Excrement information system

Abstract

To provide an excrement image display system, in which the system enables accurate detection of dirt by distinguishing between the dirt left in a toilet bowl and actual excrement, especially a small amount of excrement, that was difficult with conventional image recognition algorithms for excretion, and in addition, the system also prevents a caregiver from making an erroneous determination of medication for a care-receiver by mis-detecting and notifying that there was a bowel movement when the toilet bowl was dirty in nursing home, etc.SOLUTION: An excrement image information system of the present disclosure can accurately determine a nature of excrement by using a determination algorithm that takes into consideration toilet bowl stains and lens cover stains. Alternatively, the system encourages cleaning of a toilet bowl and enables accurate detection of excrement properties by notifying a user (e.g. care-receiver) stool viewer (e.g. caregiver) that the toilet bowl and imaging device lens are dirty.SELECTED DRAWING: Figure 10

Description

[Technical Field]

[0001] The present disclosure relates to waste information systems. [Background technology]

[0002] As disclosed in Patent Document 1, there is a determination device that determines whether or not dirt caused by the imaging device or the imaging environment has been captured by inputting image information of a target image captured inside a toilet bowl during excretion into a model trained by machine learning using a neural network. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2020-190181 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional image recognition algorithms for excretion have difficulty distinguishing between dirt remaining in the toilet bowl and actual feces, especially small amounts of feces, and accurately detecting the dirt. Furthermore, in nursing care facilities, etc., if a stain on the toilet bowl is mistakenly detected as feces and notified, caregivers may make incorrect decisions about prescribing medication for the care recipient. [Means for solving the problem]

[0005] The excrement image display system according to the present disclosure comprises: An excrement information system that displays the state of excrement on a display unit, The excrement information system includes an imaging element and an information processing device provided in a toilet bowl, the imaging element includes a light receiving unit, a lens, and a lens cover that protects the lens; and the information processing device includes a transmission unit, a storage unit, and a control unit; The storage unit ( I ) excrement learning model, and (II) storing at least one dirt learning model selected from the group consisting of a toilet bowl dirt learning model and a lens cover dirt learning model; In the information processing device, during operation, the control unit controls the imaging element so that the imaging element captures an image of the inside of the toilet bowl, thereby generating captured image information; based on the excrement learning model, the control unit estimates information about excrement as excrement estimation information from at least one captured image included in the captured image information; Based on the at least one dirt learning model, the control unit determines, as dirt estimation information, at least one piece of information selected from the group consisting of information on toilet bowl dirt adhering to the inner wall surface of the toilet bowl and information on lens cover dirt adhering to the outer peripheral surface of the lens cover, Estimating from at least one captured image included in the captured image information, controlling the transmitting unit to transmit at least one data selected from the group consisting of first data and second data to the display unit; where: the first data includes at least one of the excrement estimation information and the at least one captured image; The second data includes at least one of the dirt estimation information and the at least one captured image. [Effects of the Invention]

[0006] According to the excrement image display system of the present disclosure, the properties of excrement can be accurately determined by using a determination algorithm that takes into account at least one type of dirt selected from the group consisting of toilet bowl dirt and lens cover dirt. Alternatively, by notifying a viewer (e.g., a caregiver) of the user's (e.g., a care recipient's) stool that the toilet bowl is dirty, the viewer (e.g., a caregiver) is prompted to clean the toilet bowl, thereby enabling more accurate detection of the properties of excrement. [Brief explanation of the drawings]

