに独立であるという前提では、共分散マトリクスＲは対角行列である。そこで、Ｒ波振幅の位相情報について得られた標準偏差σの平均値を、Ｒ波振幅用のカルマンフィルタ３０－１の共分散マトリクスＲの対角成分として予め設定し、ＲＲ間隔の位相情報について得られた標準偏差σの平均値を、ＲＲ間隔用のカルマンフィルタ３０－２の共分散マトリクスＲの対角成分として予め設定し、角度変位の位相情報について得られた標準偏差σの平均値を、角度変位用のカルマンフィルタ３０－３の共分散マトリクスＲの対角成分として予め設定する。センサ雑音に関する共分散マトリクスＲは、測定環境に依存するものの、個人差による違いはさほど大きくないと考えられる。 The covariance matrix R is a diagonal matrix on the assumption that the inputs of the R wave amplitude, the RR interval, and the angular displacement are independent of each other. Therefore, the average value of the standard deviation σ obtained for the phase information of the R wave amplitude is set in advance as the diagonal component of the covariance matrix R of the Kalman filter 30-1 for the R wave amplitude, and the phase information of the RR interval is obtained. The average value of the obtained standard deviation σ is set in advance as the diagonal component of the covariance matrix R of the Kalman filter 30-2 for the RR interval, and the average value of the standard deviation σ obtained for the phase information of the angular displacement is set to the angle. It is preset as a diagonal component of the covariance matrix R of the Kalman filter 30-3 for displacement. Although the covariance matrix R regarding sensor noise depends on the measurement environment, it is considered that the difference due to individual differences is not so large.
　一方、生体システム雑音に関する共分散マトリクスＱは、生体システム雑音であり、個人差が反映される。この共分散マトリクスＱは対角行列である。共分散マトリクスＱの対角成分を変更可能なパラメータとして、生体毎に共分散マトリクスＱの対角成分の最適値を数値的にテストし、生体に応じた対角成分の値を予め設定しておくようにすればよい。以上のように、予め生体に呼吸をさせつつデータを計測し、計測したデータに基づいて共分散マトリクスＲ，Ｑを決定しておくことで、位相の推定精度を高めることができる。ここで、各パラメータの例示を行う。ｘ（ｋ）は位相θｋとドットθｋのベクトルとして表現できる。ドットθｋは位相θｋの微分である。ここでは、文字上に付した「・」をドットと呼ぶ。共分散マトリクスＱ、Ｒの定数ｑ１およびｒ１は設定可能なパラメータであり、上述の通りに設定を行う。例えば、ｑ１＝１×１０－３、ｒ１＝２．４である。 On the other hand, the covariance matrix Q regarding the biological system noise is the biological system noise, and individual differences are reflected. This covariance matrix Q is a diagonal matrix. As a parameter in which the diagonal component of the covariance matrix Q can be changed, the optimum value of the diagonal component of the covariance matrix Q is numerically tested for each living body, and the value of the diagonal component according to the living body is set in advance. You can leave it. As described above, the phase estimation accuracy can be improved by measuring the data while breathing the living body in advance and determining the covariance matrices R and Q based on the measured data. Here, an example of each parameter will be given. x (k) can be expressed as a vector of the phase θk and the dot θk. The dot θk is a derivative of the phase θk. Here, the "・" attached to the character is called a dot. The constants q1 and r1 of the covariance matrix Q and R are configurable parameters and are set as described above. For example, q1 = 1 × 10-3, r1 = 2.4.
Figure JPOXMLDOC01-appb-M000005
Figure JPOXMLDOC01-appb-M000005
　カルマンフィルタ３０（３０－１～３０－３）は、フィルタ処理を標本化部２７のサンプリング周期ごと（本実施例の例では１秒ごと）に行う。
　次に、統合処理部３１は、カルマンフィルタ３０（３０－１～３０－３）で得られたＲ波振幅の推定位相値、ＲＲ間隔の推定位相値、角度変位の推定位相値を、カルマンフィルタ３０（３０－１～３０－３）の自乗推定誤差に基づく重みを用いて重み付け平均化処理することにより、Ｒ波振幅、ＲＲ間隔、角度変位の推定位相値のデータを統合する（図５ステップＳ１１０）。 The Kalman filter 30 (30-1 to 30-3) performs the filter processing every sampling cycle of the sampling unit 27 (every second in the example of this embodiment).
