Plantar pressure analysis method, device, equipment, storage medium and program product

Abstract

The present application discloses a plantar pressure analysis method, device, equipment, storage medium and program product, which relates to the field of data processing technology. The disclosed plantar pressure analysis method comprises the following steps: obtaining pressure data of multiple plantar areas of a target user; integrating multiple pressure data to obtain a pressure data matrix; inputting the pressure data matrix into a pre-trained plantar pressure analysis model, performing header labeling and position embedding processing on each data vector in the pressure data matrix to obtain a target matrix; performing multi-head attention processing on the target matrix to extract a pressure feature vector; and obtaining the plantar pressure analysis result of the target user based on the pressure feature vector. The present application can focus on the pressure data of different plantar areas in the target matrix, capture the characteristics of the pressure data from multiple angles, and when applied to high-arch foot recognition, the plantar pressure analysis result corresponds to the high-arch foot recognition result, thereby achieving the effect of improving the accuracy of high-arch foot recognition.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a plantar pressure analysis method, device, equipment, storage medium and program product. Background Art

[0002] Conventional feature extraction algorithms typically use plantar pressure features extracted from overall foot pressure data to monitor and assess cavus. These algorithms fail to consider the data characteristics of different plantar regions, resulting in low accuracy in identifying cavus. Therefore, the challenge of analyzing plantar pressure and improving cavus identification accuracy remains urgent. Summary of the Invention

[0003] The main purpose of this application is to provide a plantar pressure analysis method, device, equipment, storage medium and program product, aiming to solve the technical problem of how to perform plantar pressure analysis and improve the accuracy of high arch foot identification.

[0004] To achieve the above objectives, the present application proposes a plantar pressure analysis method, which includes:

[0005] Obtain pressure data of multiple plantar areas of a target user;

[0006] Integrate multiple pressure data to obtain a pressure data matrix;

[0007] Input the pressure data matrix into the pre-trained plantar pressure analysis model, perform header labeling and position embedding on each data vector in the pressure data matrix to obtain the target matrix;

[0008] Perform multi-head attention processing on the target matrix to extract the pressure feature vector;

[0009] According to the pressure feature vector, the target user's plantar pressure analysis result is obtained.

[0010] In one embodiment, the step of integrating a plurality of pressure data to obtain a pressure data matrix includes:

[0011] For each pressure data, the pressure data is segmented according to the gait cycle to obtain at least one sub-pressure data;

[0012] For each gait cycle, the sub-pressure data of multiple plantar regions are integrated to obtain a sub-pressure data matrix;

[0013] At least one sub-pressure data matrix is ​​integrated to obtain a pressure data matrix.

[0014] In one embodiment, the plurality of plantar regions include a heel region, an arch region, a 1st metatarsal region, a 2nd metatarsal region, a 3rd-5th metatarsal region, a 1st phalangeal region, a 2nd phalangeal region, and a 3rd-5th phalangeal region.

[0015] In one embodiment, before the step of performing header labeling and position embedding processing on each data vector in the pressure data matrix to obtain a target matrix, the plantar pressure analysis method further includes:

[0016] According to the preset data dimension, the pressure data matrix is ​​dimensionally mapped to obtain an intermediate matrix;

[0017] The step of performing header marking and position embedding processing on each data vector in the pressure data matrix to obtain a target matrix includes:

[0018] Perform header marking and position embedding processing on each intermediate data vector in the intermediate matrix to obtain the target matrix.

[0019] In one embodiment, the step of obtaining the target user's plantar pressure analysis result based on the feature vector includes:

[0020] The classification predictor of the plantar pressure analysis model is used to perform dimension mapping and normalization on the pressure feature vector to obtain the plantar pressure analysis results of the target user.

[0021] In one embodiment, the plantar pressure analysis method further includes:

[0022] Acquire pressure data sample sets of multiple users to obtain pressure data matrix sample sets, where one pressure data sample set includes pressure data samples of multiple sole areas of one user;

[0023] Construct an initial domain adversarial transfer network;

[0024] The initial domain adversarial transfer network is trained using the pressure data matrix sample set to obtain the plantar pressure analysis model.

