Excretion detection device, excretion detection method, and program

Abstract

The present invention comprises: an identification unit that identifies, every prescribed period, the signal intensity of a reflection signal produced by reflection by the body of a person subject to monitoring irradiated with a transmission signal in a frequency band greater than or equal to a millimeter wave band; a detection unit that, on the basis of the signal intensity identified by the identification unit, detects that the person subject to monitoring has excreted; and an output unit that outputs the result of detection by the detection unit. The excretion detection device may furthermore include a storage unit that stores a threshold value between normal signal intensities for a state in which excretion has not occurred and excretion signal intensities for a state in which excretion has occurred. The detection unit may detect that the person subject to monitoring has excreted when the signal intensity identified by the identification unit reaches or exceeds the threshold value from a signal intensity below the threshold value.

Description

Excretion detection device, excretion detection method and program

[0001] The present invention relates to an excretion detection device, an excretion detection method, and a program for detecting excretion of a monitored person.

[0002] A method for automatically detecting excretion by a patient requiring care has been proposed. For example, Patent Document 1 describes a method for detecting urination by forming a positive electrode and a negative electrode using a sheet-like electrode body, and generating electricity when the positive electrode comes into contact with the patient's urine.

[0003] JP 2018-068583 A

[0004] The urine detection device described in Patent Document 1 detects urination by bringing its positive electrode into contact with the patient's urine. For this reason, the urine detection device described in Patent Document 1 needs to be cleaned every time urine is detected.

[0005] Therefore, the present invention has been made in consideration of these points, and aims to provide an excretion detection device, an excretion detection method, and a program that can further improve convenience when detecting excretion.

[0006] The first aspect of the excretion detection device of the present invention comprises an identification unit that identifies the signal strength of a reflected signal that is reflected from the body of a monitored person when irradiated with a transmission signal in a frequency band of the millimeter wave band or higher at predetermined intervals, a detection unit that detects that the monitored person has excreted based on the multiple signal intensities identified by the identification unit, and an output unit that outputs the detection results by the detection unit.

[0007] The excretion detection device may further include a memory unit that stores a threshold between a normal signal strength when no excretion has occurred and a signal strength during excretion when excretion has occurred, and the detection unit may detect that the monitored person has excreted when the signal strength identified by the identification unit changes from less than the threshold to equal to or greater than the threshold. The detection unit may detect that the monitored person has excreted based on an amount of change in the signal strength identified by the identification unit over time.

[0008] The excretion detection device may further have a memory unit that stores a first type signal strength when urine is excreted and a second type signal strength when feces is excreted, and the detection unit may detect whether the monitored person has urinated or defecate by comparing the signal strength identified by the identification unit with the first type signal strength and the second type signal strength.

[0009] The detection unit may detect that the monitored person has excreted based on the difference between the signal strength of a first reflected signal generated when a first transmission signal transmitted to a first range of the monitored person's body is reflected from the monitored person's body, and the signal strength of a second reflected signal generated when a second transmission signal transmitted to a second range other than the first range of the monitored person's body is reflected from the monitored person's body.

[0010] The detection unit may detect that the monitored person has excreted based on the phases of the reflected signal at multiple times. The excretion detection device may further include a correction unit that corrects the signal strength of the reflected signal identified by the identification unit based on a temperature measured by a temperature sensor that measures the temperature of the surrounding environment, and the detection unit may detect that the monitored person has excreted based on the signal strength after correction by the correction unit.

[0011] The detection unit may input information indicating the signal strength at multiple times identified by the identification unit as input data to a trained machine learning model that uses as input data the signal strength at multiple times of a reflected signal generated by reflection from the body of the monitored person when irradiated with a transmission signal in a frequency band of millimeter wave or higher, and output data that is the type of excretion of the monitored person, and obtain the type of excretion of the monitored person output by the machine learning model, thereby detecting the type of excretion of the monitored person.

[0012] The detection unit may further detect that the monitored person has vomited based on the signal strength at multiple times of the reflected signal when the transmitted signal transmitted to an area including the monitored person's mouth is reflected by the monitored person's body.

[0013] A second aspect of the excretion detection method of the present invention includes the steps of: executing, by a computer, a step of identifying the signal strength of a reflected signal generated by reflection from the body of a monitored person when irradiated with a transmission signal in a frequency band of the millimeter wave band or higher at predetermined intervals; a step of detecting that the monitored person has excreted based on the identified multiple signal intensities; and a step of outputting the detection results.

[0014] A third aspect of the program of the present invention causes a computer to execute the steps of: identifying the signal strength of a reflected signal that is generated by reflection from the body of a monitored person when irradiated with a transmission signal in a frequency band above the millimeter wave band at predetermined intervals; detecting that the monitored person has excreted based on the identified multiple signal strengths; and outputting the results of the detection.

[0015] The present invention has the effect of further improving convenience in detecting excretion.

[0016] 1 is a diagram showing the configuration of an excretion detection system of an embodiment; FIG. 2 shows the configuration of a measurement unit; FIG. 3 is a diagram for explaining the operation of a first measurement unit and a second measurement unit; FIG. 4 is a diagram for explaining the operation of a third measurement unit; FIG. 5 shows the configuration of an excretion detection device; and FIG. 6 is a flowchart showing the processing procedure for detecting excretion by the excretion detection device.

