Analysis device, training device, and program

Abstract

SOLUTION: An analysis device includes: a load data acquisition unit that acquires measurement data from a plurality of sensor members that are arranged along a path on which a living body walks to measure a load, in association with identification information of each individual; an image data acquisition unit that acquires an image of the living body walking along the path together with the identification information; a position detection unit that generates position data indicating which of the plurality of sensor members is pressed by each leg of the living body on the basis of the image; and a state estimation unit that estimates a state of the living body on the basis of the measurement data and the position data.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to an analysis device, a training device, and a program. [Background technology]

[0002] Patent Document 1 discloses a technology for "objectively diagnosing the condition of the hoof of a dairy cow by capturing the movement of a specific part of the cow in a captured image and diagnosing the condition of the hoof of the captured dairy cow based on the movement of this specific part (Abstract)." Patent Document 2 discloses a technology for "calculating a score indicating the health condition of a cow (Abstract)" based on "a group of three-dimensional coordinates indicating the three-dimensional shape of the cow extracted from a range image of the cow." Non-Patent Document 1 discloses a technology for early detection of lameness in cows. Patent Document 1: Japanese Patent Application Laid-Open No. 2005-253435 Patent Document 2: International Publication No. 2017 / 187719 Non-Patent Document 1 A. Van Nuffel, et al "Exploration of measurement variation of gait variables for early lameness detection in cattle using the GAITWISE" Livestock Science, Volume 156, Issues 1-3, September 2013, Pages 88-95. Summary of the Invention

[0003] A first aspect of the present invention provides an analysis device. The analysis device may include a load data acquisition unit that acquires measurement data from multiple sensor elements that measure loads and are arranged on a path along which a living organism walks, in association with identification information for each individual. Any of the analysis devices may include an image data acquisition unit that acquires images of the living organism along the path along with the identification information. Any of the analysis devices may include a position detection unit that generates, based on the images, position data indicating which of the multiple sensor elements each leg of the living organism presses. Any of the analysis devices may include a state estimation unit that estimates the state of the living organism based on the measurement data and the position data.

[0004] In any of the above analysis devices, the position detection unit may determine whether a ground contact portion of any leg of the living body straddles two or more of the sensor members. In any of the above analysis devices, when the ground contact portion straddles two or more of the sensor members, the state estimation unit may calculate a measurement value of the load of the ground contact portion based on measurement values ​​of the loads of the two or more sensor members.

[0005] In any of the above analytical devices, each of the plurality of sensor elements may be plate-shaped and have a pressing surface, and the length and width of the pressing surface of each of the sensor elements may be 20 cm or more and 50 cm or less.

[0006] In any of the above analysis devices, each of the plurality of sensor members may be plate-shaped and have a pressing surface. In any of the above analysis devices, each of the sensor members may measure a load distribution on the pressing surface. In any of the above analysis devices, the state estimation unit may estimate the state of the living body based on the load distribution on the ground-contact portions of each leg of the living body.

[0007] Any of the above analysis devices may include a speed detection unit that detects a moving speed of the living body based on the image. In any of the above analysis devices, the state estimation unit may estimate the state of the living body further based on the moving speed of the living body.

[0008] A second aspect of the present invention provides an analysis device. The analysis device may include a load data acquisition unit that is arranged on a path along which a living organism walks and acquires measurement data from a sensor member that measures the distribution of loads on the ground contact portions of each leg of the living organism in association with identification information for each individual. Any of the analysis devices may include a state estimation unit that estimates the state of the living organism based on the distribution of loads on the ground contact portions of each leg of the living organism.

[0009] In any of the above analysis devices, the condition estimation unit may estimate a type of hoof disease of the living organism.

[0010] In any of the above analysis devices, the state estimation unit may estimate the state of the living organism based on at least one feature of uneven load on each leg, difference from past measurement data of the same individual, and difference from the measurement data of other individuals.

[0011] Any of the above analysis devices may include a weight calculation unit that calculates the weight of the living body based on the measurement data.

[0012] In any of the above analysis devices, the state estimation unit may select an estimation method for estimating the state of the living organism based on an estimation result of whether the living organism is walking or stationary.

[0013] A third aspect of the present invention provides a training device. The training device includes a training data acquisition unit that acquires training data including a load distribution at the ground contact portion of each leg of a living organism in association with identification information of each individual. Any of the above training devices may include an evaluation unit that inputs the training data to a training subject model and acquires an output result indicating the state of the living organism from the training subject model. Any of the above training devices may include a training unit that adjusts the training subject model based on label data indicating the state of the individual corresponding to the identification information and the output result.

[0014] In any of the above training devices, the training subject model may output the type of hoof disease of the living organism as the state of the living organism.

[0015] Any of the above training devices may include a data processing unit that estimates which leg of the living body the load distribution included in the training data corresponds to and includes the estimation result in the training data. In any of the above training devices, the evaluation unit may input the training data including the estimation result to the training target model.

[0016] In any of the above training devices, the data processing unit may further perform at least one of noise removal and upsampling or downsampling of the data set in space or time series.

[0017] Any of the above training devices may include a data processing unit that estimates which leg of the living body the load distribution included in the training data corresponds to and includes the estimation result in the training data. In any of the above training devices, the evaluation unit may input the training data including the estimation result to the training target model.

[0018] In a fourth aspect of the present invention, there is provided a program for causing a computer to function as the analysis device according to claim 1 or 6.

[0019] The above summary of the invention does not list all of the necessary features of the present invention, and subcombinations of these features may also constitute inventions. [Brief explanation of the drawings]

[0020] [Figure 1] 1 is a block diagram showing an example of an analysis device 100 according to an embodiment of the present invention. [Figure 2] 2 is a schematic diagram illustrating an example of a sensor unit 110. FIG. [Figure 3] 1 shows an example of a plurality of sensor members 112 viewed from above. [Figure 4] FIG. 10 is a diagram showing an example of measurement results of the loads on each leg of the same individual. [Figure 5] FIG. 10 is a diagram showing an example of the measurement results of the loads of multiple individuals. [Figure 6] 1 shows an example of a time waveform of the measurement result output from one sensor member 112. [Figure 7] 10 is a diagram showing another example of the measurement result of the load in one sensor member 112. FIG. [Figure 8] 10 is a diagram showing an example of the position of the center of gravity of the load in the contact portion 211. FIG. [Figure 9] FIG. 10 is a diagram illustrating another exemplary configuration of the analysis device 100. [Figure 10] FIG. 10 is a diagram illustrating another exemplary configuration of the analysis device 100. [Figure 11] FIG. 3 is a block diagram illustrating an example of a training device 300 for training a trainee model. [Figure 12] 12 illustrates an example computer 1200 in which aspects of the present invention may be embodied, in whole or in part. DETAILED DESCRIPTION OF THE INVENTION

[0021] The present invention will be described below through embodiments of the invention, but the following embodiments do not limit the scope of the invention according to the claims. Furthermore, not all of the combinations of features described in the embodiments are necessarily essential to the solution of the invention.

