WO2022149342A1 - 排泄物判定方法、排泄物判定装置、及び排泄物判定プログラム - Google Patents

排泄物判定方法、排泄物判定装置、及び排泄物判定プログラム Download PDF

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Publication number
WO2022149342A1
WO2022149342A1 PCT/JP2021/040781 JP2021040781W WO2022149342A1 WO 2022149342 A1 WO2022149342 A1 WO 2022149342A1 JP 2021040781 W JP2021040781 W JP 2021040781W WO 2022149342 A1 WO2022149342 A1 WO 2022149342A1
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WIPO (PCT)
Prior art keywords
value
excrement
image data
determination
image
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Ceased
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PCT/JP2021/040781
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English (en)
French (fr)
Japanese (ja)
Inventor
英幸 前原
博文 金井
由佳 山田
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Panasonic Holdings Corp
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Panasonic Holdings Corp
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Priority to JP2022573930A priority Critical patent/JPWO2022149342A1/ja
Priority to CN202180087614.6A priority patent/CN116710964A/zh
Publication of WO2022149342A1 publication Critical patent/WO2022149342A1/ja
Priority to US18/217,180 priority patent/US12592001B2/en
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/90—Determination of colour characteristics
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/483—Physical analysis of biological material
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/483—Physical analysis of biological material
    • G01N33/487—Physical analysis of biological material of liquid biological material
    • G01N33/49—Blood
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/483—Physical analysis of biological material
    • G01N33/487—Physical analysis of biological material of liquid biological material
    • G01N33/493—Physical analysis of biological material of liquid biological material urine
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10024—Color image
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30004—Biomedical image processing

Definitions

  • This disclosure relates to a technique for determining excrement based on image data.
  • Patent Document 1 converts a color image into a grayscale image, calculates the magnitude of the gradient of the image from the grayscale image, classifies the magnitude of the gradient of the calculated image into bins of a histogram having a constant step size, and classifies the calculated image into bins.
  • the technique of inputting the histogram classified into bins into a classifier such as a support vector machine and determining the hardness of stool is disclosed.
  • This disclosure is made to solve such a problem, and is to provide a technique capable of accurately detecting excrement in the accumulated water of a toilet bowl.
  • the excretion determination method in one aspect of the present disclosure is an excretion determination method in an excretion determination device for determining excretion, and the processor of the excretion determination device photographs the inside of the bowl of the toilet bowl with a camera.
  • Image data of the color of excrement is acquired, and G / R value and B / R value are calculated based on the R (red) value, G (green) value, and B (blue) value contained in the image data. Then, based on the G / R value and the B / R value, it is determined whether or not the image data includes at least one image of excretion, excretion, and bleeding, and the result of the determination is output. ..
  • excrement in the accumulated water of the toilet bowl can be detected accurately.
  • excretion information such as the number of excretion times and excretion time of the care recipient is important information for grasping the health risk of the care recipient, but it is a burden on the caregiver to impose the work of recording the excretion information on the caregiver. Becomes larger.
  • the caregiver records the excretion information near the care recipient, the psychological burden on the care recipient increases. Therefore, it is required to recognize excrement from image data taken by a camera installed in the toilet bowl, generate excretion information based on the recognition result, and automatically record the generated excretion information.
  • This disclosure is made to solve such problems.
  • the excretion determination method in one aspect of the present disclosure is an excretion determination method in an excretion determination device for determining excretion, and the processor of the excretion determination device photographs the inside of the bowl of the toilet bowl with a camera.
  • Image data of the color of excrement is acquired, and G / R value and B / R value are calculated based on the R (red) value, G (green) value, and B (blue) value contained in the image data. Then, based on the G / R value and the B / R value, it is determined whether or not the image data includes at least one image of excretion, excretion, and bleeding, and the result of the determination is output. ..
  • the G / R value and the B / R value are calculated based on the R value, the B value, and the G value included in the image data, and based on the calculated G / R value and the B / R value. It is determined whether or not the image data includes at least one image of defecation, urination, and bleeding.
  • the G / R value and the B / R value are values even if the color of the excrement on the surface layer of the reservoir becomes lighter with time as the excrement on the surface layer of the reservoir gradually sinks to the bottom. Has the property of being maintained. As a result, even if the color of the excrement on the surface layer of the accumulated water fades with time due to the influence of the accumulated water in the toilet bowl, the image of the excrement can be detected accurately.
  • the image data includes the image of the defecation.
  • the G / R value and the B / R value satisfy the predetermined urination conditions, it is determined that the image data includes the image of the urination, and the G / R value and the B / R value are determined. If the predetermined bleeding condition is satisfied, it may be determined that the image data includes the image of the bleeding.
  • each image of defecation, urination, and bleeding can be accurately determined from the image data.
  • the image data when each of the G / R value, the B / R value, the R value, the G value, and the B value satisfies a predetermined black stool condition, the image data. It may be determined that the image of black stool is included in.
  • the image of the black stool is added to the image data. Since it is determined that the image data is included, it is possible to accurately detect that the image data contains an image of black stool.
  • the image data includes an image of orange stool. You may judge.
