Excrement identification methods, excrement identification devices, and excrement identification procedures

Abstract

The excrement determination device acquires image data captured by a camera installed on the toilet bowl, which can capture images of the toilet bowl in the toilet. It also acquires sensing data from sensors that detect when the user sits on and leaves the toilet. Based on changes in the sensing data, it determines whether the image data is valid or invalid. Only based on the image data that is determined to be valid, it determines that the user has either defecated or urinated, and outputs the determination result.

Description

Technical Field

[0001] This disclosure relates to techniques for determining excrement based on image data. Background Technology

[0002] Patent document 1 discloses the following technology: detecting whether defecation or urination has occurred based on the change in the water level of the sealed water formed at the bottom of the basin, and when defecation or urination is detected, the imaging unit captures images of the defecation or urination until the detection of defecation or urination ends.

[0003] However, in the technology of Patent Document 1, if the user's sitting position on the toilet changes, and an attempt is made to identify the image of excrement based on the image data captured by the camera unit, there is a possibility that the excrement may be misidentified, which requires further improvement.

[0004] Existing technical documents

[0005] Patent documents

[0006] Patent Document 1: JP 2018-126331 Summary of the Invention

[0007] This disclosure is made to address such a problem, and aims to provide a technique to prevent misidentification of excrement images based on image data even when the seating position of a user sitting on a toilet changes.

[0008] The method for determining excrement in one aspect of this disclosure is a method for determining excrement using an excrement determining device. The processor of the excrement determining device acquires image data captured by a camera installed on the toilet bowl, which can capture images of the toilet bowl in the toilet. It also acquires sensing data from sensors that detect when a user sits on and leaves the toilet. Based on changes in the sensing data, it determines whether the image data is valid or invalid. Only based on the image data that is determined to be valid, it determines whether the user has defecated or urinated, and outputs the determination result.

[0009] According to this disclosure, even if the user's sitting position on the toilet changes, it can prevent the misidentification of images of excrement based on image data. Attached Figure Description

[0010] Figure 1 This is a diagram showing the structure of the excrement determination system in Implementation 1.

[0011] Figure 2 This is a diagram illustrating the configuration of the sensor assembly and the excrement determination device in Embodiment 1.

[0012] Figure 3This is a sequence diagram illustrating the process of the excrement determination device in Embodiment 1.

[0013] Figure 4 This is a flowchart illustrating an example of the processing of the excrement determination device in Embodiment 1.

[0014] Figure 5 This is a flowchart showing the detailed process of invalidation determination.

[0015] Figure 6 This is a sequence diagram used to illustrate the invalidation determination process.

[0016] Figure 7 This is a flowchart illustrating an example of discharge detection and processing.

[0017] Figure 8 This is a block diagram illustrating an example of the structure of the excrement determination system in Embodiment 2.

[0018] Figure 9 This is a flowchart illustrating an example of invalidation determination processing in Implementation Method 2.

[0019] Figure 10 This is an explanatory diagram of the invalidity determination process in Implementation Method 2.

[0020] Figure 11 This is a block diagram illustrating an example of the structure of the excrement determination system in Embodiment 3.

[0021] Figure 12 This is a flowchart illustrating an example of calibration processing.

[0022] Figure 13 This is an illustrative diagram of the calibration process. Detailed Implementation

[0023] (The insights that form the basis of this disclosure)

[0024] In nursing facilities, the frequency and timing of bowel movements are crucial information for assessing the health risks of those being cared for. Requiring caregivers to record this information increases their workload. Furthermore, having caregivers record this information in the presence of patients increases the psychological burden on the patients. Therefore, this paper seeks to identify excrement from images captured by cameras installed in toilets, generate bowel information based on the identification results, and automatically record this information.

[0025] However, during defecation, users sometimes change their sitting posture by briefly raising their buttocks off the toilet seat when they sit back down, or by spreading their legs while sitting to wipe away waste. When the sitting posture changes, the external light entering the toilet bowl increases dramatically. Consequently, the brightness of the image data captured by the camera increases sharply, making it difficult to accurately identify waste based on the image data. At this time, although the camera's automatic exposure function is working, the camera needs time to achieve a proper exposure corresponding to the external light, making it difficult to accurately identify waste based on the image data captured during this period.

[0026] In particular, because individuals requiring caregiver assistance to sit on the toilet often need to resit multiple times before completing the sitting motion, their sitting position frequently changes. Furthermore, wiping their buttocks with toilet paper is also difficult for such individuals, leading to frequent changes in their sitting position. Therefore, it becomes challenging to accurately identify excrement from image data for such individuals.

[0027] This disclosure is made in order to address such a problem.

[0028] The method for determining excrement in one aspect of this disclosure is a method for determining excrement using an excrement determining device. The processor of the excrement determining device acquires image data captured by a camera installed on the toilet bowl, which can capture images of the toilet bowl in the toilet. It also acquires sensing data from sensors that detect when a user sits on and leaves the toilet. Based on changes in the sensing data, it determines whether the image data is valid or invalid. Only based on the image data that is determined to be valid, it determines whether the user has defecated or urinated, and outputs the determination result.

[0029] According to this structure, the validity of image data is determined based on changes in sensor data detected by sensors that detect changes in the user's sitting and getting off the toilet. Therefore, even if the external light incident on the basin changes drastically with changes in the user's sitting position, image data captured under such circumstances can be determined as invalid. Thus, excrement detection processing is no longer applied to image data showing drastic changes in external light incident on the basin. As a result, misidentification of excrement is prevented when the user's sitting position on the toilet changes.

[0030] In the above-described method for determining excrement, the image data may also be invalidated if the sensing data shows that the change in the sensing data after the person is seated exceeds a given range.

[0031] According to this structure, the image data can be invalidated if the user who is sitting on the toilet sits down again.

[0032] In the above-described method for determining excrement, the user's departure from the toilet can also be determined in the decision if the sensing data indicates that the period of departure has lasted for a first period.

[0033] According to this structure, since the user's departure from the toilet can be accurately determined when the period during which the sensing data indicates departure lasts for the first period.

[0034] In the above-described method for determining excrement, if the decision is made during a second period in which the sensing data shows that the change in the sensing data after the seating has been within the given range, at least the image data captured during the second period may be set to invalid.

[0035] According to this structure, after the sensing data indicates that the user has sat down, the image data captured up to the second period, during which the change in the sensing data is within a given range, is set to invalid. Therefore, even if the external light incident on the basin decreases sharply due to the user sitting on the toilet, the image data can be processed for excrement detection only after the camera's exposure, through the automatic exposure function, becomes a suitable exposure corresponding to the external light, thus preventing misidentification of excrement.

[0036] In the above-described method for determining excrement, the decision may also be made if the period during which the change in the sensing data after the seating is indicated to be within the given range continues for a second period.

[0037] According to this structure, seating can be determined after waiting for the camera's exposure to become a suitable exposure corresponding to the external light.

[0038] In the above-mentioned method for determining excrement, the image data may also be set to invalid if the change in the sensing data after the seating arrangement is determined falls outside the given range.

