Urination analysis method, urination analysis device, and urination analysis program

Abstract

This urination analysis device for analyzing urination: acquires image data captured by a camera placed in a toilet so as to be capable of capturing an image of a bowl portion of the toilet; upon detecting that a user has urinated through image recognition of the image data, calculates, on the basis of the image data, a urination pixel count that is the pixel count of an image showing urination; calculates a urination momentum, urination amount, and urine specific gravity on the basis of variation in the urination pixel count; and outputs the momentum, urination amount, and urine specific gravity.

Description

[Technical Field]

[0001] The present disclosure relates to techniques for analyzing urination from image data. [Background technology]

[0002] Patent Document 1 discloses a technology for converting a color image into a grayscale image, calculating the magnitude of the image gradient from the grayscale image, classifying the calculated image gradient magnitude into histogram bins of a certain step size, and inputting the binned histogram into a classifier such as a support vector machine to determine the hardness of stool, etc.

[0003] However, the technology of Patent Document 1 does not take into consideration the force of urination, the amount of urination, or the specific gravity of urine, and therefore further improvement is needed. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Special Publication No. 2020-516422 Summary of the Invention

[0005] The present disclosure has been made to solve such problems, and aims to provide a technique for calculating at least one of urination force, urination volume, and urine specific gravity.

[0006] A urination analysis method in one aspect of the present disclosure is a urination analysis method in a urination analysis device that analyzes urination, in which a processor of the urination analysis device acquires image data captured by a camera installed on the toilet so that the bowl portion of the toilet can be imaged, and when it detects that a user has urinated by image recognition of the image data, calculates a urination pixel count, which is the number of pixels in the image showing the urination, from the image data, and calculates at least one of a momentum value indicating the momentum of the urination, a urination volume indicating the amount of urination, and a urine specific gravity indicating the specific gravity of the urination based on a change in the urination pixel count, and outputs at least one of the momentum value, the urination volume, and the urine specific gravity.

[0007] According to the present disclosure, at least one of urination force, urination volume, and urine specific gravity can be calculated. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a diagram illustrating a configuration of a urine analysis system according to an embodiment of the present disclosure. [Figure 2] 1A and 1B are diagrams illustrating the arrangement positions of a sensor unit and a urine analyzer according to an embodiment of the present disclosure. [Figure 3] FIG. 2 is a diagram showing a detection area. [Figure 4] 10 is a flowchart illustrating an example of processing performed by a urine analyzer according to an embodiment of the present disclosure. [Figure 5] This is a table summarizing urination conditions. [Figure 6] 10 is a flowchart illustrating an example of a urination analysis process. [Figure 7] 10 is a graph showing the time transition of the number of urination pixels of a person with a small momentum value and a large urine specific gravity. [Figure 8] 10 is a graph showing the time transition of the number of urination pixels of a person with a large momentum value and a normal urine specific gravity. DETAILED DESCRIPTION OF THE INVENTION

[0009] (Findings underlying this disclosure) In nursing care facilities, excretion information such as the number of times and duration of excretion by care recipients is important for understanding the health risks of care recipients, but imposing the task of recording excretion information on caregivers places a heavy burden on them. Furthermore, recording excretion information while the care recipient is nearby places a heavy psychological burden on the care recipient. Therefore, there is a need for a system that recognizes excrement from image data captured by a camera installed on the toilet, generates excretion information based on the recognition results, and automatically records the generated excretion information.

[0010] Here, elderly people and diabetics who tend to have insufficient fluid intake have a higher urine specific gravity than other people, so urine specific gravity is an important indicator for managing a person's health. Also, as people age, their urination force decreases, so urination force is an important indicator for managing a person's health. Furthermore, elderly people who tend to have insufficient fluid intake have a reduced urination volume, so urination volume is an important indicator for managing a person's health.

[0011] The inventors have observed the change in color of the urinary collection area of ​​a toilet bowl after the start of urination and have found that the color of the collection area of ​​people with a high urine specific gravity, such as elderly people and diabetics, becomes significantly lighter than that of people with a low urine specific gravity. Furthermore, the inventors have observed image data of the collection area from the start to the end of urination and have found that the force of urination and the amount of urination can be calculated from the change in the number of pixels of the urination image contained in the image data.

[0012] The present disclosure has been made based on these findings.

[0013] A urination analysis method in one aspect of the present disclosure is a urination analysis method in a urination analysis device that analyzes urination, in which a processor of the urination analysis device acquires image data captured by a camera installed on the toilet so that the bowl portion of the toilet can be imaged, and when it detects that a user has urinated by image recognition of the image data, calculates a urination pixel count, which is the number of pixels in the image showing the urination, from the image data, and calculates at least one of a momentum value indicating the momentum of the urination, a urination volume indicating the amount of urination, and a urine specific gravity indicating the specific gravity of the urination based on a change in the urination pixel count, and outputs at least one of the momentum value, the urination volume, and the urine specific gravity.

[0014] According to this configuration, the number of urination pixels, which is the number of pixels in the image showing urination included in the image data, is calculated, and at least one of the urination force value, urination volume, and urine specific gravity is calculated based on the change in the number of urination pixels, and the calculation result is output. This makes it possible to calculate at least one of the force value, urination volume, and urine specific gravity, which are useful indices for managing human health.