  • the defecation condition may be a condition that the G / R value is smaller than A1% and the B / R value is smaller than A2% ( ⁇ A1%).
  • the urination condition is that the G / R value is B1% to B2% and the B / R value is B3% ( ⁇ B1%) to B4% ( ⁇ B2%). It may be a condition.
  • the bleeding condition may be a condition that the G / R value is smaller than C1% and the B / R value is smaller than C2% ( ⁇ C1%).
  • the image data includes an image of bleeding.
  • the R value, the G value, and the B value may be smaller than E, respectively.
  • the orange stool condition may be a condition in which the R value, the G value, and the B value are F1 to F2, respectively.
  • the image data when the image data contains pixel data satisfying the urination condition in an amount of the first pixel number or more, it is determined that the image data includes an image of defecation.
  • the image data includes the pixel data satisfying the defecation condition in the second pixel number or more, it is determined that the image data includes the image of the urine, and the bleeding condition in the image data.
  • the pixel data satisfying the condition is included in the number of the third pixel or more, it may be determined that the image data includes the image of the bleeding.
  • the G / R value and the G / R value and the G / R value are based on the R value, the G value, and the B value of the predetermined detection area including the pool portion of the toilet bowl in the image data.
  • the B / R value may be calculated.
  • the processing for detecting excrement can be narrowed down, so that the processing load is reduced as compared with the case where excrement is detected from the entire image data.
  • the excretion determination device in another aspect of the present disclosure is an excretion determination device that determines excretion, and is an acquisition unit that acquires color image data of excrement taken by a camera that photographs the inside of a bowl of a toilet bowl.
  • a calculation unit that calculates the G / R value and the B / R value based on the R (red) value, the G (green) value, and the B (blue) value included in the image data, and the G / R.
  • a determination unit that determines whether or not the image data includes at least one image of defecation, urination, and bleeding based on the value and the B / R value, and an output unit that outputs the result of the determination. , Equipped with.
  • the excrement determination program is an excrement determination program that causes a computer to function as an excrement determination device, and the color of the excrement taken by the computer with a camera for photographing the inside of the bowl of the toilet bowl.
  • the G / R value and the B / R value are calculated based on the R (red) value, the G (green) value, and the B (blue) value included in the image data.
  • Based on the / R value and the B / R value it is determined whether or not the image data includes at least one image of defecation, urination, and bleeding, and a process of outputting the result of the determination is executed. Let me.
  • the present disclosure can also be realized as an excrement determination system operated by such an excrement determination program.
  • a computer program can be distributed via a computer-readable non-temporary recording medium such as a CD-ROM or a communication network such as the Internet.
  • FIG. 1 is a diagram showing a configuration of an excrement determination system according to the first embodiment of the present disclosure.
  • FIG. 2 is a diagram for explaining the arrangement position of the sensor unit 2 and the excrement determination device 1 in the first embodiment of the present disclosure.
  • the excrement determination system shown in FIG. 1 includes an excrement determination device 1, a sensor unit 2, and a server 3.
  • the excrement determination device 1 is a device that determines the presence or absence of excretion by the user based on the image data taken by the camera 24. As shown in FIG. 2, the excrement determination device 1 is installed, for example, on the side surface of the water storage tank 105. However, this is only an example, and the excrement determination device 1 may be installed on the wall of the toilet, may be built in the sensor unit 2, and the installation position is not particularly limited.
  • the excrement determination device 1 is connected to the server 3 via a network.
  • the network is a wide area communication network such as the Internet.
  • the server 3 manages the user's excretion information generated by the excrement determination device 1.
  • the sensor unit 2 is hung on, for example, the edge 101 of the toilet bowl 100.
  • the sensor unit 2 is communicably connected to the excrement determination device 1 via a predetermined communication path.
  • the communication path may be a wireless communication path such as Bluetooth (registered trademark) or a wireless LAN, or may be a wired LAN.
  • the toilet bowl 100 includes an edge portion 101 and a bowl portion 102.
  • the edge 101 is located at the upper end of the toilet bowl 100 and defines the opening of the toilet bowl 100.
  • the bowl portion 102 is located below the edge portion 101 and receives defecation and urination.
  • a reservoir portion 104 for accumulating water is provided at the bottom of the bowl portion 102.
  • the pool portion 104 is provided with a drainage port (not shown).
  • the defecation and urination excreted in the bowl portion 102 are discharged to the sewer pipe through the drain port. That is, the toilet bowl 100 is a flush toilet.
  • a toilet seat 103 for a user to sit on is provided on the upper part of the toilet bowl 100.
  • the toilet seat 103 rotates up and down. The user sits with the toilet seat 103 lowered on the edge 101.
  • a water storage tank 105 for storing washing water for defecation and urination is provided behind the toilet bowl 100.
  • the sensor unit 2 includes a seating sensor 21, an illuminance sensor 22, a lighting device 23, and a camera 24.
  • the seating sensor 21 and the illuminance sensor 22 are examples of sensors that detect the user's seating and leaving on the toilet bowl 100.
