WO2019160135A1 - Simulation system, prosthetic leg blade, simulation method, and program - Google Patents

Abstract

This simulation system has: a measurement data acquisition unit for acquiring prototype measurement data which is measurement data of multiple different prototype prosthetic limbs or prototype prostheses and wearer measurement data which is data of a wearer wearing the multiple different prototype prosthetic limbs or prototype prostheses; a wearer motion data calculation unit for calculating wearer motion data by an inverse dynamic analysis on the basis of the prototype measurement data and the wearer measurement data; a motion response surface calculation unit for calculating a motion response surface by a response surface method on the basis of the wearer motion data; and a simulation execution unit which, assuming that the wearer is wearing a different prototype prosthetic limb or prototype prosthesis from the multiple different prototype prosthetic limbs or prototype prostheses, predicts motion data for the prototype prosthetic limb or prototype prosthesis and motion data for the wearer on the basis of the motion response surface.

Description

Simulation system, artificial leg blade, simulation method and program

The present invention relates to a simulation system, an artificial leg blade, a simulation method, and a program. In particular, it can be used suitably for the design of sports prostheses.
This application claims priority based on Japanese Patent Application No. 2018-027376 filed in Japan on February 19, 2018, the contents of which are incorporated herein by reference.

Conventionally, regarding golf equipment fitting technology, a golf equipment fitting system capable of calculating an accurate calculation result and outputting a simulation result in a short time even when specifications that greatly affect the swing time are changed, and golf A tool fitting program is known (see, for example, Patent Document 1). In the technique described in Patent Document 1, the acceleration, angular velocity, and the like at the time of trial hit are detected by a sensor attached to the golf club to be trial hit. Further, in the technique described in Patent Document 1, based on the acceleration, angular velocity, and the like detected at the time of a trial hit, data obtained when a golf club that is not actually hit a test is temporarily predicted is predicted by simulation. Fitting is performed.

International Publication No. 2014/132858

Consider that the simulation applied to the golf club described above is applied to a prosthetic limb (that is, a prosthetic leg or a prosthetic hand), an orthosis (that is, a device to be worn on a trunk / limb having a function disorder) or a part thereof.
When a golf club is fitted, there is almost no possibility that a user who uses the golf club will break down regardless of which golf club is selected. Therefore, the user's motion data is not detected when the golf club is tried. That is, the simulation reflecting the user operation data is not performed.
On the other hand, when fitting a prosthetic limb or orthosis or its parts, if a prosthetic limb or orthosis or its part that is not suitable for the wearer is selected, the wearer who wears the prosthetic limb or orthosis or the part is injured. There is a risk of it. For this reason, when fitting a prosthetic limb / orthoresistive or part thereof, in addition to considering the movement (behavior) of the prosthetic limb / orthoric or its part, consider the movement of the wearer of the prosthetic limb / orthoric or its part. There is a need.
In addition, the operation (behavior) of the prosthetic limb / orthorax or its components is more complicated than the operation (behavior) of the golf club. For this reason, when performing simulation of a prosthetic limb or orthosis or its parts, it is necessary to perform more complicated calculation than the simulation described in Patent Document 1.

In view of such circumstances, an object of the present invention is to provide a simulation system, a prosthetic leg blade, a simulation method, and a program capable of predicting a prosthetic limb or a brace suitable for a wearer.

The present invention has the following aspects.
<1> Fitting product measurement data that is measurement data of a plurality of different fitting prostheses or fitting devices, and wearer measurement data that are measurement data of the wearers of the plurality of fitting artificial limbs or the fitting devices Wearer operation data, which is data indicating the force generated in the wearer, is calculated by inverse dynamics analysis based on the measurement data acquisition unit, the wearer measurement data, and the wearer measurement data A wearer motion data calculation unit, a motion response curved surface calculation unit that calculates a motion response curved surface by a response surface method based on the wearer motion data, and a plurality of the fittings by the wearer based on the motion response curved surface Simulation that predicts motion data of the prosthetic limb or the brace and motion data of the wearer when it is assumed that a prosthetic limb or brace that is different from the prosthetic limb or the trial wearing brace is worn. And a tio down execution unit, the simulation system.
<2> A time response curved surface calculation unit that calculates a time response curved surface by a response surface method based on the wearer motion data, wherein the simulation execution unit is further based on the time response curved surface, The simulation system according to <1>, wherein the operation data of the appliance and the operation data of the wearer are predicted.
<3> The simulation system according to <1> or <2>, wherein the simulation execution unit predicts the motion data of the artificial limb or the orthosis and the motion data of the wearer by a basis vector method.
<4> From <1> to <3> having a selection unit that selects a prosthetic limb or a brace based on the motion data of the prosthetic limb or the brace predicted by the simulation execution unit and the motion data of the wearer. The simulation system according to any one of the above.
<5> The simulation system according to any one of <1> to <4>, wherein the motion response curved surface calculation unit calculates a motion response curved surface that is a function of quadratic or higher.
<6> Each of the plurality of trial prosthetic limbs is a trial sports prosthetic leg blade, and the simulation execution unit includes a sports prosthetic leg blade height dimension, a sports prosthetic leg rear protrusion amount, and a sports prosthetic leg blade toe length dimension. The simulation system according to any one of <1> to <5>, wherein at least one is predicted.
<7> Each of the plurality of trial prosthetic limbs is a trial sports prosthetic leg blade, and the trial sports prosthetic leg blade has a spec selected from a plurality of specs based on an L9 orthogonal table of an experimental design method. The simulation system according to any one of <1> to <6>.
<8> The simulation system according to <7>, wherein the specification includes blade rigidity, a height dimension, a backward protrusion amount, and a toe length dimension.
<9> The simulation system according to any one of <1> to <8>, wherein the simulation execution unit maximizes a predetermined objective function by repeatedly executing the simulation.
<10> The simulation system according to <9>, wherein the objective function includes at least a function related to a physical load on the wearer.
<11><1> to <10> are selected based on the motion data of the prosthetic limb or the orthosis predicted by the simulation system according to any one of <10> and the motion data of the wearer. It is a prosthetic leg blade, and the straight portion in the thickness direction of the body side end portion that is attached to the body (wearer) side through the connecting part is defined as a straight portion A, and the ground side end portion on the side installed on the ground is the ground. A side end portion B, a portion that intersects with the leaf spring main body when a perpendicular is drawn from the straight portion A in the thickness direction, is a crossing portion C, and the rearmost side when a prosthetic leg blade including a leaf spring for a prosthetic leg is attached to the body The portion located at the back surface D is the back portion D, the distance of the vertical component between the straight portion A and the ground side end B is the straight portion vertical component distance H, and the ground side end B and the intersection C The horizontal component distance Lt between the intersection horizontal component distance Lt The horizontal component distance between the intersecting portion C and the back surface portion D is set as the back surface horizontal component distance Lh, the ground side end portion B is fixed, and along the direction from the straight line portion A toward the back surface portion D. When a load of 1000 N is applied to the position P of 50 mm in the vertical direction, the linear component vertical component distance H before the prosthetic leg spring is deflected and the prosthetic leg spring after being flexed When the difference in distance from the straight line vertical component distance Ha is the amount of deflection S,
235mm ≦ H ≦ 285mm
−10 mm ≦ Lt ≦ 30 mm
220mm ≦ Lh ≦ 280mm
35mm ≦ S ≦ 45mm
It is a prosthetic leg blade.
<12> Fitting product measurement data that is measurement data of a plurality of different fitting prostheses or fitting devices, and wearer measurement data that are measurement data of the wearers of the plurality of fitting artificial limbs or fitting devices Calculating wearer motion data, which is data indicating the force generated in the wearer, based on the wearer measurement data and the wearer measurement data, by inverse dynamics analysis, and A step of calculating a motion response curved surface by a response surface method based on the wearer motion data, and a prosthetic limb that is different from the plurality of trial wear prostheses or the trial wear devices based on the motion response curved surface, or When assuming that a brace has been worn, the computer executes a step of predicting the motion data of the prosthetic limb or the brace and the motion data of the wearer. Simulation method.
<13> On the computer, fitting measurement data that is measurement data of a plurality of different fitting prostheses or fitting devices, and wearer measurement data that is measurement data of the wearers of the plurality of fitting artificial limbs or fitting devices And calculating wearer motion data, which is data indicating the force generated in the wearer, based on the wearer measurement data and the wearer measurement data by inverse dynamics analysis. And a step of calculating a motion response curved surface by a response surface method based on the wearer motion data, and a plurality of the trial prosthetic limbs or the trial wearer based on the motion response curved surface. When it is assumed that a different prosthesis or orthosis is worn, the operation data of the prosthesis or the orthosis and the operation data of the wearer are predicted. Program.

According to the present invention, it is possible to provide a simulation system, a simulation method, and a program capable of predicting a prosthetic limb or a brace suitable for a wearer.

It is a block diagram which shows an example of the structure of the design system of the prosthesis, the orthosis, or its components of 1st Embodiment. It is a figure for demonstrating the artificial leg containing an artificial leg blade. It is a figure which shows an example of 9 types of trial-wearing artificial leg blades which a wearer can actually try on among 81 types of artificial-leg blades which a design system can design. It is a block diagram which shows an example of a structure of the simulation system of the modification of 1st Embodiment. It is a flowchart for demonstrating the design method of the prosthetic leg blade most suitable for the wearer using the design system of the prosthesis, the orthosis, or its components of 1st Embodiment. It is a figure which shows an example of a trial wear artificial leg blade and a wearer's image. It is a figure for demonstrating an example of the process performed by the design system of the prosthesis, the orthosis, or its components of 1st Embodiment. It is a figure which shows an example of the image for motion analysis obtained by converting an image in order to acquire try-on goods motion data and in order to acquire wearer motion data. It is a figure for demonstrating an example of the process performed by the design system of the prosthesis, the orthosis, or its components of 1st Embodiment. It is a figure which shows an example of the leaf | plate spring for artificial legs. It is the elements on larger scale of the leaf | plate spring for artificial legs. It is a figure which shows the amount of bending of the leaf | plate spring for artificial legs.