[0015] In the above urination analysis method, the momentum value may be calculated based on a maximum value of an increase in the number of urination pixels per unit time.

[0016] According to this configuration, the momentum value is calculated based on the maximum value of the increase in the number of urination pixels per unit time, so that the momentum value can be calculated accurately.

[0017] In the above urination analysis method, the amount of urination may be calculated based on a first integrated value which is an integrated value of an increase in the number of urination pixels per unit time.

[0018] According to this configuration, the amount of urination is calculated based on the first integrated value, which is the integrated value of the increase in the number of urination pixels per unit time, so that the amount of urination can be calculated accurately.

[0019] In the above urination analysis method, the urine specific gravity may be calculated based on a second integrated value which is an integrated value of the amount of decrease in the number of urination pixels per unit time.

[0020] According to this configuration, the urine specific gravity is calculated based on the second integrated value, which is the integrated value of the decrease in the number of urination pixels per unit time, so that the urine specific gravity can be calculated accurately.

[0021] In the above-described urination analysis method, the momentum value may be calculated based on the maximum value of the increase amount and the urine specific gravity.

[0022] According to this configuration, it is possible to increase the momentum value by taking into consideration the amount of urine that has sunk from the surface layer of the reservoir to the bottom during urination, and the momentum value can be calculated more accurately.

[0023] In the above urine analysis method, the amount of urine may be calculated based on the first integrated value and the urine specific gravity.

[0024] According to this configuration, it is possible to increase the volume of urine by taking into consideration the volume of urine that sinks from the surface layer of the reservoir to the bottom during urination, and the volume of urine can be calculated accurately.

[0025] In the above urination analysis method, in calculating the momentum value, a maximum value of a derivative value of the number of urination pixels during a period from the start of urination to the end of urination may be calculated as the maximum increase amount.

[0026] According to this configuration, the momentum value is calculated based on the maximum value of the differential value per unit time of the number of urination pixels during the period from the start of urination to the end of urination, so that the momentum value can be calculated more accurately.

[0027] In the above urination analysis method, in calculating the amount of urination, an integrated value of a differential value of the number of urination pixels during a period from the start of urination to the end of urination may be calculated as the first integrated value.

[0028] According to this configuration, the amount of urination is calculated based on the integrated value of the differential value of the number of urination pixels during the period from the start of urination to the end of urination, so that the amount of urination can be calculated more accurately.

[0029] In the above urine analysis method, in calculating the urine specific gravity, an integrated value of a differential value of the number of urination pixels during a period from the end of urination until the user leaves the toilet bowl may be calculated as the second integrated value.

[0030] According to this configuration, the urine specific gravity is calculated based on the integrated value of the differential value of the number of urination pixels during the period from the end of urination until the toilet is detected as having been lifted, so that the urine specific gravity can be calculated more accurately.

[0031] In the above-described urination analysis method, the urination may be detected when pixel data exists in the image data whose G / R value, B / R value, R value, G value, and B value satisfy predetermined urination conditions.

[0032] This configuration allows accurate detection of urination.

[0033] In another aspect of the present disclosure, a urination analyzer is a urination analyzer that analyzes urination, and includes: an acquisition unit that acquires image data captured by a camera installed on the toilet so that the bowl portion of the toilet can be imaged; a first calculation unit that, when it detects that a user has urinated by image recognition of the image data, calculates a urination pixel count, which is the number of pixels in the image representing the urination, from the image data; a second calculation unit that calculates at least one of a momentum value indicating the momentum of the urination, a urination volume indicating the amount of urination, and a urine specific gravity indicating the specific gravity of the urination based on a change in the urination pixel count; and an output unit that outputs at least one of the momentum value, the urination volume, and the urine specific gravity.

[0034] According to this configuration, a urine analyzer can be provided that can obtain the same effects as the above-described urine analysis method.

[0035] In yet another aspect of the present disclosure, a urination analysis program causes a computer to function as a urination analysis device that analyzes urination, and causes the computer to execute the following process: acquire image data captured by a camera installed on a toilet so that the bowl portion of the toilet can be imaged; when it is detected that a user has urinated by image recognition of the image data, calculate a urination pixel count, which is the number of pixels in the image representing the urination, from the image data; calculate at least one of a momentum value indicating the momentum of the urination, a urination volume indicating the amount of urination, and a urine specific gravity indicating the specific gravity of the urination, based on a change in the urination pixel count; and output at least one of the momentum value, the urination volume, and the urine specific gravity.

[0036] According to this configuration, it is possible to provide a urination analysis program that can achieve the same effects as the above-described urination analysis method.

[0037] The present disclosure can also be realized as a urination analysis system operated by such a urination analysis program. Needless to say, such a computer program can be distributed on a computer-readable non-transitory recording medium such as a CD-ROM or via a communication network such as the Internet.

[0038] Note that each of the embodiments described below represents a specific example of the present disclosure. The numerical values, shapes, components, steps, and step orders shown in the following embodiments are merely examples and are not intended to limit the present disclosure. Furthermore, among the components in the following embodiments, components that are not described in the independent claims that represent the highest concept are described as optional components. Furthermore, in all of the embodiments, the respective contents can be combined.

[0039] (Embodiment) Fig. 1 is a diagram showing the configuration of a urine analysis system according to an embodiment of the present disclosure, Fig. 2 is a diagram for explaining the arrangement positions of a sensor unit 2 and a urine analyzer 1 according to an embodiment of the present disclosure.

