WO2021039923A1 - Dispositif d'estimation d'état anormal, dispositif d'estimation d'état de semelle, système et programme - Google Patents

Dispositif d'estimation d'état anormal, dispositif d'estimation d'état de semelle, système et programme Download PDF

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WO2021039923A1
WO2021039923A1 PCT/JP2020/032439 JP2020032439W WO2021039923A1 WO 2021039923 A1 WO2021039923 A1 WO 2021039923A1 JP 2020032439 W JP2020032439 W JP 2020032439W WO 2021039923 A1 WO2021039923 A1 WO 2021039923A1
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Prior art keywords
sole
foot
images
abnormal state
state estimation
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PCT/JP2020/032439
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English (en)
Japanese (ja)
Inventor
裕 竹村
里樹 築地原
光平 渡邉
啓也 小森
聖浦 周
美貴子 原口
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学校法人東京理科大学
医療法人社団誠馨会
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Priority to JP2021543015A priority Critical patent/JPWO2021039923A1/ja
Publication of WO2021039923A1 publication Critical patent/WO2021039923A1/fr

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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B10/00—Other methods or instruments for diagnosis, e.g. instruments for taking a cell sample, for biopsy, for vaccination diagnosis; Sex determination; Ovulation-period determination; Throat striking implements
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
    • A61B5/107—Measuring physical dimensions, e.g. size of the entire body or parts thereof

Definitions

  • This disclosure relates to an abnormal state estimation device, a sole state estimation device, a system, and a program.
  • the "nerve conduction test DPN (diabetic peripheral neuropathy) check” manufactured by Fukuda Colin Co., Ltd. is known.
  • a method of measuring nerve conduction velocity is useful for determining neuropathy.
  • the device used for the above "nerve conduction test DPN (diabetic peripheral neuropathy) check” is a device that measures the nerve conduction velocity, specializing in the sural nerve of the foot.
  • DPN diabetic peripheral neuropathy
  • the present disclosure has been made in view of the above circumstances, and is a non-invasive and abnormal state estimation device, a sole state estimation device, a system, which can easily estimate the abnormal state or the sole state of the subject. And the purpose of providing the program.
  • the first aspect of the present disclosure is an abnormal state estimation device, which is an image taken through the support surface when the sole of the subject is placed on each of two transparent support surfaces having different hardness. It is configured to include an image acquisition unit that acquires two foot sole images, and an abnormal state estimation unit that compares the two foot sole images and estimates the abnormal state of the subject.
  • a second aspect of the present disclosure is an abnormal state estimation system, which has a transparent support surface on which the sole of a subject is placed, and the hardness of the support surface is made different, and the support surface is passed through the support surface. It is configured to include a sole imaging device for capturing one foot image and the above-mentioned abnormal state estimation device.
  • a third aspect of the present disclosure is an abnormal state estimation program, in which a computer is photographed through the support surface when the sole of the subject is placed on each of two transparent support surfaces having different hardness.
  • This is a program for functioning as an image acquisition unit that acquires two foot images, which are the images obtained, and an abnormal state estimation unit that estimates the abnormal state of the subject by comparing the two foot images.
  • the image acquisition unit is an image of two feet taken through the support surface when the sole of the subject is placed on each of the two transparent support surfaces having different hardness. Get the back image. Then, the abnormal state estimation unit compares the two foot sole images and estimates the abnormal state of the subject.
  • the two sole images which are images taken through the support surface, are compared.
  • the abnormal state of the subject is estimated.
  • the abnormal state of the subject can be easily estimated in a non-invasive manner.
  • a fourth aspect of the present disclosure is a foot sole state estimation device, which is photographed through the support surface when the sole of the subject is placed on each of two transparent support surfaces having different hardness. It is configured to include an image acquisition unit that acquires two foot images, which are images, and a sole state estimation unit that compares the two foot images and estimates the hardness state of the sole of the subject. Has been done.
  • a fifth aspect of the present disclosure is a foot sole state estimation system, which has a transparent support surface on which the sole of a subject is placed, and has different hardness of the support surface through the support surface. It is configured to include a sole photographing device for capturing two sole images and the sole state estimating device.