[0025] In addition, to achieve the above-mentioned purpose, the present application also proposes a plantar pressure analysis device, which includes:

[0026] An acquisition module, used to acquire pressure data of multiple sole areas of a target user;

[0027] Integration module, used to integrate multiple pressure data to obtain a pressure data matrix;

[0028] The processing module is used to input the pressure data matrix into the pre-trained plantar pressure analysis model, perform header labeling and position embedding processing on each data vector in the pressure data matrix, and obtain the target matrix;

[0029] The extraction module is used to perform multi-head attention processing on the target matrix and extract the pressure feature vector;

[0030] The analysis module is used to obtain the target user's plantar pressure analysis results based on the pressure feature vector.

[0031] In addition, to achieve the above-mentioned purpose, the present application also proposes a plantar pressure analysis device, which includes: a memory, a processor, and a computer program stored in the memory and runnable on the processor, and the computer program is configured to implement the steps of the plantar pressure analysis method as described above.

[0032] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium. A computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the plantar pressure analysis method as described above are implemented.

[0033] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the steps of the plantar pressure analysis method as described above are implemented.

[0034] One or more technical solutions proposed in this application have at least the following technical effects:

[0035] The present application provides a plantar pressure analysis method, apparatus, equipment, storage medium and program product, which integrates the pressure data of different plantar areas of a target user to obtain a pressure data matrix, performs header labeling and position embedding processing on the pressure data matrix, and after obtaining a target matrix, performs multi-head attention processing to extract a pressure feature vector and obtain a plantar pressure analysis result. Since the target matrix after header labeling and position embedding processing is processed by a multi-head attention mechanism, it is possible to focus on the pressure data of different plantar areas in the target matrix, capture the characteristics of the pressure data from multiple angles, improve the accuracy of the pressure feature vector, and thus improve the accuracy of the plantar pressure analysis result. When applied to high-arch foot identification, the plantar pressure analysis result corresponds to the high-arch foot identification result, thereby achieving the effect of improving the accuracy of high-arch foot identification. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0037] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following briefly introduces the drawings required for use in the embodiments or related technical descriptions. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0038] Figure 1 This is a flow chart of the first embodiment of the plantar pressure analysis method of the present application;

[0039] Figure 2 This is a schematic diagram of the modules of the first embodiment of the plantar pressure analysis device of the present application;

[0040] Figure 3 Schematic diagram of the device structure of the hardware operating environment involved in the plantar pressure analysis method in the embodiment of the present application.

[0041] The realization of the objectives, functional features and advantages of this application will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0042] It should be understood that the specific embodiments described herein are merely used to explain the technical solutions of the present application and are not intended to limit the present application.

[0043] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0044] The main solution of the embodiment of the present application is: obtaining pressure data of multiple plantar areas of the target user; integrating multiple pressure data to obtain a pressure data matrix; inputting the pressure data matrix into a pre-trained plantar pressure analysis model, performing header labeling and position embedding processing on each data vector in the pressure data matrix to obtain a target matrix; performing multi-head attention processing on the target matrix to extract a pressure feature vector; and obtaining the plantar pressure analysis result of the target user based on the pressure feature vector.

[0045] Pes cavus is a foot deformity characterized by an arch that is significantly higher than normal. This condition can have multiple impacts and potential harm to the feet, lower limb joints, spine, and other parts of the body, which in turn can affect an individual's quality of life. Long-term untreated pedis cavus is likely to lead to serious health problems. Early prevention and intervention can greatly reduce the harm caused by pedis cavus, ensure body balance and gait stability, and reduce the risk of future joint inflammation and other problems. Therefore, real-time monitoring and assessment of pedis cavus symptoms are key to successful prevention and treatment.