[0022] FIG. 1 is a block diagram illustrating an example of an analysis device 100 according to an embodiment of the present invention. The analysis device 100 estimates the state of a living organism based on load measurement data obtained by sensor elements arranged along the walking path of the living organism. The state of a living organism refers to a health-related condition, such as illness or injury, of the living organism. The state of a living organism may be a condition indicating whether the living organism is currently ill or injured, or a condition indicating a precursor to illness or injury. The living organism may be an animal such as a cow, or may be a human. The state of a living organism may be a state in which the animal is lame, or a condition indicating a precursor to lameness. If the living organism is an animal with hooves, the state of the living organism may be the state of the hooves. Using the load measurement data enables early detection of abnormalities in the state of the living organism. For example, abnormalities in the state of the living organism can be detected early, even before changes in the living organism's appearance become apparent.

[0023] Analysis device 100 includes a load data acquisition unit 10 and a state estimation unit 50. Analysis device 100 may further include an image data acquisition unit 20 and a position detection unit 30. Analysis device 100 may further include at least one of a sensor unit 110 and an imaging unit 120. Sensor unit 110 and imaging unit 120 may be provided outside analysis device 100. For example, analysis device 100 may be a computer, and sensor unit 110 and imaging unit 120 may be external devices that communicate with analysis device 100. A program for causing the computer to function as analysis device 100 may be installed in the computer.

[0024] The sensor unit 110 is disposed on a path along which the living organism walks, and measures the load of the living organism. The sensor unit 110 may measure the load of a living organism that is walking, or may measure the load of a living organism that is stationary and not walking. The sensor unit 110 includes one or more sensor elements. Each sensor element may be in the shape of a plate having a pressure surface. Each sensor element measures the load applied to the pressure surface. One sensor element may output one measurement value. The measurement value may be the sum of the loads applied to the pressure surface. In another example, each sensor element may output the distribution of the load on one pressure surface.

[0025] The imaging unit 120 captures an image of a living organism on a path where the sensor unit 110 is provided. The imaging unit 120 may capture an image of a living organism that is walking, or may capture an image of a living organism that is not walking but is stationary. The imaging unit 120 captures an image that makes it possible to determine at least which sensor element is pressed by each leg of the living organism. The imaging unit 120 may capture an image including the pressed surface of each sensor element. The imaging unit 120 may capture a video of the living organism. In another example, the imaging unit 120 may capture a still image of the living organism including any leg of the living organism each time that leg comes into contact with a sensor element. Only one imaging unit 120 may be provided, or multiple imaging units 120 may be provided.

[0026] The load data acquiring unit 10 acquires the measurement data from the sensor unit 110 in association with the identification information of each individual. The identification information may be acquired by the sensor unit 110 and notified to the load data acquiring unit 10 together with the measurement data. The measurement data may include a timestamp indicating the date and time when the load was measured.

[0027] The image data acquisition unit 20 acquires image data captured by the imaging unit 120 in association with the identification information of each individual. The identification information may be acquired by the imaging unit 120 and notified to the image data acquisition unit 20 together with the image data. The image data may include a timestamp indicating the date and time the image was captured.

[0028] The position detection unit 30 generates position data indicating the position where each leg of the living organism presses the sensor unit 110, based on the image data acquired by the image data acquisition unit 20. The position detection unit 30 notifies the state estimation unit 50 of the position data indicating the detected position. The position data may include data indicating which sensor member each leg of the living organism presses. The position data may also include data indicating which position on the pressing surface of each sensor member each leg of the living organism presses.

[0029] The state estimation unit 50 estimates the state of the living organism based on the measurement data. The state estimation unit 50 may estimate the state of the living organism based on the position data as well. The state estimation unit 50 may calculate the load on each leg of the living organism from the measurement data and the position data. For example, if the living organism is a cow, the state estimation unit 50 may calculate the magnitude of the load on each of the cow's left front leg, left hind leg, right front leg, and right hind leg. The load on each leg is expressed as a value per unit area (for example, N / m 2 ) or may be the total load (N) at the ground contact portion of the leg. The magnitude of the load on each leg may be the maximum value of the load during the period from when the leg starts to press the sensor member to when it stops (or the period from when the leg comes into contact with the sensor member to when it separates). The state estimation unit 50 may estimate whether the living organism is walking or stationary from the measurement data or position data. A state in which the living organism is standing and not walking may be included in the stationary state. The state estimation unit 50 may calculate the walking speed from the measurement data or position data, and determine that the living organism is standing or stationary when the walking speed is below a threshold. The state estimation unit 50 may switch algorithms, such as a machine learning model, used to estimate an abnormality, depending on the estimation result of the walking state, standing, or stationary state. An abnormality estimation algorithm corresponding to the walking state and an abnormality estimation algorithm corresponding to the standing or stationary state may be preset in the state estimation unit 50.

[0030] In a healthy living organism, the load on each leg tends to be uniform when walking or standing still. The condition estimation unit 50 may estimate the state of the living organism based on the variation in the load on each leg. For example, if the difference between the load on one leg and the average load on the other legs is equal to or greater than a predetermined value, the condition estimation unit 50 may determine that the state of the one leg is abnormal. However, the method of determining the state is not limited to the method described above.

[0031] Fig. 2 is a schematic diagram illustrating an example of the sensor unit 110. Fig. 2 shows a side view of a walking living body 200. In this specification and drawings, the axis parallel to the direction of gravity is referred to as the Z axis, the axis parallel to the moving direction of the living body 200 is referred to as the X axis, and the axis perpendicular to both the X axis and the Y axis is referred to as the Y axis.

[0032] The sensor unit 110 of this example has a plurality of sensor elements 112. In this example, each sensor element 112 has a plate shape with a pressure surface 116. The pressure surface 116 is a surface capable of detecting applied pressure. The pressure surface 116 may be the surface with the largest area among the surfaces of the sensor element 112. Each sensor element 112 is arranged so that the pressure surface 116 faces upward in the Z-axis direction. The plurality of sensor elements 112 are arranged on a path 114 along which the living organism 200 walks, along the direction of travel of the living organism 200. The pressure surfaces 116 of adjacent sensor elements 112 may be in contact with each other or may be slightly spaced apart. The distance between adjacent pressure surfaces 116 is preferably shorter than the maximum length of the bottom surface of the living organism 200's leg. The distance between the pressure surfaces 116 may be equal to or less than half, or even equal to or less than one-quarter, of the maximum length of the bottom surface of the leg. This distance is, for example, equal to or less than 10 cm.

[0033] The path 114 may be, for example, an aisle in the stable of the living organism 200 or the floor of a milking area, but is not limited to these. The plurality of sensor members 112 may also be arranged in a direction (Y-axis direction) perpendicular to the direction of travel (X-axis direction). As an example, two sensor members 112 are arranged side by side in the Y-axis direction. This makes it possible to measure the load on the right leg and the load on the left leg of the living organism 200 independently.

[0034] The imaging unit 120 captures an image that makes it possible to determine which sensor member 112 is pressed by each leg of the living body 200. Although one imaging unit 120 is shown in Fig. 2, images of the legs of the living body 200 may be captured by multiple imaging units 120.

[0035] In this example, the living body 200 is provided with a tag 202 that transmits identification information of the individual. The tag 202 may be an RFID tag that transmits the identification information. The image capturing unit 120 and the sensor unit 110 may receive the identification information from the RFID tag and associate the image data and measurement data with the identification information.