  • a sixth aspect of the present disclosure is a foot condition estimation program, in which a computer is placed through the support surface when the sole of the subject is placed on each of two transparent support surfaces having different hardness. It functions as an image acquisition unit that acquires two foot images that are captured images, and a foot condition estimation unit that estimates the hardness state of the sole of the subject by comparing the two foot images. It is a program for.
  • the image acquisition unit is an image of two feet taken through the support surface when the sole of the subject is placed on each of the two transparent support surfaces having different hardness. Get the back image. Then, the foot sole state estimation unit compares the two sole images and estimates the hardness state of the sole of the subject.
  • the two sole images which are images taken through the support surface, are compared.
  • the hardness state of the sole of the subject is estimated. Thereby, the hardness state of the sole of the subject can be easily estimated in a non-invasive manner.
  • the hardness of the skin is detected from the contact area with the floor. Specifically, as shown in FIG. 1, since the skin is crushed on a hard floor, the harder the skin, the smaller the contact area. On the other hand, as shown in FIG. 2, on a soft floor, the floor is crushed, so that the difference in the ground contact area due to the hardness of the skin is small.
  • diabetic neuropathy patients have a large difference in area change rate due to the difference in floor hardness.
  • diabetes or diabetic neuropathy is estimated by comparing foot images in a stationary standing state taken through transparent floor surfaces having different hardness.
  • the acrylic plate is used as a hard floor, and the soft floor is reproduced with a transparent silicone sheet.
  • a transparent glass plate may be used instead of the acrylic plate.
  • the captured sole image is binarized and the contact area of the sole is calculated. Based on the left-right difference in the area change rate due to the hard and soft floor, the left-right difference in the area change rate of the entire sole, the left-right difference in the area change rate in the front part of the sole, and the left-right difference in the area change rate in the rear part of the sole. Make estimates for diabetes and diabetic neuropathy. At this time, multiple logistic regression analysis is used to estimate the presence or absence of diabetes and the presence or absence of diabetic neuropathy, and multiple regression analysis is used to estimate the severity of diabetes and the degree of diabetic neuropathy.
  • the abnormal state estimation system 100 of the first embodiment of the present disclosure includes an abnormal state estimation device 10 and a foot sole photographing device 50.
  • the abnormal state estimation device 10 and the foot sole photographing device 50 are connected by wire or wirelessly.
  • the abnormal state estimation device 10 and the foot sole photographing device 50 may be connected to each other via a network such as a LAN (Local Area Network) or the Internet.
  • a network such as a LAN (Local Area Network) or the Internet.
  • the foot sole photographing device 50 has a transparent support surface 52 on which the sole of the subject is placed, and is provided by the camera 54 through the mirror 56 and the support surface 52. An image is taken and transmitted to the abnormal state estimation device 10.
  • the support surface 52 is provided with a light source 58 so as to irradiate the inside of the support surface 52.
  • the sole of the foot may be scanned by a line sensor of a thin device such as a scanner to capture an image of the sole of the foot. LED lighting may be used as the light source 58.
  • the camera 54 captures an image of the sole of the foot when the transparent silicone sheet 60 is placed on the support surface 52, and the camera 54 captures the foot when the silicone sheet 60 is not placed on the support surface 52. Take a back image. As a result, the hardness of the support surface 52 can be made different, and two foot sole images can be taken through the support surface 52.
  • silicone sheets 60 having different hardness may be prepared, the hardness of the support surface 52 may be different, and two foot sole images may be taken through the support surface 52.
  • the foot sole photographing device 50 in which the silicone sheet 60 is placed on the support surface 52 instead of switching the presence or absence of the silicone sheet 60, the foot sole photographing device 50 in which the silicone sheet 60 is placed on the support surface 52 and the foot sole photographing device 50 in which the silicone sheet 60 is not placed on the support surface 52.
  • FIG. 5 is a block diagram showing a hardware configuration of the abnormal state estimation device 10 according to the first embodiment.