Hereinafter, a simulation system, a simulation method, and a program according to an embodiment of the present invention will be described with reference to the accompanying drawings.

<First Embodiment>
FIG. 1 is a block diagram illustrating an example of the configuration of a design system 1 for a prosthetic limb / orthorosis or a part thereof according to the first embodiment.
In the example shown in FIG. 1, the design system 1 includes a prosthetic leg (prosthetic leg or prosthetic hand), an orthosis (equipment to be attached to a trunk / limb with impaired function) or a part thereof, for example, a prosthetic leg blade constituting a part of the prosthetic leg. (Details are sports prosthetic blades). The design system 1 includes a motion data acquisition unit 11, a motion response curved surface calculation unit 13A, a time response curved surface calculation unit 13B, and a simulation execution unit 14. These components are realized by, for example, a hardware processor such as a CPU (Central Processing Unit) executing a program (software). Some or all of these components include LSI (Large Scale Integration), ASIC (Application Specific Integrated Circuit), FPGA (Field-Programmable Gate Array), and GPU (Graphics Prosthetic Hardware circuit unit). (including circuit), or may be realized by cooperation of software and hardware. The program may be stored in advance in a storage device such as an HDD (Hard Disk Drive) or a flash memory, or may be stored in a removable storage medium such as a DVD or CD-ROM, and the storage medium is stored in the drive device. It may be installed by being attached.

The motion data acquisition unit 11 acquires image data of a prosthetic leg (fitting product) and a prosthetic leg wearer photographed by the photographing unit A (for example, a camera).
That is, in the example illustrated in FIG. 1, the photographing unit A does not photograph an image including only the artificial leg, but captures an image including both the artificial leg and the wearer. Specifically, in the example illustrated in FIG. 1, the imaging unit A captures images of the prosthetic leg and the wearer while the wearer wearing the prosthetic leg is running as if during a short-distance running competition. During photographing by the photographing unit A, the prosthetic leg has a behavior associated with running of the wearer.
The motion data acquisition unit 11 may be other than a camera, and may acquire motion data instead of image data by using one or more sensors.
The motion data acquisition unit 11 includes a try-on product motion data acquisition unit 11A and a wearer motion data acquisition unit 11B. The try-on product operation data obtaining unit 11A obtains data (try-on product operation data) indicating the behavior (motion) occurring in the artificial leg, obtained from the image data photographed by the photographing unit A. The wearer motion data acquisition unit 11B acquires data (wearer motion data) indicating the motion of the running wearer obtained from the image data captured by the image capturing unit A.
In another example, the imaging unit A captures images of the prosthetic leg and the wearer in a state where the wearer wearing the prosthetic leg is playing a sport other than short-distance running, such as long jump, or the like. May be.

In the example shown in FIG. 1, the motion response curved surface calculation unit 13A is based on the try-on product operation data acquired by the try-on product operation data acquisition unit 11A and the wearer operation data acquired by the wearer operation data acquisition unit 11B. Then, by using the response surface method, a motion response surface obtained by extracting a method of changing the unique motion of the fitting person and a wrinkle and converting it into a quadratic function is calculated.
In the example shown in FIG. 1, since the motion response curved surface calculation unit 13A calculates a motion response curved surface converted into a quadratic function, the motion response curved surface calculation unit 13A calculates a motion response curved surface converted into a linear function. The design system 1 can predict a correct operation.
In another example, the motion response curved surface calculation unit 13A may calculate a motion response curved surface that is a function of cubic or higher.
The more complicated the operation, the higher the order of the response surface. Since the movement of the grip of the golf swing is relatively smooth, it can be handled by the primary. When the impact of running and landing enters, secondary or higher is preferable. Moreover, it is preferable that it is 4th order or less from the point which does not raise | generate overfitting.

In the example illustrated in FIG. 1, the time response curved surface calculation unit 13B is based on the try-on product operation data acquired by the try-on product operation data acquisition unit 11A and the wearer operation data acquired by the wearer operation data acquisition unit 11B. The time response surface is calculated by the response surface method.
Based on the motion response curved surface calculated by the motion response curved surface calculation unit 13A and the time response curved surface calculated by the time response curved surface calculation unit 13B, the simulation execution unit 14 does not actually try on an artificial leg blade (virtual artificial leg blade ( Specifically, the motion data of the virtual prosthetic foot blade and the motion data of the wearer when the prosthetic foot including the virtual sports prosthetic foot blade)) is temporarily tried on by the wearer are predicted. The simulation execution unit 14 includes a body motion analysis unit 14A, an FEM analysis unit 14B, a multi-purpose optimization unit 14C, and an optimum product design unit 14D.

The body motion analysis unit 14A is calculated by the motion response curved surface calculated by the motion response curved surface calculation unit 13A and the time response curved surface calculation unit 13B based on the wearer motion data acquired by the wearer motion data acquisition unit 11B. By using the time response curved surface, analysis of the wearer's movement (for example, analysis of how much load is applied to each part of the wearer's body) is performed.
The FEM (Finite Element Method) analysis unit 14B includes an operation response curved surface calculated by the motion response curved surface calculation unit 13A and a time response curved surface calculation unit based on the fitting product motion data acquired by the fitting product motion data acquisition unit 11A. By using the time response curved surface calculated by 13B, an FEM analysis of the operation of the try-on product (for example, analysis of how the prosthetic leg functions in order for the wearer to run fast) is performed. Note that the FEM analysis unit 14B is not necessarily performed by the FEM, and various discrete analysis methods such as a particle method and a rigid body link model can be used.
The multi-objective optimization unit 14C maximizes a predetermined objective function (for example, a function that prevents an excessive load (body load) from being applied to the wearer's body, a function that allows the wearer to run faster), and the like. Analyze.
The optimum product design unit 14D designs a prosthetic leg that is optimal for the wearer (specifically, a sports prosthetic leg blade) based on the analysis result in the multi-purpose optimization unit 14C.

FIG. 2 is a view for explaining an artificial leg B including an artificial leg blade (specifically, a sports artificial leg blade) B4 designed by the design system 1 shown in FIG.
In the example shown in FIG. 2, the prosthetic leg B includes a socket B1, a joint B2, a connecting part B3 that connects the socket B1 and the joint B2, a prosthetic blade B4, and a connecting part that connects the joint B2 and the prosthetic leg blade B4. B5.
The specification items of the artificial leg blade B4 include, for example, an artificial leg blade rigidity (specifically, a sports artificial leg blade rigidity) that is the rigidity of the artificial leg blade B4 and an artificial leg blade height dimension that is the height dimension of the artificial leg blade B4 ( Specifically, the sports prosthetic blade height dimension) L2, and the prosthetic blade rearward projecting amount (specifically, the sports prosthetic blade rearward projecting amount) L3, which is the rearward projecting amount of the prosthetic blade B4 with respect to the central axis B5X of the connecting portion B5, The prosthetic blade toe length dimension (specifically, the sports prosthetic leg toe length dimension) L4, which is the forward protrusion amount of the toe portion of the artificial leg blade B4 with respect to the central axis B5X of the connecting portion B5, is included.
In another example, the prosthetic blade B4 may have a specification item different from the specification item described above.

In the example of the prosthetic limb / orthoresiste or part design system 1 according to the first embodiment, based on the experimental design method, there are three probable prosthetic blade stiffness candidates “high”, “standard”, and “low”. Three levels of “high”, “standard”, and “low” are provided as candidates of the prosthetic leg blade height dimension L2 that is optimal for the wearer, and the prosthetic leg rearward protrusion amount L3 that is optimal for the wearer is set. Three levels of “large”, “standard”, and “small” are provided as candidates, and three types of “long”, “standard”, and “short” are optimal candidates for the prosthetic blade toe length dimension L4 for the wearer. A level is set.
That is, in this example, the design system 1 includes four items (“prosthetic blade rigidity”, “prosthetic blade height dimension L2”, “prosthetic blade rearward protrusion amount L3”, and “prosthetic leg blade toe length dimension L4”). The prosthetic blade B4 that is optimal for the wearer is designed (selected) from among the 81 (= 3 ⁴ ) different types of prosthetic blade B4 obtained by having the above three levels.
In another example of the prosthetic limb / equipment or its component design system 1 according to the first embodiment, a number other than 3 may be provided in the specification item of the prosthetic leg blade B4.

FIG. 3 shows nine types of try-on prosthetic blades (in detail, which can be actually tried on by the wearer) among 81 types of prosthetic blades (specifically, sports prosthetic blades) that can be designed by the design system 1 shown in FIG. FIG. 6 is a diagram showing an example of a trial wear sports prosthetic leg blade) # 1 to # 9.
In the example shown in FIG. 3, the wearer of the prosthetic blade does not try on all of the 81 kinds of prosthetic blades having different specifications in order to select an optimum prosthetic blade for the wearer, but 81 kinds of different ones. Test wear artificial leg blades # 1 to # 9 having nine different specs selected from the prosthetic leg blades having specs based on the L9 type orthogonal table in the experimental design method are tried on.

In the example shown in FIG. 3, the level of the item “Prosthetic leg blade rigidity” (Var. 1) is set to “1” (“High”) in the trial-wearing artificial leg blade # 1, and the item “Prosthetic leg blade height dimension L2” is set. The level of (Var.2) is set to “1” (“high”), the level of the item “prosthetic blade rearward protrusion L3” (Var.3) is set to “1” (“large”), and the item The level of “prosthetic blade toe length dimension L4” (Var. 4) is set to “1” (“long”).
In the trial-wearing artificial leg blade # 2, the level of the item “Prosthetic leg blade rigidity” (Var. 1) is set to “1” (“High”), and the level of the item “Prosthetic leg blade height dimension L2” (Var. 2) is set. Is set to “2” (“standard”), the level of the item “prosthetic leg blade rearward protrusion L3” (Var.3) is set to “2” (“standard”), and the item “prosthetic leg blade toe length dimension” is set. The level of “L4” (Var. 4) is set to “2” (“standard”).
In the trial wear artificial leg blade # 3, the level of the item “Prosthetic leg blade rigidity” (Var. 1) is set to “1” (“High”), and the level of the item “Prosthetic leg blade height dimension L2” (Var. 2) is set. Is set to “3” (“low”), the level of the item “prosthetic leg blade protrusion L3” (Var. 3) is set to “3” (“small”), and the item “prosthetic leg toe length dimension” is set. The level of “L4” (Var. 4) is set to “3” (“short”).

