Pressure detection shoe cover, rehabilitation stage prediction method, device and equipment

Abstract

The invention discloses a pressure detection shoe cover and a rehabilitation stage prediction method, device and equipment, the pressure detection shoe cover comprises a shoe cover body, the shoe cover body is used for fixing a shoe of a user wearing the shoe cover, and the surface of the shoe cover body is provided with a data processor; the shoe cover bottom is made of an elastic material, a pressure sensing layer is arranged on the surface of the shoe cover bottom, and the data processor is connected with the pressure sensing layer and used for collecting a plantar pressure diagram collected by the pressure sensing layer, processing the plantar pressure diagram and generating target data; the target data comprises walking feature data of a user wearing the shoe cover. The shoe cover bottom made of the elastic material can adapt to various foot types, and the elastic material has flexibility and can also adapt to users with different sole sizes, so that the pressure detection shoe cover for collecting sole pressure has good universality.

Description

Technical Field

[0001] The present application belongs to the field of wearable technology, and in particular relates to a pressure detection shoe cover, a rehabilitation stage prediction method, a device, and equipment. Background Art

[0002] In the prior art, when monitoring plantar pressure, a plantar pressure collection device is often fixed in an insole or sole of a fixed size. However, the size and shape of the soles of different users vary. Therefore, for each user, the plantar pressure collection device must be fixed in an insole or sole of the same size as the user. This results in poor versatility in the insoles and soles in which the plantar pressure collection device is mounted, which wastes resources. Furthermore, when the plantar pressure collection device is fixed in insoles or soles of different sizes, the layout of the plantar pressure collection device also varies, requiring the layout of the pressure collection device to be adjusted for each size, further resulting in poor versatility in the insoles and soles in which the plantar pressure collection device is mounted. Summary of the Invention

[0003] The embodiment of the present application provides an implementation solution different from the prior art to solve the technical problem of poor versatility of insoles and soles for installing plantar pressure collection equipment.

[0004] In a first aspect, the present application provides a pressure detection shoe cover, comprising: a shoe cover body, the shoe cover body being used to fix the shoes of a user wearing the shoe cover, and a data processor being provided on a surface of the shoe cover body; a shoe cover bottom, the shoe cover bottom being made of an elastic material, and a pressure sensing layer being provided on a surface of the shoe cover bottom, the data processor being connected to the pressure sensing layer and being used to collect a plantar pressure map collected by the pressure sensing layer, and to process the plantar pressure map to generate target data, wherein the target data includes walking characteristic data of the user wearing the shoe cover.

[0005] In a second aspect, the present application provides a method for predicting a rehabilitation stage, comprising: obtaining a plantar pressure map; inputting the plantar pressure map into a target deep learning model to obtain rehabilitation level information of the corresponding user; wherein the plantar pressure map is collected by the pressure sensing layer at the bottom of the shoe cover in the pressure detection shoe cover described in the first aspect or each possible embodiment of the first aspect.

[0006] In a third aspect, the present application provides a rehabilitation stage prediction device, comprising: an acquisition unit for acquiring a plantar pressure map; an input unit for inputting the plantar pressure map into a target deep learning model to obtain rehabilitation level information of the corresponding user; wherein, the plantar pressure map is collected by the pressure sensing layer at the bottom of the shoe cover in the pressure detection shoe cover described in the first aspect or each possible embodiment of the first aspect.

[0007] In a fourth aspect, the present application provides an electronic device comprising: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to execute any method in the first aspect or any possible implementation of the first aspect by executing the executable instructions.

[0008] In a fifth aspect, an embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any method in the first aspect or any possible implementation manner of the first aspect.

[0009] In a sixth aspect, an embodiment of the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the method described in the first aspect or any of the possible implementations of the first aspect.

[0010] The pressure detection shoe cover provided by the present application includes: a shoe cover body, the shoe cover body is used to fix the shoes of the user wearing the shoe cover, and the surface of the shoe cover body is provided with a data processor; the shoe cover bottom is made of elastic material, and the surface of the shoe cover bottom is provided with a pressure sensing layer, and the data processor is connected to the pressure sensing layer to collect the plantar pressure map collected by the pressure sensing layer, and process the plantar pressure map to generate target data, wherein the target data includes walking characteristic data of the user wearing the shoe cover. The shoe cover bottom made of elastic material can adapt to various foot shapes, and the elastic material is elastic and can also adapt to users with different sole sizes, so that the pressure detection shoe cover for collecting plantar pressure has good versatility. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following is a brief introduction to the drawings required for the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without creative work. In the drawings:

[0012] Figure 1 A schematic structural diagram of a pressure detection shoe cover provided in one embodiment of the present application;

[0013] Figure 2 A flowchart of a method for predicting a rehabilitation stage is provided for one embodiment of the present application;

[0014] Figure 3 A schematic structural diagram of a rehabilitation stage prediction device provided in one embodiment of the present application;

[0015] Figure 4A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0016] The embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings. The embodiments described below with reference to the accompanying drawings are exemplary and intended to be used to explain the present application, but should not be understood as limiting the present application.

