CN113257387B

CN113257387B - Wearable device for rehabilitation training, rehabilitation training method and system

Info

Publication number: CN113257387B
Application number: CN202110630853.3A
Authority: CN
Inventors: 印眈峰; 姚晓燕
Original assignee: Shanghai Qifeng Intelligent Technology Co ltd
Current assignee: Shanghai Medical Innovation And Development Foundation
Priority date: 2021-06-07
Filing date: 2021-06-07
Publication date: 2023-01-31
Anticipated expiration: 2041-06-07
Also published as: CN113257387A

Abstract

The embodiment of the specification provides a wearable device for rehabilitation training, a rehabilitation training method and a rehabilitation training system. The method is performed by a wearable device for rehabilitation training, comprising: generating a rehabilitation instruction, and displaying the rehabilitation instruction on a display component of the wearable device for a user to view and execute, wherein the rehabilitation instruction is used for indicating the body part of the user to move to a first position; during the period that the user executes the rehabilitation instruction, acquiring head movement information of the user, correcting the first position in real time, and indicating the body part of the user to move to the second position; and acquiring at least one rehabilitation image, wherein the rehabilitation image is used for displaying that the body part of the user is at the first position and/or the second position, and determining the completion degree of the user executing the rehabilitation instruction based on the at least one rehabilitation image.

Description

Wearable device for rehabilitation training, rehabilitation training method and system

Technical Field

The present description relates to a computer system based on a computational model of rehabilitation data processing, and more particularly, to a wearable device for rehabilitation training, a rehabilitation training method and a system.

Background

When people suffer from certain diseases (such as Parkinson's disease), problems of limb movement disorder, visual cognitive impairment and the like can occur. During the treatment and recovery phases of these diseases, patients may be required to undergo rehabilitation training. In the rehabilitation training process, how to process the rehabilitation data so that the patient can conveniently perform rehabilitation training and conveniently know the rehabilitation condition of the patient is a technical problem to be solved urgently in the field.

Disclosure of Invention

The embodiment of the specification provides a system of wearable equipment for rehabilitation training and the wearable equipment, so that rehabilitation data are processed, and the technical problems that a user is difficult to conveniently perform rehabilitation training and know the rehabilitation condition of the user are solved.

In order to solve the above technical problem, one of the embodiments of the present specification provides a rehabilitation training method, including: generating a rehabilitation instruction, and displaying the rehabilitation instruction on a display component of the wearable device for a user to view and execute, wherein the rehabilitation instruction is used for indicating the body part of the user to move to a first position; during the rehabilitation instruction executed by the user, acquiring head movement information of the user, and correcting the first position in real time to indicate that the body part of the user moves to a second position; and acquiring at least one rehabilitation image, wherein the rehabilitation image is used for displaying that the body part of the user is at the first position and/or the second position, and determining the completion degree of the user executing the rehabilitation instruction based on the at least one rehabilitation image.

One of the embodiments of the present specification provides a rehabilitation training system, including: the generating module is used for generating a rehabilitation instruction, displaying the rehabilitation instruction on a display assembly of the wearable device for a user to view and execute, wherein the rehabilitation instruction is used for indicating the body part of the user to move to a first position; the first acquisition module is used for acquiring the head movement information of the user during the rehabilitation instruction execution period of the user, correcting the first position in real time and indicating the body part of the user to move to a second position; and the second acquisition module is used for acquiring at least one rehabilitation image of the user executing the rehabilitation instruction and determining the completion degree of the user executing the rehabilitation instruction based on the at least one rehabilitation image.

One of the embodiments of the present specification provides a wearable device for rehabilitation training, including: a head support structure for wearing on the head of a user performing rehabilitation training; the display component is arranged on the head support structure and used for displaying the rehabilitation instruction and the rehabilitation data; a sensing component for acquiring the head data and the rehabilitation data of the user; the processor is in signal connection with both the display assembly and the sensing assembly; the processor is configured to: the display component is controlled to display and/or play the rehabilitation instruction, the rehabilitation data of the user acquired by the sensing component are received, and the display component is controlled to display the rehabilitation data and/or rehabilitation information corresponding to the rehabilitation data.