[0007] [Figure 1A] Diagram of excretory device in embodiment 1 [Figure 1B] Cross-sectional view of a toilet seat according to the first embodiment [Figure 2A] 1 is a block diagram including a toilet seat 2 and a server 101 included in an excrement image display system according to a first embodiment. [Figure 2B] 1 is a block diagram including a toilet seat 2 and a server 101 included in an excrement image display system according to a first embodiment. [Figure 3] Block diagram of a system according to the first embodiment [Figure 4] FIG. 1 shows a user-side information processing device 201 according to the first embodiment. [Figure 5] FIG. 1 shows a user-side information processing device 201 according to the first embodiment. [Figure 6] FIG. 1 shows a user-side information processing device 201 according to the first embodiment. [Figure 7] FIG. 1 shows a user-side information processing device 201 according to the first embodiment. [Figure 8] FIG. 1 shows a user-side information processing device 201 according to the first embodiment. [Figure 9] FIG. 1 shows a user-side information processing device 201 according to the first embodiment. [Figure 10] Flowchart in the first embodiment [Figure 11] Flowchart in the first embodiment [Figure 12A] Schematic diagram of a mask image according to the first embodiment. [Figure 12B] Schematic diagram of a captured image from which dirt has been removed by image processing according to the first embodiment. [Figure 13A] Schematic diagram of captured image information according to the first embodiment. [Figure 13B] Schematic diagram of captured image information according to the first embodiment. [Figure 13C] Schematic diagram of captured image information according to the first embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0008] (Embodiment 1) FIG. 1A is a diagram of an excretion device according to the first embodiment.

[0009] FIG. 2A is a block diagram including a toilet seat 2 and a server 101 included in the excrement image display system (hereinafter simply referred to as the "system") according to the first embodiment. (Summary) As shown in Figures 1A and 2A, a toilet 1, which is a device for excretion, has a toilet bowl, a toilet seat 2, an imaging element 4, and a toilet-side information processing device 5. A device-specific ID (i.e., an ID unique to the toilet 1) is assigned to the toilet-side information processing device 5. The toilet seat 2 is also provided with a seating sensor 3 that detects when a user sits on the toilet seat 2. The toilet seat 2 is equipped with an imaging element 4 for capturing images of the inside of the toilet. An example of the imaging element 4 is a camera. An example of the toilet-side information processing device 5 is a semiconductor element (i.e., an electric / electronic circuit) such as an IC or LSI.

[0010] As shown in FIG. 1B, the imaging element 4 is disposed inside the toilet seat 2. The imaging element 4 comprises a light receiving section (not shown) made up of an image sensor, a lens 42, and a lens cover 43. The lens cover 43 is provided to protect the lens 42. The imaging element 4 is disposed inside the toilet seat 2. The lens cover 43 forms part of the underside of the toilet seat 2. Light that passes through the lens cover 43 and reaches the lens 42 is collected by the lens 42 into the light receiving section (not shown) and converted into electronic image information. In this way, the inside of the toilet bowl As a result, the toilet-side information processing device 5 forms a captured image 502 of the inside of the toilet bowl.

[0011] In the system according to the first embodiment, an image of the excrement of a user (e.g., a care recipient) and information about the excrement are displayed on the display unit 202, which will be described later. Alternatively, in the system according to the first embodiment, (a) an image of the excrement of a user (e.g., a care recipient) and (b) an image of at least one stain selected from the group consisting of toilet bowl stains and lens cover stains are displayed on the display unit 202.

[0012] As shown in FIG. 3, the system according to the first embodiment includes a toilet 1, a server 101, and a user-side information processing device 201.

[0013] As described above, the toilet 1 includes a toilet seat 2, an image sensor 4, a seating sensor 3, and a toilet-side information processing device 5. The toilet-side information processing device 5 includes a toilet-side data transmission unit and a toilet-side control unit. The toilet-side information processing device 5 may also include a toilet-side memory unit.

[0014] Like a typical server, the server 101 includes a server-side receiving unit, a server-side transmitting unit, a server-side storage unit, and a server-side control unit. As will be described later, the server-side storage unit stores an excrement learning model.

[0015] Examples of the server 101 are a general server on the Internet, a server in a care facility, and a server in a home.

[0016] 3, the user-side information processing device 201 includes a display unit 202. The user-side information processing device 201 further includes a user-side input unit, a user-side transmitting unit, a user-side receiving unit, a user-side storage unit, and a user-side control unit. Examples of the user-side information processing device 201 include a personal computer, a tablet, and a smartphone that include a display that functions as the display unit 202.