[0036] 3 shows an example of a plurality of sensor members 112 viewed from above. In this example, sensor members 112-1 to 112-5 are lined up on the right side of the direction of travel of the living organism 200, and sensor members 112-6 to 112-10 are lined up on the left side. FIG. 3 also shows ground contact portions 211, 212, 213, and 214 where each leg of the living organism 200 comes into contact with the sensor members 112. As an example, ground contact portion 211 corresponds to the left hind leg, ground contact portion 212 corresponds to the left front leg, ground contact portion 213 corresponds to the right hind leg, and ground contact portion 214 corresponds to the right front leg. The positions of ground contact portions 211 and 212 after the legs have been moved are indicated by dashed lines.

[0037] The imaging unit 120 captures an image that allows the positions of the ground contact portions 211, 212, 213, and 214 of each sensor member 112 to be identified. This makes it possible to measure the load on each leg of the living body 200. In this example, two imaging units 120-1 and 120-2 are provided. The imaging unit 120-1 captures images of the sensor members 112-1 to 112-5 on the right side, and the imaging unit 120-2 captures images of the sensor members 112-6 to 112-10 on the left side. The imaging units 120-1 and 120-2 differ in at least one of the imaging position and the imaging angle. The imaging unit 120 can be an RGB camera, a LiDAR, a 3D camera, or the like.

[0038] The position detection unit 30 detects the positions of the ground contact portions 211 to 214. The position detection unit 30 may determine whether or not the ground contact portions 211 to 214 of any of the legs straddle the pressing surfaces 116 of two or more sensor members 112. In the example of Fig. 3, the ground contact portion 213 straddles two sensor members 112-1 and 112-2 that are adjacent to each other in the X-axis direction. Similarly, the ground contact portion 212 straddles two adjacent sensor members 112.

[0039] When any of the ground contact portions 211 to 214 straddles two or more sensor members 112, the state estimation unit 50 calculates the measurement value of the load of the ground contact portion based on the measurement values ​​of the loads of the two or more sensor members 112. For example, the state estimation unit 50 calculates the measurement value of the load of the ground contact portion 213 based on the measurement value of the load of the sensor member 112-1 and the measurement value of the load of the sensor member 112-2. The state estimation unit 50 may calculate the sum of the measurement value of the load of the sensor member 112-1 and the measurement value of the load of the sensor member 112-2 as the load of the ground contact portion 213. When any of the ground contact portions 211 to 214 does not straddle two or more sensor members 112, the state estimation unit 50 calculates the measurement value of the load of one ground contact portion based on the measurement value of the load of one sensor member 112.

[0040] The sensor unit 110 of this example has a plurality of sensor members 112 arranged along a path 114 along which the living body 200 walks. This configuration allows the use of relatively small sensor members 112. This makes it easy to arrange a plurality of sensor members 112 along the path 114 even if the path 114 is not straight. It also reduces the cost of the sensor members 112. On the other hand, when a plurality of sensor members 112 are arranged and used, the ground contact portion of the leg of the living body 200 may straddle multiple sensor members 112. In this example, by analyzing image data from the imaging unit 120, it is possible to easily detect which sensor member 112 the ground contact portion straddles. Therefore, even when a plurality of sensor members 112 are used, the load on each leg of the living body 200 can be measured with high accuracy.

[0041] The position detection unit 30 may determine whether the pressing surface 116 of any sensor member 112 is being pressed by two or more legs simultaneously. The state estimation unit 50 may process the measurement value of a sensor member 112 that is being pressed by two or more legs simultaneously as an invalid value.

[0042] The position detection unit 30 may detect the position of the ground contact portion within the plane of the pressing surface 116 of each sensor member 112. The state estimation unit 50 may set a correction value for each sensor member 112 according to the position of the ground contact portion within the plane of the pressing surface 116. The state estimation unit 50 may correct the measured value of the load of the sensor member 112 using the correction value according to the position of the ground contact portion detected by the position detection unit 30. With this configuration, even if the sensor member 112 has different sensitivity to load depending on the position within the plane of the pressing surface 116, the load of each leg of the living organism 200 can be measured with high accuracy.

[0043] The position detection unit 30 may detect the position of the contact area based on shape information set in advance regarding the shape of the contact area of ​​the living body 200 to be measured. The position detection unit 30 may detect the contact area as a contact area when the shape of the area where pressure is detected within the pressure surface 116 matches the shape information. The position detection unit 30 may detect the position of the contact area using both image data and shape information. For example, when the position of the contact area detected from the image data matches the position of the contact area detected based on the shape information, the position detection unit 30 may adopt the position as the position of the contact area.

[0044] The position detection unit 30 may detect the tip of a leg, such as a hoof, from an image of the living organism 200 to be measured, based on a leg detection model generated by machine learning. For example, the leg detection model may be a model that extracts a hoof from an image of the living organism 200 to be measured by learning in advance images of the hooves of a plurality of individuals. The leg detection model may be a model that extracts the tip of a leg from an image of the living organism 200 to be measured by learning in advance images of the legs of a plurality of individuals.

[0045] The position detection unit 30 may detect the position of the ground contact portion based on acceleration information indicating the acceleration of the legs or hooves of the living organism 200 being measured. The acceleration information may be measured by an acceleration sensor attached to each leg or each hoove of the living organism 200. The position detection unit 30 may detect the timing when the legs or hooves of the living organism 200 touch the ground based on the acceleration information. For example, the position detection unit 30 may detect the timing when the acceleration of the legs or hooves of the living organism 200 indicates 0 or a minimum value as the ground contact timing.

[0046] The position of each leg or hoof of the living organism 200 at a predetermined start timing is set as the initial position of each leg or hoof. The position detection unit 30 may calculate the movement speed of each leg or hoof of the living organism 200 by integrating, with time, the acceleration during a target period from the start timing to the ground contact timing at which the acceleration is 0 or a minimum value. In this case, the position detection unit 30 acquires acceleration information of each leg or hoof in the movement direction of the living organism 200. The position detection unit 30 may calculate the movement distance of each leg or hoof of the living organism 200 by integrating, with time, the movement speed of each leg or hoof of the living organism 200 during the target period. The position detection unit 30 may calculate a position that is the movement distance away from the initial position of each leg or hoof of the living organism 200 in the movement direction as the position of the ground contact portion of the living organism 200.

[0047] The position detection unit 30 may acquire acceleration information in multiple directions. For example, the position detection unit 30 may acquire acceleration information in two directions in a plane parallel to the ground. In this case, the in-plane position of each leg or hoof of the living organism 200 can be calculated from the acceleration information. The position detection unit 30 may acquire acceleration information in each direction of three mutually orthogonal axes. From the acceleration information, the position of each leg or hoof of the living organism 200 in three-dimensional space can be calculated.

[0048] In another example, the position where the sensor member 112 is pressed at the ground contact timing calculated from the acceleration information may be detected as the position of the ground contact part of each leg of the living body 200. In this case, the position detection unit 30 detects the position of the ground contact part from the acceleration information and the measurement data.

[0049] The position detection unit 30 may detect the position of the ground contact portion using both the image data and the acceleration information. For example, when the position of the ground contact portion detected from the image data and the position of the ground contact portion detected based on the acceleration information match, the position detection unit 30 may adopt the position as the position of the ground contact portion. The position detection unit 30 may also detect the position of the ground contact portion using the shape information described above.

[0050] As described above, the position detection unit 30 may detect the position of the ground contact portion based only on the image data of the living body 200 and the measurement data of the load. The position detection unit 30 may detect the position of the ground contact portion using both the image data of the living body 200 and the measurement data of the load. The position detection unit 30 may detect the position of the ground contact portion based only on the measurement data of the image data of the living body 200 and the measurement data of the load.