One of the embodiments of the present specification provides a computer-readable storage medium, which stores computer instructions, and when the computer reads the computer instructions in the storage medium, the computer executes the following method: generating a rehabilitation instruction, and displaying the rehabilitation instruction on a display component of the wearable device for a user to view and execute, wherein the rehabilitation instruction is used for indicating the body part of the user to move to a first position; during the rehabilitation instruction executed by the user, acquiring head movement information of the user, and correcting the first position in real time to indicate that the body part of the user moves to a second position; and acquiring at least one rehabilitation image, wherein the rehabilitation image is used for displaying that the body part of the user is at the first position and/or the second position, and determining the completion degree of the user executing the rehabilitation instruction based on the at least one rehabilitation image.

Drawings

The present description will be further explained by way of exemplary embodiments, which will be described in detail by way of the accompanying drawings. These embodiments are not intended to be limiting, and in these embodiments like numerals are used to indicate like structures, wherein:

FIG. 1 is a schematic diagram of an application scenario of a rehabilitation training system according to some embodiments of the present description;

FIG. 2 is an exemplary flow diagram of a rehabilitation training method according to some embodiments of the present description;

FIG. 3 is an exemplary flow diagram illustrating updating of initial rehabilitation instructions according to some embodiments of the present description;

FIG. 4 is a schematic diagram illustrating generation of an updated recommendation of an initial rehabilitation instruction by a rehabilitation training model according to some embodiments of the present description;

FIG. 5 is a block diagram of a rehabilitation training system according to some embodiments of the present description;

FIG. 6 is a schematic structural diagram of a wearable device for rehabilitation training according to some embodiments of the present description;

FIG. 7 is a schematic display interface diagram of a display assembly of a wearable device for rehabilitation training, according to some embodiments of the present description;

fig. 8 is a display interface schematic diagram of a display assembly of a wearable device for rehabilitation training, according to some embodiments of the present description.

Detailed Description

In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings used in the description of the embodiments will be briefly described below. It is obvious that the drawings in the following description are only examples or embodiments of the present description, and that for a person skilled in the art, the present description can also be applied to other similar scenarios on the basis of these drawings without inventive effort. Unless otherwise apparent from the context, or otherwise indicated, like reference numbers in the figures refer to the same structure or operation.

It should be understood that "system", "apparatus", "unit" and/or "module" as used herein is a method for distinguishing different components, elements, parts, portions or assemblies at different levels. However, other words may be substituted by other expressions if they accomplish the same purpose.

As used in this specification and the appended claims, the terms "a," "an," "the," and/or "the" are not intended to be inclusive in the singular, but rather are intended to be inclusive in the plural, unless the context clearly dictates otherwise. In general, the terms "comprises" and "comprising" merely indicate that steps and elements are included which are explicitly identified, that the steps and elements do not form an exclusive list, and that a method or apparatus may include other steps or elements.

Flow charts are used in this description to illustrate operations performed by a system according to embodiments of the present description. It should be understood that the preceding or following operations are not necessarily performed in the exact order in which they are performed. Rather, the various steps may be processed in reverse order or simultaneously. Meanwhile, other operations may be added to the processes, or a certain step or several steps of operations may be removed from the processes.

The wearable device of one or more embodiments of the present application can be a smart product applied to rehabilitation training of a user. The smart product may include, but is not limited to, AR (Augmented Reality), VR (Virtual Reality), MR (Mediated Reality), XR, etc. glasses and wearable devices.

In some embodiments, the wearable device may be an AR glasses, and the AR glasses may apply virtual information, images, and the like to the real world, and the real environment and the virtual object exist simultaneously superimposed on the same picture or space in real time, so as to achieve an effect of virtual-real combination. For example, when a user is performing rehabilitation training using a wearable device, virtual information and images (e.g., virtual markers, motion indication signals, etc.) generated or emitted by the wearable device may be received, as well as real environments and images (e.g., the user's own body part, surrounding environment, etc.) may be seen.

