WO2024241985A1 - Urination dynamics identification device and imaging device - Google Patents

Abstract

Provided is a urination dynamics identification device capable of identifying urination dynamics, and an imaging device. In order to identify the dynamics of urination released from a human body over time, the device comprises: an identification section (urination-related value calculation section 4) that, on the basis of an image of a surface that is caused to intersect by irradiating the released urination with planar light, identifies a split state of urination on said surface as the dynamics of urination over time; and an output section (calculation result output display section 46) that outputs information relating to the split state of the urination identified by the identification unit.

Description

Urinary behavior determination device and imaging device

The present invention relates to a urination behavior determination device and an imaging device.

　Technology for measuring the flow rate of urine released from the human body has been known for some time now in order to accurately diagnose diseases associated with prostatic hyperplasia, overactive bladder, urinary incontinence, and other urination disorders (e.g., Patent Document 1, Patent Document 2, etc.).

JP 2015-105948 A JP 2017-207317 A

Incidentally, there are types of urinary disorders that require the evaluation of various morphologically different urination dynamics, such as when the urinary stream (urine flow) from the external urethral meatus branches into two or splashes in all directions. For this reason, evaluating morphologically different urination dynamics is an important clinical point. However, conventional technology is unable to detect and evaluate such various morphologically different urination dynamics. Therefore, the only way to evaluate urination dynamics is through interviewing the patient.

The present invention aims to provide a urination behavior identification device and an imaging device that can identify urination behavior.

In order to achieve the above-mentioned object, the urination dynamics specifying device of the present invention is a urination dynamics specifying device for specifying the dynamics of urine released from the human body over time, and includes a specifying unit that specifies the urination fragmentation state on the surface as the dynamics of the urination over time based on an image of a surface that is intersected by irradiating the released urine with planar light, and an output unit that outputs information related to the urination fragmentation state specified by the specifying unit.

The urination dynamics identification device of the present invention comprises a light irradiation unit that irradiates planar light that may intersect with the released urine, and an imaging unit that images the surface from a position opposite the surface of the light, the image of the surface being an image captured by the imaging unit over time, and the identification unit preferably includes an extraction unit that extracts at least one of the number of divisions, cross-sectional area, flow rate, degree of separation, and cross-sectional shape of each divided urination based on the image of the surface as the division state of the urination.

In the urination dynamics identification device of the present invention, the light irradiation unit includes a first light irradiation unit that irradiates a planar first light having a first characteristic, and a second light irradiation unit that irradiates a planar second light having a second characteristic in parallel with the plane of the first light at a predetermined interval, and the identification unit preferably calculates the urination passing speed and cross-sectional area between the first light plane and the second light plane based on the split state of urination on the first light plane, the split state of urination on the second light plane, and the predetermined interval, and identifies the urination flow rate as a dynamics according to the urination over time.

In the urination dynamics identification device of the present invention, it is preferable that the identification unit calculates the time it takes for urination to reach the second light surface from the first light surface using a cross-correlation coefficient based on the split state of urination on the first light surface and the split state of urination on the second light surface, thereby calculating the passage speed of the urination.

In the urination behavior determination device of the present invention, it is preferable that the light irradiation unit has a cylindrical lens that emits the incident laser light in a planar manner.

In order to achieve the above objective, an imaging device that captures a specific image for identifying the dynamics of urine released from the human body over time includes a light irradiation unit that irradiates a surface of light that may intersect with the released urine, and an imaging unit that captures the surface from a position facing the surface of the light as the specific image.

　According to the present invention, it is possible to identify and evaluate the state of split urination as a dynamic state according to the passage of time of urination. As a result, it is possible to appropriately diagnose urinary disorders.