[0017] [Outline of the excretion detection system] Fig. 1 is a diagram showing the configuration of an excretion detection system S of this embodiment. The excretion detection system S is a system for non-contact detection of whether or not a monitored person, whose excretion status is being monitored, has excreted. The monitored person is, for example, a person receiving care in a nursing home. The excretion detection system S detects the state of excretion by irradiating the monitored person with a signal in a frequency band of millimeter waves or higher (hereinafter sometimes referred to as a "high-frequency signal").

[0018] When a high-frequency signal is irradiated onto a human body, the reflected wave of this high-frequency signal reflected by a non-surveyor includes a component of the reflected wave reflected by the human body, a component of the reflected wave reflected by moisture near the human body, and a component of the reflected wave reflected by objects other than moisture around the human body (e.g., metal).

[0019] For this reason, the excretion detection system S irradiates a high-frequency signal onto the human body, and detects whether or not there is a component of the reflected wave reflected by moisture near the human body by subtracting the signal strength of the component of the reflected wave reflected by the human body and objects around the human body from the signal strength of all reflected waves based on the irradiated high-frequency signal. The excretion detection system S includes a measurement unit 100, an excretion detection device 200, and a caregiver terminal 300.

[0020] The measurement unit 100 has a built-in antenna capable of transmitting and receiving high-frequency signals. The high-frequency signals are, for example, chirp signals whose frequency changes over time. The measurement unit 100 transmits the high-frequency signals toward the monitored person, receives the high-frequency signals reflected by the monitored person, and analyzes the signal strength and phase of the received high-frequency signals.

[0021] The measurement unit 100 communicates with the excretion detection device 200 via a network. The measurement unit 100 transmits information such as the signal strength and phase of the received high-frequency signal to the excretion detection device 200.

[0022] The excretion detection device 200 detects excretion by the monitored person and communicates with the measuring unit 100 and the caregiver terminal 300 via a network.

[0023] The caregiver terminal 300 is a terminal used by staff of a care facility, etc. The caregiver terminal 300 is, for example, a smartphone. The caregiver terminal 300 communicates with the excretion detection device 200 via a network.

[0024] The processing flow in the excretion detection system S will be described below. The measurement unit 100 irradiates the monitored person with a transmission signal in a frequency band of millimeter waves or higher ((1) in FIG. 1). The measurement unit 100 receives a reflected signal generated when the transmitted signal is reflected from the monitored person's body and measures the signal strength of the received reflected signal ((2) in FIG. 1). The measurement unit 100 transmits information indicating the signal strength of the received reflected signal to the excretion detection device 200. The excretion detection device 200 detects that the monitored person has excreted by analyzing the change in the signal strength of the received reflected signal over time using a method described below ((3) in FIG. 1). The excretion detection device 200 outputs information indicating the excretion detection result to the caregiver terminal 300 ((4) in FIG. 1).

[0025] The caregiver terminal 300 displays the excretion detection result on the display. In this way, the excretion detection system S outputs the excretion detection result to the caregiver terminal 300, allowing the caregiver to know that the monitored person has excreted without having to wear a tool for detecting excretion. Therefore, the excretion detection system S can improve the efficiency of the caregiver's task of checking whether the monitored person has excreted or not.

[0026] 2 shows the configuration of the measurement unit 100. The measurement unit 100 includes an antenna 11, a communication unit 12, a temperature sensor 13, a storage unit 14, and a control unit 15. The control unit 15 includes a first measurement unit 151, a second measurement unit 152, a third measurement unit 153, a temperature measurement unit 154, and a communication control unit 155.

[0027] The antenna 11 irradiates the monitored person with a high-frequency signal by transmitting a millimeter wave band transmission signal toward the monitored person's body. The antenna 11 irradiates the monitored person's body with the transmission signal from a position several meters away, for example. The antenna 11 may also be configured to irradiate the monitored person's body with the transmission signal from a close distance of several millimeters. The antenna 11 may also irradiate the monitored person's body with a transmission signal in a frequency band higher than millimeter waves. For example, the antenna 11 may irradiate the monitored person's body with a terahertz wave band transmission signal.

[0028] The antenna 11 receives a reflected signal generated when the transmitted signal is reflected from the body of the monitored person to which the transmitted signal is irradiated. The antenna 11 inputs the received reflected signal to the first measuring unit 151, the second measuring unit 152, or the third measuring unit 153.

[0029] The communication unit 12 is an interface for communicating with the excretion detection device 200. The communication unit 12 transmits information input from the communication control unit 155 to the excretion detection device 200. The temperature sensor 13 measures the temperature inside the measurement unit 100. The temperature sensor 13 inputs the measured temperature to the temperature measurement unit 154.

[0030] The storage unit 14 includes storage media such as a read-only memory (ROM), a random access memory (RAM), a hard disk, etc. The storage unit 14 stores programs that the control unit 15 executes.

[0031] The control unit 15 is, for example, a central processing unit (CPU). The control unit 15 executes the programs stored in the storage unit 14, thereby functioning as a first measurement unit 151, a second measurement unit 152, a third measurement unit 153, a temperature measurement unit 154, and a communication control unit 155.

[0032] The first measurement unit 151 measures the condition of a first area of ​​the monitored person's body by irradiating the first transmission signal onto the first area using the antenna 11. The first area is, for example, the crotch or buttocks of the monitored person. The first measurement unit 151 receives a first reflected signal, which is generated when the first transmission signal is reflected from the first area of ​​the monitored person's body, using the antenna 11. The first measurement unit 151 measures the signal strength of the received first reflected signal at predetermined intervals. The predetermined period is several seconds, several tens of seconds, or several minutes.