The "user" described in this application refers to a party in need of service, for example, a patient in need of recovery of body functions after illness or injury, a person in need of physical exercise, and the like.

The application scene of the rehabilitation training system can be used for rehabilitation training of patients with diseases or injuries needing to recover the body functions, and can also be used for daily exercise of users needing to do physical exercise. It should be understood that the above application scenarios are only examples or embodiments of the present application, and that those of ordinary skill in the art will be able to apply the present application to other similar scenarios without inventive effort based on these figures. For example, other similar medical rehabilitation training systems.

Fig. 1 is a schematic diagram of an application scenario of a rehabilitation training system according to some embodiments of the present disclosure. As shown in fig. 1, the rehabilitation training system 100 may include a processing device 110, one or more databases 130, one or more clients 140, one or more networks 150, and one or more information sources 160.

The processing device 110 may comprise a processing unit 120. In some embodiments, the processing unit 120 may be a system that analyzes and processes the collected information to generate an analysis result, for example, the processing unit 120 may acquire rehabilitation data, rehabilitation images, and other information of the user and analyze and process the information to determine the completion degree of the user executing the corresponding rehabilitation instruction.

The processing device 110 may also be a processor integrated on the wearable device, which may be in signal connection with, configured to control, and receive signals, data, etc. sent to control various components of the wearable device.

The processing unit 120 may be a server or a server group, and the servers in the group are connected via a wired or wireless network. A group of servers may be centralized, such as a data center; a server farm may also be distributed, such as a distributed system. The processing unit 120 may be centralized or distributed.

The processing unit 120 can directly access and/or access the data information stored in the database 130, and can also directly access and/or access the information of the user terminal 140 through the network 150. In some embodiments, database 130 may generally refer to a device having storage capabilities. Database 130 is primarily used to store data collected from users 120 and various data utilized, generated, and output by processing unit 120 in its operation. The database 130 may be local or remote. The connection or communication of the database 130 with the processing device 110 or a portion thereof (e.g., the processing unit 120) may be wired or wireless.

The user terminal 140 may be a tool or other entity directly associated with the service, for example, the user terminal 140 may be a wearable device directly used by the user, or may be a terminal for controlling the wearable device. In some embodiments, the user of the user end 140 may not be the user himself. For example, the user a of the user terminal 140 may use the user terminal 140 to request the user B to change the rehabilitation instruction or the difficulty of the rehabilitation instruction. For simplicity, the user of the ue 140 may also be referred to as simply a subscriber. In some embodiments, the user terminal 140 may include one or a combination of mobile devices 140-1, laptop computers 140-2, desktop computers 140-3, wearable devices 600, and the like. The mobile device 140-1 may be one or more of a smartphone, a Personal Digital Assistant (PDA), a tablet computer, and the like. The mobile device 140-1, the laptop computer 140-2, and the desktop computer 140-3 may be used to control the wearable device 600, for example, to control the wearable device 600 to generate rehabilitation instructions, obtain rehabilitation images of the user, and so on.

The network 150 may be a single network or a combination of multiple different networks. For example, the network 150 may be a Local Area Network (LAN), wide Area Network (WAN), public network, private network, public Switched Telephone Network (PSTN), the internet, wireless network, virtual network, or any combination thereof. Network 150 may also include a plurality of network access points, e.g., wired or wireless access points such as base station 150-1, base station 150-2, internet exchange points, etc., through which any data source may access network 150 and transmit information through network 150. For ease of understanding, the ue 140 in the ms service is taken as an example, but the application is not limited to this embodiment. For example, the user end 140 may be a mobile phone or a tablet computer, and the rehabilitation training system 100 of the user end 140 may be classified as a wireless network (bluetooth, wireless Local Area Network (WLAN), wi-Fi, etc.), a mobile network (2G, 3G, 4G signals, etc.), or other connection methods (virtual private network (VPN)), a shared network, near Field Communication (NFC), zigBee, etc.