Next, the integrated processing unit 31 applies the estimated phase value of the R wave amplitude obtained by the Kalman filter 30 (30-1 to 30-3), the estimated phase value of the RR interval, and the estimated phase value of the angular displacement to the Kalman filter 30 (30-1 to 30-3). By performing weighting averaging processing using weights based on the squared estimation error of 30-1 to 30-3), data of estimated phase values of R wave amplitude, RR interval, and angular displacement are integrated (FIG. 5 step S110). ..
　図１０は統合処理部３１の構成を示すブロック図である。統合処理部３１は、重み付け定数生成処理部３１０と、重み付け平均化処理部３１１とから構成される。上記のカルマンフィルタ処理では、時刻ごと（本実施例の例では１秒ごと）に推定位相値が得られると共に自乗推定誤差が更新される。 FIG. 10 is a block diagram showing the configuration of the integrated processing unit 31. The integrated processing unit 31 includes a weighting constant generation processing unit 310 and a weighting averaging processing unit 311. In the above Kalman filter processing, the estimated phase value is obtained every time (every second in the example of this embodiment), and the squared estimation error is updated.
　本実施例では、Ｒ波振幅用のカルマンフィルタ３０－１におけるカルマンフィルタ処理の自乗推定誤差をσ１、ＲＲ間隔用のカルマンフィルタ３０－２におけるカルマンフィルタ処理の自乗推定誤差をσ２、角度変位用のカルマンフィルタ３０－３におけるカルマンフィルタ処理の自乗推定誤差をσ３とする。Ｒ波振幅、ＲＲ間隔、角度変位のそれぞれのカルマンフィルタ処理の自乗推定誤差σｉ（ｉ＝１，２，３）は、次式のように表される。 In this embodiment, the Kalman filter processing self-estimation error in the Kalman filter 30-1 for R wave amplitude is σ1, the Kalman filter processing self-estimation error in the RR interval Kalman filter 30-2 is σ2, and the Kalman filter 30-3 for angular displacement. Let σ3 be the self-estimation error of the Kalman filter processing in. The square estimation error σi (i = 1, 2, 3) of each Kalman filter processing of R wave amplitude, RR interval, and angular displacement is expressed by the following equation.
Figure JPOXMLDOC01-appb-M000006
Figure JPOXMLDOC01-appb-M000006
　重み付け定数生成処理部３１０は、Ｒ波振幅、ＲＲ間隔、角度変位のそれぞれの自乗推定誤差σｉから重み付け定数αｉを式（９）により算出する。 The weighting constant generation processing unit 310 calculates the weighting constant αi from the squared estimation errors σi of the R wave amplitude, the RR interval, and the angular displacement by the equation (9).
Figure JPOXMLDOC01-appb-M000007
Figure JPOXMLDOC01-appb-M000007
　重み付け定数生成処理部３１０は、式（９）の算出をＲ波振幅、ＲＲ間隔、角度変位の各々について行う。そして、重み付け平均化処理部３１１は、重み付け定数生成処理部３１０が算出した重み付け定数αｉ（ｉ＝１，２，３）を用いて、Ｒ波振幅の推定位相値ハットｘ１、ＲＲ間隔の推定位相値ハットｘ２、角度変位の推定位相値ハットｘ３を重み付け平均化処理した位相値である統合出力値ハットｘｆ（ｋ）を式（１０）のように算出する。 The weighting constant generation processing unit 310 calculates the equation (9) for each of the R wave amplitude, the RR interval, and the angular displacement. Then, the weighted averaging processing unit 311 uses the weighting constant αi (i = 1, 2, 3) calculated by the weighting constant generation processing unit 310 to estimate the phase value hat x1 of the R wave amplitude and the estimated phase of the RR interval. The integrated output value hat xf (k), which is a phase value obtained by weighting and averaging the value hat x2 and the estimated phase value hat x3 of the angular displacement, is calculated by the equation (10).
