Data processing apparatus, data processing method, and program

Abstract

To provide a data processing apparatus, a data processing method, and a program that can reduce the time and effort in training when a user uses a myoelectric prosthesis.SOLUTION: A data processing apparatus of the present disclosure comprises: means that acquires information on a user who uses a myoelectric prosthesis; means that uses a generation model to generate an inference model that infers, from the information on the user, an electric signal for causing the myoelectric prosthesis to operate from a myoelectric signal acquired by the user; and means that outputs the generated inference model.SELECTED DRAWING: Figure 3

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a data processing device, a data processing method, and a program. [Background technology]

[0002] The following Patent Document 1 describes an electric gripping member that has a finger member support section, a sliding member that reciprocates relative to the finger member support section, an electric motor that drives the sliding member, a first finger member, and a second finger member, and that opens and closes the first finger member and the second finger member using the electric motor. It also describes that the electric gripping member is operated based on the detection results of an electromyographic sensor attached to the human body. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Laid-Open No. 2012-006133 Summary of the Invention [Problem to be solved by the invention]

[0004] In the myoelectric prosthesis including the electric gripping member of Patent Document 1, each part of the myoelectric prosthesis is operated using myoelectric signals acquired from the human body by an electromyographic sensor. In this case, it is important for user convenience that the actions performed based on the acquired myoelectric signals match the actions intended by the user wearing the myoelectric prosthesis. However, currently, in order to match the user's intended actions with the actions of the myoelectric prosthesis, a long period of training by the user, for example, two to six months, is often required. This training is one of the reasons why users hesitate to use myoelectric prostheses, hindering the widespread use of myoelectric prostheses. Given these circumstances, a method for reducing the effort required for the training described above is desired. [Means for solving the problem]

[0005] A first aspect of the technology disclosed herein is a data processing device comprising: means for acquiring information about a user who uses a myoelectric prosthesis; means for generating an inference model using information about the user, the inference model inferring an electrical signal for operating the myoelectric prosthesis from the myoelectric signal acquired from the user; and means for outputting the generated inference model.

[0006] A second aspect of the technology disclosed herein is a data processing method that includes acquiring information about a user who uses a myoelectric prosthesis, using a generative model to generate an inference model from the information about the user that infers an electrical signal for operating the myoelectric prosthesis from the myoelectric signal acquired from the user, and outputting the generated inference model.

[0007] A third aspect of the technology of the present disclosure is a program for causing a computer processor to acquire information about a user who uses a myoelectric prosthesis, use a generative model to generate an inference model from the information about the user that infers an electrical signal for operating the myoelectric prosthesis from the myoelectric signal acquired from the user, and execute a process of outputting the generated inference model. [Brief explanation of the drawings]

[0008] [Figure 1] FIG. 1 is a conceptual diagram illustrating an example of a configuration of a data processing system. [Figure 2] FIG. 1 is a conceptual diagram showing an example of the main functions of a data processing device and a myoelectric prosthesis. [Figure 3] FIG. 10 is an explanatory diagram illustrating an example of a specific process. [Figure 4] 2 shows a schematic functional configuration of a specific processing unit of the data processing device. [Figure 5] 10 is a diagram illustrating an example of an operational flow of specific processing by a data processing device. [Figure 6] FIG. 10 is an explanatory diagram illustrating an outline of another example of the identification process. DETAILED DESCRIPTION OF THE INVENTION

[0009] Below, an example of an embodiment of a data processing device, a data processing method, and a program according to the technology of the present disclosure will be described with reference to the accompanying drawings. Note that the following will show a schematic view of the scope necessary for the explanation to achieve the objectives of the present disclosure, and will mainly explain the scope necessary for explaining the relevant parts of the present disclosure. The omitted explanations will be based on publicly known technology. Furthermore, identical or equivalent components in the drawings will be given the same or similar reference numerals, and redundant explanations will be omitted. Furthermore, when a drawing contains multiple identical or equivalent components, only some of them may be given reference numerals to make the drawing easier to understand.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] FIG. 1 shows an example of the configuration of a data processing system 10 according to an embodiment.

[0017] As shown in Fig. 1, the data processing system 10 includes a data processing device 12 and a myoelectric prosthesis 14. An example of the data processing device 12 is a server. In this embodiment, the data processing device 12 is an example of a "data processing device" according to the technology of the present disclosure, and the myoelectric prosthesis 14 is an example of a "myoelectric prosthesis" according to the technology of the present disclosure.