3 is a diagram for explaining a configuration of the urination behavior specifying device. FIG. 11A and 11B are diagrams for explaining a configuration of a horizontal section light illumination unit. 11 is a diagram for explaining an example of an installation state when a horizontal section light illumination unit and a camera are installed on a toilet bowl. FIG. 13 is a flowchart illustrating an example of a urination-related value calculation process for calculating a urination-related value. FIG. 13 shows an example of split urination. FIG. 13 shows the labeling state of an example of split urination. FIG. 13 is a diagram showing an example of urination-related values stored in a predetermined area of a memory. FIG. 11 is a diagram for explaining a specific example of step S04. FIG. 11 is a diagram for explaining an example of a method for generating urination portion passing frequency distribution information. FIG. 13 is a diagram showing an example of urination portion passing frequency distribution information when urination is performed at a relatively concentrated portion. FIG. 13 is a diagram showing an example of urination portion passing frequency distribution information when a urine stream is bifurcated and excreted. FIG. 13 is a diagram showing an example of urination portion passing frequency distribution information when a urine stream is bifurcated and excreted. FIG. 13 is a diagram showing an example of urination portion passing frequency distribution information when a urine stream is bifurcated and excreted. FIG. 13 is a diagram showing a histogram based on the cross-sectional area and number of urinating individuals based on a urination motion image. FIG. 11 is a diagram for explaining another example of a method for generating urination portion passing frequency distribution information.

Below, an embodiment of the urination behavior identification device of the present invention and a program executed in a terminal device capable of communicating with the urination behavior identification device will be described with reference to the drawings. However, the present invention is not limited or restricted to the following examples.

FIG. 1 is a diagram for explaining the configuration of the urination dynamics specifying device 1. As shown in FIG. 1, the urination dynamics specifying device 1 of this embodiment includes a horizontal section light illumination unit 2 that irradiates planar horizontal section light that can intersect in a substantially horizontal direction onto urine that is released from the human body in a parabolic arc into a toilet bowl or the like, a camera 3 that captures an image of a predetermined area including the plane of the horizontal section light from a position facing the plane, and a urination-related value calculation unit 4 that calculates and outputs urination-related values related to urination dynamics including the state of urination fragmentation on the plane of the horizontal section light from the captured image.

FIG. 2 is a diagram for explaining the configuration of the horizontal section light illumination unit 2. FIG. 2 shows the horizontal section light illumination unit 2 as viewed from above, with the left-right and up-down directions in FIG. 2 corresponding to the horizontal direction, and the depth direction corresponding to the vertical direction. The horizontal section light illumination unit 2 is provided with a first laser unit 21 that emits a first laser beam in a predetermined direction, a second laser unit 22 that emits a second laser beam in a direction perpendicular to the predetermined direction, a reflecting prism 23 that reflects the second laser beam in a predetermined direction, and a cylindrical lens 24 that refracts (diffuses) the first and second laser beams that are incident on the plate-shaped base member 20 and emits them so that they spread in a horizontal plane. In FIG. 2, the laser beams are indicated by dotted lines.

In this example, the first laser light is, for example, green laser light with a wavelength of 490 to 550 nm, and the second laser light is, for example, red laser light with a wavelength of 640 to 770 nm, but this is not limiting as long as the wavelengths are different.

The first laser unit 21 and the second laser unit 22 are installed and configured with their vertical positions shifted by a predetermined distance so that the first laser light and the second laser light reflected by the reflecting prism 23 are parallel to each other at a predetermined distance (e.g., 25 mm) in the vertical direction (see the dotted arrow from the horizontal section light illumination unit 2 in FIG. 3(A) described later). This allows the second laser light reflected by the reflecting prism 23 to travel parallel to the first laser light, passing through a position vertically downward a predetermined distance from the first laser light. In FIG. 2, the first laser light and the second laser light reflected by the reflecting prism 23 are shown shifted horizontally by dotted lines for clarity, but in reality they pass along the same vertical plane and are incident on the cylindrical lens 24.

The laser light incident on the cylindrical lens 24 is diffused so as to spread in a horizontal plane, as shown in FIG. 2. This makes it possible to form a first horizontal cut surface where the first laser light spreads in a horizontal plane, and a second horizontal cut surface where the second laser light spreads in a horizontal plane, in the emission direction of the cylindrical lens 24.

Figure 3 is a diagram for explaining an example of the installation state when the horizontal section light illumination unit 2 and camera 3 are installed on the toilet bowl 6. Figure 3(A) shows the installation state when the toilet bowl 6 is viewed horizontally from the left side, and Figure 3(B) shows the installation state when the toilet bowl 6 is viewed from above.