Figure JPOXMLDOC01-appb-M000008
Figure JPOXMLDOC01-appb-M000008
　統合処理部３１は、以上のような統合処理を標本化部２７のサンプリング周期ごと（本実施例の例では１秒ごと）に行う。 The integrated processing unit 31 performs the above integrated processing every sampling cycle of the sampling unit 27 (every second in the example of this embodiment).
　次に、呼吸周波数変換部３２は、統合処理部３１によって統合された位相値を周波数に変換して呼吸周波数信号を出力する（図５ステップＳ１１１）。統合処理部３１から出力される位相値を時間微分すれば、瞬時角周波数が得られるので、この瞬時角周波数を２πで割ることにより、呼吸周波数ｆを求めることができる。こうして、呼吸周波数変換部３２は、呼吸周波数ｆのデータを出力する。
　以上の呼吸周波数測定部２の構成は国際公開ＷＯ２０１７／０９０７３２に開示されている。 Next, the respiratory frequency conversion unit 32 converts the phase value integrated by the integrated processing unit 31 into a frequency and outputs a respiratory frequency signal (step S111 in FIG. 5). Since the instantaneous angular frequency can be obtained by time-differentiating the phase value output from the integrated processing unit 31, the respiratory frequency f can be obtained by dividing this instantaneous angular frequency by 2π. In this way, the respiratory frequency conversion unit 32 outputs the data of the respiratory frequency f.
The configuration of the above-mentioned respiratory frequency measuring unit 2 is disclosed in the international publication WO2017 / 090732.
　図１１は活動量測定部３の構成を示すブロック図である。活動量測定部３は、生体の３軸加速度を測定する３軸加速度センサ３４と、３軸加速度センサ３４の測定結果に基づいて生体の活動量を算出する活動量算出部３５とから構成される。 FIG. 11 is a block diagram showing the configuration of the activity amount measuring unit 3. The activity amount measuring unit 3 is composed of a 3-axis acceleration sensor 34 that measures the 3-axis acceleration of the living body and an activity amount calculating unit 35 that calculates the activity amount of the living body based on the measurement result of the 3-axis acceleration sensor 34. ..
　Ｘ軸、Ｙ軸、およびＺ軸の測定軸を有する３軸加速度センサ３４による各測定軸の加速度の測定値を夫々、ｘｉ，ｙｉ，ｚｉとしたとき、活動量算出部３５は、下記の式（１１）に示す演算によって標準偏差を算出し、その値を生体の活動量βとする。 When the measured values of the acceleration of each measurement axis by the 3-axis acceleration sensor 34 having the X-axis, Y-axis, and Z-axis measurement axes are xi, yi, and zi, respectively, the activity amount calculation unit 35 uses the following formula. The standard deviation is calculated by the calculation shown in (11), and the value is taken as the activity amount β of the living body.
Figure JPOXMLDOC01-appb-M000009
Figure JPOXMLDOC01-appb-M000009
　式（１１）において、ｎは取得した加速度の測定値のサンプル数である。また、ａｉは、３軸の加速度の測定値の合成値であり、下記の式（１２）によって表される。ａAVは、ａｉの平均値である。 In equation (11), n is the number of samples of the acquired acceleration measured values. Further, ai is a composite value of the measured values of the accelerations of the three axes, and is represented by the following equation (12). a AV is the average value of ai.
Figure JPOXMLDOC01-appb-M000010
Figure JPOXMLDOC01-appb-M000010
　本実施例では、３軸加速度センサ３４は生体の頭部に装着され、活動量算出部３５は生体の頭部の活動量βを算出する。
　以上の活動量測定部３の構成は特開２０１７－３８８３９号公報に開示されている。 In this embodiment, the 3-axis accelerometer 34 is attached to the head of the living body, and the activity amount calculation unit 35 calculates the activity amount β of the head of the living body.