[0039] According to this structure, if the seating status changes after the seating arrangement is determined, the image data can be set to invalid.

[0040] In the above method for determining excrement, the sensing data can also be the distance value of a distance sensor or the illuminance value of an illuminance sensor.

[0041] According to this structure, since the distance measurement value of the distance sensor or the illuminance value of the illuminance sensor is used as sensing data, it is possible to accurately detect when a user sits down and leaves the seat.

[0042] In the above-described method for determining excrement, at least one of the defecation and urination may be determined using valid image data prior to the period before the latest image data.

[0043] According to this structure, since valid image data from a given period prior to the latest image data is used to determine whether at least one of the defecation or urination has occurred, the excretion detection processing can be reliably prevented from being applied to image data determined to be invalid.

[0044] In the above-described method for determining excrement, the determination can also be made by comparing the image data captured by the camera with the reference toilet color data, and the reference toilet color data can be calculated based on the color data of the area within the basin that is a given distance away from the edge of the toilet toward the storage part of the toilet.

[0045] According to this structure, since reference toilet color data is generated based on image data of the area inside the basin that is a given distance away from the edge of the basin towards the storage part of the toilet, reference toilet color data can be generated using color data of the area of ​​the basin other than the edge where it is difficult to clean the attached stains, and excrement can be detected from the image data with good accuracy.

[0046] In another embodiment of this disclosure, the excrement determination procedure causes a computer to execute the aforementioned excrement determination method.

[0047] Based on this structure, an excrement determination procedure can be provided that achieves the same effect as the excrement determination method described above.

[0048] Another aspect of this disclosure discloses an excrement determination device, which is an excrement determination device for determining excrement, comprising: a first acquisition unit that acquires image data captured by a camera mounted on the toilet bowl of a toilet; a second acquisition unit that acquires sensing data from a sensor that detects a user's sitting and leaving the toilet; a determination unit that determines whether the image data is valid or invalid based on changes in the sensing data; a determination unit that determines whether the user has performed at least one of defecation and urination based only on the image data that is determined to be valid; and an output unit that outputs the determination result.

[0049] Based on this structure, an excrement determination device can be provided that achieves the same effect as the above-mentioned excrement determination method.

[0050] Another aspect of this disclosure is a method for determining excrement using an excrement determination device. The processor of the excrement determination device acquires image data captured by a camera installed on the toilet bowl, which can capture images of the toilet bowl. Based on changes in the image data, it determines whether the image data is valid or invalid. Based only on the image data that is determined to be valid, it determines whether at least one of defecation or urination has occurred, and outputs the determination result.

[0051] Because changes in seating posture, such as sitting back down on the toilet or opening one's legs to wipe excrement, cause drastic changes in the amount of external light incident on the toilet bowl, these changes are presented as changes in image data. According to this structure, the validity or invalidation of image data is determined based on these changes. Therefore, even if the external light incident on the toilet bowl changes drastically with changes in seating posture, image data captured during this period of change can be determined as invalid. Thus, excrement detection processing is no longer applied to image data showing drastic changes in the external light incident on the toilet bowl. As a result, misidentification of excrement is prevented when the user's seating posture on the toilet changes.

[0052] In the above excrement determination method, the decision may also involve detecting a given object from the image data and, if a given condition is met indicating a sharp change in the number of pixels of the detected object, setting the image data as invalid.

[0053] If the external light incident on the basin changes drastically due to a change in seating position, this change is represented as a change in the number of pixels of a given object contained in the image data. According to this structure, if a given condition is met indicating a drastic change in the number of pixels of a given object contained in the image data, the image data is determined to be invalid. Therefore, changes in seating position can be accurately detected from the image data.

[0054] In the above method for determining excrement, the given condition may also be that the number of pixels of the object increases at a given rate of increase and decreases at a given rate of decrease.

[0055] Based on this structure, it is possible to distinguish between drastic changes in the number of pixels caused by diarrhea, urination, and bleeding, and changes in the number of pixels of a given object caused by changes in its seated state.

[0056] In the above-described excrement determination method, the given condition may also be such that the number of pixels at the t-th (t is a positive integer) sampling point is greater than P1 times the number of pixels at the (t-1)-th sampling point, and the number of pixels at at least one sampling point, i.e., the k-th (≤t-1) sampling point, within a certain period past the t-th sampling point is less than P2 (<P1) times the number of pixels at the (k-1)-th sampling point.

[0057] According to this structure, a sharp change in the number of pixels of a given object associated with a change in the seating state can be correctly detected.

[0058] In the above-described excrement determination method, the given object may also be at least one of urination, defecation, and blood.

[0059] If the external light incident on the pelvic region changes sharply due to a change in the seating state, this change appears as a change in the size of the images representing urination, defecation, and blood detected from the image data. Therefore, according to this structure, a change in the seating state can be correctly detected from the change in the number of pixels representing urination, defecation, and blood.

[0060] In the above-described excrement determination method, in the determination, at least one of defecation and urination may also be performed using valid image data given a period before the latest image data.

[0061] According to this structure, since at least one of defecation and urination is determined using valid image data given a period before the latest image data, it is possible to reliably prevent the application of excrement detection processing to image data determined to be invalid.

[0062] In the above-described excrement determination method, in the determination, at least one of defecation and urination may also be determined by comparing the image data captured by the camera and reference toilet color data, and the reference toilet color data is calculated based on the color data of the region within the pelvic region that is a given distance away from the edge of the pelvic region toward the storage portion of the toilet.

[0063] According to this structure, since the reference toilet color data is generated based on the image data of the region within the pelvic region that is a given distance away from the edge of the pelvic region toward the storage portion of the toilet, it is possible to generate the reference toilet color data using the color data of the region of the pelvic region other than near the edge where it is difficult to clean the attached stains, and excrement can be detected with good accuracy from the image data.

[0064] The excrement determination program in another aspect of the present disclosure causes a computer to execute the above-described excrement determination method.

[0065] Based on this structure, an excrement determination procedure can be provided that achieves the same effect as the excrement determination method described above.

[0066] Another aspect of this disclosure discloses an excrement determination device, which is an excrement determination device for determining excrement, comprising: an acquisition unit that acquires image data captured by a camera mounted on the toilet bowl of a toilet; a determination unit that determines whether to set the image data as valid or invalid based on changes in the image data; a determination unit that determines whether at least one of defecation and urination has occurred based on the image data determined to be valid; and an output unit that outputs the determination result.

[0067] Based on this structure, an excrement determination device can be provided that achieves the same effect as the excrement determination method described above.

[0068] This disclosure also enables an excrement determination system to operate via such an excrement determination procedure. Furthermore, it is self-evident that such a computer program can be distributed via computer-readable, non-transitory recording media such as CD-ROMs or communication networks such as the Internet.

[0069] Furthermore, the embodiments described below are all specific examples of this disclosure. The numerical values, shapes, constituent elements, steps, and order of steps shown in the following embodiments are examples and are not intended to limit the scope of this disclosure. Additionally, any constituent element in the following embodiments that is not described in the independent claim representing the highest-level concept is described as an arbitrary constituent element. Furthermore, the various elements can be combined in all embodiments.