[0051] The position detection unit 30 may generate position data from image data, etc., when set conditions are satisfied. For example, the position detection unit 30 may generate position data from image data when the magnitude of the load measured by the sensor unit 110 is equal to or greater than a predetermined value. Similarly, the imaging unit 120 and the image data acquisition unit 20 may acquire and output image data when set conditions are satisfied.

[0052] The position detection unit 30 may generate position data when the image data captured by the imaging unit 120 satisfies a set condition. For example, the position detection unit 30 may generate position data from the image data when the fluctuation in the luminance value of a predetermined pixel in the image data exceeds a predetermined value. In this case, the imaging unit 120 and the image data acquisition unit 20 acquire image data more frequently than the frequency at which the position detection unit 30 generates position data.

[0053] Each sensor member 112 may have the same shape. The length of the pressing surface 116 of one sensor member 112 in the X-axis direction is defined as L, and the width in the Y-axis direction is defined as W. If the sensor members 112 are too small, there is a high possibility that one contact area will span multiple sensor members 112. The pressing surface 116 of each sensor member 112 may be larger than the contact area of ​​each leg of the living body 200. The length L and width W of the pressing surface 116 of each sensor member 112 may be 20 cm or more. The length L and width W may also be 30 cm or more.

[0054] If the sensor element 112 becomes too large, the possibility of multiple legs stepping on one sensor element 112 at the same time increases. The sensor element 112 may not be able to separate the measured load value into components caused by multiple legs. The length L and width W of the pressing surface 116 may be smaller than the distance between the two legs of the living organism 200. This reduces the possibility of multiple legs stepping on one sensor element 112 at the same time. The length L and width W may be 50 cm or less. The length L may be larger than the width W. For example, if the living organism 200 is a cow, the distance between the front and rear legs is larger than the distance between the left and right legs. Therefore, even if the length L is large, the possibility of multiple legs stepping on one sensor element 112 at the same time is relatively small.

[0055] The sensor members 112 may be arranged in the X-axis direction over a range longer than the stride of the living body 200. The sensor members 112 may be arranged over a range of 2 m or more in the X-axis direction, or may be arranged over a range of 3 m or more.

[0056] The sensor member 112 may measure a static load (i.e., the instantaneous value of the load) or a dynamic load (i.e., the time waveform of the load). The sensor member 112 may measure a static load using a strain gauge or the like. The sensor member 112 may measure a dynamic load using a piezoelectric element or the like. The sensor member 112 may measure both static and dynamic loads. The sensor member 112 may be a plate-shaped sensor including at least one measuring element selected from the group consisting of an optical fiber sensor, a magnetic fiber sensor, a strain gauge, and a piezoelectric element.

[0057] The sensor member 112 may have a protective structure that protects a measuring element such as a strain gauge or a piezoelectric element. The protective structure is, for example, a metal or rubber cover that covers the measuring element. By providing the protective structure, the measuring element can be protected from damage due to load or contamination by excrement.

[0058] Fig. 4 is a diagram showing an example of the measurement results of the load on each leg of the same individual. The horizontal axis of Fig. 4 indicates the date of load measurement, and the vertical axis indicates the magnitude of the load. Fig. 4 shows the magnitude of the load for each of the ground contact portions 211 to 214 corresponding to the four legs.

[0059] As described above, the state estimation unit 50 may estimate the state of the living organism 200 based on a feature amount indicating the unevenness of the load on each leg. The unevenness of the load is indicated by a difference D1 between the reference value Lr and the amount of load on each leg of the living organism (in the example of FIG. 4 , the left front leg, left hind leg, right front leg, and right hind leg). The reference value Lr may be a value obtained by dividing the sum of the loads on each leg by the number of legs (or a value of ¼ of the body weight of the living organism 200), or may be a time-averaged value of the loads on the leg for which unevenness is to be calculated over a predetermined period of time. In another example, if the target leg is a front leg, the average value of the loads on the left front leg and the right front leg may be used as the reference value Lr. If the target leg is a hind leg, the average value of the loads on the left hind leg and the right hind leg may be used as the reference value Lr. In another example, the state estimation unit 50 may estimate that an abnormality has occurred in the leg at the ground-contact portion to be determined when the difference D1 between the load measured for the target leg and the reference value Lr is equal to or greater than a predetermined reference value.

[0060] The state estimation unit 50 may estimate the state of the living organism 200 based on a feature amount indicating a difference from past measurement data of the same individual. The state estimation unit 50 may estimate that an abnormality has occurred in the leg being evaluated when a feature amount indicating a change in the current difference D1 from the past difference D1 is equal to or greater than a preset reference value. The state estimation unit 50 may estimate the state of the living organism 200 based on a time differential value of the change amount. For example, when the time differential value of the change amount is increasing, the state estimation unit 50 may determine that the living organism 200 is in a state requiring early intervention.

[0061] The state estimation unit 50 may compare the past feature vector of the same individual with the feature vector of the evaluation target using any distance measure (Euclidean distance). The feature vector may be obtained by directly compressing data using a machine learning model, or by compressing designed feature vectors using a machine learning model. It is also possible to use such vectors as training data to create and evaluate an anomaly detection model based on Mahalanobis distance, etc. In this case, a model is created using data from the living organism 200 in a normal state, and an appropriate threshold is set.

[0062] The state estimation unit 50 may estimate the state of the living organism 200 based on whether the walking tendency of the living organism 200 calculated from the measurement data and image data deviates from the past walking tendency. The walking tendency includes, for example, at least one of the stride length and walking speed of the living organism 200. When the difference between the current stride length or walking speed of the living organism 200 and the past stride length or walking speed is equal to or greater than a preset reference value, it may be estimated that an abnormality has occurred in the living organism 200.

[0063] Fig. 5 is a diagram showing an example of the load measurement results for multiple individuals. The horizontal axis of Fig. 5 indicates the date of load measurement, and the vertical axis indicates the magnitude of the load. Fig. 5 shows the load measurement results for organisms 200-1, 200-2, 200-3, and 200-4. The load measurement results for each organism 200 may be the load measurement results for a common leg.

[0064] The state estimation unit 50 may estimate the state of the living organism 200 based on a feature amount indicating a difference between the load measurement data of the individual to be determined (living organism 200-1 in the example of FIG. 5) and the load measurement data of other individuals (living organisms 200-2, 200-3, and 200-4 in the example of FIG. 5). The state estimation unit 50 may estimate the state of the living organism 200 based on a difference D2 between the measurement data of the individual to be determined and the average value of the measurement data of individuals other than the individual to be determined. The state estimation unit 50 may estimate that an abnormality has occurred in the individual to be determined when the difference D2 is equal to or greater than a preset reference value.

[0065] The state estimation unit 50 may perform the following process. (1) Collect multiple sets of load data from healthy individuals as reference data, and polynomial-approximate the graph of the load on each leg over time in the reference data. The load graph is, for example, as shown in Figure 4. (2) From the measurement data of the individual being evaluated, a graph of the load over time for each leg is obtained, and the graph for each leg is approximated by a polynomial. (3) For each leg, the coefficients of the polynomial related to the measurement data are compared with the coefficients of the polynomial related to the reference data to estimate the state of each leg of the individual being evaluated. For example, the difference in coefficients for each degree of the polynomial may be calculated, and the magnitude of the difference may be used to determine whether or not an abnormality has occurred in the individual.