The configuration of the above activity amount measuring unit 3 is disclosed in Japanese Patent Application Laid-Open No. 2017-38839.
　図１２は本実施例の見守り装置の動作を説明するフローチャートである。電源制御部７は、深部体温と呼吸周波数と活動量の測定時に、電池６から深部体温測定部１と呼吸周波数測定部２と活動量測定部３とに電源電圧を供給する（図１２ステップＳ１）。 FIG. 12 is a flowchart illustrating the operation of the watching device of this embodiment. The power supply control unit 7 supplies a power supply voltage from the battery 6 to the deep body temperature measuring unit 1, the respiratory frequency measuring unit 2, and the activity measuring unit 3 when measuring the deep body temperature, the respiratory frequency, and the activity amount (FIG. 12 step S1). ).
　電源電圧が供給されたことにより、深部体温測定部１は、生体の頭部の深部体温ＴCを測定する（図１２ステップＳ２）。呼吸周波数測定部２は、生体の呼吸周波数ｆを測定する（図１２ステップＳ３）。活動量測定部３は、生体の頭部の活動量βを測定する（図１２ステップＳ４）。 When the power supply voltage is supplied, the deep body temperature measuring unit 1 measures the deep body temperature TC of the head of the living body (step S2 in FIG. 12). The respiratory frequency measuring unit 2 measures the respiratory frequency f of the living body (step S3 in FIG. 12). The activity amount measuring unit 3 measures the activity amount β of the head of the living body (FIG. 12, step S4).
　解析部４は、深部体温測定部１と呼吸周波数測定部２と活動量測定部３の測定結果に基づいて、生体が眠れる状態か眠れない状態かを判定する（図１２ステップＳ５）。具体的には、解析部４は、深部体温ＴCが深部体温閾値Ｔth（例えば３７．５℃）未満という条件と、呼吸周波数ｆが呼吸周波数閾値ｆth（例えば１０Ｈｚ）未満で一定しているという条件と、活動量βが活動量閾値βth以上となる継続時間が時間閾値ｔth（例えば１０分）未満という条件が全て成立する場合に、眠れる状態と判定する。 The analysis unit 4 determines whether the living body can sleep or cannot sleep based on the measurement results of the deep body temperature measuring unit 1, the respiratory frequency measuring unit 2, and the activity measuring unit 3 (step S5 in FIG. 12). Specifically, the analysis unit 4 keeps the condition that the core body temperature T C is less than the core body temperature threshold T th (for example, 37.5 ° C.) and the respiratory frequency f is constant below the respiratory frequency threshold value f th (for example, 10 Hz). When all the conditions that the activity amount β is equal to or more than the activity amount threshold value β th and the duration that the activity amount β is less than the time threshold value t th (for example, 10 minutes) are satisfied, it is determined to be in a sleep state.
　反対に、解析部４は、深部体温ＴCが深部体温閾値Ｔth以上という条件と、呼吸周波数ｆが呼吸周波数閾値ｆth以上で一定しないという条件と、活動量βが活動量閾値βth以上となる継続時間が時間閾値ｔth以上という条件のうち少なくとも１つが成立する場合に、眠れない状態と判定する。 On the contrary, the analysis unit 4 has a condition that the deep body temperature T C is equal to or higher than the deep body temperature threshold T th , a condition that the respiratory frequency f is not constant at the respiratory frequency threshold f th or higher, and the activity amount β is the activity threshold β th or higher. When at least one of the conditions that the duration is t th or more is satisfied, it is determined that the person cannot sleep.
　なお、解析部４は、例えば測定開始の後に呼吸周波数閾値ｆth未満となった最初の呼吸周波数ｆを基準値とし、以降の測定で得られた呼吸周波数ｆが呼吸周波数閾値ｆth未満で、かつ基準値に対する呼吸周波数ｆの変化量の絶対値が変化量閾値Δth未満であれば、呼吸周波数ｆが呼吸周波数閾値ｆth未満で一定していると判定すればよい。 The analysis unit 4 uses, for example, the first breathing frequency f that is less than the breathing frequency threshold fth after the start of measurement as a reference value, and the breathing frequency f obtained in the subsequent measurements is less than the breathing frequency threshold fth . If the absolute value of the change in the breathing frequency f with respect to the reference value is less than the change amount threshold Δth , it may be determined that the breathing frequency f is constant below the breathing frequency threshold fth .