[0070] (Implementation Method 1)

[0071] Figure 1 This is a diagram showing the structure of the excrement determination system in Embodiment 1 of this disclosure. Figure 2 This is a diagram illustrating the configuration of the sensor assembly 2 and the excrement determination device 1 in Embodiment 1 of this disclosure.

[0072] Figure 1 The excrement detection system shown includes an excrement detection device 1, a sensor assembly 2, and a server 3. The excrement detection device 1 is a device that determines the presence or absence of excrement by the user based on image data captured by the camera 24. Figure 2As shown, the excrement detection device 1 is, for example, installed on the side of the water tank 105. However, this is just one example; the excrement detection device 1 can also be installed on the wall of the toilet, or it can be built into the sensor assembly 2. The installation location is not particularly limited. The excrement detection device 1 is connected to the 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 excrement information generated by the excrement detection device 1.

[0073] like Figure 2 As shown, the sensor assembly 2 is, for example, attached to the edge 101 of the toilet bowl 100. The sensor assembly 2 and the waste detection device 1 are communicatively connected via a given communication path. The communication path can be a wireless communication path such as Bluetooth (registered trademark) or wireless LAN, or a wired LAN.

[0074] like Figure 2 As shown, the toilet 100 includes a rim portion 101 and a basin portion 102. The rim portion 101 is disposed at the upper end of the toilet 100, defining the opening of the toilet 100. The basin portion 102 is disposed below the rim portion 101, and receives defecation and urination.

[0075] A water storage section 104 is provided at the bottom of the basin 102. A drain outlet (not shown) is provided in the water storage section 104. Excrement and urine discharged into the basin 102 flow down the drain pipe through the drain outlet. That is, the toilet 100 is a water-washable toilet. A toilet seat 103 for the user to sit on is provided at the top of the toilet 100. The toilet seat 103 rotates up and down. The user sits on the toilet seat 103 in a lowered position above the edge 101z. A water tank 105 is provided at the rear of the toilet 100 to collect flushing water for excrement and urine.

[0076] Return to reference Figure 1 The sensor assembly 2 includes a seating sensor 21, an illuminance sensor 22, an illumination device 23, and a camera 24. The seating sensor 21 and the illuminance sensor 22 are examples of sensors that detect when a user sits down and leaves the toilet 100.

[0077] A seating sensor 21 is disposed on the toilet 100 and can measure the distance from the buttocks of a user sitting on the toilet 100. The seating sensor 21 may include, for example, a distance sensor that measures the distance from the buttocks of the user sitting on the toilet 100, i.e., the distance value. An example of a distance sensor is an infrared distance sensor. The seating sensor 21 measures the distance value at a given sampling rate and inputs the measured distance value to the excrement determination device 1 at the given sampling rate. The seating sensor 21 is an example of a sensor that detects the user's sitting state. The distance value is an example of sensing data representing the user's sitting and leaving the seat.

[0078] An illuminance sensor 22 is disposed on the toilet 100 to measure the illuminance within the basin 102. The illuminance sensor 22 measures the illuminance within the basin 102 at a given sampling rate and inputs the measured illuminance value to the excrement determination device 1 at the given sampling rate. The illuminance value is an example of sensing data representing the user's sitting and leaving the seat.

[0079] A lighting device 23 is provided on the toilet 100 to illuminate the inside of the basin 102. The lighting device 23 is, for example, a white LED, which illuminates the inside of the basin 102 under the control of the excrement detection device 1.

[0080] Camera 24 is capable of capturing images of the basin 102 disposed on the toilet 100. Camera 24 is, for example, a high-sensitivity and wide-angle camera, capable of capturing color images with R (red) components, G (green) components, and B (blue) components. Camera 24 captures images of the interior of the basin 102 at a given frame rate, and inputs the obtained image data to the excrement determination device 1 at a given sampling rate.

[0081] Camera 24 includes an automatic exposure unit 241. The automatic exposure unit 241 performs an automatic exposure function, controlling the exposure of camera 24 to achieve a suitable exposure corresponding to the illuminance within the basin 102. Here, the automatic exposure unit 241 can control the exposure of camera 24 based on the illuminance value detected by illuminance sensor 22.

[0082] The excrement determination device 1 includes a processor 11, a memory 12, a communication unit 13, and an entry / exit sensor 14.

[0083] The processor 11 may include, for example, a central processing unit (CPU) or an ASIC (application-specific integrated circuit). The processor 11 includes a first acquisition unit 111, a second acquisition unit 112, a decision unit 113, a determination unit 114, and an output unit 115.

[0084] The first acquisition unit 111 acquires image data captured by the camera 24 at a given sampling rate.

[0085] The second acquisition unit 112 acquires the distance value measured by the seating sensor 21 at a given sampling rate. The second acquisition unit 112 acquires the illuminance value measured by the illuminance sensor 22 at a given sampling rate.

[0086] The decision unit 113 determines whether to enable or disable the image data acquired by the first acquisition unit 111 based on changes in the sensing data from the seating sensor 21 or the illuminance sensor 22. Specifically, if the sensing data from the seating sensor 21 or the illuminance sensor 22 shows a change in sensing data exceeding a given range after seating, the decision unit 113 disables the image data. Here, the image data disabled can be image data from the present to a certain period in the past, or image data from a certain period before and after the present.

[0087] If the sensing data from the seating sensor 21 or the illuminance sensor 22 indicates that the period of leaving the seat has lasted for a first period, the determination unit 113 determines that the user has left the toilet 100. The first period is, for example, considered as a predetermined period from when the sensing data indicates that the user has left the seat to when the user gets up from the toilet 100. The first period can be, for example, a suitable value such as 5 seconds, 10 seconds, or 20 seconds.

[0088] If the second period continues during which the sensing data from the seating sensor 21 or the illuminance sensor 22 shows that the change in sensing data after seating is within a given range, the determination unit 113 will at least invalidate the image data captured during the second period. The second period is, for example, a predetermined time conceived under the following situation: external light incident on the basin 102 decreases due to seating, and the automatic exposure unit 241 of the camera 24 operates accordingly, so that the exposure of the camera 24 becomes appropriate for the reduced external light.

[0089] Furthermore, if the second period continues during which the sensing data from the seating sensor 21 or the illuminance sensor 22 shows that the change in sensing data after seating is within a given range, the determination unit 113 determines that the person is seated.

[0090] If the change in the sensing data of the seating sensor 21 or the illuminance sensor 22 after the user has confirmed sitting on the toilet 100 is outside the given range, the decision unit 113 invalidates the image data. Therefore, invalidating the image data after the user has sat down on the toilet 100 after confirming sitting prevents misidentification of excrement.