[0066] In the above-described process (3), the state estimation unit 50 may reduce the number of dimensions of the parameters to be compared (i.e., the number of parameters) using a principal component analysis technique, and then calculate the distance between the parameters. For example, if a polynomial is tenth order and has ten coefficients, the state estimation unit 50 may generate two principal component parameters from the ten coefficients using a principal component analysis technique. The state estimation unit 50 may determine that an abnormality has occurred in the individual to be evaluated when the distance between two principal component parameters related to the measurement data and two principal component parameters related to the reference data is equal to or greater than a permissible value. Furthermore, the state estimation unit 50 may use a machine learning algorithm to compare parameters for a normal case, which have been learned in advance, with parameters to be compared, to evaluate the abnormality of the living organism 200. The machine learning algorithm may be, for example, one-class SVM, isolation forest, autoencoder, or principal component analysis.

[0067] In this example, the coefficients of the polynomial obtained by polynomial approximation of the change in load over time are used as the above-mentioned feature quantity. When performing polynomial approximation on the measurement data and the reference data, the approximation may be performed under the same conditions for the target data. For example, the state estimation unit 50 may perform polynomial approximation by aligning the time axis scales in the time-varying graphs of the measurement data and the reference data. The state estimation unit 50 may also perform polynomial approximation after normalizing the magnitude of the load or aligning the zero points of the load.

[0068] The state estimation unit 50 may estimate that an abnormality has occurred in the individual being assessed when the amount of change in the current difference D2 relative to the past difference D2 is equal to or greater than a preset reference value. The state estimation unit 50 may estimate the state of the individual being assessed based on a time differential value of the amount of change. For example, when the time differential value of the amount of change is increasing, the state estimation unit 50 may determine that the individual is in a state requiring early intervention.

[0069] Fig. 6 shows an example of a time waveform of the measurement result output by one sensor element 112. The sensor element 112 of this example is capable of measuring dynamic loads. Fig. 6 shows the time waveform for one step from when one of the legs of the living body 200 comes into contact with the pressing surface 116 of the sensor element 112 until it leaves the contact. The time waveform has two peaks P1 and P2 as an example, but the time waveform may have a single peak or more peaks.

[0070] The state estimation unit 50 may estimate the state of the living body 200 based on a feature extracted from a time waveform such as that shown in Fig. 6. The state estimation unit 50 may extract a feature corresponding to a foot contact time of the living body 200 from the time waveform. The foot contact time is the length of the period from when the foot comes into contact with the pressing surface 116 of the sensor member 112 until when the foot leaves the pressing surface 116.

[0071] The state estimation unit 50 may extract, as a feature corresponding to the ground contact time, the time T1 from when the load exceeds a predetermined value until when it falls below the predetermined value. The predetermined value may be set according to a maximum value P1 of the load in the time waveform. As an example, the predetermined value may be α times the maximum value P1 (where α is greater than 0 and less than 1). As an example, α may be 0.1, or may be another value.

[0072] The condition estimation unit 50 may extract the time T2 from the first peak P1 to the last peak P2 in the time waveform as a feature corresponding to the ground contact time. In this case, the time waveform includes multiple peaks. The condition estimation unit 50 may estimate that an abnormality has occurred in the leg when the time T1 or the time T2 is equal to or less than a set reference value.

[0073] The state estimation unit 50 may estimate the state of the living body 200 based on at least one of the feature amounts described in Figures 4 to 6. However, the feature amounts for estimating the state of the living body 200 are not limited to the examples described in Figures 4 to 6.

[0074] FIG. 7 is a diagram showing another example of the load measurement results of one sensor element 112. In this example, each sensor element 112 measures the load distribution on the pressing surface 116. That is, the sensor element 112 measures the load distribution within one ground contact portion (ground contact portion 211 in FIG. 7). The spatial resolution of the load measurement on the pressing surface 116 may be 2 cm or less in both the X-axis direction and the Y-axis direction, or may be 1 cm or less. In FIG. 7, the magnitude of the load is schematically shown by the shade of the diagonal hatching. In the example of FIG. 7, the magnitude of the load is shown in three values ​​(three shades of hatching), but the sensor element 112 may be capable of measuring the magnitude of the load in more than three values.

[0075] The state estimation unit 50 estimates the state of the living organism 200 based on the distribution of the load on the ground contact portion of each leg of the living organism 200. By measuring the distribution of the load within the ground contact portion, the state of the living organism 200 can be estimated in more detail.

[0076] The condition estimation unit 50 may estimate the type of hoof disease based on the distribution of load within the ground contact portion. The type of hoof disease may be classified by the so-called disease name such as sole ulcer, white band disease, or digital dermatitis, or may be classified by the location of the affected area, or may be classified by other criteria by the user, etc.

[0077] When the living body 200 is affected by hoof disease or in the pre-disease stage, the living body 200 tends to walk in a manner that reduces the load on the affected area. Therefore, the type of hoof disease can be estimated based on the distribution of load within the contact area. The condition estimation unit 50 may extract, as feature information, the magnitude of the load at a specific point within the contact area, the shape or size of an area within the contact area where the load is below a set value, the shape or size of an area within the contact area where the load is above a set value, the position of the center of gravity of the load within the contact area, the position within the contact area where the load is minimum, or changes in these parameters. The condition estimation unit 50 may estimate the type of hoof disease by comparing this feature information with preset reference information. The condition estimation unit 50 may estimate the type of hoof disease based on a combination of multiple feature information. The condition estimation unit 50 may be configured to set a range of feature information for each type of hoof disease. If the feature information extracted from the measurement data belongs to any of the ranges of hoof disease, the condition estimation unit 50 may estimate that the living body 200 is in a state of the hoof disease.

[0078] The state estimation unit 50 may estimate the state of the living organism 200 based on a preset rule or algorithm. In another example, the state estimation unit 50 may estimate the state of the living organism 200 using a training model generated by machine learning. For example, the training model may be generated by machine learning using the load distribution of the ground contact portion of a living organism suffering from hoof disease as training data and the type of hoof disease of the living organism as label data. The state estimation unit 50 may input the load distribution of the living organism 200 to be estimated into the training model to estimate the type of hoof disease. Furthermore, the state estimation unit 50 may use a training model generated by machine learning using measurement data of the load of the ground contact portion of the living organism 200 as training data and the state of the living organism 200 as label data. In this case, the state estimation unit 50 estimates the state of the living organism 200 using the training model in the example described with reference to FIGS. 1 to 6. The generation of the training model will be described later.

[0079] FIG. 8 is a diagram showing an example of the position of the center of gravity of the load in the contact portion 211. FIG. 8 shows an initial position 221 of the center of gravity and a changed position 222. The initial position 221 is an example of the position of the center of gravity in a state where the foot is not affected by hoof disease. The changed position 222 is an example of the position of the center of gravity in a state where the foot is affected by hoof disease. The condition estimation unit 50 may estimate the type of hoof disease using the change in the center of gravity position as feature information. The condition estimation unit 50 may estimate the type of hoof disease based on at least one of the direction from the initial position 221 to the changed position 222 and the distance between the initial position 221 and the changed position 222.