　また、解析部４は、例えば測定開始の後に呼吸周波数閾値ｆth以上となった最初の呼吸周波数ｆを基準値とし、以降の測定で得られた呼吸周波数ｆが呼吸周波数閾値ｆth以上で、かつ基準値に対する呼吸周波数ｆの変化量の絶対値が変化量閾値Δth以上であれば、呼吸周波数ｆが呼吸周波数閾値ｆth以上で一定していないと判定すればよい。 Further, the analysis unit 4 uses, for example, the first respiration frequency f that becomes the respiration frequency threshold f th or more after the start of measurement as a reference value, and the respiration frequency f obtained in the subsequent measurements is the respiration frequency threshold f th or more. If the absolute value of the amount of change in the respiratory frequency f with respect to the reference value is equal to or greater than the change amount threshold Δ th , it may be determined that the respiratory frequency f is equal to or greater than the respiratory frequency threshold f th and is not constant.
　深部体温閾値Ｔth、呼吸周波数閾値ｆth、活動量閾値βth、時間閾値ｔthといった閾値は、例えばスマートフォンなどの外部装置から変更することが可能である。
　通信部８は、外部装置から無線送信された閾値を受信し、受信した閾値を解析部４に渡す。解析部４は、設定済の閾値を通信部８から受け取った閾値に更新する。 The thresholds such as the core body temperature threshold T th , the respiratory frequency threshold f th , the activity threshold β th , and the time threshold t th can be changed from an external device such as a smartphone.
The communication unit 8 receives the threshold value wirelessly transmitted from the external device, and passes the received threshold value to the analysis unit 4. The analysis unit 4 updates the set threshold value to the threshold value received from the communication unit 8.
　次に、出力部５は、深部体温ＴCの測定結果と解析部４の判定結果とを出力する（図１２ステップＳ６）。具体的には、表示部９が、深部体温ＴCの測定結果と解析部４の判定結果とを表示すればよい。解析部４の判定結果の表示は、例えば生体が眠れる状態という判定結果がでた場合には、緑色のＬＥＤを点灯し、生体が眠れない状態という判定結果がでた場合には、赤色のＬＥＤを点灯するという方法で行ってもよい。また、通信部８が、深部体温ＴCの測定結果と解析部４の判定結果とをスマートフォンなどの外部装置に無線送信するようにしてもよい。 Next, the output unit 5 outputs the measurement result of the core body temperature TC and the determination result of the analysis unit 4 (FIG. 12, step S6). Specifically, the display unit 9 may display the measurement result of the core body temperature TC and the determination result of the analysis unit 4. As for the display of the determination result of the analysis unit 4, for example, when the determination result that the living body can sleep is obtained, the green LED is turned on, and when the determination result that the living body cannot sleep is obtained, the red LED is displayed. You may do it by turning on. Further, the communication unit 8 may wirelessly transmit the measurement result of the core body temperature TC and the determination result of the analysis unit 4 to an external device such as a smartphone.
　また、解析部４は、生体が眠れない状態という判定結果がでた場合に（図１２ステップＳ７においてＹＥＳ）、出力部５に不眠警報を出力させるようにしてもよい（図１２ステップＳ８）。不眠警報の出力方法としては、例えば不眠警報発生を知らせる信号を外部装置に無線送信する等の方法がある。 Further, the analysis unit 4 may cause the output unit 5 to output an insomnia alarm when the determination result that the living body cannot sleep is obtained (YES in step S7 of FIG. 12). As a method of outputting the insomnia alarm, for example, there is a method of wirelessly transmitting a signal notifying the occurrence of the insomnia alarm to an external device.