[0091] The determination unit 114 determines whether the user has defecated or urinated solely based on image data deemed valid by the decision unit 113. Specifically, the determination unit 114 sets a detection area D1 (reference) including the storage unit 104 for valid image data. Figure 13The determination unit 114 determines whether defecation or urination has occurred by comparing the image data of the detection area D1 (hereinafter referred to as the detection area data) with the reference toilet color data. Specifically, the determination unit 114 removes pixel data with the color represented by the reference toilet color data (reference toilet color) from the detection area data. Here, the determination unit 114 removes pixel data from the detection area data whose R, G, B values ​​are within a given range relative to the reference toilet color.

[0092] Then, if the detection area data (hereinafter referred to as the determination object image data) of the detection area after removing the pixel data of the reference toilet color meets the urination condition, the determination unit 114 determines that urination has occurred. In addition, if the determination object image data meets the defecation condition, the determination unit 114 determines that defecation has occurred.

[0093] Furthermore, the determination unit 114 can determine whether the user has bleeding based on the image data that is deemed valid. In this case, the determination unit 114 can determine that the user is bleeding if the image data of the determination object meets the bleeding conditions.

[0094] Here, the reference area C2 is defined as the region within the basin 102 at a given distance from the edge 101 of the toilet 100 toward the storage section 104. Figure 13 The reference toilet color data is calculated from the image data of the reference area C2. Specifically, the reference toilet color data has the average values ​​of the R, G, and B values ​​of the reference area C2.

[0095] The output unit 115 generates discharge information that includes the determination result of the determination unit 114, and outputs the generated discharge information. Here, the output unit 115 can send the discharge information to the server 3 using the communication unit 13, or it can store the discharge information in the memory 12.

[0096] The memory 12 may include, for example, a storage device capable of storing various information, such as RAM (Random Access Memory), SSD (Solid State Drive), or flash memory. The memory 12 may store, for example, excretion information and reference toilet color data. The memory 12 may be a portable memory such as a USB (Universal Serial Bus) memory.

[0097] The communication unit 13 is a communication circuit that connects the excrement detection device 1 to the server 3 via a network. The communication unit 13 also connects the excrement detection device 1 to the sensor assembly 2 via a communication path. Excretion information includes, for example, information indicating that excretion has occurred (defecation, urination, and bleeding) and date and time information indicating the date and time of excretion. The excrement detection device 1 generates excretion information, for example, on a daily basis, and sends the generated excretion information to the server 3.

[0098] The entry / exit sensor 14 may include, for example, a distance sensor. The entry / exit sensor 14 detects when a user enters the toilet where the toilet 100 is located. Here, the distance sensor constituting the entry / exit sensor 14 has lower measurement accuracy but a wider detection range compared to the distance sensor constituting the seating sensor 21. The entry / exit sensor 14 may also replace the distance sensor with, for example, a human body sensor. The distance sensor may be, for example, an infrared distance sensor. The human body sensor detects a user located within a given distance relative to the toilet 100.

[0099] The above describes the structure of the excrement detection system. Next, we will explain the general outline of the processing of the excrement detection device 1. Figure 3 This is a sequence diagram illustrating the outline of the processing of the excrement determination device 1 in Embodiment 1 of this disclosure.

[0100] exist Figure 3 In the diagram, the first row represents the sequence of entry / exit sensors 14, which includes a human body sensor; the second row represents the sequence of entry / exit sensors 14, which includes a distance sensor; the third row represents the sequence of seating sensors 21; the fourth row represents the sequence of illuminance sensors 22; and the fifth row represents the sequence of lighting devices 23. Figure 3 In the example shown, a sequence of two sides is shown: an entry / exit sensor 14 containing a human body sensor and an entry / exit sensor 14 containing a ranging sensor. However, the excrement determination device 1 may have at least one of the entry / exit sensors 14.

[0101] At time t1, the user enters the toilet. Simultaneously, the determination unit 113 determines that the user has entered the toilet based on sensing data input from the entry / exit sensor 14 (human body sensor) or the entry / exit sensor 14 (range sensor). Here, since the entry / exit sensor 14 (human body sensor) sets the sensing data to a high level if it detects a user and to a low level if it does not detect a user, the determination unit 113 determines that the user has entered the toilet when the sensing data input from the entry / exit sensor 14 (human body sensor) is high. Furthermore, the determination unit 113 determines that the user has entered the toilet when the distance measured by the entry / exit sensor 14 (range sensor) is below a threshold A1. The threshold A1 can be a suitable value such as 50cm, 100cm, or 150cm.

[0102] Furthermore, at time t1, the decision unit 113 begins to store the sensing data input from the entry / exit sensor 14, the seating sensor 21, and the illuminance sensor 22 into the memory 12.

[0103] Furthermore, at time t1, the decision unit 113, in conjunction with the user's detection, uses the communication unit 13 to send an entry notification indicating that the user has entered the toilet to the server 3.

[0104] At time t2, the user sits on the toilet 100. Simultaneously, the distance measured from the seating sensor 21 falls below the seating detection threshold A2, and the determination unit 113 determines that the user is seated on the toilet 100. The seating detection threshold A2 has, for example, a predetermined value representing the distance from the seating sensor 21 to the user's buttocks indicating that the user is seated on the toilet 100. The seating detection threshold A2 is smaller than the threshold A1; for example, a suitable value such as 10cm, 15cm, or 20cm can be used.

[0105] Furthermore, at time t2, the illuminance value input from the illuminance sensor 22 decreases because the external light incident on the basin 102 through sitting is blocked by the user's buttocks.

[0106] Then, at time t2, the decision unit 113 activates the lighting device 23 in conjunction with the seat detection. As a result, the lighting device 23 illuminates the inside of the basin 102, ensuring the amount of light required when retrieving excrement from image data.

[0107] Then, at time t2, the decision unit 113 starts the camera 24 and captures images of the basin 102 through the camera 24. Afterwards, the first acquisition unit 111 acquires image data at a given sampling rate.

[0108] In addition, the entry notification can be sent at time t2.

[0109] During the period from time t3 to time t4, B1 occurs when the user sits down again on the toilet 100. Simultaneously, at time t3, the distance measured by the seating sensor 21 exceeds the seating detection threshold A2, and at time t4, the distance measured by the seating sensor 21 falls below the seating detection threshold A2. Furthermore, at time t3, the decision unit 113 turns off the lighting device 23, and at time t4, the decision unit 113 turns the lighting device 23 on. Additionally, the illuminance value of the illuminance sensor 22 also changes in conjunction with the distance measured by the seating sensor 21.

[0110] At time t5, the user gets off the toilet 100. Simultaneously, the distance measured by the seating sensor 21 exceeds the seating detection threshold A2. Furthermore, at time t5, the decision unit 113 turns off the lighting device 23.

[0111] At time t6, since the ranging value measured by the entry / exit sensor 14 exceeds the threshold A1, the decision unit 113 determines that the user has exited the toilet. Simultaneously, the output unit 115 uses the communication unit 13 to send an exit notification indicating that the user has exited the toilet to the server 3. Furthermore, at time t6, the output unit 115 uses the communication unit 13 to send excretion information generated based on image data to the server 3. Additionally, the exit notification and excretion information can be sent at time t7.