[0017] The storage unit is a so-called memory (especially a non-volatile memory), and the control unit is a so-called central processing unit.

[0018] (Explanation of toilet 1 operation) 10, as indicated by step 101 (i.e., S101), when it is detected that the user has sat on the toilet seat 2, the imaging element 4 starts capturing an image of the inside of the toilet bowl. In this way, the toilet-side control unit controls the imaging element 4 so that the imaging element 4 captures an image of the inside of the toilet bowl 1 (i.e., the inside of the toilet bowl).

[0019] The toilet-side information processing device 5, located inside the toilet seat, activates the image sensor 4 when it receives a seating signal from the seat sensor 3 and continues to capture images while the user is seated on the toilet seat. The image capture interval is at specified intervals, here every second, but it can be longer or shorter. The captured images are saved in the toilet-side information processing device 5 (strictly speaking, in the toilet-side storage unit, if one is provided), and the image name includes the device ID linked to the toilet seat that captured the image and the time the image was captured. Furthermore, the first image saved after image capture begins is saved with an image name that includes text that makes it clear that it is a saved image.

[0020] As indicated by step 102 (i.e., S102) in FIG. 10, when it is detected that the user has left the toilet seat 2, the image capturing is terminated, and the captured image is uploaded from the toilet side information processing device 5 (strictly speaking, from the toilet side storage unit, if provided) to the server 101. In this way, the captured image captured by the image sensor 4 is The toilet-side control unit controls the toilet-side data transmission unit so as to transmit the captured image information to the server 101.

[0021] As described above, in the system according to embodiment 1, first, as indicated as S101 in Figure 10, when the seating sensor 3 detects that the user has sat on the toilet seat 2, the toilet side control unit controls the imaging element 4 so that the imaging element 4 begins capturing images of the inside of the toilet bowl 1.

[0022] Next, the toilet-side controller controls the toilet-side storage unit to store a plurality of captured images 502 (see FIGS. 13A to 13C) captured by the imaging element 4 at predetermined time intervals in the toilet-side storage unit as captured image information 501 (see FIGS. 13A to 13C). In other words, as time passes, captured images 502 from the imaging element 4 are sequentially accumulated in the toilet-side storage unit, and captured image information 501 is formed in the toilet-side storage unit. As shown in FIGS. 13A to 13C, the captured image information 501 includes the plurality of captured images 502 and text 503 for each of the plurality of captured images 502. The text 503 includes the device ID and the time the image was captured.

[0023] 10, the toilet-side control unit controls the toilet-side data transmission unit to transmit captured image information 501 to the server 101. More specifically, in S102, the toilet-side control unit controls the toilet-side data transmission unit to transmit captured image 502 captured by the imaging element 4 to the server 101 as captured image information 501, and the server-side control unit controls the server-side receiving unit to receive captured image information 501.

[0024] Finally, when the seating sensor 3 detects that the user has left the toilet seat 2, the toilet-side control unit controls the image sensor 4 to stop capturing images of the inside of the toilet 1.

[0025] The toilet-side information processing device 5 does not need to be equipped with a toilet-side storage unit. That is, while the imaging element 4 is capturing an image of the inside of the toilet 1 (strictly speaking, the inside of the toilet bowl), the toilet-side information processing device 5 may immediately transmit the captured image 502 captured by the imaging element 4 to the server 101. In this case, the server-side control unit generates captured image information 501 and stores the generated captured image information 501 in the server-side storage unit.

[0026] In this embodiment, each captured image 502 may include an image of dirt adhering to the inner wall surface of the toilet bowl (more precisely, the toilet bowl) (particularly feces adhering to the inner peripheral surface of the toilet bowl, particularly feces adhering to the inner peripheral surface of the toilet bowl and not coming off even when flushed) and an image of dirt adhering to the outer peripheral surface of the lens cover 43. Hereinafter, in this specification, dirt adhering to the inner wall surface of the toilet bowl (more precisely, the toilet bowl) will be simply referred to as "toilet bowl dirt." Similarly, dirt adhering to the outer peripheral surface of the lens cover 43 will be simply referred to as "lens cover dirt." As used in this specification, the term "dirt" includes at least one selected from the group consisting of toilet bowl dirt and lens cover dirt.