The horizontal section light illumination unit 2 is installed at a predetermined position on the toilet 6 so that the first horizontal section P1 (first laser light) and the second horizontal section P2 (second laser light) can intersect in a substantially horizontal direction with the urine discharged from the human body into the toilet 6 (for example, the parabolic dashed-dotted line L in FIG. 3(A)). The first laser light and the second laser light are incident on and emitted from the cylindrical lens 24 so as to be parallel to each other with a predetermined distance D in the vertical direction, so that the first horizontal section P1 and the second horizontal section P2 are parallel to each other with a predetermined distance D in the vertical direction, as shown by the dotted line in FIG. 3(A).

The camera 3 is installed at an upper position facing the horizontal cut plane so that the imaging range includes an area including the range where the first horizontal cut plane P1 and the second horizontal cut plane P2 may intersect with the urine discharged into the toilet bowl 6. In this embodiment, the camera 3 is installed so that the imaging direction of the camera is aligned with the perpendicular line of the first horizontal cut plane P1 and the second horizontal cut plane P2. In FIG. 3B, an example of the imaging range is shown by a two-dot chain line. This allows the camera 3 to capture the state (e.g., the position, size, and degree of division of the urine) when the urine passes through the first horizontal cut plane P1 and the second horizontal cut plane P2. The camera 3 may have a function such as a color filter (or an RGB filter) and may be a color camera that can instantly obtain green and red color information separately. The captured moving image captured by the camera 3 is an image that allows images corresponding to the green and red color information to be distinguished from each other. The camera 3 is not limited to a color camera, and may be, for example, a spectroscopic camera.

Returning to FIG. 1, the urination-related value calculation unit 4 is realized by a computer including a memory, an input unit, an output display unit, an arithmetic processing unit, and a communication unit. By executing a program stored in the memory, the urination-related value calculation unit 4 calculates and outputs urination-related values related to urination, such as the urination dynamics of urination passing through a horizontal cross section (e.g., the number of divisions of each divided urination, cross-sectional area, flow rate, degree of separation according to distance from a specified position, cross-sectional shape, etc.) and urination flow rate, based on the captured moving image from the camera 3.

As shown in FIG. 1, the urination-related value calculation unit 4 has a color component extraction unit 41, a urination rate calculation unit 42, a urination cross-sectional area calculation unit 43, a urination flow rate calculation unit 44, a urination dynamics calculation unit 45, and a calculation result output display unit 46. The color component extraction unit 41 has a function of extracting a first urination dynamic image passing through a first horizontal cut plane P1 and a second urination dynamic image passing through a second horizontal cut plane P2 based on the captured video images from the camera 3.

The urination rate calculation unit 42 has a function of calculating the urination rate by utilizing the fact that the first urination motion image and the second urination motion image are images shifted in the vertical direction by a predetermined distance D. The urination cross-sectional area calculation unit 43 has a function of calculating the cross-sectional area of each divided urination and the total cross-sectional area of the urination. The urination flow rate calculation unit 44 has a function of calculating the urination flow rate based on the urination rate and the total cross-sectional area of the urination.

The urination dynamics calculation unit 45 has a function of calculating the urination dynamics of urination passing through a horizontal cut plane, for example, from the first urination dynamic image. The calculation result output display unit 45 has a function of outputting and displaying the calculation results calculated by each calculation unit.

FIGS. 4 to 13 are diagrams for explaining in detail the process of calculating urination-related values related to urination including urination dynamics from moving images captured by the camera 3 (hereinafter also referred to as acquired moving images) acquired by the urination-related value calculation unit 4 from the captured moving images. FIG. 4 is a flowchart for explaining an example of the urination-related value calculation process for calculating the urination-related values. The urination-related value calculation process is included in the process executed by the program stored in the memory.

In step S01, a first urination video image, for example, a green laser light, and a second urination video image, for example, a red laser light, are extracted from the acquired video image captured by the camera 3 of a subject urinating (the first urination video image and the second urination video image are separated). Note that the camera 3 captures images at, for example, 800 fps, but this is not limited to this.

In step S02, for example, image analysis is performed on the first urination video image to extract, for each image constituting the first urination video image, the individual urination pieces (lumps) that are the portions through which the green laser light has passed (transmitted) the urination and that have been split into multiple pieces, and a process is performed in which each individual piece is labeled.