[0112] At time t7, since the distance measured by the seat sensor 21 at time t5 exceeded the seat detection threshold A2 for a period of B2, the decision unit 113 ends the storage of sensing data in the memory 12 and causes the camera 24 to stop capturing images of the basin 102.

[0113] At time t8, since the high-level state of the exit sensor 14 (human body sensor) since time t7 has passed for period B4, the decision unit 113 sets the excrement determination device 1 to standby state.

[0114] Next, the details of the processing by the excrement detection device 1 will be explained. Figure 4 This is a flowchart illustrating an example of the processing of the excrement determination device 1 in Embodiment 1 of this disclosure. In the following flowchart, the sensing data is the distance value detected by the seating sensor 21.

[0115] In step S1, the decision unit 113 determines whether the user is seated on the toilet 100. Here, if the distance value obtained by the second acquisition unit 112 from the seating sensor 21 is below the seating detection threshold A2 (step S1 "Yes"), the decision unit 113 determines that the user is seated and proceeds to step S2. On the other hand, if the distance value is greater than the seating detection threshold A2 (step S1 "No"), the decision unit 113 puts the process on standby in step S1.

[0116] In step S2, the decision unit 113 performs invalidation determination processing to determine whether the image data is valid or invalid. Details of the invalidation determination processing are available using... Figure 5 To be discussed later.

[0117] In step S3, the determination unit 114 determines whether urination has been confirmed. If urination has not been confirmed (step S3 "No"), the process proceeds to step S4; if urination has been confirmed (step S3 "Yes"), the process proceeds to step S7. Confirmation of urination means that it can be determined that the image data contains an image of urination.

[0118] In step S4, the determination unit 114 performs excretion detection processing, determining, based on image data, that the user has either urinated or defecated at least once. Details of the excretion detection processing are available using... Figure 7 To be discussed later.

[0119] In step S5, if the determination unit 114 determines that urination has occurred during the excretion detection process (step S5 "Yes"), then urination is confirmed (step S6). On the other hand, if no urination is detected during the excretion detection process (step S5 "No"), the process proceeds to step S7.

[0120] In step S7, the determination unit 114 determines whether defecation has been confirmed. If defecation has been confirmed (step S7 "Yes"), the process proceeds to step S11; if defecation has not been confirmed (step S7 "No"), the process proceeds to step S8. Confirmation of defecation means that it can be determined that the image data contains an image of defecation.

[0121] In step S8, the determination unit 114 performs discharge detection processing.

[0122] In step S9, if the determination unit 114 determines that there is defecation during the excretion detection process (step S9 "Yes"), it confirms defecation (step S10). On the other hand, if the determination unit 114 does not determine that there is defecation during the excretion detection process (step S9 "No"), it proceeds to step S11.

[0123] In step S11, the decision unit 113 determines whether the user has left their seat. If the user has left their seat in step S42 (described later), the decision unit 113 determines that step S11 is "yes" and proceeds to step S12. Conversely, if the user has not left their seat (step S11 is "no"), the decision unit 113 returns the process to step S2.

[0124] In step S12, the output unit 115 uses the communication unit 13 to send the exit notification and discharge information to the server 3.

[0125] Next, we will explain the details of invalidation determination. Figure 5 This is a flowchart illustrating the details of the invalidation determination process. In step S31, the determination unit 113 samples the ranging value obtained by the second acquisition unit 112. Here, the ranging value SD(0) at the latest sampling point (t) and the ranging value SD(-1) at the previous sampling point (t-1) are sampled.

[0126] In step S32, the decision unit 113 determines whether the ranging value SD(0) is greater than the ranging value SD(-1) - width N and smaller than the ranging value SD(-1) + width N. That is, the decision unit 113 determines whether the change in the ranging value SD is within a given range. The width N can be, for example, a suitable value such as 3mm, 4mm, 5mm, 6mm, or 10mm. Twice the width N is an example of a given range.

[0127] If the change in the ranging value SD is within a given range (step S32 "Yes"), the process proceeds to step S33; if the change in the ranging value SD exceeds the given range (step S32 "No"), the process proceeds to step S38.

[0128] In step S33, the determination unit 113 determines whether the period during which the change in the ranging value SD was within a given range continued for the second period. If the period continued for the second period (step S33 "Yes"), the determination unit 113 determines that the user has sat on the toilet 100 (step S35) and proceeds to step S41. On the other hand, if the period during which the change in the ranging value SD was within a given range did not continue for the second period (step S33 "No"), the determination unit 113 starts counting the timer that is timing the second period (step S36).

[0129] In step S37, the determination unit 113 invalidates the image data, causing the process to proceed to step S41. Here, the determination unit 113 invalidates the image data by setting the pixel count data PD(t) = 0. The pixel count data PD(t) represents the count value of the pixels containing defecation, urination, and blood in the image data of the sampling point (t). For example, if there are X pixels representing defecation, Y pixels representing urination, and Z pixels representing blood in the image data of the sampling point (t), the pixel count data PD(t) = (X, Y, Z).

[0130] The decision unit 113 invalidates the image data by setting the pixel count data PD(t), i.e., the pixel count data PD(0), PD(-1), ..., PD(-20), in the image data from the latest sampling point to the 20th sampling point up to 0. Image data with pixel count data PD(0), PD(-1), ..., PD(-20) = 0 will no longer be used to detect the presence or absence of excretion in the excretion detection process described later. Therefore, by setting the pixel count data PD(0), PD(-1), ..., PD(-20) to 0, the image data from sampling point (0) to sampling point (-20) can be invalidated. Setting the pixel count data PD to 0 means setting all pixel count data PD for defecation, urination, and blood to 0.

[0131] Here, image data from the latest sampling point up to 20 sampling points is set to invalid, but this is just one example. It is also possible to invalidate only the image data from the latest sampling point, or to invalidate image data from the latest sampling point up to any sampling point other than 20, or to invalidate image data captured within a certain period before and after the latest sampling point. These are also the same in step S40 described later.

[0132] In step S38, since the change in the ranging value SD exceeds the given range, the decision unit 113 sets the seating determination to invalid.

[0133] In step S39, the decision unit 113 resets the timer that counts during the second period.

[0134] In step S40, the determination unit 113 invalidates the image data from sampling point (0) to (-20) by setting the pixel count data PD(0)...PD(-20) = 0.

[0135] In step S41, the determination unit 113 determines whether the period during which the ranging value SD(0) is greater than the seating detection threshold A2 has lasted for the first period. If the first period has lasted (step S41 "Yes"), the determination unit 113 invalidates the seating determination and determines that the person is not seated, causing the process to proceed to step S11. Figure 4 On the other hand, if the first period does not continue during the period when the ranging value SD(0) is greater than the seating detection threshold A2 (step S41 "No"), the process proceeds to step S3. Figure 4 ).

[0136] Figure 6 This is a sequence diagram used to illustrate the invalidation determination process. Figure 6 The waveform W1 in the first row indicates whether a seat has been confirmed. In waveform W1, a high level indicates a confirmed seat, and a low level indicates an invalid seat.