In the example shown in FIG. 3, the level of the item “Prosthetic leg blade stiffness” (Var. 1) is set to “2” (“Standard”) in the trial wear artificial leg blade # 4, and the item “Prosthetic leg blade height dimension” is set. The level of “L2” (Var. 2) is set to “1” (“High”), and the level of the item “Prosthetic leg rear protrusion L3” (Var. 3) is set to “2” (“Standard”). The level of the item “Prosthetic leg toe length dimension L4” (Var. 4) is set to “3” (“short”).
In the trial-wearing artificial leg blade # 5, the level of the item “Prosthetic leg blade rigidity” (Var. 1) is set to “2” (“Standard”), and the level of the item “Prosthetic leg blade height dimension L2” (Var. 2) is set. Is set to “2” (“standard”), the level of the item “prosthetic leg blade protrusion L3” (Var. 3) is set to “3” (“small”), and the item “prosthetic blade toe length dimension” is set. The level of “L4” (Var. 4) is set to “1” (“long”).
In the trial prosthetic blade # 6, the level of the item “Prosthetic leg blade rigidity” (Var. 1) is set to “2” (“Standard”), and the level of the item “Prosthetic leg blade height dimension L2” (Var. 2) is set. Is set to “3” (“low”), the level of the item “prosthetic leg blade rearward protrusion L3” (Var.3) is set to “1” (“large”), and the item “prosthetic leg blade toe length dimension” is set. The level of “L4” (Var. 4) is set to “2” (“standard”).

In the example shown in FIG. 3, the level of the item “Prosthetic leg blade stiffness” (Var. 1) is set to “3” (“Low”) in the trial-wearing artificial leg blade # 7, and the item “Prosthetic leg blade height dimension” is set. The level of “L2” (Var. 2) is set to “1” (“high”), and the level of the item “prosthetic blade rearward protrusion amount L3” (Var. 3) is set to “3” (“small”). The level of the item “Prosthetic leg toe length dimension L4” (Var. 4) is set to “2” (“Standard”).
In the trial-wearing artificial leg blade # 8, the level of the item “Prosthetic leg blade rigidity” (Var.1) is set to “3” (“Low”), and the level of the item “Prosthetic leg blade height dimension L2” (Var.2) is set. Is set to “2” (“standard”), the level of the item “prosthetic leg blade protrusion L3” (Var.3) is set to “1” (“large”), and the item “prosthetic leg toe length dimension” is set. The level of “L4” (Var. 4) is set to “3” (“short”).
In the trial-wearing artificial leg blade # 9, the level of the item “Prosthetic leg blade rigidity” (Var.1) is set to “3” (“Low”), and the level of the item “Prosthetic leg blade height dimension L2” (Var.2) is set. Is set to “3” (“low”), the level of the item “prosthetic leg blade rearward protrusion L3” (Var.3) is set to “2” (“standard”), and the item “prosthetic blade toe length dimension” is set. The level of “L4” (Var. 4) is set to “1” (“long”).

Next, a simulation system 1a according to a modification of the first embodiment will be described.
FIG. 4 is a block diagram illustrating an example of a configuration of a simulation system 1a according to a modification of the first embodiment.
The simulation system 1a supports the design of a prosthetic limb or an orthosis. A prosthetic limb is an artificial limb that is worn and used to restore the form or function of the original limb when a part of the limb is lost by cutting, and includes a prosthetic leg and a prosthetic hand. An orthosis is a device that is worn on the trunk or extremities that have impaired function.
In the modified example of the present embodiment, the description continues when the simulation system 1a designs a prosthetic leg blade (specifically, a sports prosthetic leg blade) that constitutes a part of the prosthetic leg as an example of a prosthetic limb or an orthosis.
The simulation system 1a includes a measurement data acquisition unit 11a, a wearer motion data calculation unit 12, a motion response curved surface calculation unit 13Aa, a time response curved surface calculation unit 13Ba, a simulation execution unit 14, and a selection unit 15. Yes. These components are realized, for example, when a hardware processor such as a CPU executes a program (software). Some or all of these components may be realized by hardware (circuit unit; including circuitry) such as LSI, ASIC, FPGA, GPU, etc., or by cooperation of software and hardware. Also good. The program may be stored in advance in a storage device such as an HDD or a flash memory, or may be stored in a removable storage medium such as a DVD or CD-ROM, and the storage medium is attached to the drive device. May be installed.

The measurement data acquisition unit 11a acquires try-on product measurement data that is measurement data of a plurality of different trial-wearing prosthetic legs and wearer measurement data that is measurement data of the wearers of the plurality of try-on prosthetic legs. Here, the difference may be that the specifications of the artificial leg are different. For example, the measurement data acquisition unit 11a acquires image data of a try-on prosthetic leg photographed by the photographing unit A (for example, a camera) as the fitting product measurement data. Moreover, the measurement data acquisition part 11a acquires the image data of the wearer of the trial wear artificial leg image | photographed by the imaging | photography part A as wearer measurement data.
The imaging unit A does not shoot only the artificial leg, but shoots both the artificial leg and the wearer. Specifically, the photographing unit A photographs the prosthetic leg and the wearer while the wearer wearing the prosthetic leg is running like in a short-distance running competition. During photographing by the photographing unit A, the prosthetic leg has a behavior associated with running of the wearer.
The measurement data acquisition unit 11a may acquire measurement data obtained by using one or more sensors instead of image data. Here, as an example, the case where the measurement data acquisition unit 11a acquires image data will be described.

The measurement data acquisition unit 11a includes a try-on product measurement data acquisition unit 11Aa and a wearer measurement data acquisition unit 11Ba. The try-on product measurement data acquisition unit 11Aa acquires data indicating the behavior occurring on the artificial leg (hereinafter referred to as “try-on product measurement data”) from the image data output by the imaging unit A. The try-on product measurement data acquisition unit 11 </ b> Aa outputs the acquired try-on product measurement data to the wearer operation data calculation unit 12. The wearer measurement data acquisition unit 11Ba acquires data (hereinafter referred to as “wearer measurement data”) obtained by measuring a running wearer from the image data output by the imaging unit A. The wearer measurement data acquisition unit 11Ba outputs the acquired wearer measurement data to the wearer operation data calculation unit 12.
Here, as an example, a case where image data obtained by photographing the prosthetic leg and the wearer while the wearer wearing the prosthetic leg is running will be described. In another example, the photographing unit A is obtained by photographing a prosthetic leg and a wearer in a state where a wearer wearing a prosthetic leg is playing a sport other than short-distance running such as a long jump or the like. The obtained image data may be used.

The wearer motion data calculation unit 12 acquires the try-on product measurement data output from the try-on product measurement data acquisition unit 11Aa and the wearer measurement data output from the wearer measurement data acquisition unit 11Ba. The wearer operation data calculation unit 12 is data indicating the force exerted by the wearer's whole body based on the fitting product measurement data and the wearer measurement data, in other words, data indicating the force generated in the wearer. Person motion data is calculated. The wearer movement data calculation unit 12 outputs the calculation result of the wearer movement data to the movement response curved surface calculation unit 13Aa and the time response curved surface calculation unit 13Ba. The wearer motion data calculation unit 12 calculates wearer motion data by simulating the wearer's motion. An example of the force exerted by the wearer is joint torque. Specifically, the wearer motion data calculation unit 12 calculates the joint torque by inverse dynamics analysis. Here, the inverse dynamic analysis is a method for calculating the joint torque of each joint from the position coordinates of each joint. In inverse dynamic analysis, each joint is regarded as one rigid body element, and the joint torque is calculated by formulating the coupling relationship. For example, an example of one rigid element is the lower limb.
If the mass of each rigid body is M, the moment of inertia is J, the Jacobian matrix is Cr, Cθ, the position vector r of each rigid body center of gravity, the angular velocity is ω, the external force is f, the external moment is n, and the Lagrange multiplier is λ. Equation (1) holds. In the formula (1), γ is an arbitrary constant. All symbols are in vector notation.

In equation (1), the Lagrange multiplier λ represents the joint torque.
The motion response curved surface calculation unit 13Aa acquires the wearer motion data output by the wearer motion data calculation unit 12. The motion response curved surface calculation unit 13Aa calculates a motion response curved surface by a response surface method based on the output wearer motion data. The motion response surface is a quadratic function obtained by extracting how to change the motion unique to the try-on and wrinkles.
When the motion response curved surface calculation unit 13Aa calculates a motion response curved surface converted into a quadratic function, the simulation system 1a can predict a complicated motion rather than calculating a motion response curved surface converted into a linear function.
In this embodiment, the case where the motion response curved surface calculation unit 13Aa calculates a motion response curved surface converted to a quadratic function will be described. In another example, the motion response curved surface calculation unit 13Aa functions as a function of cubic or higher. The converted motion response curved surface may be calculated.
It is preferable that the degree of the motion response curved surface calculated by the motion response curved surface calculation unit 13Aa is higher as the wearer's motion becomes more complicated. For example, since the movement of the grip of a golf swing is relatively smooth, it can be handled by a motion response curved surface converted into a linear function. However, in the case of an operation in which a landing impact such as running occurs, it is preferable that the order of the motion response curved surface is quadratic or higher. Further, from the viewpoint of making overfitting difficult to occur, the order of the motion response curved surface is preferably 4th order or less.