Figure 5 shows an example of split urination (an example in which urination is split into five chunks). For example, the first urination video image is a video image lasting 15 seconds and is composed of images with serial numbers 1 to 12000 (= 15 seconds x 800). As an example of the video image, image with serial number 250 (the 250th image) is shown in Figure 5 (A), and the parts where the green laser light passed through (transmitted) the urination are shown in white. In step S02, for each image constituting the first urination video image, the parts where the green laser light passed through (transmitted) the urination are extracted as individual pieces of split urination based on brightness, etc., and each individual piece is labeled as shown in Figure 5 (B) (labeled 1 to 5 in Figure 5 (B)). Actual individual pieces of urination have various shapes, but for the sake of convenience, Figure 5 shows an example in which the individual pieces of urination are approximately circular.

In step S03, the cross-sectional area (e.g., the area based on the number of pixels in the portion determined to be a mass) is calculated for each individual labeled in step S02, and the total cross-sectional area of urination in each image constituting the urination video image is calculated and stored in a specified area of memory. Figure 6 shows an example of urination-related values stored in a specified area of memory, and Figure 6(A) shows an example of storing the cross-sectional area of each labeled urinating individual and the total cross-sectional area for each image (per serial number) constituting the urination video image. For example, as shown in Figure 5(B), when five individuals are extracted and labeled 1 to 5, the cross-sectional area of the individual labeled 1 is stored in urinating individual labeling No. 1, and the cross-sectional area of the individual labeled 2 is stored in urinating individual labeling No. 2, and so on, storing the cross-sectional area for each labeled individual and the total cross-sectional area of individuals 1 to 5. Note that steps S02 and S03 are not limited to only targeting the first urination motion image, but also perform image analysis on the second urination motion image, but instead or in addition, image analysis may be performed on an image obtained by combining the first urination motion image and the second urination motion image (or an averaged image).

Returning to FIG. 4, in step S04, the urination motion images are divided into 20 sets, for example, with images spaced 0.05 seconds apart (one second of images (800 images) is divided into 40 images per set), and the time shift between the first urination motion image and the second urination motion image for each set is calculated and stored using the cross-correlation coefficient. In other words, the time required for a given urination to reach the first horizontal cut plane P1 and the second horizontal cut plane P2 is calculated and stored for each set. For this reason, the time required for a given urination to reach the first horizontal cut plane P1 and the second horizontal cut plane P2 varies depending on the passage of time (speed), but an approximate time can be calculated for each set.

Figure 7 is a diagram for explaining a specific example of step S04. Figure 7(A) shows an example of a graph in which the number of high brightness value pixels (equivalent to total cross-sectional area) corresponding to an individual urinating is plotted on the vertical axis for images of Serial Numbers 0 to 800 shown on the horizontal axis among the first urination motion image and the second urination motion image, and Figure 7(B) shows an example of an enlarged graph for Serial Numbers 200 to 400. Note that in Figures 7(A) and 7(B), the number of high brightness value pixels of the first urination motion image is indicated by a solid line, and the number of high brightness value pixels of the second urination motion image is indicated by a dotted line. Also, Figure 7 (C) shows an example in which, for one set of serial number images (e.g., serial numbers 201 to 240), when the serial numbers are shifted by 8 as shown on the horizontal axis (serial number difference), the cross correlation coefficient is at its highest value, resulting in a high degree of match. In other words, for the set shown in Figure 7 (C), the time shift between the first urination motion image and the second urination motion image is calculated to be 0.01 seconds, which corresponds to eight serial numbers. In this way, the time shift is calculated for each set in step S04.

Returning to FIG. 4, in step S05, the vertical velocity of urination for each set is calculated and stored based on the time shift for each set calculated in step S04 and a predetermined distance D (e.g., 25 mm) between the first horizontal cut surface P1 and the second horizontal cut surface P2.

In step S06, for example, based on an image in the set and an image shifted by the time shift calculated in step S04, the horizontal displacement from the position of the individual urinating in one image to the position of the individual urinating in the image shifted by the time shift and the time shift are calculated and stored. That is, in step S06, the displacement between images shifted in time and space is evaluated to calculate the speed. The displacement may be an average value of all the displacements of the individual urinating in each image, an average value of the displacements of a predetermined number of individuals with large cross-sectional areas among the individual urinating in each image, or the displacement of the individual with the largest cross-sectional area among the individual urinating in each image. In addition, in step S06, the displacement (or speed) of all images in the set shifted by the time shift calculated in step S04 may be calculated and the average value of these displacements (or speeds) may be stored.