[0033] The first measuring unit 151 irradiates a first transmission signal to a plurality of positions within a first range and measures the signal strength of a plurality of first reflected signals that are generated by reflection at the plurality of positions within the first range. The first measuring unit 151 may measure the phase of the received first reflected signal at predetermined intervals.

[0034] The second measurement unit 152 measures the condition of a second region of the monitored person's body by irradiating the second transmission signal to the second region using the antenna 11. The second region is, for example, a region of the monitored person's entire body that is different from the first region. The second region is preferably a region corresponding to a part of the monitored person's body whose condition does not change due to excretion or vomiting, for example, the monitored person's abdomen. The second measurement unit 152 receives, via the antenna 11, a second reflected signal generated when the second transmission signal is reflected from the second region of the monitored person's body. The second measurement unit 152 measures the signal strength of the received second reflected signal at predetermined intervals. The predetermined period is several seconds, several tens of seconds, or several minutes.

[0035] 3(a) and 3(b) are diagrams for explaining the operation of the first measuring unit 151 and the second measuring unit 152. FIG. 3(a) shows the first measuring unit 151 emitting a first transmission signal to a first region of the body of the monitored person. The dashed line in FIG. 3(a) indicates the transmission range of the first transmission signal emitted by the first measuring unit 151. FIG. 3(b) shows the second measuring unit 152 emitting a second transmission signal to a second region of the body of the monitored person. The dashed line in FIG. 3(b) indicates the transmission range of the second transmission signal emitted by the second measuring unit 152.

[0036] The first measuring unit 151 and the second measuring unit 152 radiate a first transmission signal and a second transmission signal at different timings from the commonly used antenna 11. For example, when the first measuring unit 151 transmits a first transmission signal to N locations within a first range, the first measuring unit 151 radiates the first transmission signal from the antenna 11 to the N locations within the first range at a first timing to an Nth timing. The first measuring unit 151 receives N first reflected signals generated when the first transmission signal is reflected at N locations within the first range on the body of the monitored person. Thereafter, the second measuring unit 152 radiates a second transmission signal from the antenna 11 to the second range at a timing different from the first timing to the Nth timing. The second measuring unit 152 receives a second reflected signal generated when the second transmission signal is reflected on the body of the monitored person.

[0037] The first measuring unit 151 associates the position within the first range where the first transmission signal is irradiated with the measured signal strength of the first reflected signal, and outputs the associated information to the communication control unit 155. The second measuring unit 152 outputs the measured signal strength of the second reflected signal to the communication control unit 155. The first measuring unit 151 may output information indicating the phase of the measured first reflected signal to the communication control unit 155.

[0038] The third measurement unit 153 irradiates a third transmission signal to a third region of the monitored person's body using the antenna 11. The third region is, for example, the monitored person's face including the mouth. The third measurement unit 153 receives a third reflected signal generated when the third transmission signal is reflected from the third region of the monitored person's body using the antenna 11. The third measurement unit 153 measures the signal strength of the received third reflected signal at predetermined intervals. The third measurement unit 153 irradiates the third transmission signal to multiple positions within the third region and receives multiple third reflected signals that are each reflected from these multiple positions of the irradiated third transmission signal. The third measurement unit 153 measures the signal strength of each of the multiple received third reflected signals.

[0039] FIG. 4 is a diagram illustrating the operation of the third measuring unit 153. The dashed line in FIG. 4 indicates the irradiation range of the third transmission signal irradiated by the third measuring unit 153. The third measuring unit 153 irradiates the third transmission signal to multiple positions within the third range at a timing different from the timing at which the first measuring unit 151 transmits the multiple first transmission signals and the timing at which the second measuring unit 152 transmits the second transmission signal. The third measuring unit 153 associates the positions within the third range at which the third transmission signal was irradiated with the signal strength of the measured third reflected signal, and outputs the association result to the communication control unit 155.

[0040] The temperature measurement unit 154 measures the temperature inside the measurement unit 100 using the temperature sensor 13. The temperature measurement unit 154 outputs information indicating the measured temperature to the communication control unit 155.

[0041] The communication control unit 155 communicates with the excretion detection device 200 via the communication unit 12. The communication control unit 155 transmits information indicating the signal strength and phase of the first reflected signal measured by the first measuring unit 151 to the excretion detection device 200 at predetermined intervals. The predetermined interval is, for example, longer than the period during which the first measuring unit 151 measures the signal strength of the first reflected signal. At this time, the communication control unit 155 associates the position within the first range at which the first measuring unit 151 irradiated the first transmission signal, the measured signal strength of the first reflected signal, and the timing at which the first transmission signal was transmitted, and transmits these to the excretion detection device 200. The communication control unit 155 associates the phase of the first reflected signal measured by the first measuring unit 151, the phase of the irradiated first transmission signal, and the timing at which the first transmission signal was transmitted, and transmits these to the excretion detection device 200.

[0042] The communication control unit 155 transmits information indicating the signal strength and phase of the second reflected signal measured by the second measuring unit 152 to the excretion detection device 200 at predetermined intervals. The predetermined interval is, for example, longer than the cycle at which the second measuring unit 152 measures the signal strength of the second reflected signal. At this time, the communication control unit 155 associates the signal strength of the second reflected signal measured by the second measuring unit 152 with the timing at which the second transmission signal was transmitted, and transmits this information to the excretion detection device 200.