  • the seating sensor 21 is arranged on the toilet bowl 100 so that the distance to the buttocks of the user seated on the toilet bowl 100 can be measured.
  • the seating sensor 21 is composed of, for example, a distance measuring sensor, and measures a distance measuring value which is a distance of the buttocks of a user who is seated on the toilet bowl 100.
  • An example of a distance measuring sensor is an infrared distance measuring sensor.
  • the seating sensor 21 measures the distance measurement value at a predetermined sampling rate, and inputs the measured distance measurement value to the excrement determination device 1 at a predetermined sampling rate.
  • the seating sensor 21 is an example of a sensor that detects the seating state of the user.
  • the distance measurement value is an example of sensing data indicating the user's sitting and leaving.
  • the illuminance sensor 22 is arranged in the toilet bowl 100 to measure the illuminance in the bowl portion 102.
  • the illuminance sensor 22 measures the illuminance in the bowl portion 102 at a predetermined sampling rate, and inputs the measured illuminance value to the excrement determination device 1 at a predetermined sampling rate.
  • the illuminance value is an example of sensing data indicating that the user is seated or disengaged.
  • the lighting device 23 is arranged in the toilet bowl 100 to light the inside of the bowl portion 102.
  • the lighting device 23 is, for example, a white LED, and lights the inside of the bowl portion 102 under the control of the excrement determination device 1.
  • the camera 24 is installed in the toilet bowl 100 so that the bowl portion 102 can take a picture.
  • the camera 24 is, for example, a high-sensitivity, wide-angle camera capable of capturing a color image having an R (red) component, a G (green) component, and a B (blue) component.
  • the camera 24 images the inside of the bowl portion 102 at a predetermined frame rate, and inputs the obtained image data to the excrement determination device 1 at a predetermined sampling rate.
  • the excrement determination device 1 includes a processor 11, a memory 12, a communication unit 13, and an entry / exit sensor 14.
  • the processor 11 is composed of, for example, a central processing unit (CPU) or an ASIC (application specific integrated circuit).
  • the processor 11 includes an acquisition unit 111, a calculation unit 112, a determination unit 113, and an output unit 114.
  • the acquisition unit 111 to the output unit 114 may be realized by the CPU executing an excrement determination program, or may be configured by a dedicated integrated circuit.
  • the acquisition unit 111 acquires the image data captured by the camera 24 at a predetermined sampling rate. Further, the acquisition unit 111 acquires the distance measurement value measured by the seating sensor 21 at a predetermined sampling rate. Further, the acquisition unit 111 acquires the illuminance value measured by the illuminance sensor 22 at a predetermined sampling rate.
  • the calculation unit 112 calculates the G / R value and the B / R value based on the R value, the G value, and the B value included in the image data acquired by the acquisition unit 111. Specifically, the calculation unit 112 sets the detection area D1 (FIG. 3) in the image data acquired by the acquisition unit 111, and for each of the plurality of pixel data constituting the detection area D1, the G / R value and the B / Calculate the R value.
  • the G / R value is a value obtained by dividing the G value by the R value and indicating it in%.
  • the B / R value is a value obtained by dividing the B value by the R value and expressing it in%.
  • the R value is the gradation value of the R (red) component of the pixel data
  • the G value is the gradation value of the G (green) component of the pixel data
  • the B value is the B (blue) component of the pixel data. It is a gradation value of.
  • the R value, G value, and B value take, for example, 8-bit (0 to 255) values. However, this is an example, and the R value, the G value, and the B value may be expressed by another number of bits.
  • FIG. 3 is a diagram showing the detection area D1.
  • the detection area D1 is a rectangular area including the pool portion 104 of the toilet bowl 100.
  • the calculation unit 112 may read the setting information from the memory 12 and set the detection area D1 in the image data according to the setting information.
  • the setting information is predetermined coordinate information indicating which coordinate of the detection area D1 is set in the image data. Since the toilet bowl 100 is designed so that excrement is excreted in the pool portion 104, a detection area D1 is set in the pool portion 104, and excrement is detected from the detection area D1 from the entire image data. The processing load is reduced compared to the case of detecting excrement.
  • the calculation unit 112 removes pixel data having the color (reference toilet color) indicated by the reference toilet color data from the image data of the detection area D1 (hereinafter referred to as detection area data), and the removed detection area data (hereinafter referred to as detection area data). ,
  • the determination target image data), the G / R value and the B / R value may be calculated for each pixel.
  • the calculation unit 112 detects pixel data in which the R value, the G value, and the B value are within a predetermined range with respect to the R value, the G value, and the B value of the reference toilet color, respectively, in the detection area data. It may be removed from the area data.
  • the reference toilet color data may be calculated based on the image data of the reference region in the bowl portion 102 separated by a predetermined distance from the edge portion 101 of the toilet bowl 100 to the pool portion 104 side.
  • the reference toilet color data has an average value of each of the R value, the G value, and the B value in the reference region. Since the area immediately below the edge 101 is an area where it is difficult to remove stains by cleaning, if this area is set as the reference area, it is possible to calculate the reference toilet color data that accurately indicates the color of the toilet bowl 100. It will be difficult. Therefore, the calculation unit 112 calculates the reference toilet color data based on the image data of the region separated from the edge portion 101 by a predetermined distance.