In step S07, the calculated urination speed is multiplied by the total cross-sectional area of urination and integrated over time to calculate and store the urination flow rate for each set. The total cross-sectional area of urination used to calculate the urination flow rate may be the average value of the total cross-sectional areas for each set, as with the urination speed, or the average value of the total cross-sectional area of the image with the smallest serial number in each set and the total cross-sectional area of each of the multiple images (e.g., 10 images) following that serial number, or the average value of the total cross-sectional area of the image with the largest serial number in each set and the total cross-sectional area of each of the multiple images preceding that serial number, or the average value of the total cross-sectional area of a predetermined image in each set and the total cross-sectional area of each of the multiple images following that image.

The values calculated in steps S04 to S07 are stored in correspondence with each set, as shown in FIG. 6(B). Note that the time deviation may be stored as the time, or the deviation (number of sheets) of the serial number.

In step S08, all images constituting, for example, the first urination moving image from among the acquired moving images are superimposed to generate and store urination area passing frequency distribution information. The urination area passing frequency distribution information is information that is generated, for example, by adding 1 to the image density value corresponding to pixels identified as urination individuals, so that the more pixels that are superimposed as urination individuals (i.e., the more pixels that have passed through with urine and the higher the urination passing frequency), the higher the image density value.

FIG. 8 is a diagram for explaining an example of a method for generating urination area passing frequency distribution information. FIG. 8(a) shows an image of Serial Number.X among the images constituting the first urination motion image, FIG. 8(b) shows an image of Serial Number.X+1 among the images constituting the first urination motion image, and FIG. 8(c) shows an image of Serial Number.X+2 among the images constituting the first urination motion image. When such images are superimposed, the image density value increases as the number of pixels superimposed as urination individuals increases on a pixel-by-pixel basis. In the drawing, the lower the image density value, the closer to black the color becomes, and the higher the image density value, the closer to white the color becomes. Therefore, as shown in FIG. 8(d), urination area passing frequency distribution information is generated in which the image density value is higher for the parts corresponding to the urination individuals shown in FIG. 8(a) and FIG. 8(b) than for the parts corresponding to the urination individuals shown in FIG. 8(c).

Figure 9 shows an example of urination area passing frequency distribution information when urination is concentrated in a relatively concentrated area. Since the individual urinations are concentrated in the center, as shown in Figure 9, the image density value of the center is high and the image density value of the surrounding areas gradually decreases.

FIGS. 10 to 12 are diagrams showing an example of urination section passing frequency distribution information when, for example, a urine stream is bifurcated and urination is performed. In FIG. 10 to FIG. 12, the urination shown at the top of the diagram is called the first urination section, and the urination shown at the bottom of the diagram is called the second urination section. FIG. 10 shows a case where the flow rate and degree of dispersion (dispersion degree) of the first urination section and the second urination section are approximately the same. The degree of darkness and spread of the image density value are shown to be approximately the same.

In contrast, FIG. 11 shows a case where the first urination area and the second urination area have the same degree of dispersion (degree of dispersion), but the second urination area has more overlap and a higher flow rate than the first urination area. As shown in FIG. 11, the spread of the image density values is about the same, but the degree of darkness is different. FIG. 12 shows a case where the first urination area and the second urination area have the same degree of flow rate, but the first urination area has a higher degree of dispersion (degree of dispersion) than the second urination area. As shown in FIG. 12, the spread of the image density values is wider in the first urination area than the second urination area, so the degree of darkness is different, although the flow rate is about the same.

Returning to FIG. 4, in step S09, the urination splitting state is calculated and stored based on the generated urination section passing frequency distribution information. The splitting state includes, for example, the number of splits of urination, flow rate, flow rate ratio, degree of separation, degree of dispersion, and cross-sectional shape. Note that the splitting state is not limited to this, and other parameters may be calculated and stored instead of or in addition to this, and at least one of the number of splits of urination, flow rate, flow rate ratio, degree of separation, degree of dispersion, and cross-sectional shape may be calculated and stored.