[0137] At time t1, since the user sits on the toilet 100, the ranging value SD falls below the seating detection threshold A2. Furthermore, at time t1, since the change in the ranging value SD falls within a given range, the timing of the second period begins.

[0138] At time t2, since the variation of the ranging value SD remained within a given range for the second period, seating was determined. As a result, the second period from time t1 to time t2 became an invalid interval. Image data captured within this invalid interval was invalidated. Furthermore, within each sampling point of the invalid interval, image data from the latest sampling point up to 20 sampling points prior to that point could be invalidated.

[0139] Between time points t3 and t4, the user sits down again on the toilet 100. Consequently, at time point t3, the change in the ranging value SD exceeds a given range, invalidating the seating determination and initiating an invalidation period. Although the external light incident on the basin 102 increases sharply due to this re-sitting, no excretion detection processing is applied to the image data because it is invalidated.

[0140] During time intervals t3 and t4, the ranging value SD exceeds the seating detection threshold A2, but upon the user taking their seat, the ranging value SD immediately falls below the seating detection threshold A2. That is, the period during which the ranging value SD exceeds the seating detection threshold A2 does not continue into the first period. Therefore, the invalid interval continues.

[0141] At time t4, the timing of the second period begins because the change in the ranging value SD enters the given range.

[0142] At time t5, since the variation of the ranging value SD remained within the given range for the second period, the invalid interval ended, and seating was confirmed. Thus, the image data can be made valid after waiting for the camera 24 to achieve a suitable exposure. Therefore, misidentification of excrement based on image data is prevented.

[0143] At time t6, the user begins to leave the toilet 100. As a result, since the change in the ranging value SD exceeds a given range, an invalid interval begins, and sitting down is declared invalid.

[0144] At time t7, since the ranging value SD exceeds the seating detection threshold A2, the timing of the first period begins.

[0145] At time t8, since the ranging value SD exceeded the seating detection threshold A2 for the duration of the first period, it is determined that the person is disembarking.

[0146] Next, we will explain the discharge detection and processing. Figure 7 This is a flowchart illustrating an example of discharge detection and processing.

[0147] In step S110, the determination unit 114 retrieves the reference toilet color data from the memory 12.

[0148] In step S120, the determination unit 114 acquires the image data for processing timing from the image data acquired by the first acquisition unit 111. The image data for processing timing is, for example, the image data up to a given sampling point (e.g., 20 sampling points) from the latest sampling point. However, this is just one example; the image data for processing timing can also be the image data from the latest sampling point.

[0149] In step S130, the determination unit 114 determines whether the image data for processing timing is valid. Here, the determination unit 114 determines that the image data for processing timing is invalid if the pixel count data PD in the image data for processing timing is 0, and determines that the image data for processing timing is valid if the pixel count data PD is not 0. Specifically, if all pixel count data PD for defecation, urination, and blood is 0, the image data for processing timing is determined to be invalid. If the image data for processing timing is valid (step S130 "Yes"), the process proceeds to step S140; if the image data for processing timing is invalid (step S130 "No"), the process proceeds to step S5 or step S9. Figure 4 ).

[0150] In step S140, the determination unit 114 extracts the image data (detection area data) of the detection area D1 from the image data of the processing time.

[0151] In step S150, the determination unit 114 determines whether the detection area data contains pixel data of a color different from the reference toilet color. If the detection area data contains pixel data of a color different from the reference toilet color (step S150 "Yes"), the process proceeds to step S160; if the detection area data does not contain pixel data of a color different from the reference toilet color (step S150 "No"), the process proceeds to step S5 or S9.

[0152] In step S160, the determination unit 114 generates determination object image data by removing pixel data with R, G, B values ​​that are outside a given range relative to the reference toilet color data from the detection area data.

[0153] In step S170, the determination unit 114 determines whether the image data of the determination object meets the urination condition. Here, the urination condition is the presence of pixel data within a predetermined R, G, B range indicating urination in the image data of the determination object. If the urination condition is met (step S170 "Yes"), the process proceeds to step S180; if the urination condition is not met (step S170 "No"), the process proceeds to step S190. Alternatively, the urination condition may be a condition where a given number of pixel data within the predetermined R, G, B range indicating urination exists.

[0154] In step S180, the determination unit 114 determines that the image data of the object being processed indicates urination, and the processing proceeds to step S5 or step S9. Figure 4 ).

[0155] In step S190, the determination unit 114 determines whether the image data of the determination object meets the defecation condition. Here, the defecation condition is the presence of pixel data within a predetermined R, G, B range indicating defecation in the image data of the determination object. If the defecation condition is met (step S190 "Yes"), the process proceeds to step S200; if the defecation condition is not met (step S190 "No"), the process proceeds to step S210. Alternatively, the defecation condition may be a condition where a given number of pixel data within the predetermined R, G, B range indicating defecation exists.

[0156] In step S200, the determination unit 114 determines that the image data of the object to be processed indicates defecation, and the processing proceeds to step S5 or step S9. Figure 4 ).

[0157] In step S210, the determination unit 114 determines whether the image data of the determination object meets the bleeding condition. Here, the bleeding condition is the presence of pixel data representing blood within a predetermined R, G, B range in the image data of the determination object. If the bleeding condition is met (step S210 "Yes"), the process proceeds to step S220; if the bleeding condition is not met (step S210 "No"), the process proceeds to step S230. Furthermore, the bleeding condition is the presence of a given number or more pixel data within the predetermined R, G, B range representing bleeding.

[0158] In step S220, the determination unit 114 determines that the image data of the object to be processed has bleeding, and the processing proceeds to step S5 or step S9. Figure 4 ).

[0159] In step S230, the determination unit 114 determines that there is a foreign object in the image data of the determination object. The foreign object is, for example, a diaper or toilet paper.

[0160] Thus, according to the excrement determination device 1 of Embodiment 1, the validity and invalidity of image data are determined based on changes in the sensing data of sensors that detect changes in the user's sitting and leaving the toilet. Therefore, even if the external light incident on the basin 102 changes drastically with changes in the sitting position, image data captured during this period of change can be determined as invalid image data. Consequently, excrement detection processing is no longer applied to image data showing drastic changes in the external light incident on the basin 102. As a result, misidentification of excrement can be prevented when the user's sitting position on the toilet 100 changes.

[0161] (Implementation Method 2)

[0162] In Implementation 2, if the number of pixels of an object detected from the image data changes drastically, the image data is set to invalid. Figure 8 This is a block diagram illustrating an example of the structure of the excrement determination system in Embodiment 2 of this disclosure. In Embodiment 2, the same reference numerals are used to label the same components as in Embodiment 1, and descriptions are omitted.

[0163] The excrement detection device 1A includes a processor 21A. The processor 21A includes an acquisition unit 211, a determination unit 212, a judgment unit 213, and an output unit 214. The acquisition unit 211, the judgment unit 213, and the output unit 214 are connected to... Figure 1 The first acquisition unit 111, decision unit 113, judgment unit 114 and output unit 115 are the same.