[0018] The data processing device 12 may include a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 may include at least a processor 28, a RAM 30, and a storage 32. Of these, the processor 28 is an example of a "processor" according to the technology of the present disclosure. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is an interface for connecting to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The myoelectric prosthesis 14 is a prosthetic limb that can be worn by a user, and can be configured as, for example, a myoelectric prosthesis. This myoelectric prosthesis 14 may include a computer 36, an electromyographic sensor 38, an actuator 40, an input unit 42, and a communication I / F 44. The computer 36 is an example of a "terminal device" according to the technology of the present disclosure. The computer 36 may also include a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The electromyographic sensor 38, actuator 40, and input unit 42 are also connected to the bus 52. In this embodiment, a prosthetic hand is used as an example of a myoelectric prosthesis, but a prosthetic leg may also be used.

[0020] The myoelectric sensor 38 may be a sensor that is attached to the user P (see FIG. 3) who uses the myoelectric prosthesis 14 and is capable of detecting weak electrical signals (more specifically, surface myoelectric potentials) generated in association with muscle movements of the user P. One or more myoelectric sensors 38 of this embodiment are attached to the muscles of the arm of the user P on which the myoelectric prosthesis 14 is worn, thereby acquiring myoelectric signals that are referenced when operating the myoelectric prosthesis 14.

[0021] The actuator 40 may be a movable part of the myoelectric prosthesis 14, for example, a joint part constituting one or more fingers included in an electric hand. The type of actuator 40 is not particularly limited, but may include a cylinder piston, a solenoid, a motor, etc. Furthermore, a drive source (for example, a battery) for operating the actuator 40 may be disposed in an appropriate position on the myoelectric prosthesis 14.

[0022] The input unit 42 may be a user interface for the user P or the like to input predetermined information to the myoelectric prosthesis 14. The input unit 42 may include, for example, various switches such as a power switch, a touch panel, and the like.

[0023] The communication I / F 44 is an interface for connecting to a network 54. This communication I / F 44 and the communication I / F 26 in the data processing device 12 are responsible for sending and receiving various types of information between the processor 46 and the processor 28 via the network 54.

[0024] FIG. 2 shows an example of the main functions of the data processing device 12 and the myoelectric prosthesis 14.

[0025] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0026] The storage 32 also stores a data generation model 58. The data generation model 58 is used by the specific processing unit 290. The data generation model 58 is an example of a "generative model" according to the technology of the present disclosure.

[0027] The data generation model 58 can be configured by so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction may be input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image may be input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0028] Additionally, the data generation model 58 of this embodiment may be learned using data that can be used for learning collected in advance from multiple myoelectric prosthesis users in order to generate the inference model 62 described below. This data may include not only user information about each myoelectric prosthesis user and the inference model being used, but also various data such as information about the myoelectric prosthesis being used and various signals such as myoelectric signals and electrical signals.

[0029] Furthermore, the storage 32 may store data regarding the structure of the myoelectric prosthesis 14. The data regarding the structure of the myoelectric prosthesis 14 may be used as part of a prompt when generating an inference model 62, which will be described later.

[0030] The myoelectric prosthesis 14 realizes the operation of each part by performing various processes by the processor 46. The various processes referred to here may include a reception / output process that receives a request from the user P or the like and outputs the request to the data processing device 12, and an operation control process that operates the actuator 40 based on the myoelectric signal acquired by the myoelectric sensor 38. A control program 60 for executing the various processes described above is stored in the storage 50. The processor 46 reads the control program 60 from the storage 50 and executes the read control program 60 on the RAM 48. The reception / output process is realized by the processor 46 operating as the control unit 46A in accordance with the control program 60 executed on the RAM 48.

[0031] Furthermore, the storage 32 in this embodiment can store an inference model 62 that infers an electrical signal for operating the myoelectric prosthesis 14 from the myoelectric signal acquired from the user P. This inference model 62 can be generated by the specific processing unit 290 of the data processing device 12 and stored in the storage 32. This inference model 62 can be configured as a trained model that has learned the correlation between the myoelectric signal acquired from the user P and the electrical signal for realizing the operation of the myoelectric prosthesis 14 intended by the user P. The inference model 62 can be realized by a machine learning model typified by a neural network model.

[0032] Next, the processing of the specific processing unit 290 when the data processing device 12 performs the specific processing to generate the above-mentioned inference model 62 will be described.

[0033] The identification process of this embodiment uses a data generation model 58 to generate and output an inference model 62 from information about a user P who uses the myoelectric prosthesis 14 (hereinafter, this information will be referred to as "user information"), as shown in Figure 3.

[0034] The specific processing unit 290 may include an input unit 292, a processing unit 294, and an output unit 296, as shown in FIG.

[0035] The input unit 292 acquires user input received by the input unit 42 of the myoelectric prosthesis 14. Specifically, the input unit 292 acquires, as user information, user-specific information input to the input unit 42 and a portion of the myoelectric signal acquired by the myoelectric sensor 38. Note that, although the present embodiment illustrates an example in which user information is acquired via the myoelectric prosthesis 14, the user information may also be input to the data processing device 12 via a well-known terminal device (not shown). Furthermore, if the data processing device 12 does not have information about the myoelectric prosthesis 14, the input unit 292 may acquire information about the myoelectric prosthesis 14 itself used by the user P along with the user information.