[0043] The communication control unit 155 transmits information indicating the signal strength of the third reflected signal measured by the third measuring unit 153 to the excretion detection device 200 at predetermined intervals. The predetermined interval is, for example, longer than the cycle at which the third measuring unit 153 measures the signal strength of the third reflected signal. The communication control unit 155 associates the position within the third range at which the third measuring unit 153 irradiated the third transmission signal, the measured signal strength of the third reflected signal, and the timing at which the third transmission signal was transmitted, and transmits these to the excretion detection device 200. The communication control unit 155 transmits information indicating the temperature measured by the temperature measuring unit 154 to the excretion detection device 200.

[0044] 5 shows the configuration of the excretion detection device 200. The excretion detection device 200 includes a communication unit 21, a storage unit 22, and a control unit 23. The control unit 23 includes an identification unit 231, a correction unit 232, a detection unit 233, and an output unit 234.

[0045] The communication unit 21 is an interface for communicating with the measurement unit 100 and the caregiver terminal 300. The communication unit 21 receives various types of information from the measurement unit 100. For example, the communication unit 21 receives information indicating the signal strength of the first reflected signal measured by the first measuring unit 151 at a predetermined interval.

[0046] The storage unit 22 includes a storage medium such as a ROM, a RAM, a hard disk, etc. The storage unit 22 stores a program executed by the control unit 23. The storage unit 22 stores one or more trained machine learning models. For example, the storage unit 22 stores a trained first machine learning model that takes as input data the signal intensities at multiple timings of multiple first reflected signals generated by reflection at multiple positions in a first range of the body of the monitored person to which the first transmission signal is irradiated, and the signal intensities at multiple timings of second reflected signals generated by reflection at a second range of the body of the monitored person to which the second transmission signal is irradiated, and that outputs data representing the amount of excretion by the monitored person.

[0047] The input data may include, for example, data in which the signal intensities of the first reflected signals at multiple timings are arranged in chronological order for multiple positions, and data in which the signal intensities of the second reflected signals at multiple timings are arranged in chronological order. The first machine learning model is used by the detection unit 233 to detect when the monitored person has excreted. The input data of the first machine learning model may further include information indicating a phase difference between the first transmitted signal and the first reflected signal at multiple timings.

[0048] The memory unit 22 stores a trained second machine learning model, which uses as input data the signal strength at multiple timings of a first reflected signal generated by reflection from the body of the monitored person irradiated with a first transmission signal in a frequency band equal to or higher than the millimeter wave band, and outputs as output data the type of excretion of the monitored person. This input data includes, for example, data in which the signal strength at multiple timings of the first reflected signal are arranged in chronological order. The second machine learning model is used by the detection unit 233 to detect the type of excretion of the monitored person. The first machine learning model and the second machine learning model may be configured as a single machine learning model.

[0049] The memory unit 22 stores a threshold value between the normal signal strength when the person is not defecating and the excretion signal strength when the person is defecating. The normal signal strength is the signal strength of a first reflected signal that is reflected from a first transmission signal that is irradiated onto a first area of ​​the monitored person's body when the monitored person is not defecating. The excretion signal strength is the signal strength of a first reflected signal that is reflected from a first transmission signal that is irradiated onto a first area of ​​the monitored person's body when the monitored person is defecating. This threshold value is referenced by the detection unit 233 to detect whether the monitored person has defecated.

[0050] The memory unit 22 stores a first type signal intensity, which is the signal intensity of the first reflected signal when urination is occurring, and a second type signal intensity, which is the signal intensity of the first reflected signal when feces is occurring. These are used by the detection unit 233 to detect the type of excretion of the monitored person. The memory unit 22 also stores correction data or a calculation formula that associates the measured temperature with the amount of change in signal intensity relative to the signal intensity at a predetermined reference temperature. This correction data or calculation formula is referenced by the correction unit 232 when correcting the signal intensity of the reflected signal. The reference temperature is, for example, 25°C, which is a standard room temperature.

[0051] The control unit 23 is, for example, a CPU. The control unit 23 executes the program stored in the storage unit 22, and thereby includes an identifying unit 231, a correcting unit 232, a detecting unit 233, and an output unit 234.

[0052] The identification unit 231 communicates with the measurement unit 100 via the communication unit 21. The identification unit 231 identifies, for each predetermined period, the signal strength of a reflected signal generated by reflection from the body of the monitored person to which the high-frequency signal is irradiated. For example, the identification unit 231 receives information indicating the signal strengths of the multiple first reflected signals measured by the first measurement unit 151, thereby identifying, for each predetermined period, the signal strengths of the multiple first reflected signals generated by reflection from each of multiple positions within a first range of the body of the monitored person to which the first transmission signal is irradiated.

[0053] The identifying unit 231 identifies the signal strength of the second reflected signal at multiple timings by receiving information indicating the signal strength of the second reflected signal measured by the second measuring unit 152. The identifying unit 231 identifies the phase of the first reflected signal at multiple timings by receiving information indicating the phases of the first reflected signal and the first transmission signal measured by the first measuring unit 151.

[0054] The determination unit 231 may determine the signal strength of the third reflected signal at multiple timings. By receiving information indicating the signal strengths of the multiple third reflected signals measured by the third measurement unit 153, the determination unit 231 determines, for each predetermined period, the signal strengths of the multiple third reflected signals generated by reflection at each of multiple positions in a third range of the body of the monitored person to which the third transmission signal is irradiated. The determination unit 231 determines the temperature inside the measurement unit 100. The determination unit 231 outputs information indicating the signal strengths of the identified multiple first reflected signals, second reflected signals, and third reflected signals at multiple timings to the correction unit 232. The determination unit 231 outputs information indicating the phases of the projected first reflected signals at multiple timings to the correction unit 232.