  • the abnormal state estimation device 10 includes a CPU (Central Processing Unit) 11, a ROM (Read Only Memory) 12, a RAM (Random Access Memory) 13, a storage 14, an input unit 15, a display unit 16, and communication. It has an interface (I / F) 17. Each configuration is communicably connected to each other via a bus 19.
  • CPU Central Processing Unit
  • ROM Read Only Memory
  • RAM Random Access Memory
  • storage 14 an input unit 15, a display unit 16, and communication. It has an interface (I / F) 17.
  • I / F interface
  • the CPU 11 is a central arithmetic processing unit that executes various programs and controls each part. That is, the CPU 11 reads the program from the ROM 12 or the storage 14, and executes the program using the RAM 13 as a work area. The CPU 11 controls each of the above configurations and performs various arithmetic processes according to the program stored in the ROM 12 or the storage 14.
  • the ROM 12 or the storage 14 stores an abnormal state estimation program for estimating the abnormal state of the subject.
  • the abnormal state estimation program may be one program, or may be a program group composed of a plurality of programs or modules.
  • the ROM 12 stores various programs and various data.
  • the RAM 13 temporarily stores a program or data as a work area.
  • the storage 14 is composed of an HDD (Hard Disk Drive) or an SSD (Solid State Drive), and stores various programs including an operating system and various data.
  • the input unit 15 includes a pointing device such as a mouse and a keyboard, and is used to perform various inputs.
  • the display unit 16 is, for example, a liquid crystal display and displays various types of information.
  • the display unit 16 may adopt a touch panel method and function as an input unit 15.
  • the communication interface 17 is an interface for communicating with other devices including the foot sole photographing device 50, and for example, standards such as Ethernet (registered trademark), FDDI, and Wi-Fi (registered trademark) are used.
  • Ethernet registered trademark
  • FDDI FDDI
  • Wi-Fi Wi-Fi
  • FIG. 6 is a block diagram showing an example of the functional configuration of the abnormal state estimation device 10.
  • the abnormal state estimation device 10 includes an image acquisition unit 30, a binarized image creation unit 32, and an abnormal state estimation unit 34, as shown in FIG.
  • the image acquisition unit 30 acquires two foot sole images taken by the foot sole photographing device 50.
  • the binarized image creation unit 32 binarizes each of the two acquired foot sole images and removes noise.
  • the binarized image creation unit 32 binarizes each pixel with respect to the sole image as shown in FIG. 7A by using a threshold value, so that the binarized image as shown in FIG. 7B is obtained. To create. Then, the binarized image creation unit 32 acquires a binarized image as shown in FIG. 7C by removing noise.
  • the binarized image creation unit 32 divides the binarized images obtained for each of the two sole images into three in the length direction of the sole as shown in FIG. 8, and the sole is divided into three. A binarized image of the anterior part and a binarized image of the posterior part of the sole are obtained.
  • the abnormal state estimation unit 34 estimates the presence or absence of diabetes and the presence or absence of neuropathy in diabetes as the abnormal state of the subject based on the rate of change in the area of the sole region in the two sole images.
  • the abnormal state estimation unit 34 uses the binarized image to determine the total area of the sole region, the area of each sole region of the right foot and the left foot, and the front of each sole region of the right foot and the left foot. The area of the part and the area of the rear part of each sole area of the right foot and the left foot are calculated.
  • the abnormal state estimation unit 34 Rate of change in the total area of the sole area in the two sole images, Difference between the rate of change in the area of the sole area of the right foot in the two sole images and the rate of change in the area of the sole area of the left foot in the two sole images, The difference between the rate of change in the area of the front part of the sole area of the right foot in the two sole images and the rate of change in the area of the front part of the sole area of the left foot in the two sole images, and the two sole images. Based on the difference between the rate of change in the posterior area of the sole region of the right foot and the rate of change in the posterior area of the sole region of the left foot in the two sole images, the presence or absence of diabetes and the presence or absence of neuropathy in diabetes To estimate.
  • the rate of change A of the area of the entire sole region in the two sole images is calculated by the following formula.
  • S is the area of the entire sole region (see the thin dot portion in FIG. 9) obtained from the sole image taken when the hardness of the support surface 52 is soft.