[0046] Early on, high arches may not present obvious symptoms; patients may only experience mild discomfort or even no subjective discomfort, leading them to neglect seeking medical attention. Furthermore, high arches in childhood are often dismissed as a normal developmental condition and thus are not given sufficient attention. Professional examination and diagnosis of high arches typically involves gait analysis, arch height measurement, and foot mechanics analysis, requiring an experienced podiatrist. The entire process consumes considerable time and effort from both the doctor and the patient. Furthermore, high-precision arch monitoring equipment is not yet widely available nationwide due to its high cost and time consumption, making it difficult to frequently detect high arches during routine monitoring and follow-up examinations. Therefore, the design of a portable, high-precision arch monitoring system is currently a key research focus in this field.

[0047] Current research in the field of high-arch foot health monitoring focuses primarily on the development and improvement of related monitoring hardware equipment. This requires that monitoring equipment possess integrated and automated functions for foot data collection and feature analysis. Existing equipment includes gait analysis platforms equipped with multiple high-resolution pressure sensor arrays and pressure detectors that capture foot dynamic parameters. However, based on these hardware devices, related technologies typically utilize traditional feature extraction algorithms to extract plantar pressure features based on the overall information of foot pressure data for high-arch foot disease monitoring and assessment. This fails to consider the data characteristics of different plantar regions, resulting in low accuracy in high-arch foot identification.

[0048] Therefore, the present application provides a solution, which integrates the pressure data of different plantar areas of the target user to obtain a pressure data matrix, performs header labeling and position embedding processing on the pressure data matrix, and after obtaining the target matrix, performs multi-head attention processing, extracts the pressure feature vector, and obtains the plantar pressure analysis result. Since the target matrix after header labeling and position embedding processing is processed by the multi-head attention mechanism, it can focus on the pressure data of different plantar areas in the target matrix, capture the characteristics of the pressure data from multiple angles, improve the accuracy of the pressure feature vector, and thus improve the accuracy of the plantar pressure analysis result. When applied to high-arch foot recognition, the plantar pressure analysis result corresponds to the high-arch foot recognition result, thereby achieving the effect of improving the accuracy of high-arch foot recognition.

[0049] It should be noted that the execution subject of this embodiment can be a computing service device with plantar pressure analysis, network communication, and program execution functions, such as a tablet computer, personal computer, mobile phone, etc., or an electronic device capable of performing the above functions, a plantar pressure analysis device, etc. The following uses the plantar pressure analysis device as an example to illustrate this embodiment and the following embodiments.

[0050] Based on this, the present invention provides a method for analyzing plantar pressure. Figure 1 , Figure 1 This is a flow chart of the first embodiment of the plantar pressure analysis method of the present application.

[0051] In this embodiment, the plantar pressure analysis method may include steps S100 to S500:

[0052] Step S100: Obtain pressure data of multiple sole areas of a target user.

[0053] Step S200: integrating multiple pressure data to obtain a pressure data matrix.

[0054] Step S300: Input the pressure data matrix into a pre-trained plantar pressure analysis model, perform header labeling and position embedding processing on each data vector in the pressure data matrix, and obtain a target matrix.

[0055] Step S400: Perform multi-head attention processing on the target matrix to extract the pressure feature vector.

[0056] Step S500: Obtain the target user's plantar pressure analysis result based on the pressure feature vector.

[0057] It should be noted that the target user is any user who is to undergo plantar pressure analysis, and the target task of the plantar pressure analysis may be arch type detection. Arch type detection may include high-arch foot detection, and accordingly, the target user is any user who is to undergo high-arch foot detection.

[0058] The plantar region may be divided according to the task type of the target task of the plantar pressure analysis. For example, the plantar region may include the arch region, the heel region, the metatarsal region, and the like.

[0059] In this embodiment, the plurality of plantar regions may include a heel region, an arch region, a 1st metatarsal region, a 2nd metatarsal region, a 3rd-5th metatarsal region, a 1st phalange region, a 2nd phalange region, and a 3rd-5th phalange region.

[0060] It should be noted that for arch type detection, especially for high-arch foot detection, the sole area is divided into the heel area, arch area, first metatarsal area, second metatarsal area, third to fifth metatarsal areas, first phalanx area, second phalanx area, and third to fifth phalanx areas. The target user's left and right feet are divided using the same method.