[0055] The correction unit 232 communicates with the measurement unit 100 via the communication unit 21. The correction unit 232 corrects the signal strength of the reflected signal identified by the identification unit 231 based on the temperature measured by the temperature sensor 13 that measures the temperature of the surrounding environment. The correction unit 232 reads correction data or a calculation formula from the storage unit 22, which associates the measured temperature with the amount of change in signal strength relative to the signal strength at a predetermined reference temperature. The correction unit 232 corrects the signal strength of the first reflected signal, the second reflected signal, or the third reflected signal by referring to the read correction data or calculation formula. The correction unit 232 outputs information indicating the signal strength of the corrected first reflected signal, the second reflected signal, or the third reflected signal to the detection unit 233.

[0056] [Detection of excretion based on signal strength of reflected signal] When the monitored person excretes, the transmitted signal is reflected by the moisture contained in the urine or feces, and the signal strength of the reflected signal increases. The detection unit 233 uses this principle to detect that the monitored person has excreted. The detection unit 233 detects that the monitored person has excreted based on the signal strength at multiple times identified by the identification unit 231. The signal strength may be the strength after correction by the correction unit 232, or may be the strength before correction by the correction unit 232. The detection unit 233 detects that the monitored person has excreted based on the amount of change over time in the multiple signal strengths identified by the identification unit 231.

[0057] For example, the detection unit 233 detects that the monitored person has excreted when the change in the signal intensity determined by the determination unit 231 from a reference time point is equal to or greater than a predetermined value. The reference time point is, for example, a time point stored in the memory unit 22 as the time point at which the caregiver confirmed that the monitored person had not excreted. If the change in the signal intensity determined by the determination unit 231 from the reference time point is less than a predetermined value, the detection unit 233 does not determine that the monitored person has excreted. The reference time point is, for example, when the measuring unit 100 is turned on or when calibration is performed. During calibration, for example, based on instructions from an administrator of the excretion detection system S, the determination unit 231 determines the signal intensity of the first reflected signal at that time point and the phase difference between the first reflected signal and the first transmitted signal. The predetermined value is, for example, the minimum value expected as the increase in the signal intensity of the reflected signal caused by moisture contained in urine or feces.

[0058] The detector 233 detects whether moisture derived from excreted feces or urine is present at each of the plurality of positions within the first range based on the signal intensities at a plurality of timings of a plurality of first reflected signals obtained by reflecting the irradiated first transmission signal at each of the plurality of positions. In this way, the detector 233 can detect the amount of excretion by detecting the distribution of the excreted feces or urine. For example, the detector 233 detects the amount of excretion in stages. The stages may range from several stages to a dozen stages, for example.

[0059] The detection unit 233 may detect that the monitored person has excreted using a trained machine learning model. First, the detection unit 233 reads out a trained first machine learning model stored in the storage unit 22. The detection unit 233 inputs, as input data to the read-out first machine learning model, information indicating the signal intensities at multiple timings of multiple first reflected signals reflected at multiple positions within the first range identified by the identification unit 231 and information indicating the signal intensities at multiple timings of the second reflected signal, and detects the amount of excretion of the monitored person by obtaining the amount of excretion of the monitored person output by the first machine learning model.

[0060] The detection unit 233 detects that the monitored person has excreted when the detected amount of excretion is greater than a reference value. The detection unit 233 does not detect that the monitored person has excreted when the detected amount of excretion is equal to or less than the reference value. The reference value is the maximum value of noise that the first machine learning model outputs as the amount of excretion of the monitored person when the monitored person has not excreted. The reference value may be 0 (zero).

[0061] Although an example has been described in which the detection unit 233 inputs, as input data, information indicating the signal strength at multiple timings of the first reflected signal after correction by the correction unit 232 and information indicating the signal strength at multiple timings of the second reflected signal after correction to the first machine learning model, the present invention is not limited to an example in which the detection unit 233 inputs, as input data, information indicating the signal strength at multiple timings of the first reflected signal after correction to the first machine learning model. For example, the detection unit 233 may input, as input data, information indicating the signal strength at multiple timings of the first reflected signal before correction, information indicating the signal strength at multiple timings of the second reflected signal before correction, and information indicating the internal temperature of the measurement unit 100 identified by the identification unit 231 to the first machine learning model.

[0062] The detection unit 233 may detect that the monitored person has excreted when the signal strength of the first reflected signal identified by the identification unit 231 changes from a state below a predetermined threshold to a state equal to or greater than this threshold. The predetermined threshold indicates, for example, a value between the normal signal strength of the first reflected signal when no excretion has occurred and the signal strength of the first reflected signal when excretion has occurred. On the other hand, the detection unit 233 does not detect that the monitored person has excreted when the signal strength of the first reflected signal identified by the identification unit 231 remains below the threshold.

[0063] If moisture contained in urine or feces is present within the first range where the first transmission signal is irradiated, the signal strength of the first reflected signal, which is the reflection of the first transmission signal, increases. On the other hand, since moisture contained in urine or feces is not present within the second range where the second transmission signal is irradiated, the signal strength of the second reflected signal does not increase regardless of whether the monitored person has urinated. Therefore, the detection unit 233 can detect whether the monitored person has urinated by determining the difference in signal strength between the first reflected signal and the second reflected signal.