　また、解析部４は、深部体温ＴCが所定の高温閾値Ｔhigh（３８．５℃）以上の場合に（図１２ステップＳ９においてＹＥＳ）、出力部５に高温警報を出力させるようにしてもよい（図１２ステップＳ１０）。高温警報の出力方法としては、例えば高温警報発生を知らせる信号を外部装置に無線送信する等の方法がある。 Further, the analysis unit 4 may cause the output unit 5 to output a high temperature alarm when the core body temperature TC is equal to or higher than a predetermined high temperature threshold value T high (38.5 ° C.) (YES in step S9 in FIG. 12). Good (step S10 in FIG. 12). As a method of outputting the high temperature alarm, for example, there is a method of wirelessly transmitting a signal notifying the occurrence of the high temperature alarm to an external device.
　また、解析部４は、深部体温ＴCと呼吸周波数ｆと活動量βとに基づいて生体の睡眠状態がレム睡眠かノンレム睡眠かを判定し（図１２ステップＳ１１）、判定結果を出力部５に出力させるようにしてもよい（図１２ステップＳ１２）。例えば、解析部４は、深部体温ＴCが計測開始からの平均体温以上で、呼吸周波数ｆの変化率が３０％未満で、活動量βの変化率が３０％未満であるときに、レム睡眠と判定し、深部体温ＴCが計測開始からの平均体温未満で、呼吸周波数ｆの変化率が３０％以上で、活動量βの変化率が３０％以上であるときに、ノンレム睡眠と判定する。
　見守り装置は、図１２の処理を定期的に行う。 Further, the analysis unit 4 determines whether the sleep state of the living body is REM sleep or non-REM sleep based on the core body temperature TC , the respiratory frequency f, and the activity amount β (step S11 in FIG. 12), and outputs the determination result to the output unit 5. May be output to (FIG. 12, step S12). For example, the analysis unit 4 may perform REM sleep when the core body temperature TC is equal to or higher than the average body temperature from the start of measurement, the rate of change in respiratory frequency f is less than 30%, and the rate of change in activity β is less than 30%. When the core body temperature TC is less than the average body temperature from the start of measurement, the rate of change in respiratory frequency f is 30% or more, and the rate of change in activity β is 30% or more, it is determined to be non-REM sleep. ..
The watching device periodically performs the process shown in FIG.
　本実施例では、生体の深部体温ＴCと同時に生体の睡眠状態（眠れる状態か眠れない状態）を把握することができ、生体の状態を正確に把握することができる。その結果、本実施例では、例えば投薬の要否や投薬に適したタイミングなどを把握することが可能となる。例えばインフルエンザや風邪などによって人が発熱している場合、眠りの浅いときに人を起こして解熱剤を与える方が、その後の入眠には適切である。本実施例によれば、このような投薬のタイミングの把握が可能となる。 In this embodiment, the sleeping state (sleeping state or sleepless state) of the living body can be grasped at the same time as the deep body temperature TC of the living body, and the state of the living body can be accurately grasped. As a result, in this embodiment, for example, it becomes possible to grasp the necessity of dosing and the timing suitable for dosing. For example, when a person has a fever due to influenza or a cold, it is more appropriate to wake up the person and give an antipyretic agent when he / she sleeps lightly for the subsequent sleep onset. According to this embodiment, it is possible to grasp the timing of such dosing.
　本実施例で説明した解析部４は、ＣＰＵ（Central Processing Unit）、記憶装置及びインタフェースを備えたコンピュータと、これらのハードウェア資源を制御するプログラムによって実現することができる。このコンピュータの構成例を図１３に示す。 The analysis unit 4 described in this embodiment can be realized by a computer provided with a CPU (Central Processing Unit), a storage device, and an interface, and a program for controlling these hardware resources. A configuration example of this computer is shown in FIG.