  • H is the area of the entire sole region (see the dark dot portion in FIG. 9) obtained from the sole image taken when the hardness of the support surface 52 is hard.
  • the laterality D which is the difference between the rate of change RA of the area of the sole region of the right foot in the two sole images and the rate of change LA of the area of the sole region of the left foot in the two sole images, is as follows. Obtained by the formula.
  • RS is the area of the entire sole region of the right foot obtained from the sole image taken when the hardness of the support surface 52 is soft.
  • RH is the area of the entire sole region of the right foot obtained from the sole image taken when the hardness of the support surface 52 is hard.
  • LS is the area of the entire sole region of the left foot obtained from the sole image taken when the hardness of the support surface 52 is soft.
  • LH is the area of the entire sole region of the left foot obtained from the sole image taken when the hardness of the support surface 52 is hard.
  • the difference between the rate of change RF of the front area of the sole region of the right foot in the two sole images and the rate of change LF of the area of the front part of the sole region of the left foot in the two sole images is left and right.
  • the difference DF is calculated by the following formula.
  • RFS is the area of the front part of the sole region of the right foot obtained from the sole image taken when the hardness of the support surface 52 is soft.
  • RFH is the area of the front part of the sole region of the right foot obtained from the sole image taken when the hardness of the support surface 52 is hard.
  • LFS is the area of the front part of the sole region of the left foot obtained from the sole image taken when the hardness of the support surface 52 is soft.
  • LFH is the area of the front part of the sole region of the left foot obtained from the sole image taken when the hardness of the support surface 52 is hard.
  • the laterality DB which is the difference between the rate of change RB of the area of the rear part of the sole area of the right foot in the two sole images and the rate of change LB of the area of the rear part of the sole area of the left foot in the two sole images. Is calculated by the following formula.
  • the RBS is the area of the rear part of the sole region of the right foot obtained from the sole image taken when the hardness of the support surface 52 is soft.
  • RBH is the area of the rear part of the sole region of the right foot obtained from the sole image taken when the hardness of the support surface 52 is hard.
  • the LBS is the area of the rear part of the sole region of the left foot obtained from the sole image taken when the hardness of the support surface 52 is soft.
  • LBH is the area of the rear part of the sole region of the left foot obtained from the sole image taken when the hardness of the support surface 52 is hard.
  • y is the objective variable and is the probability of taking 1.
  • x 1 , ..., X p are explanatory variables.
  • b 0 , ⁇ , b p are coefficients.
  • the coefficients b 0 , ..., B p of the regression equation for determining the presence or absence of diabetes are obtained in advance from the data (change rate A, left-right difference D, left-right difference DF, left-right difference DB) in which the presence or absence of diabetes is known. deep.
  • the coefficient left-right difference of the regression equation for determining the presence or absence of neuropathy in diabetes is obtained in advance from data (change rate A, left-right difference D, left-right difference DF, left-right difference DB) in which the presence or absence of neuropathy in diabetes is known. Keep it.
  • the foot sole imaging device 50 when the subject puts his / her foot on the support surface 52 with the silicone sheet 60 placed on the support surface 52 and is in a stationary standing state, the foot is taken by the camera 54. The sole image is taken, and the sole image is transmitted to the abnormal state estimation device 10.
  • the camera 54 when the subject puts his / her foot on the support surface 52 without placing the silicone sheet 60 on the support surface 52 and becomes a stationary standing state, the camera 54 , The sole image is taken, and the sole image is transmitted to the abnormal state estimation device 10.
  • the abnormal state estimation device 10 executes the abnormal state estimation processing routine shown in FIG.
  • step S100 the image acquisition unit 30 acquires two foot sole images received from the foot sole photographing device 50.
  • step S102 the binarized image creation unit 32 binarizes each of the two acquired sole images and removes noise. Further, the binarized image creation unit 32 divides the binarized images obtained for each of the two sole images into three in the length direction of the sole.
  • step S104 the abnormal state estimation unit 34 uses the binarized image to determine the total area of the sole region, the area of each sole region of the right foot and the left foot, and the front portion of each sole region of the right foot and the left foot. And the area of the rear part of each sole area of the right foot and the left foot.