[0064] Using this principle, the detection unit 233 may detect that the monitored person has excreted based on the difference, at multiple times, between the signal strength of a first reflected signal generated when a first transmission signal transmitted to a first area of ​​the monitored person's body is reflected from the monitored person's body and the signal strength of a second reflected signal generated when a second transmission signal transmitted to a second area other than the first area of ​​the monitored person's body is reflected from the monitored person's body. For example, the detection unit 233 detects that the monitored person has excreted when the difference between the signal strength of the first reflected signal and the signal strength of the second reflected signal is equal to or greater than a predetermined value.

[0065] The detection unit 233 does not detect that the monitored person has excreted if the difference between the signal strength of the first reflected signal and the signal strength of the second reflected signal is less than a predetermined value. The predetermined value is, for example, the minimum value expected as the increase in signal strength of the reflected signal caused by moisture contained in urine or feces. In this way, by identifying the difference between the signal strength of the first reflected signal and the signal strength of the second reflected signal, the detection unit 233 can detect that the monitored person has excreted even if it has not previously measured the signal strength of the first reflected signal when the monitored person is not excreting.

[0066] [Detection of excretion based on the phase of the reflected signal] When the monitored person excretes, the phase of the reflected signal changes due to the expansion of the diaper polymer. Therefore, the detection unit 233 detects that the monitored person has excreted based on the phase of the reflected signal at multiple timings. For example, the detection unit 233 identifies the phase difference between the first transmission signal and the first reflected signal at multiple timings. The detection unit 233 detects that the monitored person has excreted when the amount of change in the phase difference between the first transmission signal and the first reflected signal from a reference time point is equal to or greater than a threshold. The reference time point is, for example, when the measurement unit 100 is turned on or when calibration is performed.

[0067] On the other hand, the detection unit 233 does not determine that the monitored person has excreted if the change in the phase difference between the first transmission signal and the first reflected signal from the reference point is less than a threshold value, which is the minimum value assumed as the change in phase difference caused by the moisture contained in urine or feces expanding the polymer of the diaper.

[0068] The detection unit 233 may detect that the monitored person has excreted using both the signal strength of the first reflected signal and the phase of the first reflected signal. For example, in addition to inputting the signal strengths of the first reflected signals at multiple timings and the signal strengths of the second reflected signals at multiple timings as input data to the trained first machine learning model as described above, the detection unit 233 may detect the amount of excretion by further inputting the phase differences between the first reflected signals, which are reflected from the first transmitted signal, and the first transmitted signal at multiple timings as input data to the first machine learning model.

[0069] Furthermore, the detection unit 233 may detect the amount of excretion by further inputting, as input data, into the first machine learning model, phase differences at multiple timings between the phases of multiple first reflected signals generated when the irradiated first transmission signal is reflected at multiple positions in the first range of the monitored person's body and the phases of the first transmission signals before the first reflected signals are reflected. In this way, the detection unit 233 further inputs information regarding the phases of the first reflected signals as input data into the first machine learning model, thereby further improving the accuracy with which the first machine learning model detects the amount of excretion.

[0070] [Detection of type of excretion] The detection unit 233 may detect the type of excretion of the monitored person. The type of excretion is feces or urination. For example, when urination is performed, the signal strength of the reflected signal is greater than when feces is performed. Therefore, the detection unit 233 can detect whether the monitored person has excreted urination or feces based on the signal strength of the reflected signal.

[0071] First, the detection unit 233 reads out the trained second machine learning model stored in the storage unit 22. The detection unit 233 inputs, as input data, information indicating the signal strength of the first reflected signal at multiple timings identified by the identification unit 231 to the read second machine learning model, and acquires the type of excretion of the monitored person output by this second machine learning model, thereby detecting the type of excretion of the monitored person.

[0072] The detection unit 233 is not limited to detecting the type of excretion of the monitored person using the trained second machine learning model. The detection unit 233 may detect whether the monitored person has urinated or defecate by comparing the signal strength of the first reflected signal identified by the identification unit 231 with the first type signal strength stored in the memory unit 22 and the second type signal strength stored in the memory unit 22. The first type signal strength is stored in advance in the memory unit 22 as an example of the signal strength of the first reflected signal when urination has occurred. The second type signal strength is stored in advance in the memory unit 22 as an example of the signal strength of the first reflected signal when defecation has occurred.

[0073] The detection unit 233 detects that the monitored person has urinated when the difference between the signal strength of the first reflected signal identified by the identification unit 231 and the first type signal strength stored in the memory unit 22 is equal to or less than the difference between the signal strength of the first reflected signal identified by the identification unit 231 and the second type signal strength stored in the memory unit 22. On the other hand, the detection unit 233 detects that the monitored person has defecate when the difference between the signal strength of the first reflected signal identified by the identification unit 231 and the first type signal strength stored in the memory unit 22 is greater than the difference between the signal strength of the first reflected signal identified by the identification unit 231 and the second type signal strength stored in the memory unit 22. In this way, the detection unit 233 detects the type of excretion, which makes it easier for a caregiver caring for the monitored person to predict the amount of time required for care, etc.

[0074] The detection unit 233 may further detect that the monitored person has vomited based on the signal strength at multiple timings of a third reflected signal that is generated when the third transmission signal is irradiated to a third range including the monitored person's mouth and reflected by the monitored person's body. First, the detection unit 233 reads out the trained third machine learning model stored in the memory unit 22 from the memory unit 22.