[0027] Since each captured image 502 may include an image of at least one stain selected from the group consisting of toilet stains and lens cover stains, the captured image information 501 may also include an image of at least one stain selected from the group consisting of toilet stains and lens cover stains, since, as is clear from the above description, the captured image information 501 includes a plurality of captured images 502.

[0028] (Explanation of image recognition in server 101) FIG. 3 is a block diagram of the system according to the first embodiment. In FIG. 3, a server 101 includes an input image, a learning unit 102 , a recognition unit 103 , and a database 110 .

[0029] The user-side information processing device 201 has a display unit 202 and an erroneous determination correction unit 203 .

[0030] The learning unit 102 captures a large number of images of excrement in advance, extracts features from the images, and generates an excrement learning model that can respond to the features. The generated excrement learning model is stored in the server-side storage unit as the excrement learning model.

[0031] Similarly, the learning unit 102 captures in advance a large number of images of dirt adhering to the inner wall surface of a toilet bowl (more precisely, the toilet bowl) (particularly feces adhering to the inner peripheral surface of the toilet bowl, and especially feces that adhering to the inner peripheral surface of the toilet bowl and cannot be removed by flushing), extracts features from those images, and generates a toilet stain learning model that can respond to those features. The generated toilet stain learning model is stored in the server-side storage unit as the toilet stain learning model.

[0032] Similarly, the learning unit 102 previously captures a large number of images of dirt adhering to the outer peripheral surface of the lens cover 43 (i.e., images captured by the image sensor 4 when dirt is attached to the outer peripheral surface of the lens cover 43), extracts features from those images, and generates a lens cover dirt learning model that can react to those features. The generated lens cover dirt learning model is stored in the server-side storage unit as the lens cover dirt learning model.

[0033] Hereinafter, the term "at least one dirt learning model" means at least one selected from the group consisting of a toilet bowl learning model and a lens cover dirt learning model.

[0034] The recognition unit 103 has a recognition algorithm equipped with an excrement learning model and at least one dirt learning model. The recognition unit 103 recognizes excrement and dirt from the input excrement image (i.e., the captured image information 501, more specifically, the multiple captured images 502 included in the captured image information 501), and outputs the recognition results.

[0035] The database 110 stores the recognition results (for example, the presence or absence of excrement, its shape, amount, color, number of falls, toilet bowl stains, lens cover stains, etc.) The database 110 is included in the server-side storage unit.

[0036] The captured image information 501 received by the server-side receiving unit is temporarily stored in the server-side storage unit. Next, the recognition unit 103 recognizes the properties of the excrement (for example, any one stool property selected from a group consisting of seven Bristol scale index categories described below) based on the captured image information 501 (more specifically, at least one captured image 502 included in the captured image information 501), and the result is output from the recognition unit 103. The output result is stored in the database 110 (i.e., the server-side storage unit) as excrement estimation information.

[0037] Similarly, the recognition unit 103 recognizes at least one stain selected from the group consisting of toilet bowl stains and lens cover stains based on the captured image information 501, and the result is output from the recognition unit 103. The output result is stored in the database 110 (i.e., the server-side storage unit) as stain estimation information.

[0038] As is clear from the above description, based on the excrement learning model, the server-side control unit estimates information about excrement as excrement estimation information from at least one captured image 502 included in the received captured image information 501, and controls the server-side storage unit to store data (hereinafter referred to as "first data") including the excrement estimation information and the at least one captured image 502 (i.e., the at least one captured image 502 that served as the basis for estimating the excrement estimation information) in the server-side storage unit. This is indicated as step 201 (i.e., S201) and step 202 (i.e., S202) in Figure 10.