　コンピュータは、ＣＰＵ２００と、記憶装置２０１と、インタフェース装置（Ｉ／Ｆ）２０２とを備えている。Ｉ／Ｆ２０２には、深部体温測定部１のハードウェアと呼吸周波数測定部２のハードウェアと活動量測定部３のハードウェアと出力部５のハードウェア等が接続される。このようなコンピュータにおいて、本発明の見守り方法を実現させるための見守りプログラムは記憶装置２０１に格納される。ＣＰＵ２００は、記憶装置２０１に格納されたプログラムに従って本実施例で説明した処理を実行する。 The computer includes a CPU 200, a storage device 201, and an interface device (I / F) 202. The hardware of the deep body temperature measuring unit 1, the hardware of the breathing frequency measuring unit 2, the hardware of the activity measuring unit 3, the hardware of the output unit 5, and the like are connected to the I / F 202. In such a computer, the watching program for realizing the watching method of the present invention is stored in the storage device 201. The CPU 200 executes the process described in this embodiment according to the program stored in the storage device 201.
　なお、深部体温測定部１の推定部１２、呼吸周波数測定部２の呼吸周波数算出部３３、活動量測定部３の活動量算出部３５についても、図１３のようなコンピュータによって実現可能である。解析部４と推定部１２と呼吸周波数算出部３３と活動量算出部３５を別々のコンピュータで実現してもよいし、１つのコンピュータで実現してもよい。 The estimation unit 12 of the deep body temperature measurement unit 1, the respiration frequency calculation unit 33 of the respiration frequency measurement unit 2, and the activity amount calculation unit 35 of the activity amount measurement unit 3 can also be realized by a computer as shown in FIG. The analysis unit 4, the estimation unit 12, the respiratory frequency calculation unit 33, and the activity amount calculation unit 35 may be realized by separate computers, or may be realized by one computer.
　本発明は、生体の状態を把握する技術に適用することができる。 The present invention can be applied to a technique for grasping the state of a living body.
　１…深部体温測定部、２…呼吸周波数測定部、３…活動量測定部、４…解析部、５…出力部、６…電池、７…電源制御部、８…通信部、９…表示部。 1 ... Deep body temperature measurement unit, 2 ... Respiratory frequency measurement unit, 3 ... Activity measurement unit, 4 ... Analysis unit, 5 ... Output unit, 6 ... Battery, 7 ... Power supply control unit, 8 ... Communication unit, 9 ... Display unit ..

Claims (8)

  1. 　生体の深部体温を測定する第１のステップと、
    　生体の呼吸周波数を測定する第２のステップと、
    　生体の活動量を測定する第３のステップと、
    　前記深部体温と前記呼吸周波数と前記活動量とに基づいて生体が眠れる状態か眠れない状態かを判定する第４のステップと、
    　前記深部体温の測定結果と前記第４のステップの判定結果とを出力する第５のステップとを含むことを特徴とする見守り方法。     The first step to measure the core body temperature of a living body,
    The second step of measuring the respiratory frequency of the living body,
    The third step of measuring the amount of activity of the living body,
    A fourth step of determining whether the living body can sleep or cannot sleep based on the core body temperature, the respiratory frequency, and the amount of activity.
    A monitoring method comprising a fifth step of outputting the measurement result of the core body temperature and the determination result of the fourth step.
  2. 　請求項１記載の見守り方法において、
    　前記第４のステップは、前記深部体温が第１の閾値未満という条件と、前記呼吸周波数が第２の閾値未満で一定しているという条件と、前記活動量が第３の閾値以上となる継続時間が第４の閾値未満という条件が成立する場合に、生体が眠れる状態と判定し、前記深部体温が前記第１の閾値以上という条件と、前記呼吸周波数が前記第２の閾値以上で一定していないという条件と、前記活動量が前記第３の閾値以上となる継続時間が前記第４の閾値以上という条件のうち少なくとも１つが成立する場合に、生体が眠れない状態と判定するステップを含むことを特徴とする見守り方法。     In the monitoring method described in claim 1,
    In the fourth step, the condition that the core body temperature is below the first threshold value, the condition that the breathing frequency is constant below the second threshold value, and the continuation that the activity amount is equal to or more than the third threshold value. When the condition that the time is less than the fourth threshold value is satisfied, it is determined that the living body is in a sleep state, and the condition that the core body temperature is equal to or higher than the first threshold value and the respiratory frequency are constant at the second threshold value or higher. It includes a step of determining that the living body cannot sleep when at least one of the condition that the activity amount is not satisfied and the duration that the activity amount is equal to or more than the third threshold value is satisfied. A watching method characterized by that.