[0017] The terms "first" and "second" in the specification, claims and drawings of the embodiments of the present application are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the embodiments of the present application described herein can, for example, be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0018] In the prior art, when monitoring plantar pressure, the plantar pressure collection device is often fixed in an insole or sole of a fixed size. However, the size and shape of the soles of different users vary. Therefore, for different users, the plantar pressure collection device needs to be fixed in an insole or sole of the same size as the user. This makes the insole and sole where the plantar pressure collection device is installed less versatile and wastes resources. Furthermore, when the plantar pressure collection device is fixed in insoles or soles of different sizes, the layout of the plantar pressure collection device also varies, requiring the layout of the corresponding pressure collection device to be set for each size, further resulting in less versatile insole and sole where the plantar pressure collection device is installed. Furthermore, the shape of the soles of different users also varies, so it is necessary to customize insoles or soles with plantar pressure collection devices specifically for each user, resulting in a waste of resources. When using the insole or sole with plantar pressure collection devices in different scenarios, it is necessary to customize specific insoles or soles that can collect plantar pressure according to the needs of each scenario. For example, in sports scenarios, it is necessary to customize insoles or soles that can collect plantar pressure with high sampling frequency and durability; for rehabilitation scenarios, it is necessary to customize insoles or soles that can collect plantar pressure more accurately. For daily walking, it is necessary to customize insoles or soles that can collect plantar pressure that are suitable for long-term use and easy to carry. Therefore, for different usage scenarios, it is necessary to customize insoles or soles that can collect plantar pressure that meet the needs of the usage scenarios. This makes the insoles or soles less versatile, and wastes resources and financial resources. In addition, for insoles or soles of different sizes, specific pressure sensor layouts are required, that is, different preset pressure collection ranges are set. This is also a waste of manpower to a certain extent, resulting in certain limitations.

[0019] The following specific embodiments describe in detail the technical solution of the present application and how the technical solution of the present application solves the above-mentioned technical problems. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described below in conjunction with the accompanying drawings.

[0020] Figure 1 A schematic structural diagram of a pressure detection shoe cover provided by an exemplary embodiment of the present application, wherein the pressure detection shoe cover includes:

[0021] A shoe cover body 21, the shoe cover body 21 is used to fix the shoes of the user wearing the shoe cover, and a data processor 222 is provided on the surface of the shoe cover body 21;

[0022] The shoe cover bottom 22 is made of an elastic material. A pressure sensing layer 221 is provided on the surface of the shoe cover bottom 22. The data processor 222 is connected to the pressure sensing layer 221 and is used to collect the plantar pressure map collected by the pressure sensing layer 221 and process the plantar pressure map to generate target data. The target data includes walking characteristic data of the user wearing the shoe cover.

[0023] Optionally, the data processor 222 is preferably provided on both sides of the shoe cover body 21 or at the heel position.

[0024] Optionally, the data processor 222 may also be in the form of a pendant, hung on the surface of the shoe cover body 21 .

[0025] Optionally, the walking characteristic data includes at least one of the following data: gait cycle, support phase time, swing phase time, single support time, double support time, gait symmetry, full sole contact time, heel-off time and pressure center point trajectory.

[0026] Optionally, in order to make the pressure detection shoe cover better fit various foot shapes, adapt to feet of various sizes, and increase the service life of the pressure detection shoe cover, the elastic material can be made of a material with good elasticity and wear resistance to ensure a good fit in different wearing scenarios and better adapt to various foot shapes. The highly elastic material can be better stretched to adapt to feet of different sizes, thereby making the pressure detection shoe cover more versatile. In addition, the shoe cover bottom 22 made of a material with good elasticity can provide users with a better wearing experience, is also very suitable for sports scenes, and is easy to carry.

[0027] Optionally, a pressure-sensitive layer 221 can be formed by uniformly printing or coating a pressure-sensitive material on the surface of the shoe cover bottom 22. The pressure-sensitive layer 221 is located on the upper surface of the shoe cover bottom 22. The pressure-sensitive layer 221 can also be connected to the shoe cover bottom 22 by gluing, sewing, or other means.