  • the determination unit 113 determines whether or not the image data includes images of urination, defecation, and bleeding based on the G / R value and the B / R value calculated by the calculation unit 112. Specifically, the determination unit 113 may determine that the image data includes an image of urination when the G / R value and the B / R value each satisfy predetermined urination conditions. Further, when the G / R value and the B / R value satisfy the predetermined defecation conditions, the determination unit 113 may determine that the image data includes the image of defecation. Further, the determination unit 113 may determine that the image data includes an image of bleeding when the G / R value and the B / R value each satisfy predetermined bleeding conditions. Details of defecation conditions, urination conditions, and bleeding conditions will be described later.
  • the determination unit 113 may determine that the image data includes an image of urination. Further, when the determination target image data includes the pixel data satisfying the defecation condition in the second pixel number or more, the determination unit 113 may determine that the image data includes the image of urination. Further, when the determination target image data includes the pixel data satisfying the bleeding condition in the third pixel number or more, the determination unit 113 may determine that the image data includes the bleeding image.
  • the first pixel number is a preset number of pixels indicating that the pixel data satisfying the urination condition is not noise but urination pixel data.
  • the second pixel number is a preset number of pixels indicating that the pixel data satisfying the defecation condition is not noise but defecation pixel data.
  • the third pixel number is a preset number of pixels indicating that the pixel data satisfying the bleeding condition is not noise but bleeding pixel data.
  • the output unit 114 generates excretion information including the determination result by the determination unit 113, and outputs the generated excretion information.
  • the output unit 114 may transmit the excretion information to the server 3 using the communication unit 13, or may store the excretion information in the memory 12.
  • the memory 12 is composed of a storage device capable of storing various information such as a RAM (Random Access Memory), an SSD (Solid State Drive), or a flash memory.
  • the memory 12 stores, for example, excretion information, reference toilet color data, setting information, and the like.
  • the memory 12 may be a portable memory such as a USB (Universal Serial Bus) memory.
  • the communication unit 13 is a communication circuit having a function of connecting the excrement determination device 1 to the server 3 via a network.
  • the communication unit 13 has a function of connecting the excrement determination device 1 to the sensor unit 2 via a communication path.
  • the excretion information is, for example, information associated with information indicating that excretion has been performed (defecation, urination, and bleeding), date and time information indicating the date and time of excretion, and the like.
  • the excrement determination device 1 may generate excretion information on a daily basis, for example, and transmit the generated excretion information to the server 3.
  • the entry / exit sensor 14 is composed of, for example, a distance measuring sensor.
  • the entry / exit sensor 14 detects that the user has entered the toilet in which the toilet bowl 100 is installed.
  • the distance measuring sensor constituting the entry / exit sensor 14 has a lower measurement accuracy than the distance measuring sensor constituting the seating sensor 21, but has a wider detection range.
  • the range-finding sensor is, for example, an infrared range-finding sensor.
  • the entry / exit sensor 14 may be composed of, for example, a motion sensor instead of the distance measuring sensor. The motion sensor detects a user who is within a predetermined distance with respect to the toilet bowl 100.
  • FIG. 4 is a sequence diagram showing an outline of the processing of the excrement determination device 1 according to the first embodiment of the present disclosure.
  • the first line shows the sequence of the entry / exit sensor 14 composed of the motion sensor
  • the second line shows the sequence of the entry / exit sensor 14 composed of the distance measuring sensor
  • the third line shows the seating.
  • the sequence of the sensor 21 is shown
  • the fourth line shows the sequence of the illuminance sensor 22
  • the fifth line shows the sequence of the lighting device 23.
  • a sequence of both an entry / exit sensor 14 composed of a motion sensor and an entry / exit sensor 14 composed of a distance measuring sensor is shown, but the excrement determination device 1 has at least one entry / exit sensor 14. It suffices to have.
  • the calculation unit 112 determines that the user has entered the toilet based on the sensing data input from the entry / exit sensor 14 (presence sensor) or the entry / exit sensor 14 (distance measuring sensor).
  • the sensing data is set high, and when the user is no longer detected, the sensing data is set low.
  • the sensing data input from becomes high, it is determined that the user has entered the toilet.
  • the calculation unit 112 determines that the user has entered the toilet when the distance measurement value measured by the entry / exit sensor 14 (distance measurement sensor) is equal to or less than the threshold value A1.
  • the threshold value A1 an appropriate value such as 50 cm, 100 cm, 150 cm, or the like can be adopted.
  • the calculation unit 112 starts storing the sensing data input from the entry / exit sensor 14, the seating sensor 21, and the illuminance sensor 22 in the memory 12.
  • the calculation unit 112 transmits a room entry notification indicating that the user has entered the toilet to the server 3 by using the communication unit 13 in accordance with the detection of the user.
  • the user is seated on the toilet bowl 100.
  • the distance measurement value input from the seating sensor 21 becomes equal to or less than the seating detection threshold value A2, and the calculation unit 112 determines that the user has seated on the toilet bowl 100.