The number of urination fragments is a mass containing a certain number (e.g., 10) or more of pixels with image density values equal to or greater than a certain threshold (e.g., 200 or 100) based on the urination area passing frequency distribution information. In the example of Figure 9, it is calculated as "1," and in the examples of Figures 10 to 12, it is calculated as "2."

The flow rate is the flow rate for each lump, and is, for example, the total value (sum) of the image density values for each lump, based on the urination section passing frequency distribution information. In the example of Figure 9, the sum of the image density values for one lump is calculated, and in the examples of Figures 10 to 12, the sum of the image density values of the first urination section and the sum of the image density values of the second urination section are calculated.

The flow rate ratio is the ratio of the flow rate for each mass to the total flow rate. In the example of Figure 9, there is one mass, so the flow rate ratio is 1, and in the examples of Figures 10 to 12, the flow rate ratio for the first urination section and the flow rate ratio for the second urination section are calculated.

The degree of separation is the degree of separation of the lumps (the degree of dispersion between distributions), and is calculated based on the urination tract passing frequency distribution information, for example by calculating the center of gravity from all image density values (hereinafter also referred to as the overall center of gravity) and the center of gravity from the image density of each lump (hereinafter also referred to as the center of gravity of each lump), and is the total value (sum) of the distance from the overall center of gravity to the center of gravity of each lump. In the example of Figure 9, the overall center of gravity and the center of gravity of each lump are the same, so zero is calculated, and in the examples of Figures 10 to 12, the overall center of gravity and the center of gravity of each lump are different in each figure, so different values are calculated as the degree of separation.

The degree of dispersion is the degree of scattering of each lump. For example, the pixel position with the maximum image density value (hereinafter also referred to as the maximum pixel position) is identified for each lump based on the urination part passing frequency distribution information, the distribution of image density values on a line passing through the maximum pixel position (such as on a horizontal line in images such as Figures 9 to 12) is calculated, and a pixel position with an image density value that is half the maximum image density value (hereinafter also referred to as the half pixel position) is identified, and the degree of dispersion is the distance between the maximum pixel position and the half pixel position (hereinafter also referred to as the half width) for each lump. A lump with a large half width can be evaluated as having a large degree of dispersion (scattered), and a lump with a large half width can be evaluated as having a small degree of dispersion (concentrated).

The cross-sectional shape includes the circularity, which is determined from the relationship between the perimeter and area of each block, and the aspect ratio, which is the ratio of the width to the height.

Various values of the division state calculated based on the urination passage frequency distribution information are stored for each item as shown in Figure 6 (C).

Returning to FIG. 4, in step S10, the urination-related values including the calculated and stored urination flow rate and division state are output, and the urination-related value calculation process is terminated. In step S10, for example, based on the cross-sectional area information of FIG. 6(A), a histogram is generated and output, with the horizontal axis representing the size of the cross-sectional area of the urination individual and the vertical axis representing the number of urination individuals. FIG. 13(A) shows a histogram based on the cross-sectional area and number of urination individuals based on the first urination motion image, and FIG. 13(B) shows a histogram based on the urination individuals and number of individuals based on the second urination motion image. Also, FIG. 13(C) is a diagram showing a comparison between FIG. 13(A) and FIG. 13(B), and shows that there is no significant difference between the calculation results of the first urination motion image and the calculation results of the second urination motion image. Furthermore, in step S10, urination dynamics information regarding the state of division shown in FIG. 6(C) and images based on urination portion passing frequency distribution information (see FIGS. 9 to 12) are also output. In addition, in step S10, acquired moving images may also be output. Note that output in step S10 includes display, printing, sending an e-mail to a specified destination, etc.

The urination-related values including the urination flow rate and schizophrenia calculated and output by the urination dynamics specification device 1 are used for diagnosis by doctors and the like. Based on the urination-related values including the urination flow rate and schizophrenia calculated based on the urination dynamics images from the subject, doctors can diagnose the risk of prostatic hyperplasia, overactive bladder, urinary incontinence, and other urinary disorders, the possibility of contracting the disease, and the need for hospitalization. This allows the urination dynamics, which is a clinically important point, to be evaluated based on objective facts related to the morphology, distribution, and flow rate of the urination schizophrenia state and their changes over time, and allows appropriate examinations for urinary disorders. The diagnosis results may be sent to the subject via a communication line, etc., or a printed diagnosis result may be sent to the subject's address.