Information source 160 is a source that provides other information to the system. Information source 160 may be used to provide service-related information, such as road information, to the system. The information source 160 may be in the form of a single central server, or may be in the form of a plurality of servers connected via a network, or may be in the form of a large number of personal devices. When the information source exists in the form of a large number of personal devices, these devices can upload text, sound, images, videos, and the like to the cloud server in a user-generated content manner, so that the cloud server forms the information source together with the personal devices connected thereto.

Fig. 2 is an exemplary flow diagram of a rehabilitation training method according to some embodiments of the present description. As shown in fig. 2, the process 200 may include the following steps.

Step 210, generating a rehabilitation instruction, and displaying the rehabilitation instruction on a display component of the wearable device for a user to view and execute. In some embodiments, step 210 may be performed by the first generation module 510.

The rehabilitation instruction refers to an instruction for assisting the user in rehabilitation training. In some embodiments, the rehabilitation instructions may be one or more of a combination of text instructions, image instructions (e.g., pictures), voice instructions, video instructions (e.g., animated video). For example only, the image instruction may be an image with an arrow (e.g., an upward arrow, a counterclockwise arrow, etc.) indicating a direction in which the user's body part needs to be moved and a mark area indicating a location to which the user's body part needs to be moved; the animation video may be an animation video in which a certain virtual body part moves in the picture, and the body part corresponding to the user needs to move to the same position together with the virtual body part in the picture.

In some embodiments, the rehabilitation instructions may be for instructing a body part of the user to move to the first position. In some embodiments, the first location may be a location to which the corresponding body part can move in anticipation of better rehabilitation. For example only, the rehabilitation instructions may instruct the user's body part to move to the first position in the form of text instructions in combination with image instructions. For example, the rehabilitation instruction may be a text instruction of "please lift the right arm to a designated position" and an image instruction of marking the designated position, and the user may lift the right arm to move to the designated position of the image in the wearable device after receiving the rehabilitation instruction, so as to achieve the effect of performing rehabilitation training on the right arm.

In some embodiments, the rehabilitation instructions may also be used to instruct the user to perform non-athletic operations. For example, the rehabilitation instructions may instruct the user to take a break, eat, etc. In some embodiments, the rehabilitation instructions may also be used with the wearable device itself. For example, the wearable device is instructed to massage the user's head. The present embodiment is not limited with respect to the content and effect of rehabilitation instructions.

In some embodiments, the first generation module 510 may automatically generate the rehabilitation instructions through hardware, software, or a combination of software and hardware. For example, the first generation module 510 may obtain personal information, disease information, etc. of the user from the database 130, and analyze and calculate the information through the processing device 110 to automatically generate a rehabilitation instruction suitable for the user.

In some embodiments, the first generation module 510 may also receive rehabilitation instructions from the outside. For example, the first generation module 510 may receive rehabilitation instructions over the network 150 to be formulated by a doctor or rehabilitee.

In some embodiments, the first generation module 510 may display the rehabilitation instructions on a display component of the wearable device for the user to view and execute. In some embodiments, a display component may be used to display rehabilitation instructions, and further details regarding the display component may be found in fig. 6 and its associated description.

In some embodiments, the rehabilitation instructions may be a set of instructions that may include at least one instruction for instructing the user's body part to move to a first position. Understandably, the instruction set may be considered a rehabilitation regimen for the user, or a portion thereof. For an instruction set comprising a plurality of rehabilitation instructions, all or part of the plurality of rehabilitation instructions may be used for different rehabilitation training stages of the user, and for the relevant contents of the rehabilitation instructions of the different rehabilitation training stages, see the further description below.

Step 220, during the period that the user executes the rehabilitation instruction, acquiring the head movement information of the user, correcting the first position in real time, and indicating the body part of the user to move to a second position.

In some embodiments, step 220 may be performed by the first obtaining module 520.