[0075] The detection unit 233 inputs information indicating the signal strength of the multiple third reflected signals identified by the identification unit 231 at multiple timings into the read third machine learning model, and obtains the amount of vomiting of the monitored person output by this third machine learning model, thereby detecting the amount of vomiting by the monitored person. The detection unit 233 does not detect that the monitored person has vomited if the detected amount of excretion is equal to or less than a reference value. The reference value is the maximum value of noise that the first machine learning model outputs as the amount of excretion of the monitored person when the monitored person has not vomited. The reference value may be 0 (zero). In this way, the detection unit 233 detects vomiting by the monitored person, thereby shortening the time it takes for the caregiver to notice that the monitored person has vomited.

[0076] [Output of detection results] The output unit 234 communicates with the caregiver terminal 300 via the communication unit 21. The output unit 234 outputs the detection result of excretion by the detection unit 233. For example, when the detection unit 233 detects that the monitored person has excreted, the output unit 234 outputs information indicating that the monitored person has excreted, the amount of excretion, and the type of excretion to the display of the caregiver terminal 300. When the detection unit 233 does not detect that the monitored person has excreted, the output unit 234 does not output information indicating that the monitored person has excreted, etc. to the display of the caregiver terminal 300.

[0077] The output unit 234 may output the result of the vomiting detection by the detection unit 233. For example, when the detection unit 233 detects that the monitored person has vomited, the output unit 234 outputs information indicating that the monitored person has vomited and information indicating the amount of vomit to the display of the caregiver terminal 300. When the detection unit 233 does not detect that the monitored person has responded, the output unit 234 does not output information indicating that the monitored person has vomited.

[0078] The output unit 234 may output information indicating that it has been detected that the monitored person has excreted when the signal strength of the first reflected signal and the phase of the first reflected signal satisfy a predetermined condition. For example, the output unit 234 outputs information indicating that it has been detected that the monitored person has excreted when the difference between the signal strength of the first reflected signal and the signal strength of the second reflected signal is equal to or greater than a predetermined value and the amount of change in the phase difference between the first transmitted signal and the first reflected signal from a reference time point is equal to or greater than a threshold.

[0079] The output unit 234 does not output information indicating that the monitored person has excreted if the difference between the signal strength of the first reflected signal and the signal strength of the second reflected signal is less than a predetermined value, or if the amount of change in the phase difference between the first transmitted signal and the first reflected signal from the reference time point is less than a threshold value. In this way, the output unit 234 can reduce the risk of erroneously outputting information indicating that the monitored person has excreted.

[0080] [Processing During Training of Machine Learning Model] The following describes the processing during training of the above-mentioned first machine learning model. First, the identification unit 231 uses pairs of input data: information indicating the signal strength at multiple timings of a first reflected signal generated by reflection at multiple positions in a first range of the body of the monitored person to which the first transmission signal is irradiated; and information indicating the signal strength at multiple timings of a reflected signal generated by reflection at a second range of the body of the monitored person to which the second transmission signal is irradiated immediately before or immediately after the irradiation of the first transmission signal; and creates multiple training data sets in which the amount of excretion of the monitored person measured visually or by a weight sensor, etc., immediately before or immediately after the irradiation of the first transmission signal is used as correct answer data corresponding to the input data. The first machine learning model is generated by the identification unit 231 by machine learning the created multiple training data using, for example, a method such as random forest.

[0081] Similarly, the processing during training of the second machine learning model will be described. For training the second machine learning model, the identification unit 231 creates a plurality of training data sets in which, instead of the amount of excretion of the monitored person measured immediately before or immediately after the first transmission signal is irradiated, the type of excretion of the monitored person visually confirmed immediately before or immediately after the second transmission signal is irradiated is used as the correct answer data corresponding to the input data. The created training data is the same as the training data used to generate the first machine learning model, except for the correct answer data.

[0082] If it is desired to have the first machine learning model or the second machine learning model detect both the amount and type of excretion, the identification unit 231 may create learning data in which the amount of excretion of the monitored person measured immediately before or immediately after the first transmission signal is irradiated is used as the first correct answer data corresponding to the input data, and the type of excretion of the monitored person confirmed immediately before or immediately after the first transmission signal is irradiated is used as the second correct answer data corresponding to the input data.

[0083] Similarly, the processing during training of the third machine learning model will be described. In the processing during training of the third machine learning model, instead of using the signal intensities of the multiple first reflected signals at multiple timings as input data, the identification unit 231 creates multiple pieces of training data using the signal intensities of multiple third reflected signals at multiple timings that are generated by reflection at multiple positions within a third range of the body of the monitored person to which the third transmission signal is irradiated as input data. The processing during training of the third machine learning model is similar to the processing during training of the first machine learning model except that the signal intensities of the multiple third reflected signals at multiple timings are used as input data, and therefore will not be described again.

[0084] 6 is a flowchart showing the processing procedure for detecting excretion by the excretion detection device 200. This processing procedure starts, for example, while the measurement unit 100 and the excretion detection device 200 are operating. First, the identification unit 231 identifies, for each predetermined period, the signal strengths of multiple first reflected signals generated by reflection at multiple positions in a first range of the body of the monitored person to which multiple first transmission signals are irradiated. The identification unit 231 identifies, for each predetermined period, the signal strengths of multiple second reflected signals generated by reflection at a second range of the body of the monitored person to which second transmission signals are irradiated (S101).

[0085] The determination unit 231 determines the phase difference between the irradiated first transmission signal and the first reflected signal reflected from the first transmission signal for each predetermined period (S102). The determination unit 231 determines the internal temperature of the measurement unit 100 (S103). The correction unit 232 corrects the signal intensities of the first reflected signals and second reflected signals determined by the determination unit 231 based on the internal temperature of the measurement unit 100 determined by the determination unit 231 (S104).