  • step S106 the abnormal state estimation unit 34 Rate of change in the total area of the sole area in the two sole images, Difference between the rate of change in the area of the sole area of the right foot in the two sole images and the rate of change in the area of the sole area of the left foot in the two sole images, The difference between the rate of change in the area of the front part of the sole area of the right foot in the two sole images and the rate of change in the area of the front part of the sole area of the left foot in the two sole images, and the two sole images.
  • the abnormal state estimation unit 34 displays the estimation result on the display unit 16 and ends the abnormal state estimation processing routine.
  • the foot sole of the subject is supported when the sole of the subject is placed on each of the two transparent support surfaces having different hardness.
  • the presence or absence of diabetes in the subject and the presence or absence of neuropathy in diabetes are estimated by comparing the two sole images, which are images taken through the surface. Thereby, the presence or absence of diabetes and the presence or absence of neuropathy in diabetes can be easily estimated in a non-invasive manner.
  • the abnormal state estimation unit 34 of the abnormal state estimation device 10 estimates the severity of diabetes and the degree of neuropathy in diabetes as the abnormal state of the subject based on the rate of change in the area of the sole region in the two sole images. To do.
  • the abnormal state estimation unit 34 uses the binarized image obtained from each of the two sole images, as in the first embodiment, and uses the area of the entire sole region, the right foot and the left foot. The area of each sole area of the foot, the area of the front part of each sole area of the right foot and the left foot, and the area of the rear part of each sole area of the right foot and the left foot are obtained. Further, the abnormal state estimation unit 34 obtains the rate of change A, the laterality D, the laterality DF, and the laterality DB, as in the first embodiment.
  • the abnormal state estimation unit 34 uses the above-mentioned rate of change A, laterality D, laterality DF, and laterality DB as explanatory variables, and uses the regression equation of the multiple regression analysis shown in the following equation to determine the severity of diabetes. And estimate the degree of neuropathy in diabetes.
  • y is the objective variable and is the probability of taking 1.
  • x 1 , ..., X p are explanatory variables.
  • b 0 , ⁇ , b p are coefficients.
  • Coefficient b 0 of the regression formula for determining the severity of diabetes, ⁇ ⁇ ⁇ , b p is severity known data diabetes (rate of change A, laterality D, laterality DF, laterality DB) in advance from the I'll ask for it.
  • the coefficient b 0 of the regression formula for determining the degree of neuropathy in diabetes, ⁇ ⁇ ⁇ , b p is degree known data (the change rate A of neuropathy in diabetes, laterality D, laterality DF, It is obtained in advance from the left-right difference DB).
  • the foot sole of the subject is supported when the sole of the subject is placed on each of the two transparent support surfaces having different hardness.
  • the severity of diabetes in the subject and the degree of neuropathy in diabetes are estimated by comparing the two foot images, which are images taken through the surface. This makes it possible to estimate the severity of diabetes and the degree of neuropathy in diabetes of a subject in a non-invasive manner and easily.
  • the third embodiment is different from the first embodiment in that the hardness state of the sole of the subject is estimated.
  • the foot sole state estimation system of the third embodiment of the present disclosure includes the foot sole state estimation device 310 shown in FIG. 11 and the foot sole imaging device 50.
  • the foot sole state estimation device 310 and the foot sole photographing device 50 are connected by wire or wirelessly.
  • the foot sole state estimation device 310 and the foot sole photographing device 50 may be connected via a network such as LAN or the Internet.
  • the sole state estimation device 310 includes a CPU 11, a ROM 12, a RAM 13, a storage 14, an input unit 15, a display unit 16, and a communication interface (I / F) 17. Each configuration is communicably connected to each other via a bus 19.
  • the CPU 11 is a central arithmetic processing unit that executes various programs and controls each part. That is, the CPU 11 reads the program from the ROM 12 or the storage 14, and executes the program using the RAM 13 as a work area. The CPU 11 controls each of the above configurations and performs various arithmetic processes according to the program stored in the ROM 12 or the storage 14.