The time response curved surface calculation unit 13Ba acquires the wearer motion data output by the wearer motion data calculation unit 12. The time response curved surface calculation unit 13Ba calculates a time response curved surface by a response surface method based on the acquired wearer motion data.
The simulation execution unit 14a is a prosthetic leg that is different from the prosthetic leg imaged by the imaging unit A based on the motion response curved surface calculated by the motion response curved surface calculation unit 13Aa and the time response curved surface calculated by the time response curved surface calculation unit 13Ba. When it is assumed that the user wears, the motion data of the artificial leg assumed to be worn and the motion data of the wearer are predicted. In other words, the simulation execution unit 14a includes the operation data of the virtual prosthetic leg blade when the artificial leg including the artificial leg blade (virtual prosthetic leg blade (specifically, the virtual sports prosthetic leg blade)) that is not actually tried on is temporarily tried on by the wearer. Predict the wearer's motion data.
The simulation execution unit 14a includes a body motion analysis unit 14Aa, an FEM analysis unit 14Ba, a multipurpose optimization unit 14Ca, and an optimum product design unit 14Da.

The body motion analysis unit 14Aa acquires the motion response curved surface output by the motion response curved surface calculation unit 13Aa and the time response curved surface output by the time response curved surface calculation unit 13Ba. The body motion analysis unit 14Aa analyzes the wearer's motion based on the acquired motion response curved surface and the time response curved surface. An example of analysis of a wearer's movement is an analysis of how much load is applied to various parts of the wearer's body. The body motion analysis unit 14Aa outputs the analysis result of the wearer's motion to the multipurpose optimization unit 14Ca.
The FEM (Finite Element Method) analysis unit 14Ba acquires the motion response surface output by the motion response surface calculation unit 13Aa and the time response surface output by the time response surface calculation unit 13Ba. The FEM analysis unit 14Ba performs FEM analysis of the operation of the try-on product based on the acquired motion response curved surface and the time response curved surface. An example of FEM analysis is an analysis of how the prosthesis is functioning for the wearer to run faster. The FEM analysis unit 14Ba outputs the result of the FEM analysis to the multipurpose optimization unit 14Ca.

The multi-objective optimization unit 14Ca acquires the analysis result of the wearer's motion output from the body motion analysis unit 14Aa and the FEM analysis result output from the FEM analysis unit 14Ba. The multi-objective optimization unit 14Ca performs an analysis for maximizing (optimizing) a predetermined objective function based on the acquired analysis result of the wearer's movement and the FEM analysis result. An example of the objective function is a function that prevents an excessive load (body load) from being applied to the wearer's body, a function that allows the wearer to run faster, and the like. The multi-objective optimization unit 14Ca outputs an analysis result for maximizing (optimizing) a predetermined objective function to the optimum product design unit 14Da.
The optimum product design unit 14Da acquires an analysis result for maximizing (optimizing) the predetermined objective function output from the multi-objective optimization unit 14Ca. The optimum product design unit 14Da designs a prosthetic leg that is optimal for the wearer (specifically, a sports prosthetic leg blade) based on the obtained analysis result. The optimum product design unit 14Da outputs the result of the design of the prosthetic leg optimum for the wearer to the selection unit 15. Specifically, the optimal product design unit 14Da includes the “prosthetic leg blade rigidity”, the “prosthetic leg blade height dimension L2”, the “prosthetic leg rear protrusion amount L3”, and the “prosthetic leg blade” included in the four items described above. Information indicating each value (value range) with “toe length dimension L4” is output to selection unit 15.

The selection unit 15 acquires the result of the design of the prosthesis most suitable for the wearer output by the optimal product design unit 14Da. The selection unit 15 selects an optimal prosthesis for the wearer based on the acquired result of the design of the optimal prosthesis for the wearer. Specifically, the selection unit 15 includes the “prosthetic leg blade rigidity”, the “prosthetic leg blade height dimension L2”, the “prosthetic leg rear protrusion amount L3”, and the “prosthetic leg blade toe length” output from the optimum product design unit 14Da. Information indicating each value (range of values) of the length dimension L4, the acquired "prosthetic leg blade rigidity", "prosthetic leg blade height dimension L2", "prosthetic leg blade rearward protrusion amount L3", A prosthetic leg having a specification close to each value (value range) with the “prosthetic blade toe length dimension L4” is selected from prosthetic legs manufactured in advance. The selection unit 15 outputs information indicating the selected artificial leg.

In the modified example of the above-described embodiment, the simulation system 1a has been described as including the body motion analysis unit 14Aa and the time response curved surface calculation unit 13Ba, but is not limited to this example. For example, the simulation system 1a may not include the time response curved surface calculation unit 13Ba. In this case, the body motion analysis unit 14Aa analyzes the wearer's motion based on the acquired motion response curved surface. Further, the FEM analysis unit 14Ba performs FEM analysis of the operation of the try-on product based on the acquired operation response curved surface. Since the simulation system 1a includes the time response curved surface calculation unit 13Ba, it is possible to improve the accuracy of the analysis of the wearer's motion performed by the body motion analysis unit 14Aa, and the FEM of the try-on product performed by the FEM analysis unit 14Ba. Analysis accuracy can be improved.
In the modification of the embodiment described above, the simulation system 1a has been described as including the FEM analysis unit 14Ba. However, the present invention is not limited to this example. For example, the simulation system 1a may not include the FEM analysis unit 14Ba. In this case, the multi-objective optimization unit 14Ca performs an analysis for maximizing (optimizing) a predetermined objective function based on the acquired analysis result of the wearer's movement. By providing the FEM analysis unit 14Ba in the simulation system 1a, it is possible to improve the accuracy of analysis for maximizing (optimizing) a predetermined objective function executed by the multi-objective optimization unit 14Ca.

The operation of the design system of this embodiment will be described.
FIG. 5 is a flowchart for explaining a design method of a prosthetic leg blade (specifically, a sports prosthetic leg blade) that is most suitable for a wearer using the prosthetic limb / orthoresiste or part design system 1 according to the first embodiment. Here, the operation of the design system 1 of the first embodiment will be mainly described, but the operation of the simulation system 1a of a modification of the present embodiment will also be described.
In step S10, a reference artificial leg blade (specifically, a reference sports artificial leg blade) is designed. Specifically, nine types of trial-wearing artificial leg blades (specifically, trial-sporting artificial leg blades) # 1 to # 9 shown in FIG. 3 are designed as reference artificial leg blades.
Step S10 can also be applied to the operation of the simulation system 1a.
Next, in step S20, the photographing unit A photographs nine types of trial wear artificial leg blades # 1 to # 9 and images of the wearer. Specifically, the photographing unit A is a trial prosthetic leg blade # 1 in a state where the wearer wearing each of the nine types of trial prosthetic leg blades # 1 to # 9 is running like a short distance running competition. Take pictures of # 9 and the wearer.

FIG. 6 is a diagram showing an example of the image of the trial-wearing prosthetic leg blade (specifically, a trial-sporting prosthetic leg blade) and the wearer C imaged in step S20 of FIG.
In the example shown in FIG. 6, the wearer C wears a prosthetic leg B configured in the same manner as the prosthetic leg B shown in FIG. 2, and the prosthetic leg B includes nine types of trial-wearing prosthetic leg blades # 1 to # 9. For example, a trial-wearing prosthetic leg blade # 1 is provided.
That is, in the example shown in FIG. 6, the photographing unit A wears a prosthetic leg B provided with (attached to) a trial prosthetic leg blade # 1 as a prosthetic leg blade (specifically, a sports prosthetic leg blade) B4. An image of the person C running at full power is taken.
Step S20 can also be applied to the operation of the simulation system 1a.

Returning to the description of FIG. 5, next, in step S <b> 30, the design system 1 designs an artificial leg blade (specifically, a sports artificial leg blade) B <b> 4 that is optimal for the wearer C.
Step S30 can also be applied to the operation of the simulation system 1a.
Hereinafter, with reference to FIG. 7, the detail of the process in which the design system 1 performed by step S30 performs design of the artificial leg blade B4 optimal for the wearer C is demonstrated. Here, the operation of the design system 1 of the first embodiment will be mainly described, but the operation of the simulation system 1a of a modification of the present embodiment will also be described.
FIG. 7 is a diagram for explaining an example of processing executed by the design system 1 for a prosthetic limb / equipment of the first embodiment or its parts in step S30 of FIG.
In step S31, the try-on product operation data acquiring unit 11A obtains data (try-on product operation data) indicating the behavior (operation) occurring in the prosthetic leg blade B4, which is obtained from the image data captured by the image capturing unit A in step S20 in FIG. ) To get.
Specifically, the fitting product motion data acquisition unit 11A is in a state where the wearer C wearing the artificial leg B provided with the trial wearing artificial leg blade (specifically, a trial sports artificial leg blade) # 1 is running at full power. From the image data, try-on product operation data indicating the behavior occurring in the try-on prosthetic leg blade # 1 is acquired. Similarly, the wearer C wearing the prosthetic leg B provided with the trial prosthetic leg blades (specifically, the trial sports prosthetic leg blades) # 2 to # 9 is running at full power in the try-on product operation data acquisition unit 11A. From the image data of the state, try-on product operation data indicating the behavior occurring in the try-on artificial leg blades # 2 to # 9 is acquired.

In the case of the simulation system 1a, in step S31, the try-on product measurement data obtaining unit 11Aa obtains try-on product measurement data from the image data output by the photographing unit A (image data photographed in step S20 in FIG. 5). .
Specifically, the fitting product measurement data acquisition unit 11Aa images a state in which the wearer C wearing the artificial leg B to which the trial-wearing artificial leg blade (specifically, a trial sports prosthetic blade) # 1 is attached is running. Try-on product measurement data is acquired from the image data obtained by this. Similarly, the fitting product measurement data acquisition unit 11Aa displays the state in which the wearer C wearing the artificial leg B to which the trial prosthetic leg blades (specifically, the trial sports prosthetic leg blades) # 2 to # 9 are attached is running. Try-on measurement data is acquired from image data obtained by imaging.