[0036] The user-specific information included in the user information may be information related to the characteristics of user P. Specifically, it may include at least one, and more preferably several, of user P's age, sex, height, weight, muscle mass, and skeletal size, but is not limited to these.

[0037] Similarly, the myoelectric signal included in the user information may be a sample of the myoelectric signal obtained from the user P. The myoelectric signal may be collected, for example, by the user P using the myoelectric prosthesis 14 wearing the myoelectric prosthesis 14 and performing a predetermined movement with the myoelectric sensor 38 attached at a predetermined position.

[0038] The processing unit 294 performs specific processing using the data generation model 58. Specifically, a prompt generated based on user information is input to the data generation model 58, and a generation result is obtained.

[0039] The output unit 296 transmits the results of the specific processing to the myoelectric prosthesis 14. The myoelectric prosthesis 14 stores the inference model 62 as the received result of the specific processing in the storage 50. As a result, when the control unit 46A acquires a myoelectric signal from the user P wearing the myoelectric prosthesis 14, it can use the inference model 62 to generate an electrical signal for operating the myoelectric prosthesis, and operate the actuator 40 based on the electrical signal.

[0040] The data processing device 12 according to this embodiment, having the above-described configuration, makes it possible to generate an inference model 62 that can be used by a user P when using the myoelectric prosthesis 14, from the user information of the user who uses the myoelectric prosthesis 14. This makes it possible to significantly shorten or essentially eliminate the long training period that was previously required when the user P used the myoelectric prosthesis 14.

[0041] Next, the operation of the data processing system 10 having the above-described configuration will be described.

[0042] An example of the flow of the specific processing will be described with reference to Fig. 5. The flow of the specific processing shown in Fig. 5 is an example of a "data processing method" according to the technology of the present disclosure. The flow of the specific processing is implemented by processor 28 executing specific processing program 56.

[0043] The flow of the specific processing in this embodiment includes at least a step of acquiring information about the user P who uses the myoelectric prosthesis 14 (corresponding to step S300 described later), a step of using the data generation model 58 to generate an inference model 62 that infers an electrical signal for operating the myoelectric prosthesis 14 from the myoelectric signal acquired from the user P based on the information about the user P (corresponding to step S304 described later), and a step of outputting the generated inference model 62 (corresponding to step S306 described later).

[0044] More specifically, first, in step S300, the identification processing unit 290 determines whether or not user information of a specific user P has been acquired, which is a predetermined trigger condition.

[0045] If the user information is acquired (step S300; Yes), the data processing system 10 proceeds to step S302. On the other hand, if the user information is not acquired (step S300; No), the data processing system 10 enters a standby state until the user information is acquired.

[0046] In step S302, the processing unit 294 generates a prompt by adding an instruction for obtaining the result of a specific process to the text representing the input. Specifically, the processing unit 294 converts the user information into text information and adds an instruction for obtaining a desired output using the data generation model 58 to the text information, thereby generating a prompt to be input to the data generation model 58. The generated prompt may be, for example, "User P is 50 years old, male, 170 cm tall, weighs 65 kg, and has little muscle mass. Please generate an inference model that can infer an electrical signal for operating the myoelectric prosthetic hand XXX from the myoelectric signal obtained from this user P." Additionally, additional information (such as functional information of the myoelectric prosthetic hand XXX or sample data of the myoelectric signal obtained from user P) may be added to this prompt.

[0047] In step S304, the processing unit 294 inputs the generated prompt into the data generation model 58 and acquires the result of the specific processing based on the output of the data generation model 58. Specifically, an inference model 62 is acquired that infers an electrical signal for operating the myoelectric prosthesis 14 from the myoelectric signal acquired from the user P.

[0048] In step S306, the output unit 296 outputs the inference model 62 as a result of the identification process to the myoelectric prosthesis 14, and the identification process ends. The inference model 62 output by the output unit 296 is stored in the storage 50 in the myoelectric prosthesis 14 and then used for the operation of the myoelectric prosthesis 14.

[0049] Once the inference model 62 is stored in the storage 50, the myoelectric prosthesis 14 becomes available for use by the user P. Therefore, the user P can make the myoelectric prosthesis 14 perform the desired movement simply by wearing the myoelectric prosthesis 14 and attaching the myoelectric sensor 38 to a predetermined position. Furthermore, in this embodiment, the electrical signals for operating the myoelectric prosthesis 14 can be identified mainly by the computer 36 within the myoelectric prosthesis 14, so the myoelectric prosthesis 14 can be used without considering the communication environment.