[0061] It is understood that the pressure data may include pressure signals collected by a wearable foot pressure collection device during the target user's walking. The pressure data may include pressure signals of the target user's left foot and / or right foot. Preferably, the pressure data may include pressure signals of the target user's left foot and right foot.

[0062] Taking the target task of detecting high arches as an example, if the pressure data includes the pressure signal of the target user's left foot, the plantar pressure analysis result can be the high arch foot detection result of the target user's left foot; if the pressure data includes the pressure signal of the target user's right foot, the plantar pressure analysis result can be the high arch foot detection result of the target user's right foot; if the pressure data includes the pressure signals of the target user's left and right feet, the plantar pressure analysis result can be the high arch foot detection result of the target user's left and right feet.

[0063] In addition, it is understood that before inputting the pressure data into the plantar pressure analysis model for analysis, the multiple pressure data need to be integrated into a pressure data matrix. In the pressure data matrix, a data vector may include pressure signals of different foot regions in the same gait cycle.

[0064] Furthermore, plantar pressure analysis can be achieved through a pre-trained plantar pressure analysis model. The plantar pressure analysis model may include a feature extractor and a classification predictor. The feature extractor can extract a pressure feature vector from the pressure data features, and the classification predictor can predict the arch type of the target user based on the pressure feature vector.

[0065] Continuing with the example of the target task of detecting high arches, the feature extractor can extract the arch feature vector, and the classification predictor can obtain the classification probability that the target user has high arches based on the arch feature vector.

[0066] In this embodiment, the feature extractor can be implemented using a Transformer framework. The feature extractor can include a first LayerNorm layer, a linear layer, a second LayerNorm layer, a header tag layer, a PositionEmbedding layer, a Dropout layer, a Transformer encoding layer, a third LayerNorm layer, and an output layer, connected in sequence. In a specific implementation, after the pressure data matrix is ​​input into the feature extractor, the header tag layer can be used to perform header tagging on each data vector in the pressure data matrix, adding a classification tag to each data vector. The Position Embedding layer can be used to embed position information into the header tags of each data vector in the header-tagged pressure data matrix, adding a corresponding position vector to each data vector to obtain a target matrix. Then, after calculations using modules such as the multi-head self-attention mechanism, residual connections, and a multi-layer perceptron in the Transformer encoding layer, the headers in the Transformer encoding layer output matrix are extracted to obtain a pressure feature vector. Finally, the pressure feature vector is predicted using a classification predictor to obtain the foot pressure analysis result. Preferably, the strategy of the Position Embedding layer is 1D-Position Embedding, that is, one-dimensional position embedding. Compared with other strategies, one-dimensional position embedding requires less computational complexity and is concise and effective.

[0067] In a feasible embodiment, step S200 may include: for each pressure data, dividing the pressure data according to the gait cycle to obtain at least one sub-pressure data; for each gait cycle, integrating the sub-pressure data of multiple plantar areas to obtain a sub-pressure data matrix; integrating at least one sub-pressure data matrix to obtain a pressure data matrix.

[0068] It should be noted that the pressure data matrix can include pressure signals from a single gait cycle or from multiple gait cycles. If the pressure data matrix includes pressure signals from a single gait cycle, the plantar pressure analysis model can process multiple pressure data matrices to obtain corresponding classification probabilities, with the largest of the multiple classification probabilities being used as the plantar pressure analysis result. If the pressure data matrix includes pressure signals from multiple gait cycles, the plantar pressure analysis model can directly obtain the corresponding classification probability as the plantar pressure analysis result based on the pressure data matrix.