Next, in step S32, the wearer movement data acquisition unit 11B obtains data indicating the movement of the wearer C running at full power (the wearer movement) obtained from the image data captured by the photographing unit A in step S20 of FIG. Data).
Specifically, the wearer operation data acquisition unit 11B is a wearer who is running with full power from image data in a state where the wearer C wearing the artificial leg B with the trial-wearing artificial leg blade # 1 is running with full power. Wearer operation data indicating the operation of C is acquired. Similarly, the wearer motion data acquisition unit 11B is running at full power from the image data in a state where the wearer C wearing the artificial leg B equipped with the trial wear artificial leg blades # 2 to # 9 is running at full power. Wearer operation data indicating the operation of the wearer C is acquired.

In the case of the simulation system 1a, in step S32, the wearer measurement data acquisition unit 11Ba acquires wearer measurement data from the image data output by the imaging unit A (image data captured in step S20 of FIG. 5). .
Specifically, the wearer measurement data acquisition unit 11Ba obtains the wearer from the image data obtained by imaging the state in which the wearer C wearing the artificial leg B to which the trial wear artificial leg blade # 1 is attached is running. Get measurement data. Similarly, the wearer measurement data acquisition unit 11Ba obtains from the image data obtained by imaging the state in which the wearer C wearing the artificial leg B to which the trial wearing artificial leg blades # 2 to # 9 are attached is running. Get wearer measurement data.

Here, an example of a method for acquiring the try-on product operation data and the wearer operation data in the design system 1 will be described. The try-on product operation data and the wearer operation data are acquired by measuring the position (coordinates) of the reflective marker attached to the whole body of the wearer who is the subject of photographing.
・ Measurement method Operation data is acquired from the reflection marker attached to the whole body of the subject. This is done by reflecting the infrared rays emitted by the camera from the marker coated with the retroreflective material and capturing the three-dimensional spatial coordinates of each marker on the computer. In other words, the operation data such as the try-on product operation data and the wearer operation data are reflected light obtained by reflecting the infrared rays emitted from each of the plurality of imaging units A by the reflection marker attached to the wearer who is the subject of photographing. Is obtained by measuring the three-dimensional spatial coordinates of the reflective marker. From the three-dimensional spatial coordinates of each reflective marker acquired by two or more cameras, the translational and rotational speed, acceleration, and travel distance of the entire body (of the fitting person), each segment, and each joint, Calculate physical features such as angles. In addition, a plurality of force plates buried in the ground are used to measure ground reaction forces in the three directions of front and rear, left and right, and vertical, and moments around each axis. By applying inverse dynamics calculation to the above-described three-dimensional space coordinates and ground reaction force data, translational and rotational forces and moments in the arbitrarily defined whole body, each segment, and each joint are calculated. This measurement method can also be applied to the simulation system 1a.

FIG. 8 shows an operation obtained by converting the image shown in FIG. 6 in order to acquire the try-on product operation data in step S31 of FIG. 7 and to acquire the wearer operation data in step S32 of FIG. It is a figure which shows an example of the image for an analysis.
In the example shown in FIG. 8, the trial wearing artificial leg blade (specifically, a trial sports artificial leg blade) # 1 shown in FIG. 6 is expressed by 24 points. By creating and analyzing the motion analysis image as shown in FIG. 8 every unit time, it is possible to calculate the acceleration applied to the positions corresponding to 24 points. As a result, it is possible to calculate the behavior (operation) of the trial-wearing prosthetic leg blade # 1 during the full power travel of the wearer C wearing the prosthetic leg B provided with the trial-wearing prosthetic leg blade # 1.
In the example shown in FIG. 8, the wearer C is reproduced on the computer of the design system 1. Specifically, for example, the portion from the right hand to the fingertip of the wearer C in the example shown in FIG. 6 is represented by four points. By creating and analyzing a motion analysis image as shown in FIG. 8 every time unit time elapses, it is possible to calculate the acceleration applied to the portion from the right hand of the wearer C to the fingertip. Similarly, the acceleration concerning each site | part of the wearer's C whole body is computable. As a result, the body load applied to each part of the wearer C's whole body can be calculated while the wearer C who wears the artificial leg B provided with the trial wear artificial leg blade # 1 is running at full power.
Even when the simulation system 1a acquires the fitting product measurement data and the wearer measurement data, the motion analysis image can be used.

Here, we will explain the running data. The running data is a measurement result of motion data based on the three-dimensional spatial coordinates of the reflective marker, and corresponds to try-on product motion data and wearer motion data. The running data is expressed by equation (2).

The equation (2) will be described. The measured running data is represented by f ₁ to f _m (m is an integer of m> 1). m is the number of each of the try-on product operation data and the wearer operation data, and here corresponds to the number of blades. In the present embodiment, the measured running data is expressed by f ₁ to f ₉ . The time t is discretized into t = {t1,..., Tn}. In this embodiment, since the trial run was performed with nine blades, the running data is from f ₁ to f ₉ , but the value varies depending on the number of trial runs of the blades. Further, fj (ti) is running data of the wearer who wears the j-th artificial leg blade (specifically, a sports artificial leg blade). Specifically, fj (ti) indicates the position coordinates {rx, ry, rz} of each marker attached to the wearer's body.
When the four specifications (design variables) of each of the nine prosthetic blades are represented by {wi, xj, yj, zj} (j = 1... 9), the relationship of Expression (2) is obtained. Then, the equation (2) is solved for each ti.

Here, w, x, y, z representing the four specifications (design variables) are design variables, w is the first specification (sport prosthetic leg blade rigidity), and x is the second specification (sports). Prosthetic leg blade height dimension), y is a third spec (sport prosthetic leg blade rearward protrusion amount), and z is a fourth spec (sport prosthetic leg blade toe length dimension). In Formula (2), numbers 1 to n in w ₁ to w _n , x ₁ to x _n , y ₁ to y _n , and z ₁ to z _n correspond to the numbers of the blades.
By solving equation (2), equation (3) is obtained.

In Equation (3), a ₁ to a ₁₅ are coefficients of the response surface.
There is generally no exact solution in equation (2). Therefore, a generalized inverse matrix A ⁺ (also called a Moore-Penrose inverse matrix or a pseudo inverse matrix) is used to solve the equation (2). This is a technique for obtaining an approximate solution when there is no exact solution. That is, this is a technique for obtaining a solution that minimizes the error between the approximate solution and the exact solution. Since this is a general mathematical method, details are omitted. Here, in order to solve the equation (2), numerical calculation software “MATLAB (registered trademark)” manufactured by MathWorks was used.
The coefficients a ₁ to a ₁₅ are values corresponding to the skill of the test runner (the try-on person) and the habit of running. That is, by this process, even if w, x, y, and z are changed to specifications that are not actually measured, running data represented by the function f is obtained as shown in Expression (4). . In other words, the following is obtained as an approximate value of the body position coordinate {rx, ry, rz} data of each marker with respect to an arbitrary {w, x, y, z} by the equation (4). The running data for any blade that has not been measured is expressed as shown in equation (4). Equation (4) is a response surface.

The motion response phase and the time response phase can be calculated from Formula (2) to Formula (4).
The running data described above also corresponds to try-on product measurement data and wearer measurement data. Moreover, Formula (2) to Formula (4) is applicable also to the simulation system 1a.

Returning to the description of FIG. 7, in step S33, the motion response surface calculation unit 13A performs the response surface method based on the try-on product motion data acquired in step S31 and the wearer motion data acquired in step S32. Thus, an action response curved surface obtained by extracting the manner of changing the unique action of the fitting person C and the wrinkle and converting it into a quadratic function is calculated. Here, the description will be continued for the case where the motion response phase is calculated by the above-described equations (2) to (5).
Step S33 can also be applied when the motion response curved surface calculation unit 13Aa of the simulation system 1a calculates a motion response curved surface.

Next, in step S34, the time response curved surface calculation unit 13B calculates a time response curved surface by a response surface method based on the try-on product motion data acquired in step S31 and the wearer motion data acquired in step S32. . The time response curved surface can be calculated by equations (5) to (7). Equation (5) is running data.

In Equation (5), g ₁ to g _m (m is an integer satisfying m> 1) is measured running data. m is the number of each of the try-on product operation data and the wearer operation data, and here corresponds to the number of blades. In the present embodiment, the measured running data is expressed as g ₁ to g ₉ . The time t is discretized into t = {t1,..., Tn}. In this embodiment, since the trial run was performed with nine blades, the running data is from g ₁ to g ₉ , but the value varies depending on the number of trial runs of the blades. Further, gj (ti) is running data of the wearer who wears the jth artificial leg blade (specifically, a sports artificial leg blade). Specifically, gj (ti) indicates the position coordinates {rx, ry, rz} of each marker attached to the wearer's body.
When the four specifications (design variables) of each of the nine prosthetic blades are represented by {wi, xj, yj, zj} (j = 1... 9), the relationship of Expression (2) is obtained. Then, the equation (5) is solved for each ti.

Here, w, x, y, z representing the four specifications (design variables) are design variables, w is the first specification (sport prosthetic leg blade rigidity), and x is the second specification (sports). Prosthetic leg blade height dimension), y is a third spec (sport prosthetic leg blade rearward protrusion amount), and z is a fourth spec (sport prosthetic leg blade toe length dimension). In Equation (5), the numbers 1 to n in w ₁ to w _n , x ₁ to x _n , y ₁ to y _n , and z ₁ to z _n correspond to the numbers of the blades.
By solving equation (2), equation (6) is obtained.