  • the seating detection threshold value A2 has, for example, a predetermined value indicating that the distance measurement value from the seating sensor 21 to the user's buttocks has seated the user on the toilet bowl 100.
  • the seating detection threshold value A2 is smaller than the threshold value A1, and appropriate values such as 10 cm, 15 cm, and 20 cm can be adopted.
  • the external light entering the bowl portion 102 due to sitting is blocked by the user's buttocks, so that the illuminance value input from the illuminance sensor 22 is reduced.
  • the calculation unit 112 turns on the lighting device 23 when the seating is detected.
  • the lighting device 23 lights the inside of the bowl portion 102, and the amount of light required for detecting excrement from the image data is secured.
  • the calculation unit 112 activates the camera 24 and causes the camera 24 to take a picture of the inside of the bowl unit 102. After that, the acquisition unit 111 acquires image data at a predetermined sampling rate.
  • the room entry notification may be sent at timing t2.
  • the user's toilet bowl 100 is re-sitting.
  • the distance measurement value of the seating sensor 21 exceeds the seating detection threshold value A2, and at the timing t4, the distance measurement value of the seating sensor 21 is below the seating detection threshold value A2.
  • the calculation unit 112 turns off the lighting device 23, and at the timing t4, the calculation unit 112 turns on the lighting device 23.
  • the illuminance value of the illuminance sensor 22 also changes in conjunction with the distance measurement value of the seating sensor 21.
  • the calculation unit 112 turns off the lighting device 23.
  • the distance measurement value of the entry / exit sensor 14 exceeds the threshold value A1, so the calculation unit 112 determines that the user has left the toilet.
  • the output unit 114 transmits an exit notification indicating that the user has left the toilet to the server 3 using the communication unit 13. Further, at the timing t6, the output unit 114 transmits the excretion information generated based on the image data to the server 3 using the communication unit 13.
  • the exit notification and excretion information may be transmitted at timing t7. Further, at timing t6, the image data used for detecting excrement may be transmitted.
  • the distance measurement value of the seating sensor 21 exceeded the seating detection threshold value A2 for the period B2 at the timing t5, so that the calculation unit 112 ends the storage of the sensing data in the memory 12 and the camera 24.
  • the shooting in the bowl portion 102 is completed.
  • the calculation unit 112 puts the excrement determination device 1 in the standby state.
  • FIG. 5 is a flowchart showing an example of a process in which the excrement determination device 1 generates image data to be transmitted.
  • step S21 the calculation unit 112 determines whether or not the user is seated on the toilet bowl 100.
  • the calculation unit 112 determines that the user has been seated, and performs the process in step S22. Proceed to.
  • the calculation unit 112 waits for processing in step S1.
  • the calculation unit 112 determines whether or not the urination pixel count data PD (X) is smaller than the urination pixel count data PD (-20).
  • the pixel count data PD for urination is the number of pixels of pixel data that satisfies the urination condition in the detection area data.
  • the urination pixel count data PD (-20) is urination pixel count data included in the detection area data of the sampling point (t-20) 20 sample points before the latest sampling point (t).
  • the urination pixel count data PD (X) is the maximum value of the urination pixel count data PD in the past from the sampling point (t-20) after the processing of FIG. 5 is started.
  • step S22 If the urination pixel count data PD (X) is smaller than the urination pixel count data PD (-20) (YES in step S22), the process proceeds to step S23, and the urination pixel count data PD (X) is the urination pixel. When the count data is PD (-20) or more (NO in step S22), the process proceeds to step S25.
  • the pixel count data PD (-20) of urination before the 20 sampling points was adopted, but this is an example, and the pixel count data PD (t) before any sample point may be adopted. This also applies to the pixel count data PD (-20) for defecation, which will be described later.
  • step S23 the calculation unit 112 updates the urination pixel count data PD (X) by substituting the urination pixel count data PD (-20) for the urination pixel count data PD (X).
  • step S24 the calculation unit 112 updates the RGB data of urination.
  • the RGB data of urination is the average value of the R value, G value, and B value of the pixel data satisfying the urination condition of each block R1 when the detection area data is divided into a plurality of blocks R1 (FIG. 3). be.
  • the block R1 is image data obtained by dividing the detection area data corresponding to the detection area D1 into a total of 64 pieces of, for example, 8 rows ⁇ 8 columns.
  • each block R1 is assigned a block number from 1 to 64 in the order of raster scanning from the upper left block R1 to the lower right block R1.
  • the RGB data of urination is data composed of an average value of R values, an average value of G values, and an average value of B values of pixel data satisfying the urination condition in each block R1.
  • the calculation unit 112 determines the average value of the R values, the average value of the G values, and the average value of the B values of the pixel data of these Q1 units. And are calculated as RGB data of urination.
  • the RGB data of urination is composed of 64 R-values, G-values, and B-values.
  • the block R1 is a block obtained by dividing the detection area data into 8 rows ⁇ 8 columns, but this is an example, and the block R1 sets the detection area data to n (n is an integer of 2 or more). It may be a block partitioned by rows ⁇ m (m is an integer of 2 or more) columns.