[0069] It is understandable that the pressure data of a plantar area collected by the wearable plantar pressure collection device may include pressure signals within at least one gait cycle. For each pressure signal, the pressure signal may be divided into at least one sub-pressure signal according to the gait cycle of the target user. A sub-pressure signal is a pressure signal of a plantar area within a gait cycle. For a gait cycle, the sub-pressure signals of each plantar area within the gait cycle are integrated to obtain a sub-pressure data matrix, and the number of columns of the sub-pressure data matrix may be the number of plantar areas. In the case where the pressure data matrix includes pressure signals within multiple gait cycles, the sub-pressure data matrices of multiple gait cycles are integrated to obtain a pressure data matrix, and the number of columns of the pressure data matrix may be the number of plantar areas, and the number of rows may be the number of gait cycles.

[0070] In a feasible implementation, before "performing header labeling and position embedding processing on each data vector in the pressure data matrix to obtain a target matrix", the plantar pressure analysis method may also include: performing dimension mapping on the pressure data matrix according to a preset data dimension to obtain an intermediate matrix.

[0071] Correspondingly, “performing header marking and position embedding processing on each data vector in the pressure data matrix to obtain a target matrix” includes: performing header marking and position embedding processing on each intermediate data vector in the intermediate matrix to obtain a target matrix.

[0072] It's important to note that after the pressure data matrix is ​​input into the plantar pressure analysis model, it must first be dimensionally mapped to convert it into the dimensional form used by the feature extractor within the plantar pressure analysis model. This dimension mapping is implemented using the linear layer of the Transformer framework. The default data dimension can be determined based on the model scale and the purpose of the plantar pressure analysis.

[0073] Continuing with the target task of detecting high arches, for example, if the pressure data for the target user's left foot during a gait cycle is {L1, L2...L8}, and the pressure data for the target user's right foot is {R1, R2...R8}, then the sub-pressure data matrix is ​​{L1, L2...L8, R1, R2...R8}, and its specific size can be 16×T. After dimensioning, the sub-pressure data matrix can be mapped into a 16×512 matrix. After header labeling and position embedding, this matrix can be processed to obtain a 17×512 target matrix. Multi-head attention processing is performed on the target matrix to extract the pressure feature vector.

[0074] In a feasible implementation, step S500 may include: using a classification predictor of a plantar pressure analysis model to perform dimension mapping and normalization processing on the pressure feature vector to obtain a plantar pressure analysis result of the target user.

[0075] It should be noted that the classification predictor of the plantar pressure analysis model can include a fully connected layer and a softmax layer. The fully connected layer performs dimension mapping on the pressure feature vector, and the softmax layer uses this dimension-mapped pressure feature vector to obtain the classification probability, i.e., the foot pressure analysis result. The fully connected layer of the classification predictor can perform dimension mapping on the pressure feature vector based on the actual purpose of the plantar pressure analysis, obtaining a result vector corresponding to the target dimension, which serves as the input to the softmax layer of the classification predictor.

[0076] In a feasible implementation, the plantar pressure analysis method may further include: obtaining pressure data sample sets of multiple users to obtain a pressure data matrix sample set, where one pressure data sample set includes pressure data samples of multiple plantar areas of a user; constructing an initial domain adversarial transfer network; and using the pressure data matrix sample set to train the initial domain adversarial transfer network to obtain a plantar pressure analysis model.

[0077] It should be noted that the plantar pressure analysis model can be a DANN (Domain-Adversarial Neural Network). The initial domain-adversarial transfer network can include a feature extractor, a classification predictor, and a domain discriminator. During training, the domain discriminator can be used to classify pressure data matrix samples, forming an adversarial relationship with the initial feature extractor, promoting the feature extractor's extraction of domain-invariant features and reducing the impact of inter-individual differences in foot pressure data distribution on actual recognition results.

[0078] During the specific training process, the pressure data matrix samples are passed through the feature extractor G f After mapping, it is transformed into the feature vector corresponding to the input, and the feature vector is passed through the classification predictor G y and domain discriminator G d The internal mapping of the sample plantar pressure analysis results and sample classification results are obtained respectively, and the sample classification label d is substituted into the sample classification label d i , we can get the domain discriminator loss function L d , as shown in Formula 1; similarly, substitute the sample label y i The classification predictor loss function L can be obtained yAfter adding the gradient reversal layer, the total loss function of the initial domain adversarial transfer network is as follows: Formula 2; Among them, the final optimization goal of the total loss function E is as follows: Formula 3, which needs to minimize the loss of the classification predictor loss function part and maximize the loss of the classification predictor loss function part. Thus, the classification predictor G y and the domain discriminator G d There is a relationship similar to that between generation and discrimination. The former makes the extracted feature vector make the classification predictor classification effect better, and the latter makes the domain discriminator have stronger discrimination ability. The two confront each other, and finally achieve a mutual balance between the classification predictor loss function and the domain discriminator loss function.