[0028] Optionally, the pressure sensing layer 221 is made of a pressure-sensitive material that can respond to pressure changes of varying degrees and convert the pressure changes of varying degrees into electrical signals.

[0029] Optionally, the pressure sensing layer 221 and the shoe cover bottom 22 may be of the same size or different sizes.

[0030] Optionally, the data processor 222 does not overlap with the pressure sensing layer.

[0031] Optionally, the data processor 222 is an annular structure and is located outside the pressure sensing layer. The annular structure of the data processor 222 may be the same as or different from the outer contour of the shoe cover bottom 22 .

[0032] Optionally, a wireless module is provided at the heel position of the shoe cover body 21 , and the wireless module is connected to the data processor 222 , and is used to send the target data and the plantar pressure map to a terminal device for display.

[0033] Optionally, the terminal device can be a smartphone, a computer, or other device with data storage, data analysis, and real-time monitoring capabilities. Specifically, a dedicated application is installed in the terminal device, and the user interface provided by the application can display information such as the plantar pressure map and target data for the user to view. In addition, the user interface can also allow the user to adjust the pressure acquisition parameter range of the pressure detection shoe cover.

[0034] Optionally, the data processor 222 includes a microprocessor and a signal processing unit; the microprocessor is used to collect the plantar pressure map of the pressure sensing layer 221; the signal processing unit is used to: pre-process the plantar pressure map to obtain a first pressure map of standard size, wherein the pre-processing includes normalization processing, image edge detection processing and matching processing; and process the first pressure map to obtain target data.

[0035] Optionally, in order to improve the accuracy of the plantar pressure map collected by the pressure sensing layer 221, the pressure sensing layer 221 needs to be calibrated. Specifically, the pressure sensing layer 221 can be calibrated by static calibration or dynamic calibration, wherein the static calibration method means that when no pressure is applied to the bottom 22 of the shoe cover, the data collected by the microprocessor is zero. Zero-point drift can be eliminated by static calibration. The dynamic calibration method means that when multiple different pressure values ​​are applied at multiple positions of the pressure detection shoe cover, the pressure value collected by the pressure sensing layer 221 at each of the multiple positions is the same as the pressure value applied at the position. That is, the nonlinear response of the pressure sensing layer 221 is corrected by polynomial fitting.

[0036] For example, a pressure of 0 Newton may be applied at point A of the pressure detection shoe cover, a pressure of 50 Newtons may be applied at point B, a pressure of 100 Newtons may be applied at point C, a pressure of 150 Newtons may be applied at point D, and a pressure of 200 Newtons may be applied at point F. The parameters of the pressure sensing layer 221 need to be adjusted so that the pressure sensing layer 221 detects a pressure of 0 Newton at point A, a pressure of 50 Newtons at point B, a pressure of 100 Newtons at point C, a pressure of 150 Newtons at point D, and a pressure of 200 Newtons at point F.

[0037] Optionally, the pressure detection shoe cover is also provided with adaptive calibration, which can perform adaptive calibration according to the user's actual application situation by using real-time zero drift compensation, dynamic pressure range adjustment, temperature compensation and applicable frequency adaptation, so as to improve the accuracy of the plantar pressure map collected by the pressure detection shoe cover.

[0038] Specifically, to ensure the accuracy and consistency of the target data and eliminate individual differences, and to facilitate cross-user comparisons, the plantar pressure map needs to be preprocessed. First, since the size and shape of each user's foot vary greatly, it is not convenient for subsequent feature extraction. Therefore, the foot shape needs to be normalized. To ensure the quality of the normalized image, filtering and denoising technology is needed to improve the image quality of the plantar pressure map. To more accurately obtain the contour of the sole corresponding to the plantar pressure map, the Sobel edge detection algorithm can be used to locate the plantar contour. The two farthest points at the upper and lower ends of the contour are then used as the heel point and toe point (i.e., the first metatarsal bone), respectively. The fifth metatarsal bone is identified based on the first metatarsal bone using template matching. Finally, the heel point and toe point are used as the reference points for foot length, and the positions of the first and fifth metatarsal heads are used as the reference points for foot width. After identifying the reference points, the plantar pressure map is normalized according to foot length and foot width and mapped onto a standard size grid, where the standard size can be 130 units long and 50 units wide.

