WO2022038710A1 - Procédé de visualisation, programme de visualisation, et dispositif de visualisation - Google Patents

Procédé de visualisation, programme de visualisation, et dispositif de visualisation 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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English (en)
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 JP2022543871A priority Critical patent/JP7420264B2/ja
Priority to PCT/JP2020/031261 priority patent/WO2022038710A1/fr
Publication of WO2022038710A1 publication Critical patent/WO2022038710A1/fr

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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

La présente invention concerne un dispositif de visualisation comprenant : une unité de mesure de température corporelle profonde (1) destinée à mesurer la température corporelle profonde d'un corps vivant ; une unité de mesure de fréquence respiratoire (2) destinée à mesurer la fréquence respiratoire du corps vivant ; une unité de mesure de quantité d'activité (3) destinée à mesurer la quantité d'activité du corps vivant ; une unité d'analyse (4) destinée à déterminer, sur la base de la température corporelle profonde, de la fréquence respiratoire et de la quantité d'activité, si le corps vivant se trouve dans un état de somnolence ou dans un état d'insomnie ; et une unité de sortie (5) destinée à délivrer en sortie un résultat de la mesure de température corporelle profonde et un résultat de la détermination par l'unité d'analyse (4).
PCT/JP2020/031261 2020-08-19 2020-08-19 Procédé de visualisation, programme de visualisation, et dispositif de visualisation WO2022038710A1 (fr)

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PCT/JP2020/031261 WO2022038710A1 (fr) 2020-08-19 2020-08-19 Procédé de visualisation, programme de visualisation, et dispositif de visualisation

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Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20050209512A1 (en) * 2004-03-16 2005-09-22 Heruth Kenneth T Detecting sleep
JP2013172899A (ja) * 2012-02-27 2013-09-05 Toyota Infotechnology Center Co Ltd 覚醒度推定装置
JP2016028662A (ja) * 2014-07-25 2016-03-03 船井電機株式会社 睡眠評価装置および睡眠評価方法
WO2017090732A1 (fr) * 2015-11-25 2017-06-01 日本電信電話株式会社 Procédé et dispositif d'évaluation respiratoire
JP2019141358A (ja) * 2018-02-21 2019-08-29 株式会社デンソー 枕装置、枕調整システム、及び枕調整方法
JP2020003291A (ja) * 2018-06-27 2020-01-09 日本電信電話株式会社 生体内温度測定装置および生体内温度測定方法

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20050209512A1 (en) * 2004-03-16 2005-09-22 Heruth Kenneth T Detecting sleep
JP2013172899A (ja) * 2012-02-27 2013-09-05 Toyota Infotechnology Center Co Ltd 覚醒度推定装置
JP2016028662A (ja) * 2014-07-25 2016-03-03 船井電機株式会社 睡眠評価装置および睡眠評価方法
WO2017090732A1 (fr) * 2015-11-25 2017-06-01 日本電信電話株式会社 Procédé et dispositif d'évaluation respiratoire
JP2019141358A (ja) * 2018-02-21 2019-08-29 株式会社デンソー 枕装置、枕調整システム、及び枕調整方法
JP2020003291A (ja) * 2018-06-27 2020-01-09 日本電信電話株式会社 生体内温度測定装置および生体内温度測定方法

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