[0039] Similarly, based on at least one dirt learning model selected from the group consisting of a toilet bowl dirt learning model and a lens cover dirt learning model, the server-side control unit estimates information on at least one dirt selected from the group consisting of toilet bowl dirt and lens cover dirt as dirt estimation information from at least one captured image 502 included in the received captured image information 501, and controls the server-side storage unit to store data (hereinafter referred to as "second data") including the dirt estimation information and the at least one captured image 502 (i.e., the at least one captured image 502 that served as the basis for estimating the dirt estimation information) in the server-side storage unit. This is indicated as step 201 (i.e., S201) and step 202 (i.e., S202) in Figure 10.

[0040] Finally, as indicated as step 203 (i.e., S203) in FIG. 10, if the server-side control unit determines that the first data includes an image of excrement, the server-side control unit controls the server-side transmitting unit to transmit the first data from the server-side memory unit to the user-side receiving unit, and the user-side control unit controls the user-side receiving unit to receive the first data.

[0041] Similarly, when the server-side control unit determines that the second data includes an image of at least one stain selected from the group consisting of toilet bowl stains and lens cover stains, the server-side control unit controls the server-side transmitting unit to transmit the second data from the server-side memory unit to the user-side receiving unit, and the user-side control unit controls the user-side receiving unit to receive the second data.

[0042] (Explanation of dirt detection) In FIG. 10, the images uploaded by the toilet 1 are classified into image groups for each excretion event in the server 101 using text added to the first image saved after image capture begins (for example, text 503 shown as "13:05:00" in FIG. 13A). Captured image information 501 is input to a recognition algorithm equipped with an excrement learning model that can respond to the characteristics of excrement and a dirt learning model that can respond to the characteristics of dirt, and the recognition results, i.e., excretion information such as the presence or absence of excrement, its shape, amount, color, and number of drops, as well as the presence or absence of dirt, are output. The output recognition results are stored in the database 110.

[0043] If even one image of excrement is detected among the captured images 502 included in the captured image information 501, the result of excrement detection and the captured image 502 that is the basis for this detection are selected as first data, and the result and the captured image 502 are displayed on the user-side information processing device 201.

[0044] Similarly, if even one image of dirt is detected among the captured images 502 included in the captured image information 501, the result of the dirt detection (i.e., toilet dirt estimation information) and the captured image 502 that is the basis for this detection are selected as second data, and the result and the captured image 502 are displayed on the user-side information processing device 201.

[0045] In addition, only when an image of excrement or dirt is detected among the multiple captured images 502 included in the captured image information 501, the recognition result (i.e., the result of detecting excrement or dirt) and the captured image 502 that is the basis for the result may be selected, and the result and the captured image 502 may be displayed on the user-side information processing device 201.

[0046] Furthermore, when selecting multiple recognition results and images, all the results and images may be displayed on the user-side information processing device 201, or only a representative result may be displayed. The representative result is an image with a high reliability, which is a parameter of the recognition result (i.e., an image that is judged to be excrement when excrement is detected). The image may be selected using various parameters, such as an image with a high probability of being an image of the object, an image with minimal blur, or an image with the largest object area.

[0047] 10, the system according to this embodiment includes one learning model, which includes an excrement learning model and a dirt learning model. In other words, the excrement image display system includes one learning model, which includes an excrement learning model. Furthermore, the one learning model includes at least one dirt learning model selected from the group consisting of a toilet bowl dirt learning model and a lens cover dirt learning model.

[0048] Alternatively, the system according to this embodiment may include two learning models corresponding to an excrement learning model and a stain learning model, as shown in Fig. 11. In other words, the excrement image display system may include two or more learning models, one of which may be an excrement learning model, and the other of which may be at least one stain learning model selected from the group consisting of a toilet bowl stain learning model and a lens cover stain learning model.