The urination dynamics specifying device 1 in this embodiment is a urination dynamics specifying device 1 for specifying the dynamics of urination released from the human body over time, and based on the acquired moving image obtained by irradiating planar light onto urine released from a subject into a toilet or the like and capturing an image of the intersecting plane, the urination-related value calculation unit 4 specifies the urination split state on that plane as the dynamics of urination over time, and outputs information regarding the urination split state. This makes it possible to specify and evaluate the urination split state as the dynamics of urination over time. As a result, it is possible to perform an appropriate diagnosis for urinary disorders.

The urination dynamics specification device 1 also includes a horizontal section light illumination unit 2 that irradiates a planar light that may intersect with the released urine, and a camera 3 that captures the plane of the light from a position opposite the plane, and the urination-related value calculation unit 4 extracts at least one of the number of divisions, cross-sectional area, flow rate, degree of separation, and cross-sectional shape of each divided urination as the division state of the urination based on the moving image of the plane acquired by the camera 3. This makes it possible to more specifically specify and evaluate the values of items that provide a more detailed analysis of the division state of the urination.

The horizontal section light illumination unit 2 includes a first laser unit 21 that irradiates a planar first laser light having a first wavelength, and a second laser unit 22 that irradiates a planar second laser light having a second wavelength, and generates a first horizontal section P1 and a second horizontal section P2 that are parallel to each other at a predetermined interval D, and the urination-related value calculation unit 4 calculates the urination passing speed and the cross-sectional area between the first horizontal section P1 and the second horizontal section P2 based on the split state of urination on the first horizontal section P1 and the split state of urination on the second horizontal section P2 and the predetermined interval D, and specifies the urination flow rate as a dynamic state according to the time course of urination. This makes it possible to specify and evaluate not only the split state but also the urination flow rate, thereby improving the accuracy of diagnosis for urinary disorders. The horizontal section light illumination unit 2 also has a cylindrical lens that emits the incident laser light in a horizontal plane. This simplifies the structure of the horizontal section light illumination unit 2.

The urination-related value calculation unit 4 also calculates the time it takes for urination to reach the second horizontal cut plane P2 from the first horizontal cut plane P1 using a cross-correlation coefficient based on the split state of urination on the first horizontal cut plane P1 and the split state of urination on the second horizontal cut plane P2, and calculates the urination passage velocity. This allows the urinary flow velocity of urination to be determined efficiently and accurately.

(Modification)
In the above-mentioned embodiment, an example was described in which the urination behavior specification device 1 is installed in a facility such as a hospital, and the urination behavior including the schizophrenia is specified based on a moving image of urination from a subject who comes to the hospital. However, the present invention is not limited to this. Only the horizontal section light illumination unit 2 and the camera 3 shown in Fig. 1 and Fig. 3 (imaging device) of the urination behavior specification device 1 are installed in a first facility such as a hospital, and only the urination-related value calculation unit 4 shown in Fig. 1 is installed as a urination behavior specification device in a second facility different from the first facility, and the first facility may capture a moving image of urination from the subject and transmit it to the second facility, and the second facility may specify the urination behavior including the schizophrenia based on the received moving image of urination, and return the result of the specification to the facility that sent the moving image. In this case, a doctor or the like at the first facility may make a diagnosis based on the result of the specification, or a doctor at the second facility may make a diagnosis based on the result of the specification, and a result including the diagnosis result may be returned to the facility that sent the moving image.

In the above-described embodiment, an example has been described in which the horizontal section light illumination unit 2 includes a first laser unit 21 that irradiates a planar first laser light having a first wavelength, and a second laser unit 22 that irradiates a planar second laser light having a second wavelength. However, the present invention is not limited to this, as long as it irradiates two types of light having different optical characteristics (e.g., wavelength, polarization, etc.). The camera is not limited to a color camera, etc., as long as it can distinguish images according to the characteristics of the irradiated light. For example, a polarized camera may be used when irradiating two types of light having different polarizations. In addition, when identifying and diagnosing the split state of urination shown in step S09 of FIG. 4, the horizontal section light illumination unit may include one laser unit, and the urination-related value calculation unit 4 may perform the processes of steps S08 to S10 of FIG. 4. In addition, the horizontal section light illumination unit may include three or more laser units, and the split state may be identified in more detail from the information on urination in three types of planes.
In addition, in the above-described embodiment, an example was shown in which the laser light is expanded into a surface by the cylindrical lens 24 to illuminate the object, but the present invention is not limited to this as long as the illumination can form surface light. For example, slit light that forms surface light through a narrow gap, or LEDs that are lined up in a straight line (the surface to be formed) to form surface light may be used.