[0040] The urine analysis system shown in FIG. 1 includes a urine analyzer 1, a sensor unit 2, and a server 3. The urine analyzer 1 is a device that analyzes a user's urination based on image data captured by a camera 24. The urine analyzer 1 is installed, for example, on the side of a water storage tank 105 as shown in FIG. 2. However, this is just one example, and the urine analyzer 1 may be installed on the wall of a toilet or built into the sensor unit 2; the installation location is not particularly limited. The urine analyzer 1 is connected to a server 3 via a network. The network is, for example, a wide area communication network such as the Internet. The server 3 manages the user's excretion information generated by the urine analyzer 1.

[0041] The sensor unit 2 is hung on, for example, the edge 101 of the toilet bowl 100 as shown in Figure 2. The sensor unit 2 is connected to the urination analyzer 1 via a predetermined communication path so that they can communicate with each other. The communication path may be a wireless communication path such as Bluetooth (registered trademark) or wireless LAN, or it may be a wired LAN.

[0042] 2, the toilet 100 includes a rim 101 and a bowl 102. The rim 101 is located at the top of the toilet 100 and defines the opening of the toilet 100. The bowl 102 is located below the rim 101 and receives bowel movements and urination.

[0043] A reservoir 104 for collecting water (collected water) is provided at the bottom of the bowl 102. A drain outlet (not shown) is provided in the reservoir 104. Defecations and urine excreted in the bowl 102 are discharged through the drain outlet into a sewer pipe. In other words, the toilet 100 is a flush toilet. A toilet seat 103 for a user to sit on is provided at the top of the toilet 100. The toilet seat 103 rotates up and down. The user sits with the toilet seat 103 lowered onto the rim 101. A water storage tank 105 for storing flushing water for flushing defecation and urine is provided at the rear of the toilet 100.

[0044] Referring back to Fig. 1, 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 a user sitting on and leaving the toilet bowl 100.

[0045] The seating sensor 21 is disposed on the toilet 100 so as to be able to measure the distance to the buttocks of a user seated on the toilet 100. The seating sensor 21 is configured, for example, as a distance measurement sensor, and measures a distance value that is the distance to the buttocks of a user seated on the toilet 100. An example of a distance measurement sensor is an infrared distance measurement sensor. The seating sensor 21 measures the distance value at a predetermined sampling rate and inputs the measured distance value to the urination analyzer 1 at the predetermined sampling rate. The seating sensor 21 is an example of a sensor that detects the seated state of a user. The distance value is an example of sensing data that indicates whether the user is sitting or leaving the seat.

[0046] The illuminance sensor 22 is disposed in the toilet 100 to measure the illuminance inside the bowl portion 102. The illuminance sensor 22 measures the illuminance inside the bowl portion 102 at a predetermined sampling rate and inputs the measured illuminance value at the predetermined sampling rate to the urination analyzer 1. The illuminance value is an example of sensing data that indicates whether the user is sitting or leaving the seat.

[0047] The lighting device 23 is disposed in the toilet 100 to illuminate the inside of the bowl portion 102. The lighting device 23 is, for example, a white LED. For example, when the processor 11 detects that a user is seated based on sensing data from the seating sensor 21 or the illuminance sensor 22, the lighting device 23 is turned on under the control of the processor 11, and when the processor 11 detects that the user has left the seat based on sensing data from the seating sensor 21 or the illuminance sensor 22, the lighting device 23 is turned off under the control of the processor 11. By turning on the lighting device 23, the illuminance required for the camera 24 to photograph the bowl portion 102 is ensured.

[0048] The camera 24 is installed on the toilet 100 so as to be able to photograph the bowl portion 102. The camera 24 is, for example, a high-sensitivity, wide-angle camera that is capable of capturing color images having R (red), G (green), and B (blue) components. The camera 24 captures images of the inside of the bowl portion 102 at a predetermined frame rate and inputs the obtained image data into the urination analyzer 1 at a predetermined sampling rate.

[0049] The urination analyzer 1 includes a processor 11, a memory 12, a communication unit 13, and an entrance / exit sensor .

[0050] The processor 11 is configured, for example, by a central processing unit (CPU) or an ASIC (application specific integrated circuit). The processor 11 includes an acquisition unit 111, a first calculation unit 112, a second calculation unit 113, and an output unit 114. The acquisition unit 111 to the output unit 114 may be realized by the CPU executing a urination analysis program, or may be configured by a dedicated integrated circuit.

[0051] The acquisition unit 111 acquires image data captured by the camera 24 at a predetermined sampling rate. The acquisition unit 111 also acquires distance measurements measured by the seating sensor 21 at the predetermined sampling rate. The acquisition unit 111 also acquires illuminance values ​​measured by the illuminance sensor 22 at the predetermined sampling rate.

[0052] When the first calculation unit 112 detects that the user has urinated by performing image recognition on the image data acquired by the acquisition unit 111, it calculates the number of urination pixels, which is the number of pixels in the image showing urination, from the image data.

[0053] Details of the image recognition are as follows. That is, the first 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. Then, the first calculation unit 112 may detect that the user has urinated if pixel data exists in the image data in which the G / R value, the B / R value, the R value, the G value, and the B value satisfy a predetermined urination condition. The urination condition will be described later.