  • step S11 of FIG. 6, which will be described later RGB data of urination having such a data structure is included in the excretion information and transmitted, so that the amount of data of the excretion information is reduced. Further, since the RGB data of urination is mosaic-like data, it is possible to protect privacy as compared with the case of managing the image data itself.
  • the RGB data of urination at the sampling point where the pixel count data PD of urination is maximum is set as the RGB data to be transmitted.
  • step S25 the calculation unit 112 determines whether or not the defecation pixel count data PD (X) is smaller than the defecation pixel count data PD (-20).
  • the pixel count data PD for defecation is the number of pixels of pixel data that satisfies the defecation condition included in the detection area data.
  • the defecation pixel count data PD (-20) is urination pixel count data included in the detection area data of the sampling point (t-20) 20 sample points before the latest sampling point (t).
  • the defecation pixel count data PD (X) is the maximum value of the defecation pixel count data PD in the past from the sampling point (t-20) after the processing of FIG. 5 is started.
  • step S25 If the defecation pixel count data PD (X) is smaller than the defecation pixel count data PD (-20) (YES in step S25), the process proceeds to step S26, and the defecation pixel count data PD (X) is the defecation pixel. When the count data is PD (-20) or more (NO in step S25), the process proceeds to step S28.
  • step S26 the calculation unit 112 updates the defecation pixel count data PD (X) by substituting the defecation pixel count data PD (-20) for the defecation pixel count data PD (X).
  • step S27 the calculation unit 112 updates the RGB data of defecation.
  • the data structure of the RGB data of defecation is the same as the RGB data of urination.
  • the RGB data of defecation at the sampling point where the pixel count data PD of defecation is the maximum is set as the RGB data to be transmitted.
  • step S28 the calculation unit 112 determines whether or not the user has left.
  • the calculation unit 112 determines that the user has left the seat if the period in which the distance measurement value of the seating sensor 21 exceeds the seating detection threshold value A2 continues for the period B2, and the distance measurement value is less than the seating detection threshold value A2.
  • the period in which the distance measurement value exceeds the seating detection threshold value A2 does not continue for the period B2, it may be determined that the user has not left the seat. If it is determined that the user has left (NO in step S28), the process returns to step S22, and if it is determined that the user has left (YES in step S28), the process ends.
  • FIG. 6 is a flowchart showing an example of the process from when the user sits down to when the user leaves the excrement determination device 1 in the first embodiment. The flowchart of FIG. 6 is performed in parallel with the flowchart of FIG.
  • step S1 the calculation unit 112 determines whether or not the user is seated on the toilet bowl 100.
  • the calculation unit 112 determines whether or not the distance measurement value acquired from the seating sensor 21 by the acquisition unit 111 is equal to or less than the seating detection threshold value A2, so that the user can use the toilet bowl 100. Determine if you are seated. If it is determined that the user is seated on the toilet bowl 100 (YES in step S1), the process proceeds to step S2, and if it is determined that the user is not seated on the toilet bowl 100 (NO in step S1), the process proceeds to step S2. Wait at S1.
  • step S2 the determination unit 113 determines whether or not urination is confirmed. If urination is not confirmed (NO in step S2), the process proceeds to step S3, and if urination is confirmed (YES in step S2), the process proceeds to step S6. Confirmation of urination means that it can be concluded that the image data includes an image of urination.
  • step S3 the determination unit 113 executes an excretion detection process for determining that the user has performed at least one of urination and defecation based on the image data.
  • the details of the excretion detection process will be described later with reference to FIG. 7.
  • step S4 when the determination unit 113 determines that urination is present in the excretion detection process (YES in step S4), the determination unit 113 confirms urination (step S5). On the other hand, if urination is not detected in the excretion detection process (NO in step S4), the process proceeds to step S6.
  • step S6 the determination unit 113 determines whether or not defecation is confirmed. If defecation is confirmed (YES in step S6), the process proceeds to step S10, and if defecation is not confirmed (NO in step S6), the process proceeds to step S7. Confirmation of defecation means that it can be concluded that the image data includes an image of defecation.
  • step S7 the determination unit 113 executes the excretion detection process.
  • step S8 when the determination unit 113 determines that defecation is present in the excretion detection process (YES in step S8), the determination unit 113 confirms defecation (step S9). On the other hand, if the determination unit 113 does not determine that there is defecation in the excretion detection process (NO in step S8), the determination unit 113 proceeds to the process in step S10.
  • step S10 the calculation unit 112 determines whether or not the user has left.
  • the calculation unit 112 may determine that the user has left the seat when the period in which the distance measurement value of the seating sensor 21 exceeds the seating detection threshold value A2 continues for the period B2. If the distance measurement value is larger than the seating detection threshold value A2 (YES in step S10), the process proceeds to step S11, and if the distance measurement value is equal to or less than the seating detection threshold value A2 (NO in step S10), the process returns to step S2. ..
  • step S11 the output unit 114 transmits the exit notification and the excretion information to the server 3 using the communication unit 13.