[0039] Foot length is the distance from the heel to the end of the first metatarsal bone, while foot width is the distance from the first metatarsal head to the fifth metatarsal head. 130 units is approximately 26 centimeters, and 50 units is approximately 10 centimeters.

[0040] Optionally, during the normalization process, the aspect ratio of the plantar pressure map before normalization and the aspect ratio of the plantar pressure map after normalization are maintained the same. Furthermore, bilinear interpolation resampling is used to map the pressure values ​​of the plantar pressure map before normalization to the plantar pressure map after normalization in the same proportion.

[0041] Optionally, the first pressure map is a two-dimensional map of time and pressure, and time characteristic information and space characteristic information are extracted from the first pressure map to calculate the target data based on the time characteristic information and the space characteristic information.

[0042] Specifically, the unilateral heel strike moment h1 and the unilateral heel strike moment h2 are extracted from the first pressure map, and the interval time between h2 and h1 is calculated, and the interval time is the gait cycle; the heel strike moment h3 and the toe-off moment t1 are extracted from the first pressure map, and a first difference between t1 and h3 is calculated, and the first difference is the stance phase time; the number of gait cycles within 60 seconds is detected from the first pressure map, and a second difference between the number of cycles and the stance phase time is calculated, and the second difference is the swing phase time;

[0043] Based on the first pressure map, multiple left stance phases and multiple right stance phases are extracted. Left and right stance phases with the same duration are obtained from the phases. The overlap between the left and right stance phases is calculated, and the overlap is defined as the double stance time. A third difference is calculated by subtracting the double stance time from the stance phase time, and the third difference is defined as the single stance time. The ratio of the left stance phase to the right stance phase is calculated, and the ratio represents the gait symmetry.

[0044] Obtain a preset threshold, obtain the pre-taken pressure value of the heel and the pressure value of the forefoot area from the first pressure map, when the pressure values ​​of the heel and forefoot areas exceed the threshold at the same time, record the start and end of the period, calculate the duration, which is the contact time of the entire sole of the foot; obtain the first moment when the heel point pressure value drops to the preset threshold from the first pressure map, and obtain the second moment when the heel touches the ground for the first time after the first moment, calculate the duration between the first moment and the second moment, which is the heel-off time. According to the first pressure map, calculate the COP value at each moment to form a trajectory, and then calculate the trajectory length and left and right front and back offsets to obtain the trajectory of the pressure center point. Among them, the time mentioned above refers to the duration.

[0045] In summary, the pressure-sensing shoe cover provided in the application includes: a shoe cover body, which is used to secure the shoe of the user wearing the shoe cover, and a data processor disposed on the surface of the shoe cover body; a shoe cover bottom, which is made of an elastic material and has a pressure-sensing layer disposed on the surface of the shoe cover bottom; and a data processor connected to the pressure-sensing layer, which is used to collect a plantar pressure map captured by the pressure-sensing layer and process the plantar pressure map to generate target data. The target data includes walking characteristic data of the user wearing the shoe cover. The shoe cover bottom made of elastic material can adapt to various foot shapes, and the elastic material is elastic, which can also accommodate users with different foot sizes, making the pressure-sensing shoe cover highly versatile for collecting plantar pressure. The shoe cover also utilizes advanced pressure-sensitive materials, a pressure-sensing layer, and a data processor to achieve high-frequency sampling and real-time data processing, improving the real-time and accuracy of target data acquisition. Furthermore, a zero-drift compensation method is employed to further improve the real-time and accuracy of target data acquisition.

[0046] Figure 2 A flowchart of a method for predicting a rehabilitation stage is provided as an exemplary embodiment of the present application; wherein the method includes the following steps S201-S202:

[0047] S201, obtaining a plantar pressure map;

[0048] S202. Input the plantar pressure map into a target deep learning model to obtain the corresponding user's recovery level information; wherein, the plantar pressure map is collected by the pressure sensing layer at the bottom of the shoe cover in the aforementioned pressure detection shoe cover.

[0049] Optionally, the target deep learning model may be a CNN-LSTM model. For details about the CNN-LSTM model, please refer to the prior art and will not be described in detail here.