[0054] The G / R value is the value obtained by dividing the G value by the R value, expressed as a percentage. The B / R value is the value obtained by dividing the B value by the R value, expressed as a percentage. 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, and the B value is the gradation value of the B (blue) component of the pixel data. The R value, G value, and B value each take on an 8-bit value (0 to 255), for example. However, this is just an example, and the R value, G value, and B value may be expressed using other numbers of bits.

[0055] The first calculation unit 112 may count the number of pixels of pixel data that satisfy the urination condition in the image data, and calculate the counted number of pixels as the number of urination pixels. The number of urination pixels is the number of pixels in the image that shows urination.

[0056] The first calculation unit 112 may set a detection area D1 (FIG. 3) in the image data acquired by the acquisition unit 111, and determine that the user has urinated if pixel data satisfying the urination condition is present within the detection area D1. Furthermore, the first calculation unit 112 may count the number of pixels of pixel data satisfying the urination condition in the detection area D1, and calculate the counted number of pixels as the number of urination pixels.

[0057] FIG. 3 is a diagram showing detection area D1. Detection area D1 is a rectangular area that includes the toilet bowl basin 104. The first calculation unit 112 reads setting information from memory 12 and sets detection area D1 in the image data according to the setting information. The setting information is predetermined coordinate information that indicates at what coordinates in the image data detection area D1 is to be set. The toilet 100 is designed so that excrement is excreted in the bowl basin 104, so by setting detection area D1 in the bowl basin 104 and detecting excrement from detection area D1, the processing load is reduced compared to detecting excrement from the entire image data.

[0058] The second calculation unit 113 calculates at least one of a momentum value indicating the momentum of urination, a urination volume indicating the amount of urination, and a urine specific gravity indicating the specific gravity of urination, based on the change in the number of urination pixels. In the following description, it is assumed that the second calculation unit 113 calculates all of the momentum value, the urination volume, and the urine specific gravity.

[0059] The second calculation unit 113 may calculate the momentum value based on the maximum increase per unit time of the number of urination pixels calculated by the first calculation unit 112. Here, the second calculation unit 113 may calculate the maximum value of the differential value of the number of urination pixels during the urination period from the start of urination to the end of urination as the maximum increase.

[0060] The second calculation unit 113 may calculate the amount of urination based on the first integrated value, which is the integrated value of the increase in the number of urination pixels per unit time. Here, the second calculation unit 113 may calculate the integrated value of the differential value of the number of urination pixels during the urination period as the first integrated value.

[0061] The second calculation unit 113 may calculate the urine specific gravity based on a second integrated value, which is an integrated value of the decrease in the number of urination pixels per unit time. Here, the second calculation unit 113 may calculate, as the second integrated value, an integrated value of the differential value of the number of urination pixels during the period from the end of urination until the user leaves the toilet 100.

[0062] The output unit 114 generates excretion information including the momentum value, urination volume, and urine specific gravity calculated by the second calculation unit 113, and outputs the generated excretion information. Here, 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 urination information may include the urination time, image data including an image of urination, and sensing data from the seating sensor 21 and the illuminance sensor 22.

[0063] The memory 12 is configured with a storage device capable of storing various types of information, such as a random access memory (RAM), a solid state drive (SSD), or a flash memory. The memory 12 stores, for example, excretion information, reference toilet color data, and setting information. The memory 12 may be a portable memory such as a universal serial bus (USB) memory.

[0064] The communication unit 13 is a communication circuit having a function of connecting the urine analyzer 1 to the server 3 via a network. The communication unit 13 has a function of connecting the urine analyzer 1 to the sensor unit via a communication path. The excretion information is information in which, for example, information indicating that excretion has occurred (defecation, urination, and bleeding) is associated with date and time information indicating the date and time of excretion. The urine analyzer 1 may generate excretion information on a daily basis, for example, and transmit the generated excretion information to the server 3.

[0065] The entry / exit sensor 14 is composed of, for example, a distance measurement sensor. The entry / exit sensor 14 detects that a user has entered the toilet in which the toilet 100 is installed. Here, the distance measurement sensor that constitutes the entry / exit sensor 14 has lower measurement accuracy but a wider detection range than the distance measurement sensor that constitutes the seating sensor 21. The distance measurement sensor is, for example, an infrared distance measurement sensor. The entry / exit sensor 14 may be composed of, for example, a human presence sensor instead of a distance measurement sensor. The human presence sensor detects a user who is within a predetermined distance from the toilet 100.

[0066] The above is the configuration of the urine analysis system. Next, we will explain the processing of the urine analyzer 1. Figure 4 is a flowchart showing an example of the processing of the urine analyzer 1 according to the embodiment of the present disclosure.

[0067] In step S1, the first calculation unit 112 determines whether or not the user has sat on the toilet bowl 100. Here, if the distance measurement value acquired by the acquisition unit 111 from the seat sensor 21 is equal to or less than the seat detection threshold (YES in step S1), the first calculation unit 112 determines that the user has sat, and proceeds to step S2. On the other hand, if the distance measurement value is greater than the seat detection threshold (NO in step S1), the first calculation unit 112 waits in step S1. The seat detection threshold can be any appropriate value, such as 10 cm, 15 cm, or 20 cm.

[0068] In step S2, the acquisition unit 111 acquires image data from the camera 24.