The head movement information refers to information related to head movement. E.g., whether the head generates motion, the size of the motion value generated by the head, etc. Since the user usually moves the body part to the first position of the virtual image in the wearable device during the execution of the rehabilitation instruction, if the head of the user moves, the virtual image seen by the user may deviate from the picture of the real environment, which may cause problems that the user cannot accurately execute the rehabilitation instruction, the wearable device cannot accurately acquire the data of the user for performing the rehabilitation training, and the like.

The first obtaining module 520 may obtain the head movement information of the user in various ways. In some embodiments, the first acquisition module 520 may acquire the head information of the user through a sensing component of the wearable device. For example, the sensing component may detect the head movement of the user through a motion sensing technology, for example, it may directly detect whether the head of the user moves and the magnitude of the motion value. For more details on the sensing assembly, reference may be made to fig. 6 and its associated description.

In some embodiments, the first obtaining module 520 may obtain the motion information of the head of the user through a virtual image of the wearable device. For example, the wearable device may generate two marker points on a virtual image while generating the virtual image, where one marker point may change position with the head movement of the user and the other marker point may not change position with the head movement of the user. At the starting time and the ending time of the user executing the rehabilitation instruction, the first obtaining module 520 may obtain the relative position between the two mark points at the two times, respectively, to obtain the head movement information of the user. For example, if the relative positions of the two mark points at the two moments change, it indicates that the head of the user moves, and the distance between the two mark points or the change value of the coordinates is the size of the head movement value of the user.

Since the user's head movement during execution of rehabilitation instructions affects the rehabilitation training, it is desirable to reduce the influence of the head movement on the rehabilitation training as much as possible. In some embodiments, the first acquisition module 520 may make a real-time correction of the first position, indicating that the body part of the user is moving to the second position. The second position may be a position corresponding to the rehabilitation training effect when the user moves to the first position after the user generates the head movement and the head movement is not generated.

In some embodiments, the virtual image of the wearable device may have a proportional translation relationship with the frame of the real environment. For example, the ratio of the virtual image of the wearable device to the real environment is 1:20, the ratio may be a conversion ratio of distance, coordinate, length, size of objects present in the two.

In some embodiments, the first obtaining module 520 may perform real-time correction of the first position based on the translation relationship. For example, when the acquired head movement information of the user is "5 cm above the head", according to the conversion relationship, for example, the ratio of the conversion relationship is 1:20, the first obtaining module 520 may adjust the position of the virtual image in the wearable device downward by 0.25cm as a whole.

By acquiring the head information of the user, whether the virtual image seen by the user is deviated from the picture of the real environment can be judged quickly and effectively. And after the head movement generated by the user is acquired, the deviation between the virtual image and the picture of the real environment can be eliminated by correcting the first position, so that the accuracy of the user in executing the rehabilitation instruction and the rehabilitation training effect can be improved.

Step 230, obtaining at least one rehabilitation image, wherein the rehabilitation image is used for displaying that the body part of the user is at the first position and/or the second position, and determining the completion degree of the user executing the rehabilitation instruction based on the at least one rehabilitation image. In some embodiments, step 230 may be performed by the second acquisition module 530.

The rehabilitation image refers to an image during which the user performs rehabilitation instructions. The rehabilitation image may be used to display the user's body part in the first position and/or the second position. For example, the rehabilitation image may be an image of the user moving the body part to the first position.

In some embodiments, the second obtaining module 530 may obtain at least one rehabilitation image by taking a picture. For example, the second acquiring module 530 may be a camera integrated on a wearable device, an image acquiring terminal, or the like. In some embodiments, the second obtaining module 530 may obtain a plurality of rehabilitation images within a predetermined time or a plurality of predetermined time intervals, so as to better judge the rehabilitation training effect of the user according to the rehabilitation images.

In some embodiments, the second obtaining module 530 may determine the completion of the user's execution of the rehabilitation instructions based on the at least one rehabilitation image. The degree of completion refers to the degree to which the user has completed executing the rehabilitation instructions. In some embodiments, the completion may be in the form of numbers, percentages, text, or any combination thereof. For example, the completion degree may be "90 points", "100%", "good recovery", or the like.