[0080] As explained in FIG. 8, the condition estimation unit 50 may estimate the type of hoof disease suffered by an individual based on changes over time in characteristic information extracted from measurement data of the same individual. The condition estimation unit 50 may estimate the type of hoof disease suffered by the leg of the individual being estimated by comparing the characteristic information explained in FIGS. 7 and 8 between the leg of the individual being estimated and other legs of the same individual. The comparison process between legs of the same individual is the same as the example in FIG. 4. The condition estimation unit 50 may estimate the type of hoof disease suffered by the individual being estimated by comparing the characteristic information explained in FIGS. 7 and 8 between the individual being estimated and multiple other individuals. The comparison process between individuals is the same as the example in FIG. 5.

[0081] In the example described in FIGS. 7 and 8, the condition estimation unit 50 may estimate the type of hoof disease of the living organism 200 without using image data of the living organism 200. In this case, the analysis device 100 may not include the imaging unit 120, the image data acquisition unit 20, and the position detection unit 30. In another example, even in the example described in FIGS. 7 and 8, the condition estimation unit 50 may estimate the type of hoof disease of the living organism 200 using image data of the living organism 200. The condition estimation unit 50 may determine, based on the image data, which leg of the living organism 200 each contacts with the ground extracted from the measurement data. This makes it easier to determine which leg of the living organism 200 is affected by hoof disease.

[0082] The condition estimation unit 50 may use the load distribution on the ground contact portion to determine a condition of the living organism 200 other than the type of hoof disease. The condition estimation unit 50 may determine a condition such as lameness of the living organism 200 based on the load distribution.

[0083] Fig. 9 is a diagram showing another example of the configuration of analysis device 100. Analysis device 100 of this example further includes a speed detection unit 40 in addition to the configuration of analysis device 100 shown in Fig. 1. The other configuration of analysis device 100 is similar to that of analysis device 100 of any of the aspects described with reference to Figs. 1 to 8.

[0084] The speed detection unit 40 detects the moving speed of the living body 200 based on the image data. The image data in this example includes images showing changes in the position of the living body 200 at multiple times. The state estimation unit 50 in this example estimates the state of the living body 200 based further on the moving speed of the living body 200.

[0085] The state estimation unit 50 may estimate the state of the living organism 200 by comparing the moving speed of the living organism 200 with a reference value. For example, the state estimation unit 50 may use the past moving speed of the same individual or the moving speed of another individual as the reference value. When the moving speed of the living organism 200 becomes slower than the moving speeds of the past or other individuals, the state estimation unit 50 may estimate that there is a high possibility that some abnormality has occurred in the living organism 200.

[0086] In another example, the state estimation unit 50 may select measurement data to be used to estimate the state of the living organism 200 using the moving speed of the living organism 200. Each measurement data may be stored in association with the moving speed of the living organism 200 at the time of measurement. For example, even for measurement data of the same individual, the feature amounts and feature information described with reference to FIGS. 1 to 8 may change between a stationary state and a moving state. When comparing past and current measurement data of the same individual, if the current measurement data is obtained by measuring the living organism 200 in a stationary state, the state estimation unit 50 may extract past measurement data obtained by measuring the living organism 200 in a stationary state. Similarly, if the current measurement data is obtained by measuring the living organism 200 in a moving state, the state estimation unit 50 may extract past measurement data obtained by measuring the living organism 200 in a moving state. Even when comparing measurement data between the individual to be estimated and another individual, the state estimation unit 50 may extract measurement data that share a common stationary or moving state.

[0087] The state estimation unit 50 may select measurement data used to estimate the state of the living organism 200 according to the magnitude of the moving speed. For example, even in measurement data of the same individual, the feature amounts and feature information described with reference to Figures 1 to 8 may change between a state in which the individual is moving fast and a state in which the individual is moving slowly. The state estimation unit 50 may extract and compare measurement data with similar magnitude of moving speed.

[0088] The speed detection unit 40 may detect, based on the image data, the acceleration of the living body 200. In the example described with reference to Fig. 9, the state estimation unit 50 may use the acceleration of the living body 200 instead of the moving speed of the living body 200.

[0089] FIG. 10 is a diagram showing another example of the configuration of the analysis device 100. The analysis device 100 of this example further includes a weight calculation unit 60 in addition to the configuration of the analysis device 100 of any of the aspects described with reference to FIGS. 1 to 9. The weight calculation unit 60 calculates the weight of the living organism 200 based on the measurement data. The weight calculation unit 60 may calculate the weight of the living organism 200 from the total load on each leg of the living organism 200 in a stationary state. The weight calculation unit 60 may notify the administrator of the living organism 200, etc., of the calculated weight. The weight calculation unit 60 may also notify the state estimation unit 50 of the calculated weight.

[0090] The state estimation unit 50 may estimate the state of the living organism 200 based on the weight of the living organism 200. The state estimation unit 50 may estimate the state of the living organism 200 based on a change in the weight of the living organism 200. The state estimation unit 50 may estimate that an abnormality has occurred in the living organism 200 when a change in the weight of the living organism 200 within a predetermined period exceeds a set reference value.

[0091] In another example, the state estimation unit 50 may select measurement data to be used to estimate the state of the living organism 200 using the weight of the living organism 200. Each measurement data may be stored in association with the weight of the living organism 200 at the time of measurement. For example, even if the measurement data is of the same individual, the feature amount (e.g., the magnitude of the load) and feature information described with reference to FIGS. 1 to 8 may change depending on changes in weight. When comparing past and current measurement data of the same individual, the state estimation unit 50 may compare measurement data with similar weights. Similarly, when comparing measurement data between an individual to be estimated and another individual, the state estimation unit 50 may compare measurement data with similar weights.

[0092] 11 is a block diagram showing an example of a training device 300 that trains a training target model. When measurement data including a distribution of loads on the living organism 200 is input, the training target model outputs the state of the living organism 200. The state estimation unit 50 described with reference to FIGS. 1 to 10 may estimate the state of the living organism 200 using the training target model.

[0093] The training device 300 trains a training target model by machine learning using training data. The training data may include the distribution of loads on the ground contact portions of each leg of the living organism 200. The training data may include at least a portion of the measurement data and image data described in FIGS. 1 to 10, or may include data generated from the measurement data and image data. The data generated from the measurement data and image data may be the above-mentioned feature amount or feature information, or may be the moving speed or weight of the living organism 200.

[0094] The training device 300 of this example includes a training data acquisition unit 310, a data processing unit 340, an evaluation unit 320, and a training unit 330. At least a part of the training data acquisition unit 310, the evaluation unit 320, and the training unit 330 may be a computer. A program for causing the computer to function as the training device 300 may be installed in the computer. The program may be stored in a computer-readable medium such as a memory, or may be transmitted to the computer via a communication line.

[0095] The training data acquisition unit 310 acquires the training data and the identification information of each individual in association with each other. The training data may be generated from measurement data of the actual living organism 200.

[0096] The data processing unit 340 performs a predetermined process on the training data. The data processing unit 340 estimates which leg of the living organism 200 the load distribution included in the training data corresponds to, and includes the estimation result in the training data. The data processing unit 340 may estimate which leg of the living organism 200 the data included in the training data corresponds to, based on image data of the living organism 200. The data processing unit 340 may estimate which leg of the living organism 200 each contact point corresponds to, based on the relative position of each contact point included in the training data.

[0097] The data processing unit 340 may further perform at least one of noise removal and upsampling or downsampling of a data set in a spatial or temporal series on the training data. Noise removal may be a process of removing a predetermined frequency component from a data sequence included in the training data. Upsampling may be a process of interpolating data on a spatial or temporal axis from a data sequence included in the training data. Downsampling may be a process of thinning data on a spatial or temporal axis from a data sequence included in the training data.