[0069] In step S3, the first calculation unit 112 sets a detection area D1 in the image data acquired by the acquisition unit 111, and identifies pixel data of interest from the detection area D1. Here, the pixel data of interest is identified in raster scan order, for example.

[0070] In step S4, the first calculation unit 112 calculates a G / R value and a B / R value from the R value, G value, and B value of the target pixel data.

[0071] In step S5, the first calculation unit 112 determines whether the G / R value and the B / R value satisfy the urination condition. If the G / R value and the B / R value satisfy the urination condition (YES in step S5), the process proceeds to step S6. If the G / R value and the B / R value do not satisfy the urination condition (NO in step S5), the process returns to step S3, and the next pixel data of interest is identified.

[0072] In step S6, the first calculation unit 112 determines whether the R value, G value, and B value satisfy the urination condition. If the R value, G value, and B value satisfy the urination condition (YES in step S6), the process proceeds to step S7. If the R value, G value, and B value do not satisfy the urination condition (NO in step S6), the process returns to step S3, and the next pixel data of interest is specified.

[0073] Figure 5 is a table summarizing the urination conditions. In Figure 5, Low is the lower threshold value of the range that satisfies the urination condition, and High is the upper threshold value of the range that satisfies the urination condition.

[0074] The urination condition is that the G / R value is not less than A1% and not more than A2%, the B / R value is not less than A3% and not more than A4%, the R value is not less than B1 and not more than B2, the G value is not less than B1 and not more than B2, and the B value is not less than B1 and not more than B2. However, A3% < A1% and A4% < A2%. Also, B2 may be the maximum value of the gradation value.

[0075] Specifically, A1 is, for example, not less than 80 and not more than 90, preferably not less than 83 and not more than 87.

[0076] A2 is, for example, not less than 100 and not more than 110, preferably not less than 103 and not more than 107.

[0077] A3 is, for example, not less than 45 and not more than 55, preferably not less than 48 and not more than 52.

[0078] A4 is not less than 92 and not more than 103, preferably not less than 95 and not more than 99.

[0079] When the image data is 8 bits, B1 is, for example, not less than 95 and not more than 105, preferably not less than 98 and not more than 102. When the number of bits of the image data is arbitrary, B1 is, for example, not less than 37% and not more than 41%, preferably not less than 38% and not more than 40%.

[0080] When the image data is 8 bits, B2 is, for example, 245 or more and 255 or less, and preferably 250 or more and 255 or less. When the number of bits of the image data is arbitrary, B2 is, for example, 96% or more and 100% or less, and preferably 98% or more and 100% or less.

[0081] The conditions for the R value, G value, and B value may be omitted from the urination conditions.

[0082] In step S7, the first calculation unit 112 counts up the number of urination pixels.

[0083] In step S8, the first calculation unit 112 determines whether all of the target pixel data have been identified from the detection area D1. If they have been identified (YES in step S8), the process proceeds to step S9. If they have not been identified (NO in step S8), the process returns to step S3, where the next target pixel data is identified.

[0084] In step S9, the first calculation unit 112 initializes the number of urination pixels by setting the number of urination pixels to 0. This prepares for counting the number of urination pixels for the next pixel data of interest.

[0085] In step S10, the first calculation unit 112 determines whether or not the user has left the toilet 100. Here, the first calculation unit 112 may determine that the user has left the toilet 100 if the distance measurement value acquired by the acquisition unit 111 from the seat sensor 21 remains greater than the seat detection threshold for a predetermined period of time or longer. If the user has left the seat (YES in step S10), the process ends, and if the user has not left the seat (NO in step S10), the process returns to step S2, and the next image data is acquired.

[0086] 6 is a flowchart showing an example of a urination analysis process. The urination analysis process is a process for calculating a momentum value, a urination volume, and a urine specific gravity from image data. The flowchart in FIG. 6 is executed in parallel with the flowchart in FIG. 4.

[0087] In step S21, the first calculation unit 112 detects the start of urination. Here, in the flowchart of FIG. 4, after detecting that the user is seated (YES in step S1), the first calculation unit 112 may determine that the user has started urination when it detects pixel data that satisfies the urination condition for the first time. As described above, the pixel data that satisfies the urination condition is pixel data whose G / R value, B / R value, R value, G value, and B value satisfy the urination condition shown in FIG. 5. Note that the first calculation unit 112 may determine that the user has started urination when it detects a predetermined number of pixels or more of pixel data that satisfies the urination condition.

[0088] In step S22, the second calculation unit 113 acquires the number of urination pixels. Here, the second calculation unit 113 may acquire the latest number of urination pixels calculated in step S7 of Fig. 4. As a result, every time the flowchart of Fig. 6 is repeated, the latest number of pixels is acquired in step S22, and time-series data of the number of urination pixels is obtained.

[0089] In step S23, the second calculation unit 113 calculates a derivative value ΔP of the number of urination pixels. An example of the derivative value ΔP is the amount of change in the number of urination pixels per unit time. An example of the unit time is the sampling period. Therefore, the second calculation unit 113 may calculate the derivative value ΔP(t) by subtracting the number of urination pixels P(t-1) at the immediately previous sample point from the number of urination pixels P(t) at the latest sample point (t). Note that the unit time may be n (n is an integer) times the sampling period. The derivative value ΔP(t) may be a value obtained by dividing the amount of change in the number of urination pixels per unit time by the unit time.