In Equation (6), b ₁ to b ₁₅ are coefficients of the response surface.
There is generally no exact solution in equation (5). Therefore, a generalized inverse matrix B ⁺ (also called Moore-Penrose inverse matrix or pseudo inverse matrix) is used to solve the equation (5). This is a technique for obtaining an approximate solution when there is no exact solution. That is, it is a technique for obtaining a solution that minimizes the error between the approximate solution and the exact solution. Since this is a general mathematical method, details are omitted. Here, in order to solve the equation (5), numerical calculation software “MATLAB (registered trademark)” manufactured by MathWorks was used.

The coefficients b ₁ to b ₁₅ are values corresponding to the skill of the test runner (the try-on person) and the habit of running. That is, even if w, x, y, and z are changed to specifications that are not actually measured by this process, running data represented by the function g is obtained as shown in Expression (7). . In other words, the following is obtained as an approximate value of the body position coordinate {rx, ry, rz} data of each marker with respect to an arbitrary {w, x, y, z} by the equation (7). The running data for any blade that has not been measured is expressed as shown in equation (7). Equation (7) is a response surface.

The time response situation can be calculated by Equation (5) to Equation (7).
The running data described above also corresponds to try-on product measurement data and wearer measurement data. Moreover, Formula (5) to Formula (7) is applicable also to the simulation system 1a.
In Equation (5), g ₁ to g ₉ are the operating times of the prosthetic limbs provided with each trial wearing prosthetic leg blade (specifically, a trial sports prosthetic blade). The coefficients b ₁ to b ₁₅ obtained by the equation (6) are values corresponding to the wearer's operating time.
Next, g ₁ (w, x, y, z) is calculated based on Expression (7).
Step S34 can also be applied when the motion response curved surface calculation unit 13Aa of the simulation system 1a calculates a time response curved surface.

Next, in step S35, the simulation executing unit 14 uses a prosthetic blade (virtual prosthetic blade (not described in detail) that is not actually tried on the basis of the motion response curved surface calculated in step S33 and the time response curved surface calculated in step S34. Is a virtual sports prosthetic leg blade)) (that is, prosthetic leg blades other than the trial prosthetic leg blades # 1 to # 9 out of 81 different types of prosthetic leg blades (specifically, sports prosthetic leg blades)). The operation data of the virtual prosthetic leg blade and the operation data of the wearer when B is temporarily tried on by the wearer C are predicted. Specifically, the simulation execution unit 14 uses the basis vector method to generate virtual artificial leg blades having specifications different from the specifications of the trial-wearing artificial leg blades # 1 to # 9. Specifically, the simulation execution unit 14 performs the simulation while changing the shape of the prosthetic blade based on a certain pattern as needed.
In the example illustrated in FIG. 7, the simulation execution unit 14 uses a basis vector method, but in other examples, the simulation execution unit 14 may use a method other than the basis vector method.
The basis vector method is a method for obtaining an optimal shape by combining basic shape candidates (basis vectors) that can be considered by a designer with respect to an original shape. Each basis vector has a weighting coefficient, and the basis vector changes its shape by multiplying the weighting coefficient. In this embodiment, the sports prosthetic blade height dimension, the sports prosthetic leg rearward protrusion amount, and the sports prosthetic leg toe length dimension correspond to the basis vector, and the sports prosthetic leg blade height dimension and the sports prosthetic leg blade rearward protrusion amount. The simulation is executed while sequentially changing the toe length dimension of the sports prosthetic blade. The golf shaft is a simple rod and does not change its shape, but the prosthesis has various shape variations. By using the basis vector method, you can specify how to change each part. At this time, the simulation can be executed without cutting the mesh again by deforming each finite element used in the simulation by using the basis vector with the same weight.
Step S35 can also be applied when the simulation execution unit 14a of the simulation system 1a predicts the motion data of the virtual artificial leg blade and the motion data of the wearer.
After step S35, in the simulation system 1a, the simulation execution unit 14a outputs the prediction result of the motion data of the virtual artificial leg blade and the prediction result of the wearer's motion data to the selection unit 15. The selection unit 15 acquires the prediction result of the motion data of the virtual prosthetic leg blade output from the simulation execution unit 14a and the prediction result of the motion data of the wearer, and the prediction result of the acquired motion data of the virtual prosthetic leg blade and the wear The prosthetic blade is selected based on the prediction result of the person's motion data. For example, the selection unit 15 selects a prosthetic leg blade having specifications close to the four items (parameters) described above based on the prediction result of the virtual prosthetic leg movement data and the prediction result of the wearer movement data. For example, the selection unit 15 may select a prosthetic blade based on a statistic such as an average value of four items (parameters). By comprising in this way, an artificial leg blade can be selected objectively irrespective of subjectivity.

Details of step S35 will be described with reference to FIG.
FIG. 9 is a diagram for explaining an example of processing executed by the design system 1 for the prosthetic limbs / orthorosis or the component of the first embodiment in step S35 of FIG.
In step S35A, the body motion analysis unit 14A uses the motion response curved surface calculated in step S33 and the time response curved surface in step S34 based on the wearer motion data acquired in step S32. Analysis of the movement of the wearer C (for example, analysis of how much body load is applied to each part of the wearer's body) is performed.
Next, in step S35B, the FEM analysis unit 14B uses the motion response curved surface calculated in step S33 and the time response curved surface calculated in step S34 based on the try-on product motion data acquired in step S31. FEM analysis of the operation of a try-on product (prosthetic leg B including the above-mentioned 81 different types of prosthetic leg blades (specifically, sports prosthetic leg blades)) (e.g. Analyze how it works). This FEM analysis is, for example, an analysis by a dynamic finite element method that can be executed by using commercially available finite element method software.

Next, in step S35C, the multi-objective optimization unit 14C performs a predetermined objective function (for example, a function that prevents the wearer C from applying an excessive physical load, a function that allows the wearer C to run faster), and the like. Perform analysis to maximize. Specifically, the multi-objective optimization unit 14C maximizes the predetermined objective function described above by repeatedly executing simulation.
Next, in step S35D, the optimal product design unit 14D selects the optimal prosthetic blade for the wearer C from among the 81 types of prosthetic foot blades having the above-described different specifications based on the analysis result in the multipurpose optimization unit 14C. Design (select) B4. In other words, the prosthetic leg blade B4 that is most suitable for the wearer C may be a prosthetic leg blade B4 other than the above-described trial-wearing prosthetic leg blades (specifically, trial-sporting prosthetic leg blades) # 1 to # 9.
Steps S35A to S35D can be applied to processing performed by the simulation execution unit 14a of the simulation system 1a.

The result of having performed simulation using the simulation system 1a of the modification of this embodiment is demonstrated. A prosthetic blade was designed using the simulation system 1a.
FIG. 10 is a diagram illustrating an example of a plate spring for a prosthetic leg. FIG. 10 shows a leaf spring 100 for an artificial leg. FIG. 11 is a partially enlarged view of a leaf spring for a prosthetic leg. FIG. 11 shows an enlarged view of a plate spring for a prosthetic leg included in the range E shown in FIG. 10 and 11, the vertical downward direction when the prosthetic leaf spring 100 is used is the vertical direction, and the direction orthogonal to the vertical direction is the horizontal direction.
In the leaf spring 100 for a prosthetic leg, the straight portion in the thickness direction of the body side end attached to the body (wearer) side through the connecting component is defined as a straight portion A, and the ground side end on the side installed on the ground Is the ground side end B, and when the perpendicular line is dropped from the straight line portion A in the thickness direction, the portion that intersects the leaf spring main body is the intersection C, and when the artificial leg blade including the leaf spring 100 for the artificial leg is attached to the body. A portion located on the most back side is defined as a back portion D.
Further, in the leaf spring 100 for a prosthetic leg, the distance of the vertical component between the straight portion A and the ground side end B is defined as the straight portion vertical component distance H, and the horizontal between the ground side end B and the intersection C is set. The distance between the component distances is defined as the intersection horizontal component distance Lt, and the horizontal component distance between the intersection C and the back surface portion D is defined as the back surface horizontal component distance Lh.
A straight line portion in the thickness direction orthogonal to the straight line portion A is defined as a straight line portion T.
FIG. 12 is a diagram illustrating the amount of bending of the prosthetic leaf spring. FIG. 12 shows a case where the ground side end portion B is fixed and a load of 1000 N is applied to the position P of 50 mm in the vertical direction along the direction from the straight line portion A to the back surface portion D. When a load of 1000 N is applied to the position P in the vertical direction, the prosthetic leaf spring 100 is bent in the vertical direction. A difference in distance between the straight portion vertical component distance H before the prosthetic leg spring 100 is bent and the straight portion vertical component distance Ha after the prosthetic leg spring 100a is bent is defined as a deflection amount S.