[0050] Alternatively, a large number of labeled plantar pressure sample images can be obtained and used to train an initial deep learning model. Each sample image is then fed into the initial deep learning model, which is then trained to produce an intermediate deep learning model and training values. If the loss between the training values ​​and the labels is less than a preset loss, the intermediate deep learning model meets the model requirements of this solution and can be put into use. The intermediate deep learning model obtained from the most recent training is then used as the target deep learning model. During the training process, a multi-task learning strategy is employed, combining classification and regression loss functions to optimize the model parameters of the initial deep learning model. The Adam optimizer and an appropriate learning rate adjustment strategy are used to ensure convergence of the trained intermediate deep learning model. Data augmentation and regularization techniques are used to improve the generalization ability of the intermediate deep learning model. Finally, the performance of the intermediate deep models is evaluated on a test set, and the best intermediate deep learning model is selected as the target deep learning model for practical application. The entire process of the target deep learning model combines the advantages of spatial and temporal feature extraction, enabling efficient analysis of plantar pressure data and high-precision gait parameter estimation and rehabilitation level prediction. Targeted data augmentation is also performed.

[0051] Optionally, the plantar pressure image input to the target deep learning model needs to be transformed into an image of size (T, H, W, C). T is the length of the time series, H and W are the height and width of the plantar pressure image, respectively, and C is the number of channels. The CNN in the target deep learning model extracts spatial features from the input image, primarily through two convolutional layers. The first convolutional layer can have a kernel size of 3×3, 32 kernels, a stride of 1, and uniform padding. The second convolutional layer can have a kernel size of 3×3, 64 kernels, a stride of 1, and uniform padding. Afterwards, a ReLU activation function and a max pooling layer are added after each convolutional layer to enhance nonlinear representation capabilities. Finally, the output of the convolutional layer is flattened into a two-dimensional feature vector, which is then used in the LSTM layer in the target deep learning model to perform time series modeling. The LSTM layer is a single, bidirectional layer with 128 hidden units. The input of the LSTM layer is the spatial feature vector for each time step, and the output is a sequence feature. Finally, the sequence features are processed through a fully connected layer, and the output layer uses a softmax activation function to classify and output the recovery level prediction task, resulting in recovery level information. The recovery level information can be any of the following: 3, 4, 5, or 6 stages, with a higher number of stages indicating a better recovery level.

[0052] Optionally, the target deep learning model can be set in an application. In the application, a user rehabilitation plan can also be given based on the plantar pressure map and rehabilitation level information.

[0053] Figure 3A schematic diagram of a rehabilitation stage prediction device provided as an exemplary embodiment of the present application; wherein the device comprises:

[0054] An acquisition unit 31 is used to acquire a plantar pressure map;

[0055] An input unit 32 is used to input the plantar pressure map into a target deep learning model to obtain the rehabilitation level information of the corresponding user;

[0056] The plantar pressure map is acquired by collecting the pressure sensing layer at the bottom of the pressure detection shoe cover.

[0057] It should be understood that the device embodiments and the method embodiments may correspond to each other, and similar descriptions may refer to the method embodiments. To avoid repetition, they will not be described in detail here. Specifically, the device can perform the above-mentioned method embodiments, and the aforementioned and other operations and / or functions of each module in the device are the corresponding processes in each method in the above-mentioned method embodiments, which will not be described in detail here for the sake of brevity.

[0058] The apparatus of the embodiment of the present application is described above from the perspective of functional modules in conjunction with the accompanying drawings. It should be understood that the functional module can be implemented in hardware form, can be implemented by instructions in software form, or can be implemented by a combination of hardware and software modules. Specifically, the steps of the method embodiment in the embodiment of the present application can be completed by the hardware integrated logic circuit and / or software form instructions in the processor, and the steps of the method disclosed in the embodiment of the present application can be directly embodied as being executed by a hardware decoding processor, or can be executed by a combination of hardware and software modules in the decoding processor. Optionally, the software module can be located in a mature storage medium in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory, and the processor reads the information in the memory and completes the steps in the above method embodiment in conjunction with its hardware.

[0059] Figure 4 : is a schematic block diagram of an electronic device provided in an embodiment of the present application, and the electronic device may include:

[0060] The memory 301 and the processor 302 are configured to store computer programs and transmit the program code to the processor 302. In other words, the processor 302 can call and run the computer program from the memory 301 to implement the method in the embodiment of the present application.

[0061] For example, the processor 302 may be configured to execute the above method embodiments according to instructions in the computer program.

[0062] In some embodiments of the present application, the processor 302 may include but is not limited to:

[0063] General-purpose processor, digital signal processor (DSP), application-specific integrated circuit (ASIC), field-programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware components, etc.

[0064] In some embodiments of the present application, the memory 301 includes but is not limited to:

[0065] Volatile memory and / or non-volatile memory. Non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. Volatile memory can be random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of RAM are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link DRAM (SLDRAM), and direct RAM bus random access memory (DR RAM).