[0049] In either case, the learning model is stored in a server-side storage unit. 12A, the algorithm that inputs captured image information 501 and outputs the presence or absence of dirt as a recognition result compares a pre-stored image without dirt with at least one image selected from the group consisting of an image immediately after the start of shooting and an image immediately after flushing in captured image information 501, extracts a dirty area 603 in the image through image processing, and creates a mask image 604 obtained by binarizing (i.e., drawing in black and white only) the dirty area 603 in a puddle area 602 in the toilet bowl interior area 601, as shown in FIG. 12A, and extracts a white area corresponding to the dirty area 603 included in mask image 604. If the number of pixels is equal to or greater than a specified threshold, it may be determined that there is dirt.

[0050] The mask image 604 may be created by extracting the excrement area using an algorithm that outputs excrement information as a recognition result.

[0051] Furthermore, the mask image 604 may be used in an algorithm that outputs excretion information as a recognition result, to improve the recognition accuracy of excretion information by deleting the stained area 603 through image processing, as shown in FIG. 12B.

[0052] Furthermore, when the image is displayed on the user-side information processing device 201, the mask image 604 may be displayed by pixelating at least one area selected from the group consisting of the excrement area and the stain area 603 using image processing, or by replacing it with another image such as a specific texture. This allows users who do not want to directly view at least one area selected from the group consisting of the excrement and the stain to be encouraged to view the captured image 502.

[0053] (Explanation of display on user-side information processing device 201) As described above, the display unit 202 can display the excretion record by the user entering an ID and password on the web or on the app.

[0054] Figure 4 shows an example of an excretion record displayed on the display unit 202. As shown in Figure 4, the excretion record is displayed on the display unit 202, showing the time the user sat on the toilet seat 2, when the excretion occurred, whether the excretion was feces (i.e., stool) or urine (i.e., urination), and if it was feces, the shape of the excretion at that time, which category it falls into on the seven-category Bristol scale, the color of the excretion, the amount of the excretion, and the number of times the excretion fell, all of which are shown on the graph 205. If it was urine, the color and amount of urine are also shown on the graph 205, showing the excretion record.

[0055] Graph 205 is composed of three graphs with the horizontal axis representing time from 0:00:00 to 23:59:59. The top graph displays the time spent sitting on the toilet seat from the image capture time noted in the image name as a square wave.

[0056] In the middle row, the imaging time marked on the image determined to be urine by the fecal urine detection is read and a bar-shaped figure is displayed on the graph. In this case, the figure may be shown in the same color as the urine color determination result, with the height changed depending on the urine volume, or the urine color and urine volume may be displayed as text within the figure as shown in Figure 5, or text may be displayed as an annotation near the figure as shown in Figure 6.

[0057] Furthermore, in the lower row, the image capture time marked on the image determined to be stool by the feces / urine presence detection is read and a bar-shaped figure is displayed on the graph. In this case, the figure may display the stool shape recognition result as text within the figure, or may display an iconized figure of the Bristol scale index imitating the shape of stool. The figure may also be shown in the same color as the stool color determination result, with its height changed depending on the stool volume, or the stool color, stool volume, and number of stool drops may be displayed as text within the figure, or text may be displayed as an annotation near the figure.

[0058] Furthermore, as shown in Fig. 7, the user-side information processing device 201 of this embodiment can display the image that is the basis for the judgment result (i.e., one captured image 502 that is the basis for the judgment result) by clicking or touching a figure that represents the judgment result displayed on the graph 205. The image may be displayed on the graph 205 from the beginning. The image displayed at this time may be displayed in a form that pops up as an image display area 206 separate from the graph, or may be displayed in an image display area 206 provided in the same window as the graph, as shown in Fig. 8.

[0059] In this way, in the excrement image display system according to embodiment 1, when the excrement recognition results (i.e., excrement estimation information) are displayed on the display unit 202, a viewer (e.g., a caregiver) of the user's (e.g., a care recipient's) excrement can easily recognize whether or not the user has excrement, and as a result, can easily understand the user's health condition.