When a user wearing a prosthetic leg blade including a prosthetic leg spring 100 runs, in order to maximize the speed, the prosthetic leg spring 100 satisfies the following (1) to (4). A favorable result was obtained.
(1) 235 mm ≦ H ≦ 285 mm
(2) −10 mm ≦ Lt ≦ 30 mm
(3) 220 mm ≦ Lh ≦ 280 mm
(4) 35mm ≦ S ≦ 45mm
In addition, when H is too small, it is felt hard for the wearer, and there is a possibility that the prosthetic leg side hurts. When H is too large, a sense of stability in the deflection direction cannot be obtained. Therefore, it is preferably 245 mm ≦ H ≦ 275 mm, more preferably 250 mm ≦ H ≦ 270 mm.
If Lt is too small, the risk of so-called “knee break” increases. Knee bending means that a moment acts in a direction in which the knee joint portion of the artificial leg bends. When a knee break occurs, the wearer cannot escape. When Lt is too large, it becomes difficult to take a forward leaning posture, and it becomes difficult to increase the running speed. In addition, there is a high possibility that the toe of the prosthetic blade touches the ground, and the risk of falling over and falling is increased. Therefore, it is preferably 0 mm ≦ Lt ≦ 20 mm, more preferably 5 mm ≦ Lt ≦ 15 mm.
In addition, when Lh is too small, the rebound direction of the blade approaches upward, and it is difficult to increase the running speed. When Lh is too large, the heel part hits the buttocks during running. Therefore, it is preferably 230 mm ≦ Lh ≦ 270 mm, more preferably 240 mm ≦ Lh ≦ 260 mm.
Moreover, when S is too small, it will be hard for a wearer and will become bad as a feeling of wear. If S is too large, the deflection return timing will be delayed, making it difficult to increase the running speed. Therefore, it is preferably 37 mm ≦ S ≦ 43 mm, more preferably 39 mm ≦ S ≦ 41 mm. However, since S depends on the muscle strength and weight of the wearer, it cannot be said unconditionally.
In addition, in order to maximize the stepping force of the user who wears the prosthetic blade blade including the prosthetic leaf spring 100, it is preferable that the prosthetic leaf spring 100 satisfies the following (1a) to (4a). was gotten.
(1a) 185 mm ≦ H ≦ 235 mm
(2a) −10 mm ≦ Lt ≦ 30 mm
(3a) 140 mm ≦ Lh ≦ 200 mm
(4a) 35 mm ≦ S ≦ 45 mm
Moreover, when the user who wears the prosthetic leg blade including the prosthetic leg spring 100 is a beginner, the prosthetic leg spring 100 preferably satisfies the following (1b) to (4b). Obtained. If the user wearing the prosthetic blade is a beginner, the knee bending moment is minimized. In general, knee bending is the greatest risk of falls for a prosthetic leg user. Therefore, a prosthetic leg with a minimized knee bending moment is suitable for a general prosthetic leg user in order to minimize the risk of falling. In this case, it is preferable that the rigidity of the leaf spring 100 for the artificial leg is preferably 55 to 75 mm, the height is 185 to 235 mm, the toe length is 50 to 90 mm, and the heel length is 220 to 280 mm. It was.
(1b) 185 mm ≦ H ≦ 235 mm
(2b) 50 mm ≦ Lt ≦ 90 mm
(3b) 220 mm ≦ Lh ≦ 280 mm
(4b) 55 mm ≦ S ≦ 75 mm

<Summary of First Embodiment>
In the design system 1 of the prosthetic limbs / equipment or parts thereof according to the first embodiment, as described above, a plurality of trial limbs / equipment or parts thereof at the time of wearing a plurality of trial limbs / equipment or parts thereof having different specifications are used. Acquired by the operation data acquisition unit 11 and the operation data acquisition unit 11 for acquiring the operation data of the try-on product which is operation data and the wearer operation data which is the operation data of the wearer of a plurality of trial wear artificial limbs and orthoses or parts thereof The motion response curved surface calculation unit 13A that calculates the motion response curved surface by the response surface method based on the fitted product motion data and the wearer motion data, and the try-on product motion data and the wearer motion acquired by the motion data acquisition unit 11 A time response surface calculator 13B that calculates a time response surface by a response surface method based on the data, an action response surface, and a time response surface Based on the above, when a virtual prosthesis, orthosis, or part that is not actually tried on is temporarily tried on by a wearer, a simulation is performed to predict the motion data of the virtual prosthesis, the brace, or the part, and the wearer's movement data. Part 14 is provided.
Therefore, according to the design system 1 of the prosthetic limb / equipment or a part thereof according to the first embodiment, a prosthetic limb that is not actually tried on as a prosthetic limb / equipment suitable for the wearer or a part thereof while ensuring the safety of the wearer.・ You can select the brace or its parts.

<Summary of Modifications of First Embodiment>
The simulation system 1a of the modification of 1st Embodiment is the measurement data of the fitting product measurement data which are the measurement data of a plurality of different trial-wearing prosthetics or the trial-wearing device, and the measurement data of the wearer of the plurality of trial-wearing prosthetics or the trial-wearing device. Based on the measurement data acquisition unit 11a that acquires certain wearer measurement data, the wearer measurement data, and the wearer measurement data, the wearer operation data that is data indicating the force generated in the wearer is reversed. Wearer motion data calculation unit 12 calculated by dynamic analysis, motion response curved surface based on the wearer motion data, motion response curved surface calculation unit 13Aa that calculates the response surface by response surface method, and wear based on motion response curved surface When it is assumed that the person wears a plurality of trial prosthetic limbs or a prosthetic limb or orthosis different from the trial orthosis, the operation data of the prosthesis or the orthosis and the operation data of the wearer And a simulation execution unit 14a to predict.
Based on the motion response surface, if it is assumed that the wearer wears a different prosthetic limb or brace from a plurality of trial limbs or trial braces, the prosthetic limb or brace motion data and the wearer motion data are predicted. Therefore, the simulation system 1a of the modified example of the first embodiment can predict a prosthetic limb or a brace suitable for the wearer.

Second Embodiment
Hereinafter, a second embodiment of a prosthetic limb / orthoresiste or its part design system, prosthesis / orthoral or its part design method and program according to the present invention will be described with reference to the accompanying drawings.
The prosthetic limb / equipment or part design system 1 according to the second embodiment is configured in the same manner as the prosthetic limb / equipment or part design system 1 according to the first embodiment described above, except as described below. Therefore, according to the prosthetic limb / equipment or its part design system 1 according to the second embodiment, the same effects as those of the prosthetic limb / equipment or its part design system 1 according to the first embodiment described above are obtained, except as described below. be able to.

As described above, the design system 1 for the prosthetic limb / equipment or its part according to the first embodiment designs a prosthetic limb blade (specifically, a sports prosthetic leg blade). The design system 1 designs a prosthetic limb (prosthetic leg or prosthetic hand) other than a prosthetic braid, an orthosis (a device to be worn on a trunk or limb with a function disorder), or parts thereof.
Specifically, the prosthetic limb / orthoresistive device or its part design system 1 according to the second embodiment is the same as the prosthetic limb / orthorid or its part design system 1 according to the first embodiment (that is, virtual prosthetic limb / virtual orthosis, etc.). For example, prosthetic limbs, wrist joint orthosis, shoulder joint orthosis, cervical vertebra orthosis, thoracic vertebra orthosis, lumbar orthosis, sacroiliac orthosis, hip joint orthosis, knee joint orthosis, short leg orthosis and any of those parts Do the design.

As mentioned above, although the form for implementing this invention was demonstrated using embodiment, this invention is not limited to such embodiment at all, In the range which does not deviate from the summary of this invention, various deformation | transformation and substitution Can be added. You may combine the structure as described in each embodiment mentioned above. For example, the second embodiment and the modified example of the first embodiment may be combined.

A program for realizing each function of the design system 1 of the prosthesis / orthoresistive or its parts and the function of the simulation system 1a is recorded on a computer-readable recording medium, and the program recorded on the recording medium is recorded on the computer The processing of each unit described above may be performed by causing the system to read and execute. Here, “loading and executing a program recorded on a recording medium into a computer system” includes installing the program in the computer system. The “computer system” here includes an OS and hardware such as peripheral devices. Further, the “computer system” may include a plurality of computer devices connected via a network including a communication line such as the Internet, WAN, LAN, and dedicated line.
The “computer-readable recording medium” refers to a storage device such as a flexible medium, a magneto-optical disk, a portable medium such as a ROM or a CD-ROM, and a hard disk incorporated in a computer system. As described above, the recording medium storing the program may be a non-transitory recording medium such as a CD-ROM. The recording medium also includes a recording medium provided inside or outside that is accessible from the distribution server in order to distribute the program. The code of the program stored in the recording medium of the distribution server may be different from the code of the program that can be executed by the terminal device. That is, the format stored in the distribution server is not limited as long as it can be downloaded from the distribution server and installed in a form that can be executed by the terminal device.

Note that the program may be divided into a plurality of parts, downloaded at different timings, and combined in the terminal device, or the distribution server that distributes each of the divided programs may be different. Furthermore, the “computer-readable recording medium” holds a program for a certain period of time, such as a volatile memory (RAM) inside a computer system that becomes a server or a client when the program is transmitted via a network. Including things. The program may be for realizing a part of the functions described above. Furthermore, what can implement | achieve the function mentioned above in combination with the program already recorded on the computer system, what is called a difference file (difference program) may be sufficient.

Further, part or all of the above-described functions may be realized as an integrated circuit such as an LSI (Large Scale Integration). Each function described above may be individually made into a processor, or a part or all of them may be integrated into a processor. Further, the method of circuit integration is not limited to LSI, and may be realized by a dedicated circuit or a general-purpose processor. In addition, when an integrated circuit technology that replaces LSI appears due to the advancement of semiconductor technology, an integrated circuit based on the technology may be used.