  • the ROM 12 or the storage 14 stores a foot sole state estimation program for estimating the foot sole state of the subject.
  • the sole state estimation program may be one program, or may be a program group composed of a plurality of programs or modules.
  • FIG. 11 is a block diagram showing an example of the functional configuration of the sole state estimation device 310.
  • the foot sole state estimation device 310 includes an image acquisition unit 30, a binarized image creation unit 32, and a foot sole state estimation unit 334.
  • the sole state estimation unit 334 estimates the hardness state of the sole of the subject based on the rate of change in the area of the sole region in the two sole images.
  • the sole state estimation unit 334 uses the binarized image obtained from each of the two sole images to determine the total area of the sole region, the area of each sole region of the right foot and the left foot, and the area of each sole region of the right foot and the left foot. The area of the front part of each sole area of the right foot and the left foot, and the area of the rear part of each sole area of the right foot and the left foot are calculated. In addition, the sole state estimation unit 334 estimates the hardness state of the entire sole of the subject based on the rate of change in the area of the entire sole region in the two sole images.
  • the sole state estimation unit 334 estimates the hardness state of the sole of the right foot of the subject based on the rate of change in the area of the sole region of the right foot in the two sole images. In addition, the sole state estimation unit 334 estimates the hardness state of the sole of the left foot of the subject based on the rate of change in the area of the sole region of the left foot in the two sole images. In addition, the sole state estimation unit 334 estimates the hardness state of the front part of the sole of the right foot of the subject based on the rate of change in the area of the front part of the sole region of the right foot in the two sole images.
  • the sole state estimation unit 334 estimates the hardness state of the front part of the sole of the left foot of the subject based on the rate of change in the area of the front part of the sole region of the left foot in the two sole images. In addition, the sole state estimation unit 334 estimates the hardness state of the rear part of the sole of the right foot of the subject based on the rate of change in the area of the rear part of the sole region of the right foot in the two sole images. In addition, the sole state estimation unit 334 estimates the hardness state of the rear part of the left foot of the subject based on the rate of change in the area of the rear part of the sole region of the left foot in the two sole images.
  • the foot sole imaging device 50 when the subject puts his / her foot on the support surface 52 with the silicone sheet 60 placed on the support surface 52 and is in a stationary standing state, the foot is taken by the camera 54. The sole image is taken, and the sole image is transmitted to the sole state estimation device 310.
  • the camera 54 when the subject puts his / her foot on the support surface 52 without placing the silicone sheet 60 on the support surface 52 and becomes a stationary standing state, the camera 54 , The sole image is taken, and the sole image is transmitted to the sole state estimation device 310.
  • the sole state estimation device 310 executes the sole state estimation processing routine shown in FIG.
  • the same processing as the abnormal state estimation processing routine in the first embodiment is designated by the same reference numerals, and detailed description thereof will be omitted.
  • step S100 the image acquisition unit 30 acquires two foot sole images taken by the foot sole photographing device 50.
  • step S102 the binarized image creation unit 32 binarizes each of the two acquired sole images and removes noise. Further, the binarized image creation unit 32 divides the binarized images obtained for each of the two sole images into three in the length direction of the sole.
  • step S104 the sole state estimation unit 334 uses the binarized image to determine the total area of the sole region, the area of each sole region of the right foot and the left foot, and the front of each sole region of the right foot and the left foot. The area of the part and the area of the rear part of each sole area of the right foot and the left foot are calculated.
  • step S300 the sole state estimation unit 334 estimates the hardness state of the entire sole of the subject based on the rate of change in the area of the entire sole region in the two sole images.
  • the sole state estimation unit 334 estimates the hardness state of the sole of the right foot of the subject based on the rate of change in the area of the sole region of the right foot in the two sole images.
  • the sole state estimation unit 334 estimates the hardness state of the sole of the left foot of the subject based on the rate of change in the area of the sole region of the left foot in the two sole images.