[0060] Similarly, in the excrement image display system according to embodiment 1, when the dirt recognition result (i.e., at least one dirt estimation information selected from the group consisting of toilet bowl dirt information and lens cover dirt information) is displayed on the display unit 202, the viewer (e.g., a care worker) can easily recognize whether or not there is dirt, and as a result, is encouraged to clean the inside of the toilet bowl.

[0061] 2B, the toilet-side information processing device 5 may have a recognition unit 103. In other words, the toilet-side information processing device 5 may also function as the server 101.

[0062] The toilet seat 2 shown in FIG. 2B displays data on the display unit of the user's information processing device.

[0063] The toilet seat 2 is equipped with an imaging element 4 and a toilet-side information processing device 5.

[0064] In the toilet seat 2 shown in Figure 2B, the toilet-side information processing device 5 includes a toilet-seat-side transmitting unit, a toilet-seat-side memory unit, and a toilet-seat-side control unit. The toilet-seat-side memory unit stores at least one dirt learning model selected from the group consisting of (I) an excrement learning model and (II) a toilet bowl dirt learning model and a lens cover dirt learning model. Meanwhile, the user-side information processing device further includes a user-side receiving unit.

[0065] The toilet-side information processing device 5 included in the toilet seat 2 shown in FIG. 2B performs the following operations during operation: The process is carried out.

[0066] First, the toilet seat control unit generates captured image information 501 by controlling the imaging element 4 so that the imaging element 4 captures an image of the inside of the toilet bowl.

[0067] Next, based on the excrement learning model, the toilet seat-side control unit estimates excrement information from at least one captured image 502 included in the captured image information 501 as excrement estimation information.

[0068] Furthermore, based on at least one dirt learning model, the toilet seat side control unit estimates at least one piece of information selected from the group consisting of information on toilet bowl dirt adhering to the inner wall surface of the toilet bowl and information on lens cover dirt adhering to the outer surface of the lens cover as dirt estimation information from at least one captured image 502 included in the captured image information 501.

[0069] Finally, the server-side control unit controls the server-side transmitting unit to transmit at least one data selected from the group consisting of the first data and the second data to the user-side receiving unit. As above, the first data includes excrement estimation information and at least one captured image 502 (i.e., at least one captured image 502 that served as the basis for estimating the excrement estimation information), and the second data includes dirt estimation information and at least one captured image 502 (i.e., at least one captured image 502 that served as the basis for estimating the dirt estimation information).

[0070] (Explanation of correction of judgment results) In this embodiment, the judgment result displayed on the display unit 202 can be corrected. As shown in Fig. 9, the user-side information processing device 201 is provided with a judgment result display box 207 in which judgment result text is input, and a pull-down box 208 is provided next to the judgment result display box 207. By clicking or touching the pull-down box 208, a list of judgment results is displayed, and the user-side information processing device 201 of this embodiment has an erroneous judgment correction unit 203 that corrects the judgment result displayed in the judgment result display box 207 by clicking or touching the judgment result that the user wants to confirm. At this time, the figure representing the judgment result displayed on the graph is also redrawn in accordance with the correction content.

[0071] (Explanation of the control unit hardware) The system of this embodiment is a computer system. The computer system mainly comprises a processor (i.e., a central processing unit that functions as a control unit) and a memory (i.e., a storage unit) as hardware. In the server 101 and the user-side information processing device, the system operates by the processor (i.e., a central processing unit that functions as a control unit) executing a program recorded in the memory (i.e., a storage unit). In the toilet-side information processing device included in the toilet, the system also operates by the processor (i.e., a central processing unit that functions as a control unit) executing a program recorded in the memory (i.e., a storage unit).

[0072] The program may be pre-recorded in memory, provided via a telecommunications line, or provided recorded on a non-transitory recording medium such as a computer system-readable memory card, optical disk, or hard disk drive. The processor is composed of one or more electronic circuits including a semiconductor integrated circuit (IC) or a large-scale integrated circuit (LSI). The integrated circuits such as ICs and LSIs referred to here are called by different names depending on the degree of integration, and include integrated circuits called system LSIs, VLSIs (Very Large Scale Integration), and ULSIs (Ultra Large Scale Integration).