In step S08 of FIG. 4 in the above-mentioned embodiment, an example was shown in which all images constituting the acquired moving image are superimposed to generate and store urination area passing frequency distribution information as in method A of FIG. 14, but this is not limited to this. For example, as shown in method B of FIG. 14, for a certain frame (Serial Number), a predetermined number (e.g., 100) of images from the frame are superimposed to generate urination area passing frequency distribution information, thereby generating urination area passing frequency distribution information (urination area passing frequency distribution information shifted by one frame) for each of all frames. Also, as shown in method C of FIG. 14, it is possible to divide the frame into a predetermined number (e.g., 100), superimpose the divided images into a predetermined number, and generate urination area passing frequency distribution information for each divided number (urination area passing frequency distribution information based on completely separate sets of 100 frames). This allows more detailed urination dynamics to be identified, such as, for example, a change in the number of divisions during urination.

In the above embodiment, an example was described in which the first horizontal cut plane and the second horizontal cut plane are parallel, and the camera's imaging direction is set in a direction that coincides with the perpendicular line of the first and second horizontal cut planes. This makes it possible to prevent the distance between the first and second horizontal cut planes from being constant, and to prevent the camera from taking distorted images without performing complex calculations, thereby enabling accurate calculation of urination-related values including the split state. However, this is not limited to this, and the camera's imaging direction may not be set in a direction that coincides with the perpendicular line of the first and second horizontal cut planes. In addition, the first horizontal cut plane and the second horizontal cut plane are assumed to be planes whose perpendicular lines are vertical, that is, horizontal planes, but this is not limited to this, and the first horizontal cut plane and the second horizontal cut plane are not necessarily required to be aligned with the horizontal plane as long as they are parallel to each other, and may be planes that are slightly misaligned with the horizontal plane.

1: Urinary behavior specification device 2: Horizontal section light illumination unit 3: Camera 4: Urination-related value calculation unit

Claims (6)

A urination behavior specification device for specifying a behavior of urine discharged from a human body over time, comprising:
an identification unit that identifies a state of division of urination on the plane based on an image of a plane crossed by irradiating a planar light onto the released urination as a dynamic state according to the time course of the urination;
and an output unit that outputs information regarding the urination split state identified by the identification unit. A light irradiation unit that irradiates planar light that can intersect with the released urine;
an imaging unit that images the surface of the light from a position facing the surface of the light,
the image of the surface is an image captured by the imaging unit over time,
The urination dynamics specifying device according to claim 1, wherein the specifying unit includes an extracting unit that extracts at least one of the number of fragments, cross-sectional area, flow rate, degree of separation, and cross-sectional shape of each fragmented urination based on the image of the surface as the fragmentation state of the urination. the light irradiation unit includes a first light irradiation unit that irradiates a first light having a first characteristic and a planar first light, and a second light irradiation unit that irradiates a second light having a second characteristic in parallel with a surface of the first light at a predetermined interval;
The urination dynamics determining device of claim 2, wherein the determining unit calculates a passing speed and a cross-sectional area of the urination between the first light plane and the second light plane based on the fragmentation state of the urination on the first light plane, the fragmentation state of the urination on the second light plane, and the specified interval, and determines a urination flow rate as a dynamics according to the time course of the urination. The urination dynamics determination device according to claim 3, wherein the determination unit calculates the time it takes for urination to reach the second light surface from the first light surface using a cross-correlation coefficient based on the split state of urination on the first light surface and the split state of urination on the second light surface, thereby calculating the passage speed of the urination. The urination behavior determination device according to claim 2, wherein the light irradiation unit has a cylindrical lens that emits incident laser light in a planar manner. An imaging device for capturing identification images for identifying a dynamic state of urine discharged from a human body over time, comprising:
A light irradiation unit that irradiates planar light that can intersect with the released urine;
an imaging unit that captures an image of the surface of the light from a position facing the surface as the identification image;

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