  3. 　請求項１または２記載の見守り方法において、
    　前記第１のステップは、生体の頭部の表皮温度を測定するステップと、この表皮温度の測定結果に基づいて生体の深部体温を算出するステップとを含み、
    　前記第２のステップは、生体の心電位波形と生体の呼吸運動による加速度とを測定するステップと、この心電位波形と加速度の測定結果に基づいて生体の呼吸周波数を算出するステップとを含み、
    　前記第３のステップは、生体の頭部の加速度を測定するステップと、この加速度の測定結果に基づいて生体の活動量を算出するステップとを含むことを特徴とする見守り方法。     In the monitoring method according to claim 1 or 2,
    The first step includes a step of measuring the epidermis temperature of the head of the living body and a step of calculating the core body temperature of the living body based on the measurement result of the epidermis temperature.
    The second step includes a step of measuring the electrocardiographic waveform of the living body and an acceleration due to the respiratory movement of the living body, and a step of calculating the respiratory frequency of the living body based on the measurement result of the electrocardiographic waveform and the acceleration.
    The third step is a monitoring method including a step of measuring the acceleration of the head of the living body and a step of calculating the amount of activity of the living body based on the measurement result of the acceleration.
  4. 　請求項１乃至３のいずれか１項に記載の見守り方法において、
    　生体が眠れない状態と判定された場合に不眠警報を出力する第６のステップをさらに含むことを特徴とする見守り方法。     In the monitoring method according to any one of claims 1 to 3,
    A monitoring method further comprising a sixth step of outputting an insomnia alarm when it is determined that the living body cannot sleep.
  5. 　請求項１乃至４のいずれか１項に記載の見守り方法において、
    　前記深部体温が高温閾値以上の場合に高温警報を出力する第７のステップをさらに含むことを特徴とする見守り方法。     In the monitoring method according to any one of claims 1 to 4,
    A monitoring method further comprising a seventh step of outputting a high temperature alarm when the core body temperature is equal to or higher than a high temperature threshold value.
  6. 　請求項１乃至５のいずれか１項に記載の見守り方法において、
    　前記深部体温と前記呼吸周波数と前記活動量とに基づいて生体の睡眠状態がレム睡眠かノンレム睡眠かを判定する第８のステップと、
    　前記第８のステップの判定結果を出力する第９のステップとをさらに含むことを特徴とする見守り方法。     In the monitoring method according to any one of claims 1 to 5,
    The eighth step of determining whether the sleep state of the living body is REM sleep or non-REM sleep based on the core body temperature, the respiration frequency, and the amount of activity.
    A monitoring method comprising further including a ninth step for outputting a determination result of the eighth step.
  7. 　請求項１乃至６のいずれか１項に記載の各ステップをコンピュータに実行させることを特徴とする見守りプログラム。 A watching program characterized by having a computer execute each step according to any one of claims 1 to 6.
  8. 　生体の深部体温を測定するように構成された深部体温測定部と、
    　生体の呼吸周波数を測定するように構成された呼吸周波数測定部と、
    　生体の活動量を測定するように構成された活動量測定部と、
    　前記深部体温と前記呼吸周波数と前記活動量とに基づいて生体が眠れる状態か眠れない状態かを判定するように構成された解析部と、
    　前記深部体温の測定結果と前記解析部の判定結果とを出力するように構成された出力部とを備えることを特徴とする見守り装置。     A deep body temperature measuring unit configured to measure the core body temperature of a living body,
    A respiratory frequency measuring unit configured to measure the respiratory frequency of a living body,
    An activity measuring unit configured to measure the activity of a living body,
    An analysis unit configured to determine whether the living body can sleep or cannot sleep based on the core body temperature, the respiratory frequency, and the amount of activity.
    A monitoring device including an output unit configured to output the measurement result of the core body temperature and the determination result of the analysis unit.
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