[0098] 1 to 10 from the measurement data and image data included in the training data, the data processing unit 340 may extract at least one of the feature amounts, feature information, movement speed, and weight explained in Figures 1 to 10 and include it in the training data. By using feature amounts, etc. as training data, it may be possible to create a model with less learning data than when using measured load values ​​as training data (the amount of calculation can be reduced and the memory capacity for storing the model to be trained can be reduced), or it may be easier to interpret the inference results of the model.

[0099] The evaluation unit 320 inputs the training data to the training target model. The evaluation unit 320 may input the training data including the above-mentioned estimation result by the data processing unit 340 to the training target model. The training target model estimates and outputs the state of the living organism 200 according to the input training data. The state of the living organism 200 may be the presence or absence of an abnormality such as lameness, or may be the type of hoof disease. The evaluation unit 320 obtains an output result indicating the state of the living organism 200 from the training target model.

[0100] The training unit 330 adjusts the training target model based on the label data indicating the state of the individual corresponding to the identification information and the output result. The training unit 330 adjusts the training model so that the output result of the training target model approaches the label data. The adjustment of the training target model may be a process of adjusting parameters such as weighting coefficients in the calculation processing of the training target model.

[0101] The label data may be generated from the results of observing the state of the actual living organism 200. The label data may include the results of observing the state of the living organism 200 after the time point of measurement of the measurement data included in the training data. This makes it possible to generate a training object model that can estimate a precursor to an abnormality such as lameness. The label data may include the results of observing the state of the living organism 200 at different points in time. In other words, the label data may include results that indicate a time series transition of the state of the living organism 200. The training object model may estimate a time series transition of the state of the living organism 200 when measurement data is input.

[0102] The training device 300 may generate a training object model using training data and label data of a living organism 200 in which an abnormality is detected. The training device 300 may also generate a training object model using training data of a living organism 200 in which no abnormality is detected. In this case, label data may not be used. In this case, the training object model estimates the occurrence of an abnormality when measurement data input as an estimation object deviates from the measurement data included in the training data. The training device 300 may generate a training object model using training data and label data of a living organism 200 in which an abnormality is detected and a living organism 200 in which no abnormality is detected.

[0103] The training model may output a score indicating the degree of abnormality such as lameness, etc. Analysis device 100 may notify the user of the occurrence of an abnormality if the score exceeds a threshold.

[0104] The label data may include information indicating the cause of an abnormality such as lameness or hoof disease. The information may be generated based on a diagnosis by a veterinarian or the like. When an abnormality is predicted in the living body 200, the training model may also predict the cause.

[0105] Various embodiments of the present invention may be described with reference to flowcharts and block diagrams, where the blocks may represent (1) stages of a process in which operations are performed or (2) sections of an apparatus responsible for performing the operations. Particular stages and sections may be implemented by dedicated circuitry, programmable circuitry provided with computer-readable instructions stored on a computer-readable medium, and / or a processor provided with computer-readable instructions stored on a computer-readable medium. Dedicated circuitry may include digital and / or analog hardware circuitry, and may include integrated circuits (ICs) and / or discrete circuits. Programmable circuitry may include reconfigurable hardware circuitry, including logical AND, OR, XOR, NAND, NOR, and other logical operations, flip-flops, registers, memory elements such as field programmable gate arrays (FPGAs), programmable logic arrays (PLAs), and the like.

[0106] A computer-readable medium may include any tangible device capable of storing instructions that are executed by an appropriate device, such that the computer-readable medium having instructions stored thereon comprises an article of manufacture containing instructions that can be executed to create means for performing the operations specified in the flowcharts or block diagrams. Examples of computer-readable media may include electronic, magnetic, optical, electromagnetic, and semiconductor storage media. More specific examples of computer-readable media may include floppy disks, diskettes, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), electrically erasable programmable read-only memory (EEPROM), static random access memory (SRAM), compact disc read-only memory (CD-ROM), digital versatile disc (DVD), Blu-ray (RTM) disc, memory stick, integrated circuit card, and the like.

[0107] The computer readable instructions may include either assembler instructions, Instruction Set Architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state-setting data, or source or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk®, JAVA®, C++, etc., and conventional procedural programming languages ​​such as the “C” programming language or similar programming languages.

[0108] The computer-readable instructions may be provided to a processor or programmable circuit of a programmable data processing device, such as a general-purpose computer, a special-purpose computer, or another computer, either locally or via a wide-area network (WAN) such as a local area network (LAN) or the Internet, which executes the computer-readable instructions to create means for performing the operations specified in the flowcharts or block diagrams. Here, the computer may be a personal computer (PC), a tablet computer, a smartphone, a workstation, a server computer, a general-purpose computer, a special-purpose computer, or the like, or may be a computer system in which multiple computers are connected. Such a computer system in which multiple computers are connected is also called a distributed computing system, and is a broad definition of computers. In a distributed computing system, multiple computers collectively execute a program by each executing a portion of the program and passing data between computers as needed during program execution.

[0109] Examples of processors include a computer processor, a central processing unit (CPU), a processing unit, a microprocessor, a digital signal processor, a controller, a microcontroller, etc. A computer may have one or more processors. In a multiprocessor system with multiple processors, each processor executes a portion of a program and passes data between processors as needed during program execution, allowing the multiple processors to collectively execute a program. For example, in multitasking, each of the multiple processors may execute a portion of each task in small chunks by switching tasks at time slice intervals. In this case, which portion of a program each processor executes changes dynamically. Which portion of a program each of the multiple processors executes may also be statically determined by multiprocessor-aware programming.

[0110] 12 illustrates an example of a computer 1200 in which aspects of the present invention may be embodied, in whole or in part. Programs installed on the computer 1200 may cause the computer 1200 to function as or perform operations associated with an apparatus or one or more sections of the apparatus according to embodiments of the present invention, and / or to perform a process or steps of a process according to embodiments of the present invention. Such programs may be executed by the CPU 1212 to cause the computer 1200 to perform specific operations associated with some or all of the blocks of the flowcharts and block diagrams described herein.

[0111] A computer 1200 according to this embodiment includes a CPU 1212, a RAM 1214, a graphics controller 1216, and a display device 1218, which are interconnected by a host controller 1210. The computer 1200 also includes input / output units such as a communication interface 1222, a storage device 1224 such as a hard disk drive, a DVD-ROM drive 1226, and an IC card drive, which are connected to the host controller 1210 via an input / output controller 1220. The computer also includes legacy input / output units such as a ROM 1230 and a keyboard 1242, which are connected to the input / output controller 1220 via an input / output chip 1240.

[0112] The CPU 1212 operates according to programs stored in the ROM 1230 and the RAM 1214, thereby controlling each unit. The graphics controller 1216 acquires image data generated by the CPU 1212 into a frame buffer or the like provided in the RAM 1214 or into the graphics controller 1216 itself, and causes the image data to be displayed on the display device 1218.

[0113] The communication interface 1222 communicates with other electronic devices via a network. The storage device 1224 stores programs and data used by the CPU 1212 in the computer 1200. The DVD-ROM drive 1226 reads programs or data from a DVD-ROM 1227 and provides the programs or data to the storage device 1224 via the RAM 1214. The IC card drive reads programs and data from an IC card and / or writes programs and data to an IC card.