[0079] Formula 1 is:

[0080]

[0081] Formula 2 is:

[0082]

[0083] Formula 3 is:

[0084]

[0085] x i is the i-th pressure data matrix sample, θ f is the model parameter of the feature extractor, θ y are the model parameters of the classification predictor, θ d is the model parameter of the domain discriminator, λ is the weight parameter, is the prediction result of the classification predictor, is the classification result of the domain discriminator, is the target model parameter of the feature extractor, are the target model parameters of the classification predictor, are the target model parameters of the domain discriminator.

[0086] It can be understood that the domain discriminator includes a gradient reversal layer and a fully connected layer. During the training process, as shown in Formula 4, the gradient reversal layer is used to automatically reverse the gradient direction during the back propagation process, and to achieve the identity transformation during the forward propagation process, thereby achieving confrontation between the feature extractor and the domain discriminator. Formula 4 is:

[0087] R(x)=x

[0088]

[0089] Among them, x is the input data of the gradient reversal layer, R is the function of the gradient reversal layer, and I is the identity matrix.

[0090] In addition, the fully connected layer is mainly used to extract feature mapping. The length of the mapping result is a vector of the number of individual subjects in the sample. This vector is converted into sample classification probability through the Softmax layer. Finally, the sample with the largest classification probability is selected to obtain the individual subject number in the sample.

[0091] Among them, the calculation formula of the Softmax layer is as follows:

[0092]

[0093] z j Input the j-th dimension data in the Softmax layer input vector.

[0094] Therefore, this embodiment provides a plantar pressure analysis method, which integrates the pressure data of different plantar areas of the target user to obtain a pressure data matrix, performs header labeling and position embedding processing on the pressure data matrix, and after obtaining the target matrix, performs multi-head attention processing to extract the pressure feature vector and obtain the plantar pressure analysis result. Since the target matrix after header labeling and position embedding processing is processed by the multi-head attention mechanism, the pressure data of different plantar areas in the target matrix can be paid attention to, and the characteristics of the pressure data are captured from multiple angles, which improves the accuracy of the pressure feature vector and thus improves the accuracy of the plantar pressure analysis result. When applied to high-arch foot recognition, the plantar pressure analysis result corresponds to the high-arch foot recognition result, thereby achieving the effect of improving the accuracy of high-arch foot recognition.

[0095] This application provides a plantar pressure analysis device, such as Figure 2 As shown, the plantar pressure analysis device may include:

[0096] An acquisition module 10 is used to acquire pressure data of multiple sole areas of a target user;

[0097] An integration module 20 is used to integrate multiple pressure data to obtain a pressure data matrix;

[0098] The processing module 30 is used to input the pressure data matrix into the pre-trained plantar pressure analysis model, perform header labeling and position embedding processing on each data vector in the pressure data matrix, and obtain a target matrix;

[0099] An extraction module 40 is used to perform multi-head attention processing on the target matrix to extract a pressure feature vector;

[0100] The analysis module 50 is used to obtain the target user's plantar pressure analysis result according to the pressure feature vector.

[0101] For more implementation details of the specific implementation of the above-mentioned plantar pressure analysis device, please refer to the description of the specific implementation of the plantar pressure analysis method in the above embodiment. For the sake of brevity of the description, they will not be repeated here.

[0102] The present application provides a plantar pressure analysis device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the plantar pressure analysis method in the above-mentioned embodiment one.