  • the sole state estimation unit 334 estimates the hardness state of the front part of the sole of the right foot of the subject based on the rate of change in the area of the front part of the sole region of the right foot in the two sole images. In addition, the sole state estimation unit 334 estimates the hardness state of the front part of the sole of the left foot of the subject based on the rate of change in the area of the front part of the sole region of the left foot in the two sole images. In addition, the sole state estimation unit 334 estimates the hardness state of the rear part of the sole of the right foot of the subject based on the rate of change in the area of the rear part of the sole region of the right foot in the two sole images.
  • the sole state estimation unit 334 estimates the hardness state of the rear part of the left foot of the subject based on the rate of change in the area of the rear part of the sole region of the left foot in the two sole images.
  • the sole state estimation unit 334 displays the estimation result on the display unit 16 and ends the foot sole state estimation processing routine.
  • the foot condition estimation system when the sole of the subject is placed on each of two transparent support surfaces having different hardness.
  • the hardness state of the sole of the subject is estimated by comparing the two sole images, which are images taken through the support surface. Thereby, the hardness state of the sole of the subject can be easily estimated in a non-invasive manner.
  • the average value of the rate of change in the area of the entire sole area in the two sole images The average value of the laterality of the rate of change in the area of the sole area of the right foot and the left foot in the two sole images, The mean value of the laterality of the lateral difference in the area of the anterior part of the sole area of the right foot and the left foot in the two sole images, and the rate of change of the area of the posterior part of the sole area of the right foot and the left foot in the two sole images. Shows the result of comparing the average value of the left-right difference.
  • ⁇ Modification example 1> In the above embodiment, the case of estimation using multiple logistic regression analysis and multiple regression analysis has been described as an example, but the present invention is not limited to this.
  • the evaluation value may be obtained based on the following formula and estimated according to the evaluation value.
  • Evaluation value rate of change in the area of the entire sole area A + score 1 + score 2 + score 3 + score 4
  • the score 1 is the difference between the rate of change RA of the area of the sole region of the right foot in the two sole images and the rate of change LA of the area of the sole region of the left foot in the two sole images.
  • D is 0.6 or more, it becomes 0.1, and in other cases, it becomes 0.
  • Left-right difference DF which is the difference between the rate of change RF of the area of the front part of the sole area of the right foot in the two sole images and the rate of change LF of the area of the front part of the sole area of the left foot in the two sole images.
  • the score 2 becomes 0.1, and in other cases, the score 2 becomes 0.
  • the laterality DB which is the difference between the rate of change RB of the area of the rear part of the sole area of the right foot in the two sole images and the rate of change LB of the area of the rear part of the sole area of the left foot in the two sole images, is If it is 0.1 or more, the score 3 becomes 0.1, and in other cases, the score 3 becomes 0.
  • the score 4 is 0.5 if it is Charcot's foot and 0 if it is not Charcot's foot. In the case of Charcot's foot, scores 1 to 3 are set to 0.
  • Charcot's foot is a flat foot caused by severe neuropathy in diabetes and destruction of the arch joint.
  • ⁇ Modification 2> In addition, using two foot images as inputs, a neural network that estimates the presence or absence of diabetes, the presence or absence of neuropathy in diabetes, the severity of diabetes, or the degree of neuropathy in diabetes is used to determine the presence or absence of diabetes and nerves in diabetes. The presence or absence of disability, the severity of diabetes, or the degree of neuropathy in diabetes may be estimated.
  • ⁇ Modification example 3> the case of estimating diabetes or neuropathy in diabetes as an abnormal state of the subject has been described as an example, but the present invention is not limited to this. If the abnormal state can be estimated by comparing the two sole images, the abnormal state of the subject other than diabetes and neuropathy in diabetes may be estimated.
  • the system includes one foot sole imaging device and one abnormal state estimation device or foot sole state estimation device has been described as an example, but the present invention is not limited to this.
  • it may be a system including a plurality of foot sole imaging devices and one abnormal state estimation device or foot sole state estimation device configured as a server.
  • the sole imaging device may transmit the thickness and hardness of the transparent silicone sheet together with the sole image. Good.
  • Rate of change A, laterality D, laterality DF, and laterality DB are used to estimate the presence or absence of diabetes, the presence or absence of neuropathy in diabetes, the severity of diabetes, or the degree of neuropathy in diabetes.