  • This excretion information includes RGB data of urination, RGB data of defecation, pixel count data PD (X) of urination, pixel count data PD (X) of defecation calculated by the flowchart of FIG.
  • FIG. 7 is a flowchart showing an example of excretion detection processing.
  • step S110 the calculation unit 112 acquires the reference toilet color data from the memory 12.
  • step S120 the calculation unit 112 acquires the image data of the processing timing among the image data acquired by the acquisition unit 111.
  • the image data at the processing timing is, for example, image data before a predetermined sampling point (for example, 20 sampling points) from the latest sampling point.
  • a predetermined sampling point for example, 20 sampling points
  • the image data at the processing timing may be the image data at the latest sampling point.
  • step S130 the calculation unit 112 extracts the image data (detection area data) of the detection area D1 from the image data of the processing timing.
  • step S140 the calculation unit 112 determines whether or not the detection area data includes pixel data of a color different from the reference toilet color. If the detection area data contains pixel data of a color different from the reference toilet color (YES in step S140), the process proceeds to step S150, and the detection area data contains pixel data of a color different from the reference toilet color. If not (NO in step S140), the process proceeds to step S4 or S8 (FIG. 6).
  • step S150 the calculation unit 112 removes pixel data having an R value, a G value, and a B value outside a predetermined range with respect to the R value, the G value, and the B value of the reference toilet color data from the detection area data. , Generates the image data to be judged.
  • step S160 the calculation unit 112 calculates the G / R value and the B / R value for each pixel data of the image data to be determined.
  • step S170 the determination unit 113 determines whether the urination condition is satisfied in the image data to be determined or whether the pixel data is included in the first pixel count or more.
  • FIG. 8 is a table summarizing defecation conditions, urination conditions, and bleeding conditions. In FIG. 8, Low is the lower limit threshold value in the range that satisfies the condition, and High is the upper limit threshold value in the range that satisfies the condition.
  • the urination condition is that the G / R value is B1% or more and B2% or less, and the B / R value is B3% or more and B4% or less. However, B3% ⁇ B1% and B4% ⁇ B2%.
  • step S180 the determination unit 113 determines that there is urination with respect to the image data to be processed, and proceeds to the process in step S4 or step S8 (FIG. 4).
  • step S190 the determination unit 113 determines whether or not the pixel data satisfying the defecation condition is included in the determination target image data by the number of second pixels or more.
  • the defecation condition is that the G / R value is 0% or more and A1% or less, and the B / R value is 0% or more and A2% or less. However, A2% ⁇ A1%.
  • the process proceeds to step S200, and when the pixel data satisfying the defecation condition is less than the second pixel number (NO in step S190). The process proceeds to step S210.
  • step S200 the determination unit 113 determines that there is defecation with respect to the image data to be processed, and proceeds to the process in step S4 or step S8 (FIG. 4).
  • step S210 the determination unit 113 determines whether or not the pixel data satisfying the bleeding condition is included in the determination target image data by the number of third pixels or more.
  • the bleeding condition is that the G / R value is 0% or more and C1% or less, and the B / R value is 0% or more and C2% or less. However, C2% ⁇ C1%.
  • the process proceeds to step S220, and when the pixel data satisfying the bleeding condition is less than the second pixel number (NO in step S210). The process proceeds to step S230.
  • step S220 the determination unit 113 determines that there is bleeding in the image data to be processed, and proceeds to the process in step S4 or step S8 (FIG. 4).
  • step S230 the determination unit 113 determines that there is an image of a foreign substance in the image data to be determined, and proceeds to step S4 or step S8 (FIG. 4).
  • the foreign substance is, for example, a diaper, toilet paper, or the like.
  • A1 is, for example, 80 or more and 90 or less, preferably 83 or more and 87 or less.
  • A2 is, for example, 40 or more and 50 or less, preferably 43 or more and 47 or less.
  • B1 is, for example, 80 or more and 90 or less, preferably 83 or more and 87 or less.
  • B2 is, for example, 100 or more and 110 or less, preferably 103 or more and 107 or less.
  • B3 is, for example, 45 or more and 55 or less, preferably 48 or more and 52 or less.
  • B4 is 92 or more and 103 or less, preferably 95 or more and 99 or less.
  • C1 is, for example, 32 or more and 42 or less, preferably 35 or more and 39 or less.
  • C2 is, for example, 22 or more and 31 or less, preferably 25 or more and 29 or less.
  • the image data includes at least one image of defecation, urination, and bleeding based on the G / R value and the B / R value.
  • the G / R value and the B / R value have a characteristic that the values are maintained even if the color of the excrement on the surface layer of the accumulated water becomes lighter with time. As a result, the excrement of the accumulated water of the toilet bowl can be detected accurately.
  • FIG. 9 is a block diagram showing an example of the configuration of the excrement determination system according to the second embodiment.
  • the same components as those in the first embodiment are designated by the same reference numerals, and the description thereof will be omitted.
  • the processor 21A of the excrement determination device 1A includes an acquisition unit 211, a calculation unit 212, a determination unit 213, and an output unit 214.