[0090] In step S24, the second calculation unit 113 calculates the first integrated value TP1(t) by adding the derivative value ΔP(t) calculated in step S23 to the first integrated value TP1(t-1) recorded in the memory 12. The first integrated value TP1(t) is an integrated value of the derivative value ΔP from the start of urination to the present. During the period from the start of urination to the immediate end of urination, the number of urination pixels increases, so the derivative value ΔP(t) takes a positive value. However, as urination approaches the end, the number of urination pixels decreases, so the derivative value ΔP(t) takes a negative value. Note that the second calculation unit 113 may calculate the first integrated value TP1(t) by integrating only the positive derivative value ΔP(t).

[0091] In step S25, if the latest differential value ΔP(t) is greater than the maximum value ΔPmax of the differential value ΔP recorded in the memory 12, the second calculation unit 113 updates the maximum value ΔPmax with the latest differential value ΔP(t). On the other hand, if the latest differential value ΔP(t) is equal to or less than the maximum value ΔPmax, the second calculation unit 113 does not update the maximum value ΔPmax. As a result, the maximum value ΔPmax indicates the maximum value of the differential value ΔP from the start of urination to the present.

[0092] In step S26, the second calculation unit 113 detects the end of urination. Here, the second calculation unit 113 may detect the end of urination when the decrease in the number of urination pixels continues for a certain period of time. In particular, the second calculation unit 113 may detect the end of urination when the negative differential value ΔP continues for a certain period of time or more. The certain period of time may be an appropriate value such as 0.1 seconds, 0.5 seconds, 1 second, or 2 seconds.

[0093] If the end of urination is detected (YES in step S26), the process proceeds to step S27, and if the end of urination is not detected (NO in step S26), the process returns to step S22.

[0094] In step S27, the second calculation unit 113 calculates the differential value ΔP(t).

[0095] In step S28, the second calculation unit 113 calculates the second integrated value TP2(t) by adding the derivative value ΔP(t) calculated in step S27 to the second integrated value TP2(t-1) recorded in the memory 12. The second integrated value TP2(t) is the integrated value of the derivative value ΔP from the end of urination to the present. After the end of urination, the number of urination pixels does not basically increase, so the derivative value ΔP basically takes a negative value. Therefore, the second integrated value TP2(t) also takes a negative value. Note that the second calculation unit 113 may calculate the second integrated value TP2(t) by integrating only the negative derivative value ΔP(t).

[0096] In step S29, the first calculation unit 112 determines whether or not the user has left the toilet 100. Details of this process are the same as those in step S10 of Fig. 4. If it is determined that the user has left the seat (YES in step S29), the process proceeds to step S30, and if it is not determined that the user has left the seat (NO in step S29), the process returns to step S22.

[0097] In step S30, the second calculation unit 113 calculates the absolute value of the second integrated value TP2 calculated in step S28 as the urine specific gravity. Here, the absolute value of the second integrated value TP2 is calculated as the urine specific gravity in order to make the second integrated value TP2 a positive value. However, this is just an example, and the second calculation unit 113 may calculate the value of the second integrated value TP2 as the urine specific gravity as it is.

[0098] In step S31, the second calculation unit 113 calculates a momentum value based on the maximum value ΔPmax updated in step S25 and the urine specific gravity calculated in step S30. In detail, the second calculation unit 113 calculates the momentum value β by the following formula (1), where α is the urine specific gravity, β is the momentum value, and C1 is a predetermined constant.

[0099] β=ΔPmax×α×C1 (1) The larger the urine specific gravity α, the greater the amount of urine that sinks to the bottom of the reservoir 104, and the sinking amount of urine reduces the maximum value ΔPmax, resulting in an underestimated momentum value. Therefore, the second calculation unit 113 calculates the momentum value using equation (1) to inflate this underestimated momentum value. The constant C1 is a predetermined constant for converting ΔPmax×α into a momentum value.

[0100] In step S32, the second calculation unit 113 calculates the amount of urine based on the first integrated value TP1 calculated in step S24 and the urine specific gravity α. In detail, the second calculation unit 113 calculates the amount of urine γ by the following formula (2), where γ is the amount of urine and C2 is a predetermined constant.

[0101] γ=TP1×α×C2 (2) The larger the urine specific gravity α, the greater the amount of urine that sinks to the bottom of the reservoir 104, and the first integrated value TP1 decreases due to the amount of urine that sinks, resulting in an underestimated amount of urine. Therefore, the second calculation unit 113 calculates the amount of urine using equation (2) to increase the underestimated amount of urine. The constant C2 is a predetermined constant for converting TP1×α into the amount of urine.

[0102] In step S33, the output unit 114 generates excretion information including the urine specific gravity, the momentum value, and the amount of urine excreted, and transmits the excretion information to the server 3 using the communication unit 13.

[0103] Figure 7 is a graph showing the time transition of the number of urination pixels of a person with a small momentum value and a large urine specific gravity. In Figure 7, the vertical axis represents the number of urination pixels, and the horizontal axis represents time. This is also true for the graph in Figure 8.