[0114] The ROM 1230 stores therein a boot program or the like that is executed by the computer 1200 upon activation, and / or programs that depend on the hardware of the computer 1200. The input / output chip 1240 may also connect various input / output units to the input / output controller 1220 via a parallel port, a serial port, a keyboard port, a mouse port, etc.

[0115] The programs are provided by a computer-readable medium such as a DVD-ROM 1227 or an IC card. The programs are read from the computer-readable medium, installed in the storage device 1224, RAM 1214, or ROM 1230, which are also examples of computer-readable media, and executed by the CPU 1212. Information processing described in these programs is read by the computer 1200, and causes cooperation between the programs and the various types of hardware resources described above. An apparatus or a method may be configured by implementing information manipulation or processing in accordance with the use of the computer 1200.

[0116] For example, when communication is performed between the computer 1200 and an external device, the CPU 1212 may execute a communication program loaded into the RAM 1214 and instruct the communication interface 1222 to perform communication processing based on the processing described in the communication program. Under the control of the CPU 1212, the communication interface 1222 reads transmission data stored in a transmission buffer processing area provided in the RAM 1214, the storage device 1224, the DVD-ROM 1227, or a recording medium such as an IC card, and transmits the read transmission data to the network, or writes reception data received from the network to a reception buffer processing area or the like provided on the recording medium.

[0117] The CPU 1212 may read all or a necessary portion of a file or database stored on an external recording medium such as the storage device 1224, the DVD-ROM drive 1226 (DVD-ROM 1227), an IC card, etc. into the RAM 1214, and perform various types of processing on the data on the RAM 1214. The CPU 1212 then writes back the processed data to the external recording medium.

[0118] Various types of information, such as various types of programs, data, tables, and databases, may be stored on the recording medium and may undergo information processing. CPU 1212 may perform various types of processing on data read from RAM 1214, including various types of operations, information processing, conditional judgment, conditional branching, unconditional branching, information search / replacement, etc., as described throughout this disclosure and specified by the instruction sequences of the programs, and write the results back to RAM 1214. CPU 1212 may also search for information in a file, database, etc. on the recording medium. For example, if multiple entries each having an attribute value of a first attribute associated with an attribute value of a second attribute are stored on the recording medium, CPU 1212 may search for an entry that matches a condition specified by the attribute value of the first attribute from among the multiple entries, read the attribute value of the second attribute stored in the entry, and thereby obtain the attribute value of the second attribute associated with the first attribute that satisfies a predetermined condition.

[0119] The above-described programs or software modules may be stored in a computer-readable medium on or near the computer 1200. A recording medium such as a hard disk or RAM provided in a server system connected to a dedicated communication network or the Internet can also be used as a computer-readable medium, thereby providing the programs to the computer 1200 via the network.

[0120] Although the present invention has been described above using embodiments, the technical scope of the present invention is not limited to the scope described in the above embodiments. It will be apparent to those skilled in the art that various modifications and improvements can be made to the above embodiments. It is clear from the claims that such modifications and improvements can also be included within the technical scope of the present invention.

[0121] It should be noted that the execution order of each process, such as operations, procedures, steps, and stages, in the devices, systems, programs, and methods shown in the claims, specifications, and drawings is not specifically stated as "before," "prior to," etc., and that the processes can be performed in any order unless the output of a previous process is used in a subsequent process. Even if the operational flow in the claims, specifications, and drawings is described using "first," "next," etc. for convenience, this does not mean that the processes must be performed in this order. [Explanation of symbols]

[0122] 10 Load data acquisition unit, 20 Image data acquisition unit, 30 Position detection unit, 40 Speed ​​detection unit, 50 State estimation unit, 60 Weight calculation unit, 100 Analysis device, 110 Sensor unit, 112 Sensor member, 114 Path, 116 Pressing surface, 120 Imaging unit, 200 Living body, 202 Tag, 211, 212, 213, 214 Ground contact part, 221 Initial position, 222 Changed position, 300 Training device, 310 Training data acquisition unit, 320 Evaluation unit, 330 Training unit, 340 Data processing unit

Claims

1. a load data acquisition unit that acquires measurement data from a plurality of sensor members that measure loads and are arranged on a path along which the living body walks, in association with identification information of each individual living body; an image data acquisition unit that acquires an image of the living body along the path together with the identification information; a position detection unit that generates position data indicating which of the plurality of sensor members is pressed by each leg of the living body based on the image; a state estimation unit that estimates a state of the living body based on the measurement data and the position data; An analysis device comprising:

2. the position detection unit determines whether a ground contact portion of any leg of the living body straddles two or more of the sensor members; When the ground contact portion straddles two or more of the sensor members, the state estimation unit calculates the measurement value of the load of the ground contact portion based on the measurement values ​​of the loads of the two or more sensor members, and when the ground contact portion does not straddle two or more of the sensor members, the state estimation unit calculates the measurement value of the load of the ground contact portion based on the measurement value of the load of one of the sensor members. The analysis device according to claim 1 .

3. Each of the plurality of sensor members has a plate shape with a pressing surface. The analysis device according to claim 1 .

4. Each of the plurality of sensor members has a plate shape having a pressing surface, Each of the sensor members measures a distribution of a load on the pressing surface, The state estimation unit estimates the state of the living body based on a distribution of loads on the ground contact portions of each leg of the living body. The analysis device according to claim 1 .

5. a speed detection unit that detects a moving speed of the living body based on the image; The state estimation unit estimates the state of the living body further based on the moving speed of the living body. The analysis device according to claim 1 .

6. a load data acquisition unit that is arranged on a path along which the living body walks and acquires measurement data from a sensor member that measures the distribution of loads at the ground contact portions of each leg of the living body, in association with identification information of each individual living body; a state estimation unit that estimates a state of the living body based on a distribution of loads on the ground contact portions of each leg of the living body; An analysis device comprising:

7. The condition estimation unit estimates the type of hoof disease of the living organism. The analysis device according to claim 6.

8. The state estimation unit estimates the state of the living body based on at least one feature of unevenness of the load on each leg, a difference from the past measurement data of the same individual, and a difference from the measurement data of another individual. The analysis device according to claim 1 or 6.

9. a weight calculation unit that calculates the weight of the living body based on the measurement data The analysis device according to claim 1 or 6.

10. The state estimation unit selects an estimation method for estimating the state of the living body based on an estimation result of whether the living body is walking or stationary. The analysis device according to claim 1 or 6.

11. a training data acquisition unit that acquires training data including a distribution of loads on the ground contact portions of each leg of a living organism in association with identification information of each individual living organism; an evaluation unit that inputs the training data into a training object model and obtains an output result indicating the state of the living body from the training object model; a training unit that adjusts the training target model based on label data indicating the state of the individual corresponding to the identification information and the output result; A training device comprising:

12. The training model outputs the type of hoof disease of the living organism as the state of the living organism.

12. The training device of claim 11.

13. a data processing unit that estimates which leg of the living body the load distribution included in the training data corresponds to and includes the estimation result in the training data; The evaluation unit inputs the training data including the estimation result into the training target model.

13. A training device according to claim 11 or 12.

14. a data processing unit that estimates which leg of the living body the load distribution included in the training data corresponds to and includes the estimation result in the training data; The evaluation unit inputs the training data including the estimation result into the training target model.

13. A training device according to claim 11 or 12.

15. A program for causing a computer to function as the analysis device according to claim 1 or 6.

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