[0164] The decision unit 212 detects a given object from the image data, and invalidates the image data if a given condition (hereinafter referred to as the invalid condition) is met, indicating a sharp change in the number of pixels of the detected object. The invalid condition will be described later. The given object is at least one of urination, defecation, and blood.

[0165] Next, the processing of the excrement determination device 1A in Embodiment 2 will be explained. The main routine of the excrement determination device 1A and Figure 4 The same. Furthermore, in the excrement determination device 1A, the excrement detection and processing are the same. Figure 7 The invalid determination process differs between the excrement determination device 1A and the excrement determination device 1; therefore, the invalid determination process will be explained below.

[0166] Figure 9This is a flowchart illustrating an example of the invalidation determination process in Implementation Method 2. In step S51, the determination unit 212 shifts the pixel count data PD. Specifically, the determination unit 212 sets the pixel count data PD(1) to PD(20) such that pixel count data PD(0) is set to pixel count data PD(1), pixel count data PD(1) is set to pixel count data PD(2), ..., and pixel count data PD(19) is set to pixel count data PD(20).

[0167] In step S52, the decision unit 212 obtains the latest image data.

[0168] In step S53, the decision unit 212 detects the number of pixels for urination, defecation, and blood from the latest image data. Here, the decision unit 212 extracts a detection area D1 from the latest image data and detects the number of pixels for urination by counting pixel data within a predetermined R, G, B range representing urination in the extracted detection area D1. Similarly, the decision unit 212 detects the number of pixels for defecation by counting pixel data within a predetermined R, G, B range representing defecation in the detection area D1 of the latest image data. Furthermore, the decision unit 212 detects the number of pixels for blood by counting pixel data within a predetermined R, G, B range representing blood in the detection area D1 of the latest image data.

[0169] In step S54, the determination unit 212 sets the pixel count data PD(0) to the number of pixels detected from the latest image data for urination, defecation and blood.

[0170] In step S55, the determination unit 212 determines whether the pixel count data PD satisfies the invalid condition. Here, the invalid condition is a condition where |PD(0)|>|PD(-1)|×P1 and at least one pixel count data PD(k) during the (Q×T) period is |PD(k)|<|PD(k-1)|×P2.

[0171] k is an integer less than or equal to -1. P1 can be a suitable value such as 5, 6, or 7. P2 is a value smaller than P1, and can be a suitable value such as 2, 3, or 4. Q is a suitable value such as 3, 4, or 5. T is the sampling period. Furthermore, if at least one pixel count data PD in urine, feces, or blood satisfies the invalidity condition, the decision unit 212 determines that it is "yes" in step S55. Alternatively, the decision unit 212 may use the total value of urine, feces, and blood as the pixel count data PD.

[0172] If the pixel count data PD meets the invalid condition (step S55 "Yes"), the process proceeds to step S56; if the pixel count data PD does not meet the invalid condition (step S55 "No"), the process proceeds to step S3. Figure 4 ).

[0173] In step S56, the determination unit 212 is set to PD(0), PD(-1), ..., PD(Q) = 0. Therefore, the image data at sampling points (t), (t-1), ..., (tQ) are invalidated. For example, when Q = 3, the four image data corresponding to the pixel count data PD(0), PD(-1), PD(-2), and PD(-3) are invalidated. If the processing in step S56 ends, the process proceeds to step S3. Figure 4 ).

[0174] Figure 10 This is an explanatory diagram of the invalidation determination process in Implementation Method 2. In this example, |PD(0)|>|PD(-1)|×P1. Furthermore, |PD(-2)|<|PD(-3)|×P2. Therefore, the pixel count data PD satisfies the invalidation condition. Thus, the period from sampling point (0) to sampling point (t-3) is set as the invalid interval.

[0175] Since the sitting position changes, such as sitting down again on the toilet or opening one's legs to wipe excrement, the external light incident on the basin changes drastically. Therefore, the change in the amount of light is presented as a change in pixel count data (PD).

[0176] Therefore, if a given condition indicating a sharp change in pixel count data PD is met, the determination unit 212 determines that the image data in the case of a sharp change is invalid. Thus, excretion detection processing is no longer applied to image data during periods of sharp change in external light incident on the basin. As a result, misidentification of excrement can be prevented if the user's sitting position changes while seated on the toilet 100.

[0177] Alternatively, an invalid condition can be PD(0) > PD(-1) × P1 and at least one PD(k) within the period (Q × T) is PD(k). <PD(k-1)×P2。

[0178] Furthermore, invalid conditions can be conditions such as having intervals in the period Q×T where the pixel count data PD increases with a slope of more than a given increase rate and intervals where the pixel count data PD decreases with a slope of a given decrease rate.

[0179] (Implementation Method 3)

[0180] Implementation method 3 performs calibration processing for the reference toilet color. Figure 11This is a block diagram illustrating an example of the structure of the excrement determination system in Embodiment 3. Furthermore, in Embodiment 3, the same reference numerals are used for the same components as in Embodiments 1 and 2, and descriptions are omitted.

[0181] The processor 31 of the excrement determination device 1B includes a first acquisition unit 311, a second acquisition unit 312, a determination unit 313, a judgment unit 314, an output unit 315, and a calibration execution unit 316. The first acquisition unit 311 to the output unit 315 and... Figure 1 The first acquisition section 111 to the output section 115 are the same.

[0182] The calibration execution unit 316 performs calibration processing to determine the color of the reference toilet.

[0183] Figure 12 This is a flowchart illustrating an example of calibration processing.

[0184] In step S71, the calibration execution unit 316 acquires the image data captured by the camera 24, performs pattern matching and other processing on the acquired image data, and detects the mark. Figure 13 This is an explanatory diagram of the calibration process. Mark M1 is located at a given position on the edge 101 of the toilet 100. Mark M1 is a symbol used to set the detection area D1 and the reference area C2 for the image data.

[0185] In step S72, the calibration execution unit 316 determines whether the flag M1 can be detected. If the flag M1 can be detected (step S72 "Yes"), the process proceeds to step S73; if the flag M1 cannot be detected (step S72 "No"), the process proceeds to step S76.

[0186] In step S73, the calibration execution unit 316 sets a detection area D1 and a reference area C2 for the image data. Here, setting information is predetermined, specifying which coordinates of the image data are used to set the detection area D1 and the reference area C2, with the marker M1 as a reference. Therefore, the calibration execution unit 316 sets the detection area D1 and the reference area C2 in the image data according to the setting information, starting from the marker M1. The detection area D1 is a rectangular area containing the storage portion 104. The reference area C2 is a rectangular area within the basin 102 that is a given distance away from the edge portion 101 towards the storage portion 104 and does not include the storage portion 104.

[0187] In step S74, the calibration execution unit 316 calculates the toilet reference color data from the reference region C2. The toilet reference color data is the average of the R, G, and B values ​​of each pixel data constituting the reference region C2.

[0188] In step S75, the calibration execution unit 316 stores the calibration result in the memory 12. The calibration result includes the coordinates of the vertex of the set detection area D1, the coordinates of the vertex of the reference area C2, and the reference toilet color data.