[0066] In some embodiments of the present application, the computer program may be divided into one or more modules, which are stored in the memory 301 and executed by the processor 302 to implement the method provided by the present application. The one or more modules may be a series of computer program instruction segments capable of implementing specific functions, and the instruction segments are used to describe the execution process of the computer program in the electronic device.

[0067] like Figure 4 As shown, the electronic device may further include:

[0068] The transceiver 303 may be connected to the processor 302 or the memory 301 .

[0069] The processor 302 may control the transceiver 303 to communicate with other devices. Specifically, the processor 302 may send information or data to other devices or receive information or data sent by other devices. The transceiver 303 may include a transmitter and a receiver. The transceiver 303 may further include one or more antennas.

[0070] It should be understood that the various components in the electronic device are connected via a bus system, wherein the bus system includes not only a data bus but also a power bus, a control bus and a status signal bus.

[0071] The present application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a computer, enables the computer to perform the method of the above-mentioned method embodiment. In other words, the present application also provides a computer program product containing instructions, which, when executed by a computer, enables the computer to perform the method of the above-mentioned method embodiment.

[0072] When software is used for implementation, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function according to the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center via a wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) method. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integrated. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a digital video disc (DVD)), or a semiconductor medium (e.g., a solid state drive (SSD)).

[0073] Those skilled in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0074] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the modules is merely a logical function division. In actual implementation, there may be other division methods, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or modules, which can be electrical, mechanical or other forms.

[0075] Modules described as separate components may or may not be physically separate, and components displayed as modules may or may not be physical modules, i.e., they may be located in one place or distributed across multiple network elements. Some or all of the modules may be selected based on actual needs to achieve the purpose of the present embodiment. For example, the functional modules in the various embodiments of the present application may be integrated into a processing module, or each module may exist physically separately, or two or more modules may be integrated into a single module.

[0076] The above are only specific embodiments of the present application, but the scope of protection of this 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.

Claims

1. A pressure detection shoe cover, characterized in that: include: a shoe cover body, the shoe cover body being used to fix the shoes of a user wearing the shoe cover, and a data processor being provided on a surface of the shoe cover body; The bottom of the shoe cover is made of an elastic material, and a pressure sensing layer is provided on the surface of the bottom of the shoe cover. The data processor is connected to the pressure sensing layer and is used to collect a plantar pressure map collected by the pressure sensing layer and process the plantar pressure map to generate target data, wherein the target data includes walking characteristic data of the user wearing the shoe cover.

2. The pressure detection shoe cover according to claim 1, characterized in that: The data processor does not overlap with the pressure sensing layer.

3. The pressure detection shoe cover according to claim 1, characterized in that: The data processor is a ring-shaped structure and is located on the periphery of the pressure sensing layer.

4. The pressure detection shoe cover according to claim 1, characterized in that: A wireless module is provided at the heel position of the shoe cover body, and the wireless module is connected to the data processor and is used to send the target data and the plantar pressure map to a terminal device for display.

5. The pressure detection shoe cover according to claim 1, characterized in that: The data processor includes a microprocessor and a signal processing unit; The microprocessor is used to collect the plantar pressure map of the pressure sensing layer; The signal processing unit is used for: Preprocessing the plantar pressure map to obtain a first pressure map of standard size, wherein the preprocessing includes normalization processing, image edge detection processing, and matching processing; The first pressure map is processed to obtain target data.

6. The pressure detection shoe cover according to claim 5, characterized in that: When no pressure is applied to the bottom of the shoe cover, the data collected by the microprocessor is zero.

7. A method for predicting the stage of rehabilitation, characterized in that: include: Obtain plantar pressure maps; Inputting the plantar pressure map into a target deep learning model to obtain the corresponding user's recovery level information; The plantar pressure map is acquired by collecting the pressure sensing layer at the bottom of the pressure detection shoe cover according to any one of claims 1 to 6.

8. A rehabilitation stage prediction device, characterized in that: include: an acquisition unit, for acquiring a plantar pressure map; An input unit, configured to input the plantar pressure map into a target deep learning model to obtain rehabilitation level information of the corresponding user; The plantar pressure map is acquired by collecting the pressure sensing layer at the bottom of the pressure detection shoe cover according to any one of claims 1 to 6.

9. An electronic device, characterized in that: include: processor; as well as a memory for storing executable instructions of the processor; Wherein, the processor is configured to perform the method of claim 7 by executing the executable instructions.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to claim 7 is implemented.

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