[0104] The start of urination is detected at time t0. The end of urination is detected at time t1. During the urination period from time t0 to time t1, the amount of urine in the reservoir 104 increases, so the number of urination pixels also increases with an average slope K1. After time t1, the urine on the surface of the reservoir 104 gradually sinks to the bottom of the reservoir 104 due to urine specific gravity, so the number of urination pixels gradually decreases with an average slope K2. Therefore, the above-mentioned second integrated value TP2 increases as the amount of urine sinking to the bottom of the reservoir 104 increases. Furthermore, as the amount of urine sinking to the bottom increases, urine specific gravity increases.

[0105] Figure 8 is a graph showing the time transition of the number of urination pixels of a person with a large momentum value and a normal urine specific gravity. As shown in Figure 8, it can be seen that in the urination period of a person with a large momentum value, the number of urination pixels increases rapidly with an average slope K1 in the latter part of the urination period. Furthermore, a person with a normal urine specific gravity has a small urine specific gravity, so the amount of urine that sinks to the bottom of the reservoir 104 is small. Therefore, the average slope K2 of the number of urination pixels after time t1 shown in Figure 8 is gentler than the average slope K2 shown in Figure 7.

[0106] Thus, the time transition of the number of urination pixels has a waveform that differs depending on the momentum value, urination volume, and urine specific gravity. Therefore, by analyzing the change in the number of urination pixels, the momentum value, urination volume, and urine specific gravity can be calculated.

[0107] The present disclosure can employ the following modifications.

[0108] (1) In step S31 of FIG. 6, the second calculation unit 113 calculates the momentum value using equation (1), but this is just an example, and the maximum value ΔPmax may be calculated as the momentum value.

[0109] (2) In step S32 of FIG. 6, the second calculation unit 113 calculated the amount of urine using equation (2), but this is just one example, and the absolute value of the first integrated value TP1 or the first integrated value TP1 may be calculated as the amount of urine.

[0110] (3) In the above embodiment, seating and leaving the seat are detected based on the distance measurement value of the seat sensor 21. However, this is merely an example, and seating and leaving the seat may be detected based on the illuminance value of the illuminance sensor 22. In this case, the user's seating may be detected when the illuminance value is equal to or less than the seating detection threshold for illuminance. Alternatively, the user's leaving the seat may be detected when the illuminance value remains greater than the seating detection threshold for illuminance for a certain period of time or more.

[0111] (4) The first integrated value TP1 is the integrated value of the differential value ΔP, but may be the integrated value of the number of urination pixels from the start of urination to the end of urination.

[0112] (5) The second integrated value TP2 is the integrated value of the differential value ΔP, but it may be "the first integrated value TP1 at the end of urination" minus "the integrated value of the number of urination pixels (t) from the end of urination to leaving the seat." [Industrial Applicability]

[0113] INDUSTRIAL APPLICABILITY The present disclosure is useful for managing a user's health based on the characteristics of urination, since it provides information about the characteristics of the user's urination.

Claims

1. A urination analysis method in a urination analyzer for analyzing urination, comprising: a processor of the urine analyzer, Acquiring image data captured by a camera installed on the toilet bowl so as to be able to capture an image of the bowl portion of the toilet bowl; When it is detected that the user has urinated by image recognition of the image data, a urination pixel number is calculated from the image data, which is the number of pixels of the image showing the urination; calculating at least one of a momentum value indicating the momentum of the urination, a urination volume indicating the volume of the urination, and a urine specific gravity indicating the specific gravity of the urination based on the change in the number of urination pixels; outputting at least one of the momentum value, the urination volume, and the urine specific gravity; The momentum value is calculated based on a maximum value of an increase in the number of urination pixels per unit time. Urine analysis method.

2. A urination analysis method in a urination analyzer for analyzing urination, comprising: a processor of the urine analyzer, Acquiring image data captured by a camera installed on the toilet bowl so as to be able to capture an image of the bowl portion of the toilet bowl; When it is detected that the user has urinated by image recognition of the image data, a urination pixel number is calculated from the image data, which is the number of pixels of the image showing the urination; calculating at least one of a momentum value indicating the momentum of the urination, a urination volume indicating the volume of the urination, and a urine specific gravity indicating the specific gravity of the urination based on the change in the number of urination pixels; outputting at least one of the momentum value, the urination volume, and the urine specific gravity; The amount of urination is calculated based on a first integrated value which is an integrated value of an increase in the number of urination pixels per unit time. Urine analysis method.

3. A urination analysis method in a urination analyzer for analyzing urination, comprising: a processor of the urine analyzer, Acquiring image data captured by a camera installed on the toilet bowl so as to be able to capture an image of the bowl portion of the toilet bowl; When it is detected that the user has urinated by image recognition of the image data, a urination pixel number is calculated from the image data, which is the number of pixels of the image showing the urination; calculating at least one of a momentum value indicating the momentum of the urination, a urination volume indicating the volume of the urination, and a urine specific gravity indicating the specific gravity of the urination based on the change in the number of urination pixels; outputting at least one of the momentum value, the urination volume, and the urine specific gravity; The urine specific gravity is calculated based on a second integrated value which is an integrated value of a decrease in the number of urination pixels per unit time. Urine analysis method.

4. The momentum value is calculated based on the maximum value of the increase amount and the urine specific gravity. The method for analyzing urine according to claim 1.

5. The amount of urine is calculated based on the first integrated value and the urine specific gravity. The method for analyzing urine according to claim 2.