  • the present invention is not limited to this.
  • at least one of rate of change A, laterality D, laterality DF, and laterality DB can be used to determine the presence or absence of diabetes, the presence or absence of neuropathy in diabetes, the severity of diabetes, or the degree of neuropathy in diabetes. You may try to estimate.
  • LED lighting is used as the light source 58
  • illumination having a specific wavelength such as near infrared may be used as the light source 58.
  • a specific wavelength at which the presence or absence of diabetes and the presence or absence of neuropathy in diabetes are more prominent may be obtained in advance, and the illumination of the specific wavelength may be used as the light source 58.
  • processors other than the CPU may execute various processes executed by the CPU reading software (program) in the above embodiment.
  • the processors include PLD (Programmable Logic Device) whose circuit configuration can be changed after the manufacture of FPGA (Field-Programmable Gate Array), and ASIC (Application Specific Integrated Circuit) for executing ASIC (Application Special Integrated Circuit).
  • An example is a dedicated electric circuit or the like, which is a processor having a circuit configuration designed exclusively for the purpose.
  • the abnormal state estimation process or the sole state estimation process may be executed by one of these various processors, or a combination of two or more processors of the same type or different types (for example, a plurality of FPGAs, etc.). And a combination of CPU and FPGA, etc.).
  • the hardware structure of these various processors is, more specifically, an electric circuit in which circuit elements such as semiconductor elements are combined.
  • the program is a non-temporary storage medium such as a CD-ROM (Compact Disk Read Only Memory), a DVD-ROM (Digital entirely Disk Online Memory), and a USB (Universal Serial Bus) memory. It may be provided in the form. Further, the program may be downloaded from an external device via a network.
  • (Appendix 2) A non-temporary storage medium that stores a program that can be executed by a computer to perform anomalous state estimation processing.
  • the abnormal state estimation process is When the sole of the subject was placed on each of the two transparent support surfaces having different hardness, two sole images, which are images taken through the support surface, were acquired.
  • a non-temporary storage medium that estimates the abnormal state of the subject by comparing the two foot sole images.
  • (Appendix 3) With memory With at least one processor connected to the memory Including The processor When the sole of the subject was placed on each of the two transparent support surfaces having different hardness, two sole images, which are images taken through the support surface, were acquired.
  • a foot sole state estimation device that estimates the hardness state of the sole of the subject by comparing the two sole images.
  • a non-temporary storage medium that stores a program that can be executed by a computer to execute the sole state estimation process.
  • the sole state estimation process is When the sole of the subject was placed on each of the two transparent support surfaces having different hardness, two sole images, which are images taken through the support surface, were acquired.

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Abstract

Selon la présente invention, un dispositif d'imagerie de semelle (50) capture deux images de semelle par l'intermédiaire d'une surface de support transparente (52) sur laquelle la semelle du sujet est placée tout en faisant en sorte que la surface de support (52) présente une dureté différente à chaque instant de la capture. Ce dispositif d'estimation d'état de semelle compare les deux images de semelle pour estimer l'état anormal du sujet ou l'état de dureté de la semelle.
PCT/JP2020/032439 2019-08-28 2020-08-27 Dispositif d'estimation d'état anormal, dispositif d'estimation d'état de semelle, système et programme WO2021039923A1 (fr)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPWO2022224916A1 (fr) * 2021-04-19 2022-10-27

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
HITOYA ET AL.: "Severity prediction of patients with diabetic neuropathy using plantar images contacting the floor with different hardness", THE PROCEEDINGS OF JSME ANNUAL CONFERENCE ON ROBOTICS AND MECHATRONICS, 8 June 2019 (2019-06-08) *

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPWO2022224916A1 (fr) * 2021-04-19 2022-10-27
WO2022224916A1 (fr) * 2021-04-19 2022-10-27 合同会社画像技術研究所 Dispositif d'acquisition d'informations biologiques
JP7253298B2 (ja) 2021-04-19 2023-04-06 合同会社画像技術研究所 生体情報取得装置

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