  • the acquisition unit 211, the calculation unit 212, and the output unit 214 are the same as the acquisition unit 111, the calculation unit 112, and the output unit 114.
  • each of the G / R value and the B / R value calculated by the calculation unit 212 and the R value, the G value, and the B value included in the image data (or the image data to be determined) are predetermined. If the black stool condition is satisfied, it is determined that the image data includes the image of the black stool.
  • Black stool is a defecation with a dark gray color. Black stool is a defecation that may be excreted when sodium monopotassium citrate or the like is taken. Such black stool cannot be accurately determined under the defecation conditions shown in the first embodiment. Therefore, in this embodiment, the black stool condition is defined.
  • the determination unit 213 determines that the image data includes the image of orange stool. ..
  • orange stool orange stool
  • orange stool condition is specified.
  • FIG. 10 is a flowchart showing an example of excretion detection processing according to the second embodiment.
  • the processing of steps S110 to S220 is the same as that in FIG.
  • step S210 If there is no pixel data satisfying the bleeding condition for the number of third pixels or more (NO in step S210), the process proceeds to step S230.
  • step S230 the determination unit 113 determines whether or not the pixel data satisfying the black stool condition exists in the determination target image data by the number of fourth pixels or more.
  • the fourth pixel number is a preset number of pixels indicating that the pixel data satisfying the black stool condition is not noise but black stool pixel data.
  • FIG. 11 is a table summarizing the black stool conditions.
  • the black stool condition is that the G / R value is D1% or more and D2% or less, the B / R value is D3% or more and D4% or less, and each of the R value, G value, and B value is 0 or more and E or less.
  • the image data is 8 bits, the R value, the G value, and the B value each take a value of 0 to 255, so that E is 0 or more and 255 or less.
  • D1 is, for example, 85 or more and 95 or less, preferably 88 or more and 92 or less.
  • D2 is, for example, 105 or more and 115 or less, preferably 108 or more and 112 or less.
  • D3 is, for example, 55 or more and 65 or less, preferably 58 or more and 62 or less.
  • D4 is, for example, 105 or more and 115 or less, preferably 108 or more and 112 or less.
  • E is, for example, 85 or more and 95 or less, preferably 88 or more and 92 or less when the image data is 8 bits.
  • E is, for example, 33% or more and 37% or less, preferably 34% or more and 36% or less.
  • step S230 When the pixel data satisfying the black stool condition exists in the fourth pixel number or more (YES in step S230), the process proceeds to step S240, and when the pixel data satisfying the black stool condition is less than the fourth pixel number (NO in step S230). ), The process proceeds to step S250.
  • step S240 the determination unit 113 determines that there is black stool in the image data to be processed, and proceeds to the process in step S4 or step S8 (FIG. 4). In this case, defecation is confirmed in step S6 (FIG. 6).
  • step S250 the determination unit 113 determines whether or not the pixel data satisfying the orange stool condition exists in the determination target image data by the number of fifth pixels or more.
  • the fifth pixel number is a preset number of pixels indicating that the pixel data satisfying the orange stool is not noise but orange stool pixel data.
  • FIG. 12 is a table summarizing the orange flight conditions.
  • the orange stool condition is a condition that the R value, the G value, and the B value are F1 or more and F2 or less, respectively.
  • F1 and F2 take a value of 0 to 255.
  • F1 is, for example, 95 or more and 105 or less, preferably 98 or more and 102 or less.
  • F1 is, for example, 37% or more and 41% or less, preferably 38% or more and 40% or less.
  • F2 is, for example, 245 or more and 255 or less, preferably 250 or more and 255 or less.
  • F2 is, for example, 96% or more and 100% or less, preferably 98% or more and 100% or less.
  • step S250 When the number of pixel data satisfying the orange stool condition exists in the fifth pixel number or more (YES in step S250), the process proceeds to step S260, and when the pixel data satisfying the orange stool condition is less than the fifth pixel number (NO in step S250). ), The process proceeds to step S270.
  • step S260 the determination unit 113 determines that there is orange stool for the image data to be processed, and proceeds to the process in step S4 or step S8 (FIG. 4). In this case, defecation is confirmed in step S6 (FIG. 6).
  • step S270 the determination unit 113 determines that there is an image of a foreign substance in the image data to be determined, and proceeds to step S4 or step S8 (FIG. 4).
  • the foreign substance is, for example, a diaper, toilet paper, or the like.
  • the image data includes black stool and orange stool.
  • the orange stool condition shown in step S250 may be provided after the case where the number of pixel data satisfying the urination condition is equal to or greater than the number of first pixels (YES in step S270).
  • the determination unit 113 may determine that there is orange stool when the number of pixel data satisfying the urination condition but also the orange stool condition is present in the fifth pixel number or more.
  • step S170 of FIG. 7 the condition of YES is that the pixel data satisfying the urination condition exists in the first pixel number or more, but this is an example, and if the pixel data satisfying the urination condition exists. , YES may be determined. In this case, if there is even one pixel data satisfying the urination condition, it is determined that there is an image of urination. This also applies to defecation conditions, bleeding conditions, and orange stool conditions.

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