[0050] As explained above, the data processing method according to this embodiment also makes it possible to generate an inference model 62 that can be used by a user P when using the myoelectric prosthesis 14, based on the user information of the user. This makes it possible to significantly reduce or essentially eliminate the long training period that was previously required for the user P to use the myoelectric prosthesis 14. Furthermore, by shortening or eliminating the training period that was a burden on the user, it is expected that the use of the myoelectric prosthesis 14 will be promoted.

[0051] In the data processing system 10 of the embodiment described above, an example has been given in which the inference model 62 generated by the data processing device 12 is stored in the storage 50 in the myoelectric prosthesis 14 and used. However, the data processing device 12 of the present disclosure is not limited to this configuration. Another embodiment of the present disclosure will be described below with reference to FIG. 6. Note that FIG. 6 shows an overview of specific processing by a data processing system according to another embodiment of the present disclosure, where FIG. 6(A) is a diagram showing an overview of the operation of each unit when the specific processing is executed, and FIG. 6(B) is a diagram showing an overview of the operation of each unit when the myoelectric prosthesis 14 is operated.

[0052] A data processing system 10A according to another embodiment differs from the above-described embodiment in that the output destination of the inference model 62A generated by the data processing device 12A is the storage 32 of the data processing device 12A, rather than the storage 50 of the myoelectric prosthesis 14. Note that, other than the above-described points, the data processing system 10A is generally similar to the data processing system 10 according to the first embodiment, and therefore, a description of the points similar to those of the data processing system 10 will be omitted.

[0053] During the identification process in the data processing system 10A, as shown in Figure 6(A), first, the data processing device 12A acquires user information of the user P who uses the myoelectric prosthesis 14. Next, a prompt including the acquired user information is generated and input into the data generation model 58, generating an inference model 62A. The generated inference model 62A is then stored in the storage 32 of the data processing device 12A, completing the series of identification processes.

[0054] Once the generation of the inference model 62A described above is complete, the user P wears the myoelectric prosthesis 14 and begins using it. In the data processing system 10A, as shown in FIG. 6(B), when use of the myoelectric prosthesis 14 begins, the myoelectric signal acquired by the myoelectric sensor 38 is transmitted to the data processing device 12A via the communication I / F 44 of the myoelectric prosthesis 14. The data processing device 12A, which has received the myoelectric signal, uses the inference model 62A stored in the storage 32 to output an electrical signal for operating the myoelectric prosthesis 14 and transmits the output result to the myoelectric prosthesis 14. The myoelectric prosthesis 14 operates the actuator 40 based on the received electrical signal.

[0055] The data processing device 12A and data processing method according to this embodiment can also be expected to have the same effects as those described in the first embodiment. In addition, by adopting a configuration in which the inference model 62A is stored in the data processing device 12A, as in this embodiment, the inference model 62A can be easily updated, and the inference accuracy of the inference model 62A currently in use can be improved relatively easily.

[0056] In order to further improve the inference accuracy of the above-described inference models 62, 62A, it is also possible to implement so-called online learning, etc., using data collected when the user P actually uses the model. Also, data collected when multiple users actually use the model may be collected by the data processing device 12, 12A and used to improve the accuracy of the inference model generated by the data generation model 58. Also, in order to improve the inference accuracy of the data generation model 58, data augmentation may be used to increase the amount of data used for learning.

[0057] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[0058] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0059] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0060] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0061] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0062] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0063] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[0064] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0065] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0066] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0067] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0068] 10 Data Processing System 12 Data Processing Device 14 Myoelectric prostheses 290 Special Processing Department 292 Input section 294 Processing Section 296 Output Section

Claims

1. A means for acquiring information about a user who uses a myoelectric prosthesis; A means for generating an inference model that infers an electrical signal for operating the myoelectric prosthesis from the myoelectric signal acquired from the user based on information about the user using a generation model; and a means for outputting the generated inference model. Data processing device.

2. The information about the user is composed of at least one of user-specific information including at least one of the user's age, sex, height, weight, muscle mass, and skeletal size, and data of myoelectric signals acquired from the user.

2. The data processing device according to claim 1.

3. The myoelectric prosthesis includes a terminal device for operating each component, and the means for outputting the inference model outputs the inference model to the terminal device.

2. The data processing device according to claim 1.

4. Acquire information about users of myoelectric prostheses, Using a generative model, an inference model is generated from information about the user, which infers an electrical signal for operating the myoelectric prosthesis from the myoelectric signal acquired from the user; outputting the generated inference model; Data processing methods.

5. The computer processor Acquire information about users of myoelectric prostheses, Using a generative model, an inference model is generated from information about the user, which infers an electrical signal for operating the myoelectric prosthesis from the myoelectric signal acquired from the user; Execute a process to output the generated inference model. program.

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