[0189] refer to Figure 13 Previously, the area directly below the edge portion 101 within the basin 102 was designated as the reference area C1. This area is difficult to clean, making it hard to remove stains. Therefore, if reference toilet color data is calculated from the reference area C1, there is a possibility that the calculated reference toilet color data accurately representing the color of the basin 102 may not be accurate due to the influence of stains. To address this, the calibration execution unit 316 calculates reference color data from the reference area C2.

[0190] In step S76, the calibration execution unit 316 does not store the calibration result in the memory 12 and ends the process.

[0191] Thus, according to the excrement determination device 1B of Embodiment 3, suitable reference toilet color data can be calculated.

[0192] The present disclosure can be adapted to the following variations.

[0193] (1) The excrement determination device 1 can further execute the invalid determination process shown in Embodiment 2 based on the invalid determination process shown in Embodiment 1. In this case, Figure 5 If step S41 is "No", proceed the process to... Figure 9 Step S51 is sufficient. The invalidation determination process of Embodiment 1 is effective, for example, for detecting changes in the seating state caused by sitting down again. On the other hand, the invalidation determination process of Embodiment 2 is effective for detecting changes in the seating state caused by a user opening their legs. Therefore, by combining Embodiment 1 and Embodiment 2, it is possible to detect changes in the seating state caused by sitting down again and changes in the seating state caused by opening one's legs.

[0194] (2) In Figure 5 In the flowchart, the ranging value of the seating sensor 21 is used to determine whether the image data is invalid or valid, but the illuminance value detected by the illuminance sensor 22 can also be used to determine whether the image data is invalid or valid. In this case, in step S32, SD(0) represents the illuminance value at sampling point (0), and SD(-1) represents the illuminance value at sampling point (t-1). Furthermore, the seating detection threshold A2 in step S41 adopts a predetermined illuminance value representing the user leaving the toilet 100.

[0195] (3) The calibration process shown in Implementation 3 can also be applied to Implementation 2.

[0196] Industrial availability

[0197] The excrement determination device of this disclosure is useful in techniques for determining excretion based on image data.

Claims

1. A method for determining excrement, comprising a method for determining excrement using an excrement determining device. The processor of the excrement determination device performs the following processing: Obtain image data captured by a camera installed on the toilet bowl, capable of capturing images of the toilet bowl area inside the toilet. Acquire sensing data from sensors that detect when a user sits on and leaves the toilet. The decision to enable or disable the image data is based on the changes in the sensed data. The determination that at least one of the user has defecated or urinated is based solely on the image data deemed valid. Output the result of the determination.

2. The method for determining excrement according to claim 1, wherein, In the decision, if the sensing data shows that the change in the sensing data after the seating exceeds a given range, the image data is set to invalid.

3. The method for determining excrement according to claim 2, wherein, In the decision, if the sensing data indicates that the period of the user leaving the toilet lasted for a first period, it is determined that the user has left the toilet.

4. The method for determining excrement according to claim 2 or 3, wherein, In the decision, if the period during which the sensing data shows that the change in the sensing data after the seating is within the given range continues for a second period, at least the image data captured during the second period will be invalidated.

5. The method for determining excrement according to claim 2 or 3, wherein, In the decision, the seating is determined if the period during which the sensing data shows that the change in the sensing data after the seating has been within the given range has continued for a second period.

6. The method for determining excrement according to claim 5, wherein, In the decision, if the change in the sensing data becomes outside the given range after the seating arrangement is determined, the image data is set to invalid.

7. The method for determining excrement according to claim 1 or 2, wherein, The sensing data is the distance value measured by the distance sensor or the illuminance value measured by the illuminance sensor.

8. The method for determining excrement according to claim 1 or 2, wherein, In the determination, valid image data prior to the given period relative to the latest image data is used to determine whether at least one of the defecation and urination has occurred.

9. The method for determining excrement according to claim 1 or 2, wherein, In the determination, at least one of the following is determined: either defecation or urination has occurred, by comparing image data captured by the camera with reference toilet color data. The reference toilet color data is calculated based on the color data of the area within the basin that extends a given distance from the edge of the toilet toward the storage portion of the toilet.

10. A computer program product comprising an excrement determination program, the excrement determination program causing a computer to execute the excrement determination method according to any one of claims 1 to 9.

11. An excrement determination device for determining excrement, The excrement determination device includes: The first acquisition unit acquires image data captured by a camera installed on the toilet bowl, which is capable of capturing images of the toilet bowl in the toilet. The second acquisition unit acquires sensing data from sensors that detect when a user sits on and leaves the toilet. The decision unit determines whether to enable or disable the image data based on changes in the sensed data. The determination unit determines, based solely on the image data deemed valid, that at least one of the user has defecated or urinated; and The output unit outputs the result of the determination.

12. A method for determining excrement, comprising a method for determining excrement using an excrement determining device. The processor of the excrement determination device performs the following processing: Obtain image data captured by a camera installed on the toilet bowl, capable of capturing images of the toilet bowl area inside the toilet. The decision to set the image data as valid or invalid is based on the changes in the image data. The determination of whether at least one person has defecated or urinated is based solely on the image data deemed valid. Output the result of the determination.

13. The method for determining excrement according to claim 12, wherein, In the decision, a given object is detected from the image data, and if a given condition is met indicating a sharp change in the number of pixels of the detected object, the image data is set to invalid.

14. The method for determining excrement according to claim 13, wherein, The given condition is that the number of pixels of the object increases at a given rate of increase and decreases at a given rate of decrease.

15. The method for determining excrement according to claim 13 or 14, wherein, The given conditions are: the number of pixels at sampling point t is greater than P1 times the number of pixels at sampling point (t-1), and the number of pixels at at least one sampling point (i.e., sampling point k) over a certain period of time since sampling point t is less than P2 times the number of pixels at sampling point (k-1), where t is a positive integer, k ≤ t-1, and P2... <P1。 16. The method for determining excrement according to claim 13 or 14, wherein, The given object is at least one of urine, feces, and blood.

17. The method for determining excrement according to claim 12 or 13, wherein, In the determination, valid image data prior to the given period relative to the latest image data is used to determine whether at least one of the defecation and urination has occurred.

18. The method for determining excrement according to claim 12 or 13, wherein, In the determination, at least one of the following is determined: either defecation or urination has occurred, by comparing image data captured by the camera with reference toilet color data. The reference toilet color data is calculated based on the color data of the area within the basin that extends a given distance from the edge of the basin towards the storage portion of the toilet.

19. A computer program product comprising an excrement determination program, said excrement determination program causing a computer to execute the excrement determination method according to any one of claims 12 to 18.

20. An excrement detection device for detecting excrement, The excrement determination device includes: The acquisition unit acquires image data captured by a camera installed on the toilet bowl of the toilet. a determination unit that determines to make the image data valid or invalid based on a change in the image data; and The determination unit determines, based on the image data deemed valid, whether at least one of the individuals has defecated or urinated. and The output unit outputs the result of the determination.

Images