[0073] Furthermore, a field-programmable gate array (FPGA), which is programmed after the LSI is manufactured, or a logic device capable of reconfiguring the connections within the LSI or the circuit partitions within the LSI, can also be employed as a processor. Multiple electronic circuits may be integrated into a single chip or distributed across multiple chips. Multiple chips may be integrated into a single device or distributed across multiple devices. The computer system referred to here includes a microcontroller having one or more processors and one or more memories. Therefore, a microcontroller may also be composed of one or more electronic circuits, including a semiconductor integrated circuit or a large-scale integrated circuit. [Industrial Applicability]

[0074] The excrement image display system of the present disclosure accurately determines the properties of excrement by using a determination algorithm that takes into account at least one type of dirt selected from the group consisting of toilet bowl dirt and lens cover dirt. The system notifies a viewer (e.g., caregiver) of the toilet bowl user's (e.g., care recipient's) excrement that the toilet bowl is dirty, thereby encouraging the viewer to clean the toilet bowl. As a result of cleaning, the properties of the excrement can be more accurately estimated. The present disclosure also includes a program that causes a computer to execute the excrement image display system of the present disclosure. [Explanation of symbols]

[0075] 1 toilet 2 toilet seats 3 Seat sensor 4. Image sensor 42 Lens 43 Lens cover 5. Toilet-side information processing device 101 Server 102 Learning Department 103 Recognition part 110 databases 201 User side information processing device 202 Display section 205 graphs 206 Image display area 207 Judgment result display box 208 Drop-down box 501 Captured image information 502 Captured images 503 Text 601 Toilet bowl internal area 602 Puddle area 603 Dirt area 604 Mask Images

Claims

1. An excrement information system that displays the state of excrement on a display unit, The excrement information system includes an imaging element and an information processing device provided in a toilet bowl, the imaging element includes a light receiving unit, a lens, and a lens cover that protects the lens; and the information processing device includes a transmission unit, a storage unit, and a control unit; The storage unit (I) a fecal learning model; and (II) storing at least one dirt learning model selected from the group consisting of a toilet bowl dirt learning model and a lens cover dirt learning model; In the information processing device, during operation, the control unit controls the imaging element so that the imaging element captures an image of the inside of the toilet bowl, thereby generating captured image information; based on the excrement learning model, the control unit estimates information about excrement as excrement estimation information from at least one captured image included in the captured image information; Based on the at least one dirt learning model, the control unit determines, as dirt estimation information, at least one piece of information selected from the group consisting of information on toilet bowl dirt adhering to the inner wall surface of the toilet bowl and information on lens cover dirt adhering to the outer peripheral surface of the lens cover, Estimating from at least one captured image included in the captured image information, controlling the transmitting unit to transmit at least one data selected from the group consisting of first data and second data to the display unit; where: the first data includes at least one of the excrement estimation information and the at least one captured image; the second data includes at least one of the dirt estimation information and the at least one captured image; Excrement information system.

2. the at least one dirt learning model includes the toilet dirt learning model, and the dirt estimation information is information on toilet dirt adhering to an inner wall surface of the toilet. The excrement information system according to claim 1.

3. The at least one dirt learning model includes the lens cover dirt learning model, and the dirt estimation information is information on lens cover dirt attached to an outer peripheral surface of the lens cover. The waste information system according to claim 1 .

4. When the at least one piece of data transmitted by the transmitting unit is displayed on the display unit, the image displayed has a mosaic applied to an area where an image of excrement and an image of dirt included in the image are captured by image processing. The waste information system according to claim 1 .

5. When the at least one piece of data transmitted by the transmission unit is displayed on the display unit, both the image of excrement and the image of dirt included in the image are replaced with other images in the displayed image. The waste information system according to claim 1 .

Images