[0103] Reference below Figure 3 , which shows a schematic diagram of the structure of a plantar pressure analysis device suitable for implementing the embodiments of the present application. The plantar pressure analysis device in the embodiments of the present application may include, but is not limited to, mobile terminals such as laptop computers, PDAs (Personal Digital Assistants), and PADs (Portable Application Descriptions), as well as fixed terminals such as desktop computers. Figure 3 The plantar pressure analysis device shown is merely an example and should not limit the functions and scope of use of the embodiments of the present application.

[0104] like Figure 3As shown, the plantar pressure analysis device may include a processing device 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes based on programs stored in a read-only memory (ROM) 1002 or programs loaded from a storage device 1003 into a random access memory (RAM) 1004. RAM 1004 also stores various programs and data required for the operation of the plantar pressure analysis device. Processing device 1001, ROM 1002, and RAM 1004 are interconnected via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to I / O interface 1006: input devices 1007 including, for example, a touch screen, touchpad, keyboard, mouse, image sensor, microphone, etc.; output devices 1008 including, for example, a liquid crystal display (LCD) and speakers; storage device 1003 including, for example, a magnetic tape or hard disk; and communication device 1009. The communication device 1009 can allow the plantar pressure analysis device to communicate with other devices wirelessly or wired to exchange data. Although the figure shows a plantar pressure analysis device with various systems, it should be understood that it is not required to implement or have all of the systems shown. More or fewer systems can be implemented or provided instead.

[0105] In particular, according to the embodiments disclosed in the present application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, the embodiments disclosed in the present application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program comprising program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0106] The plantar pressure analysis device provided in this application, utilizing the plantar pressure analysis method described in the aforementioned embodiment, can address the technical problem of how to perform plantar pressure analysis and improve the accuracy of identifying high-arched feet. Compared to related technologies, the plantar pressure analysis device provided in this application achieves the same beneficial effects as the plantar pressure analysis method described in the aforementioned embodiment. Other technical features of this plantar pressure analysis device are the same as those disclosed in the aforementioned embodiment and are not further elaborated here.

[0107] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0108] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

[0109] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, computer program) stored thereon, and the computer-readable program instructions are used to execute the plantar pressure analysis method in the above embodiment.

[0110] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems or devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0111] The computer-readable storage medium may be included in the plantar pressure analysis device, or may exist independently without being assembled into the plantar pressure analysis device.

[0112] The computer-readable storage medium carries one or more programs. When the one or more programs are executed by the plantar pressure analysis device, the plantar pressure analysis device: obtains pressure data of multiple plantar areas of the target user; integrates the multiple pressure data to obtain a pressure data matrix; inputs the pressure data matrix into a pre-trained plantar pressure analysis model, performs header labeling and position embedding processing on each data vector in the pressure data matrix to obtain a target matrix; performs multi-head attention processing on the target matrix to extract a pressure feature vector; and obtains the plantar pressure analysis result of the target user based on the pressure feature vector.

[0113] Computer program code for performing the operations of the present application may be written in one or more programming languages, or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on the remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0114] The flow charts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present application. In this regard, each box in the flow chart or block diagram can represent a module, program segment or a part of code, and the module, program segment or a part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented by a dedicated hardware-based system that performs the specified function or operation, or can be implemented by a combination of dedicated hardware and computer instructions.

[0115] The modules described in the embodiments of the present application may be implemented in software or hardware, wherein the name of a module does not necessarily limit the unit itself.

[0116] The computer-readable storage medium provided in this application stores computer-readable program instructions (i.e., a computer program) for executing the aforementioned plantar pressure analysis method. This computer-readable storage medium addresses the technical problem of performing plantar pressure analysis and improving the accuracy of identifying high-arched feet. Compared to related technologies, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the plantar pressure analysis method provided in the aforementioned embodiments, and are not further elaborated here.

[0117] The present application also provides a computer program product, comprising a computer program, which implements the steps of the above-mentioned plantar pressure analysis method when executed by a processor.