[0086] The detection unit 233 reads out the trained first machine learning model stored in the storage unit 22. The detection unit 233 inputs, as input data to the read out first machine learning model, information indicating the signal strengths at multiple timings of the multiple first reflected signals identified by the identification unit 231, information indicating the phase differences at multiple timings between the identified first transmission signal and the first reflected signal, and information indicating the signal strengths at multiple timings of the second reflected signal, and obtains the amount of excretion of the monitored person output by this first machine learning model, thereby detecting the amount of excretion of the monitored person.

[0087] The output unit 234 determines whether the amount of excretion detected by the detection unit 233 is greater than 0 (S105). If the amount of excretion detected by the detection unit 233 is greater than 0 (YES in S105), the output unit 234 outputs the detection result to the display of the caregiver terminal 300 (S106) and ends the process. On the other hand, if the amount of excretion detected by the detection unit 233 is 0 in the determination of S105 (NO in S105), the output unit 234 does not output the detection result and returns to the process of S101.

[0088] [Effects of the excretion detection device 200 of this embodiment] In the excretion detection device 200 of this embodiment, the output unit 234 outputs the excretion detection result to the caregiver terminal 300, so that the caregiver can know that the monitored person has excreted and can immediately start caring for the monitored person. Therefore, the output unit 234 can make it possible for the caregiver to omit the task of checking whether the monitored person has excreted or not.

[0089] The present invention has been described above using embodiments, but the technical scope of the present invention is not limited to the scope described in the above embodiments, and various modifications and changes are possible within the scope of the gist of the present invention. For example, all or part of the device can be configured by functionally or physically distributing or integrating in any unit. Furthermore, new embodiments resulting from any combination of multiple embodiments are also included in the embodiments of the present invention. The effects of the new embodiments resulting from the combination also have the effects of the original embodiments.

[0090] REFERENCE SIGNS LIST 11 Antenna 12 Communication section 13 Temperature sensor 14 Memory section 15 Control section 21 Communication section 22 Memory section 23 Control section 100 Measurement unit 151 First measurement section 152 Second measurement section 153 Third measurement section 154 Temperature measurement section 155 Communication control section 200 Excretion detection device 231 Identification section 232 Correction section 233 Detection section 234 Output section 300 Caregiver terminal S Excretion detection system

Claims

1. An excretion detection device comprising: an identification unit that identifies the signal strength of a reflected signal generated by reflection from the body of a monitored person when irradiated with a transmission signal in a frequency band of millimeter wave or higher for each predetermined period of time; a detection unit that detects when the monitored person has excreted based on the multiple signal strengths identified by the identification unit; and an output unit that outputs the detection results by the detection unit.

2. The excretion detection device of claim 1 further comprises a memory unit that stores a threshold value between the normal signal strength when no excretion has occurred and the excretion signal strength when excretion has occurred, and the detection unit detects that the monitored person has excreted when the signal strength identified by the identification unit changes from below the threshold value to above the threshold value.

3. The excretion detection device of claim 1, wherein the detection unit detects that the monitored person has excreted based on the amount of change in the signal strength identified by the identification unit over time.

4. The excretion detection device of claim 1, further comprising a memory unit that stores a first type signal strength when urine is excreted and a second type signal strength when feces is excreted, and the detection unit detects whether the monitored person has urinated or defecates by comparing the signal strength identified by the identification unit with the first type signal strength and the second type signal strength.

5. An excretion detection device as described in any one of claims 1 to 3, wherein the detection unit detects that the monitored person has excreted based on the difference in signal strength between a first reflected signal generated when a first transmission signal transmitted to a first range of the monitored person's body is reflected from the monitored person's body and a second reflected signal generated when a second transmission signal transmitted to a second range other than the first range of the monitored person's body is reflected from the monitored person's body.

6. An excretion detection device according to any one of claims 1 to 4, wherein the detection unit detects that the monitored person has excreted based further on the phases of the reflected signals at multiple times.

7. An excretion detection device as described in any one of claims 1 to 4, further comprising a correction unit that corrects the signal strength of the reflected signal identified by the identification unit based on the temperature measured by a temperature sensor that measures the temperature of the surrounding environment, and the detection unit detects that the monitored person has excreted based on the signal strength after correction by the correction unit.

8. The excretion detection device described in any one of claims 1 to 4, wherein the detection unit inputs information indicating the signal strength at multiple times identified by the identification unit as input data to a trained machine learning model that receives input data from a transmitted signal in a frequency band of millimeter wave or higher and reflects the reflected signal from the body of the monitored person when irradiated with the transmitted signal, and outputs the type of excretion of the monitored person as output data, thereby detecting the type of excretion of the monitored person.

9. An excretion detection device as described in any one of claims 1 to 4, wherein the detection unit further detects that the monitored person has vomited based on the signal strength at multiple times of the reflected signal when the transmitted signal transmitted to an area including the monitored person's mouth is reflected by the monitored person's body.

10. An excretion detection method executed by a computer, comprising the steps of: identifying, for a predetermined period of time, the signal strength of a reflected signal generated by reflection from the body of a monitored person when irradiated with a transmission signal in a frequency band above the millimeter wave band; detecting that the monitored person has excreted based on the identified multiple signal strengths; and outputting the detection results.

11. A program that causes a computer to execute the steps of: identifying the signal strength of a reflected signal that is generated when a transmission signal in a frequency band above the millimeter wave band is irradiated on the body of a monitored person at predetermined intervals; detecting that the monitored person has excreted based on the identified multiple signal strengths; and outputting the results of the detection.

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