6. In calculating the momentum value, a maximum value of a differential value of the number of urination pixels during a period from the start of urination to the end of urination is calculated as a maximum value of the increase amount. The method for analyzing urine according to claim 1.

7. In calculating the amount of urination, an integrated value of a differential value of the number of urination pixels during a period from the start of urination to the end of urination is calculated as the first integrated value. The method for analyzing urine according to claim 2.

8. In calculating the urine specific gravity, an integrated value of a differential value of the number of urination pixels during a period from the end of urination to the time when the user leaves the toilet bowl is calculated as the second integrated value. The method for analyzing urine according to claim 3.

9. In the detection of urination, when pixel data exists in the image data in which a G / R value, a B / R value, an R value, a G value, and a B value satisfy a predetermined urination condition, the urination is detected. The method for analyzing urine according to any one of claims 1 to 8.

10. A urination analyzer for analyzing urination, an acquisition unit that acquires image data captured by a camera installed on the toilet bowl so as to be able to capture an image of the bowl; a first calculation unit that calculates a urination pixel number, which is the number of pixels of an image showing the urination, from the image data when it is detected that the user has urinated by performing image recognition on the image data; and a second calculation unit that calculates at least one of a momentum value indicating the momentum of the urination, a urination volume indicating the volume of the urination, and a urine specific gravity indicating the specific gravity of the urination based on the change in the number of urination pixels; an output unit that outputs at least one of the momentum value, the urination volume, and the urine specific gravity, The momentum value is calculated based on a maximum value of an increase in the number of urination pixels per unit time. Urine analyzer.

11. A urination analyzer for analyzing urination, comprising: an acquisition unit that acquires image data captured by a camera installed on the toilet bowl so as to be able to capture an image of the bowl; a first calculation unit that calculates a urination pixel number, which is the number of pixels of an image showing the urination, from the image data when it is detected that the user has urinated by performing image recognition on the image data; and a second calculation unit that calculates at least one of a momentum value indicating the momentum of the urination, a urination volume indicating the volume of the urination, and a urine specific gravity indicating the specific gravity of the urination based on the change in the number of urination pixels; an output unit that outputs at least one of the momentum value, the urination volume, and the urine specific gravity, The amount of urination is calculated based on a first integrated value which is an integrated value of an increase in the number of urination pixels per unit time. Urine analyzer.

12. A urination analyzer for analyzing urination, comprising: an acquisition unit that acquires image data captured by a camera installed on the toilet bowl so as to be able to capture an image of the bowl; a first calculation unit that calculates a urination pixel number, which is the number of pixels of an image showing the urination, from the image data when it is detected that the user has urinated by performing image recognition on the image data; and a second calculation unit that calculates at least one of a momentum value indicating the momentum of the urination, a urination volume indicating the volume of the urination, and a urine specific gravity indicating the specific gravity of the urination based on the change in the number of urination pixels; an output unit that outputs at least one of the momentum value, the urination volume, and the urine specific gravity, The urine specific gravity is calculated based on a second integrated value which is an integrated value of a decrease in the number of urination pixels per unit time. Urine analyzer.

13. A urination analysis program that causes a computer to function as a urination analyzer that analyzes urination, The computer, Acquiring image data captured by a camera installed on the toilet bowl so as to be able to capture an image of the bowl portion of the toilet bowl; When it is detected that the user has urinated by image recognition of the image data, a urination pixel number is calculated from the image data, which is the number of pixels of the image showing the urination; calculating at least one of a momentum value indicating the momentum of the urination, a urination volume indicating the volume of the urination, and a urine specific gravity indicating the specific gravity of the urination based on the change in the number of urination pixels; the momentum value is calculated based on a maximum value of an increase in the number of urination pixels per unit time; Execute a process to output at least one of the momentum value, the urination volume, and the urine specific gravity. Urine analysis program.

14. A urination analysis program that causes a computer to function as a urination analyzer that analyzes urination, The computer, Acquiring image data captured by a camera installed on the toilet bowl so as to be able to capture an image of the bowl portion of the toilet bowl; When it is detected that the user has urinated by image recognition of the image data, a urination pixel number is calculated from the image data, which is the number of pixels of the image showing the urination; calculating at least one of a momentum value indicating the momentum of the urination, a urination volume indicating the volume of the urination, and a urine specific gravity indicating the specific gravity of the urination based on the change in the number of urination pixels; the amount of urination is calculated based on a first integrated value which is an integrated value of an increase in the number of urination pixels per unit time, Execute a process to output at least one of the momentum value, the urination volume, and the urine specific gravity. Urine analysis program.

15. A urination analysis program that causes a computer to function as a urination analyzer that analyzes urination, The computer, Acquiring image data captured by a camera installed on the toilet bowl so as to be able to capture an image of the bowl portion of the toilet bowl; When it is detected that the user has urinated by image recognition of the image data, a urination pixel number is calculated from the image data, which is the number of pixels of the image showing the urination; calculating at least one of a momentum value indicating the momentum of the urination, a urination volume indicating the volume of the urination, and a urine specific gravity indicating the specific gravity of the urination based on the change in the number of urination pixels; the urine specific gravity is calculated based on a second integrated value which is an integrated value of a decrease in the number of urination pixels per unit time, Execute a process to output at least one of the momentum value, the urination volume, and the urine specific gravity. Urine analysis program.

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