In addition, the following additional notes are disclosed regarding the above description.
(Appendix 1) A plurality of try-on prosthetic limbs / apparatuses or parts thereof having different specifications, and a plurality of try-on prosthetic limbs / apparatuses or operation data of the parts thereof; An operation data acquisition unit for acquiring wearer operation data which is operation data of the wearer of the brace or its parts;
A motion response surface calculation unit that calculates a motion response surface by a response surface method based on the try-on product motion data and the wearer motion data acquired by the motion data acquisition unit;
A time response surface calculation unit that calculates a time response surface by a response surface method based on the try-on product operation data and the wearer operation data acquired by the operation data acquisition unit;
Based on the motion response curved surface and the time response curved surface, the virtual prosthesis, the orthosis or the part of the virtual prosthesis, the orthosis, or the part that is not actually tried on is temporarily tried by the wearer, A design system for a prosthetic limb / orthoresiste or a part thereof having a simulation execution unit for predicting the wearer's motion data.
(Supplementary Note 2) The motion response curved surface calculated by the motion response curved surface calculation unit is functionalized into a quadratic or higher function.
The prosthetic limb / orthoresister or part design system according to appendix 1.
(Appendix 3) The plurality of trial prosthetic limbs and orthoses or parts thereof are a plurality of trial sports prosthetic blades,
The items of the specifications of the plurality of trial-wearing sports prosthetic blades include at least one of the sports prosthetic blade height dimension, the sports prosthetic blade rearward protrusion amount, and the sports prosthetic blade toe length dimension,
A design system for the prosthetic limb or orthosis or parts thereof according to appendix 1 or appendix 2.
(Additional remark 4) The said simulation execution part produces | generates the virtual sports prosthetic leg blade which has a specification different from each specification of these trial wearing sports prosthetic leg blades by using a basis vector method.
The prosthetic limb / orthoresister or part design system according to appendix 3.
(Appendix 5) The plurality of trial sports prostheses are:
From 81 types of specifications obtained by having each of the four items including the sports prosthetic blade height dimension, the sports prosthetic blade rear protrusion amount, and the sports prosthetic blade toe length dimension having three levels, Nine specifications selected based on the L9 orthogonal table in the design of experiments,
The prosthetic limb / orthoresister or the parts design system according to appendix 3 or appendix 4.
(Appendix 6) The four items are sports prosthetic leg blade rigidity, sports prosthetic leg blade height dimension, sports prosthetic leg blade protrusion amount, and sports prosthetic leg blade toe length dimension.
A prosthetic limb / orthoresister or part design system according to appendix 5.
(Appendix 7) The simulation execution unit maximizes a predetermined objective function by repeatedly executing the simulation.
The prosthetic limb / orthorm or a part design system according to any one of supplementary notes 1 to 6.
(Supplementary Note 8) The objective function includes at least the body load of the wearer.
A prosthetic limb / orthoresister or part design system according to appendix 7.
(Supplementary note 9) A plurality of try-on prosthetic limbs / apparatus or parts thereof having different specifications, and a plurality of try-on prosthetic limbs / apparatus or parts of the prosthesis operation data, An operation data acquisition step for acquiring wearer operation data which is operation data of the wearer of the brace or its parts;
An operation response surface calculation step for calculating an operation response surface by a response surface method based on the try-on product operation data and the wearer operation data acquired in the operation data acquisition step;
A time response surface calculation step for calculating a time response surface by a response surface method based on the try-on product operation data and the wearer operation data acquired in the operation data acquisition step;
Based on the motion response curved surface and the time response curved surface, the virtual prosthesis, the orthosis or the part of the virtual prosthesis, the orthosis, or the part that is not actually tried on is temporarily tried by the wearer, And a simulation execution step for predicting the wearer's motion data.
(Appendix 10)
A plurality of try-on prosthetic limbs / apparatus or parts thereof when wearing a plurality of try-on prosthetic limbs / apparatus or parts thereof; An operation data acquisition step for acquiring the wearer operation data which is the operation data of the wearer;
An operation response surface calculation step for calculating an operation response surface by a response surface method based on the try-on product operation data and the wearer operation data acquired in the operation data acquisition step;
A time response surface calculation step for calculating a time response surface by a response surface method based on the try-on product operation data and the wearer operation data acquired in the operation data acquisition step;
Based on the motion response curved surface and the time response curved surface, the virtual prosthesis, the orthosis or the part of the virtual prosthesis, the orthosis, or the part that is not actually tried on is temporarily tried by the wearer, And a simulation execution step for predicting the movement data of the wearer.

DESCRIPTION OF SYMBOLS 1 ... Design system, 11 ... Motion data acquisition part, 11A ... Try-on product motion data acquisition part, 11B ... Wearer motion data acquisition part, 13A ... Motion response curved surface calculation part, 13B ... Time response curved surface calculation part, 14 ... Simulation execution 14A ... body motion analysis unit 14B ... FEM analysis unit 14C ... multipurpose optimization unit 14D ... optimal product design unit A ... photographing unit B ... prosthetic leg B1 ... prosthetic socket B2 ... prosthetic joint B3 ... Prosthetic foot connection part, B4 ... Prosthetic leg blade, B5 ... Prosthetic leg connection part, B5X ... Center axis, C ... Wearer, L2 ... Prosthetic leg blade height dimension, L3 ... Prosthetic leg rear protrusion amount, L4 ... Prosthetic leg blade toe length dimension, DESCRIPTION OF SYMBOLS 1a ... Simulation system, 11a ... Measurement data acquisition part, 11Aa ... Try-on product measurement data acquisition part, 11Ba ... Wearer measurement data acquisition part, 12 ... Wearer movement Data calculation unit, 13Aa ... motion response curved surface calculation unit, 13Ba ... time response curved surface calculation unit, 14a ... simulation execution unit, 14Aa ... body motion analysis unit, 14Ba ... FEM analysis unit, 14Ca ... multipurpose optimization unit, 14Da ... optimal product Design department, 15 ... Selection department

Claims (13)

Measurement data for acquiring try-on product measurement data that is measurement data of a plurality of different try-on prostheses or try-on devices, and wearer measurement data that is measurement data of a wearer of the plurality of try-on prosthetics or the test-on devices An acquisition unit;
Based on the wearer measurement data and the wearer measurement data, a wearer operation data calculation unit that calculates wearer operation data, which is data indicating the force generated in the wearer, by reverse dynamic analysis,
Based on the wearer motion data, a motion response curved surface calculation unit that calculates a motion response curved surface by a response surface method;
Based on the motion response curved surface, when it is assumed that the wearer wears a plurality of prosthetic limbs or prosthetic limbs different from the trial rigging, the operation data of the prosthetic limbs or the orthotics and the wear A simulation execution unit that predicts motion data of a person. Based on the wearer motion data, a time response curved surface calculation unit that calculates a time response curved surface by a response surface method,
The simulation system according to claim 1, wherein the simulation execution unit predicts the operation data of the artificial limb or the orthosis and the operation data of the wearer based on the time response curved surface. The simulation system according to claim 1 or 2, wherein the simulation execution unit predicts motion data of the prosthetic limb or the orthosis and motion data of the wearer by a basis vector method. 4. The method according to claim 1, further comprising: a selection unit that selects a prosthetic limb or an orthosis based on the motion data of the prosthetic limb or the orthosis predicted by the simulation execution unit and the motion data of the wearer. The simulation system according to one item. The simulation system according to any one of claims 1 to 4, wherein the motion response curved surface calculation unit calculates a motion response curved surface that is functionalized into a quadratic or higher function. Each of the plurality of trial prosthetic limbs is a trial sports prosthetic blade,
The simulation execution unit predicts at least one of a sports prosthetic leg blade height dimension, a sports prosthetic leg blade rearward protrusion amount, and a sports prosthetic leg blade toe length dimension.
The simulation system according to any one of claims 1 to 5. Each of the plurality of trial prosthetic limbs is a trial sports prosthetic blade,
The trial sports prosthetic blade has a spec selected from a plurality of specs based on the L9 orthogonal table of the experimental design method.
The simulation system according to any one of claims 1 to 6. The specifications include blade rigidity, height dimension, rearward protrusion amount, and toe length dimension.
The simulation system according to claim 7. The simulation execution unit maximizes a predetermined objective function by repeatedly executing a simulation.
The simulation system according to any one of claims 1 to 8. The objective function includes at least a function related to a body load of the wearer,
The simulation system according to claim 9. A prosthetic blade selected based on the motion data of the prosthetic limb or the orthosis predicted by the simulation system according to any one of claims 1 to 10, and the motion data of the wearer. There,
The straight-line portion in the thickness direction of the body-side end portion attached to the body (wearer) side through the connecting component is defined as a straight-line portion A, and the ground-side end portion on the side installed on the ground is defined as the ground-side end portion B. The portion that intersects with the leaf spring body when the perpendicular is drawn from the straight portion A in the thickness direction is defined as the intersection portion C, and the portion that is located on the most back side when the artificial leg blade including the leaf spring for the artificial leg is attached to the body is the back surface. Part D,
The distance of the vertical component between the straight line part A and the ground side end part B is defined as a straight line part vertical component distance H, and the distance of the horizontal component distance between the ground side edge part B and the intersection part C is defined as a cross part. A horizontal component distance Lt is set, a horizontal component distance between the intersection C and the back surface portion D is set as a back surface horizontal component distance Lh, the ground side end portion B is fixed, and the straight portion A is transferred to the back surface portion D. When a load of 1000 N is applied vertically to a position P of 50 mm along the direction of heading, the straight portion vertical component distance H before the prosthetic leg spring is deflected and the prosthetic leg spring are deflected. When the difference in distance from the straight portion vertical component distance Ha after bending is defined as the deflection amount S,
235mm ≦ H ≦ 285mm
−10 mm ≦ Lt ≦ 30 mm
220mm ≦ Lh ≦ 280mm
35mm ≦ S ≦ 45mm
Is a prosthetic blade. A step of acquiring fitting product measurement data which is measurement data of a plurality of different fitting prostheses or fitting devices, and wearer measurement data which is measurement data of a wearer of the plurality of fitting artificial limbs or the fitting devices; ,
Based on the wearer measurement data and the wearer measurement data, calculating wearer operation data that is data indicating the force generated in the wearer by inverse dynamics analysis; and
Based on the wearer motion data, calculating a motion response surface by a response surface method;
Based on the motion response curved surface, when it is assumed that the wearer wears a plurality of prosthetic limbs or prosthetic limbs different from the trial rigging, the operation data of the prosthetic limbs or the orthotics and the wear A computer-implemented simulation method comprising: predicting a person's motion data. On the computer,
A step of acquiring fitting product measurement data which is measurement data of a plurality of different fitting prostheses or fitting devices, and wearer measurement data which is measurement data of a wearer of the plurality of fitting artificial limbs or the fitting devices; ,
Based on the wearer measurement data and the wearer measurement data, calculating wearer operation data that is data indicating the force generated in the wearer by inverse dynamics analysis; and
Based on the wearer motion data, calculating a motion response surface by a response surface method;
Based on the motion response curved surface, when it is assumed that the wearer wears a plurality of prosthetic limbs or prosthetic limbs different from the trial rigging, the operation data of the prosthetic limbs or the orthotics and the wear For predicting the motion data of a person.

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