[0118] The computer program product provided in this application can solve the technical problem of how to perform plantar pressure analysis and improve the accuracy of identifying high-arched feet. Compared with related technologies, the beneficial effects of the computer program product provided in this application are the same as those of the plantar pressure analysis method provided in the above-mentioned embodiment, and will not be elaborated here.

[0119] The above description is only part of the embodiments of the present application and does not limit the patent scope of the present application. All equivalent structural transformations made by using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect application in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A plantar pressure analysis method, characterized in that: The plantar pressure analysis method comprises: Obtain pressure data of multiple plantar areas of a target user; Integrating a plurality of the pressure data to obtain a pressure data matrix; Input the pressure data matrix into a pre-trained plantar pressure analysis model, perform header labeling and position embedding processing on each data vector in the pressure data matrix, and obtain a target matrix; Performing multi-head attention processing on the target matrix to extract a pressure feature vector; Obtaining a plantar pressure analysis result of the target user according to the pressure feature vector; Before the step of performing header marking and position embedding processing on each data vector in the pressure data matrix to obtain a target matrix, the plantar pressure analysis method further includes: Performing dimension mapping on the pressure data matrix according to a preset data dimension to obtain an intermediate matrix; The step of performing header marking and position embedding processing on each data vector in the pressure data matrix to obtain a target matrix includes: Perform header marking and position embedding processing on each intermediate data vector in the intermediate matrix to obtain a target matrix.

2. The plantar pressure analysis method according to claim 1, wherein: The step of integrating the plurality of pressure data to obtain a pressure data matrix includes: For each of the pressure data, segment the pressure data according to the gait cycle to obtain at least one sub-pressure data; For each gait cycle, integrating the sub-pressure data of a plurality of the plantar regions to obtain a sub-pressure data matrix; At least one of the sub-pressure data matrices is integrated to obtain the pressure data matrix.

3. The plantar pressure analysis method according to claim 1, wherein: The plurality of plantar regions include a heel region, an arch region, a first metatarsal region, a second metatarsal region, a third to fifth metatarsal region, a first phalanx region, a second phalanx region, and a third to fifth phalanx region.

4. The plantar pressure analysis method according to claim 1, wherein: The step of obtaining the target user's plantar pressure analysis result based on the feature vector includes: The classification predictor of the plantar pressure analysis model is used to perform dimension mapping and normalization processing on the pressure feature vector to obtain the plantar pressure analysis result of the target user.

5. The plantar pressure analysis method according to any one of claims 1 to 4, characterized in that: The plantar pressure analysis method further comprises: Acquire pressure data sample sets of multiple users to obtain a pressure data matrix sample set, wherein one pressure data sample set includes pressure data samples of multiple sole areas of one user; Construct an initial domain adversarial transfer network; The initial domain adversarial transfer network is trained using the pressure data matrix sample set to obtain the plantar pressure analysis model.

6. A plantar pressure analysis device, characterized in that: The plantar pressure analysis device comprises: An acquisition module, used to acquire pressure data of multiple sole areas of a target user; an integration module, configured to integrate the plurality of pressure data to obtain a pressure data matrix; a processing module, configured to input the pressure data matrix into a pre-trained plantar pressure analysis model, perform header labeling and position embedding processing on each data vector in the pressure data matrix, and obtain a target matrix; An extraction module, configured to perform multi-head attention processing on the target matrix to extract a pressure feature vector; an analysis module, configured to obtain a plantar pressure analysis result of the target user based on the pressure feature vector; The plantar pressure analysis device is further configured to perform dimension mapping on the pressure data matrix according to a preset data dimension to obtain an intermediate matrix; The processing module is specifically used to perform header marking processing and position embedding processing on each intermediate data vector in the intermediate matrix to obtain a target matrix.

7. A plantar pressure analysis device, characterized in that: The plantar pressure analysis device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, and is configured to implement the steps of the plantar pressure analysis method according to any one of claims 1 to 5 through the computer program.

8. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the plantar pressure analysis method according to any one of claims 1 to 5 are implemented.

9. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the steps of the plantar pressure analysis method according to any one of claims 1 to 5 are implemented.

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