In some embodiments, the second acquisition module 530 may acquire a standard image. The standard image corresponds to a standard completion of the rehabilitation instruction. The standard image is an image of the user executing the rehabilitation instruction corresponding to the standard completion degree. The standard completion degree refers to a judgment index corresponding to the standard completion degree of the user executing the rehabilitation instruction. In some embodiments, the standard completion may be a standard value, for example, a standard completion of 100%. In some embodiments, the standard completion may also be a range of values, for example, 90% to 100% standard completion.

In some embodiments, the second acquisition module 530 may acquire the standard image when the body part of the user happens to move to the first position or the second position. In some embodiments, the user may be a doctor, a rehabilitee, a test person of a wearable device, or the like. For example only, after generating or formulating a rehabilitation instruction, the doctor may wear the wearable device to execute the rehabilitation instruction, and when the body part of the doctor moves to the first position, the second obtaining module 530 may obtain a corresponding standard image.

In some embodiments, the second obtaining module 530 may determine the completion of the user executing the rehabilitation instruction based on the comparison of the standard image and the at least one rehabilitation image. In some embodiments, the comparison result may be a deviation value of the standard image and the rehabilitation image. For example, the offset value may be a positional offset value of the body part of the user in the two images. For example, if the rehabilitation instruction is that the user's left foot is advanced by 50cm, and the left foot of the user is advanced by 40cm in the rehabilitation image, the deviation value is 10cm.

In some embodiments, the second obtaining module 530 may determine the completion degree of the rehabilitation instruction executed by the user according to the ratio of the deviation value to the standard value. The criterion value may be a movement value for which the user's body part has just moved to the first position, e.g. the user has taken the left foot forward by 50cm in the previous example. Continuing with the previous example, if the deviation value of the comparison result is 10cm, the ratio of the deviation value to the standard value is 20%, which indicates that the rehabilitation training effect of the user still remains 20%, and the second obtaining module 530 may determine that the completion degree of the user executing the rehabilitation instruction is 80%.

In some embodiments, for an instruction set containing a plurality of rehabilitation instructions, the second obtaining module 530 may determine the completion of each instruction based on at least one rehabilitation image and each instruction of the instruction set. For example, the second obtaining module 530 may obtain a standard image corresponding to each rehabilitation instruction, and determine the completion of each instruction according to a comparison result between the rehabilitation image and the standard image.

In some embodiments, the second fetch module 530 may also determine the completion of the set of instructions based on the completion of each instruction described above. For example, the second obtaining module 530 may calculate the plurality of completion degrees by averaging, weighted averaging, median taking and the like after obtaining the completion degree of each rehabilitation instruction, and determine the obtained calculation result as the completion degree of the instruction set.

By determining the completion degree of the user executing the instruction set, the rehabilitation training effect of the user in the current rehabilitation training stage can be more comprehensively evaluated, so that whether a further rehabilitation training scheme is formulated for the user can be accurately judged.

It should be noted that the above description related to the flow 200 is only for illustration and description, and does not limit the applicable scope of the present specification. Various modifications and alterations to flow 200 will be apparent to those skilled in the art in light of this description. However, such modifications and variations are still within the scope of the present specification.

Fig. 3 is an exemplary flow diagram illustrating updating of initial rehabilitation instructions according to some embodiments of the present description. As shown in fig. 3, the process 300 may include the following steps.

Step 310, acquiring physiological data of the user. In some embodiments, step 310 may be performed by third acquisition module 560.

Physiological data refers to data that is related to a physiological characteristic. Such as heart rate, blood pressure, body temperature, etc. In some embodiments, the third acquisition module 560 may monitor the physical condition of the user through the sensing component to acquire physiological data of the user. For example, the sensing component may monitor the heartbeat of the user, and thereby obtain the heart rate of the user. For more details on the sensing assembly, reference may be made to fig. 6 and its associated description.

Step 320, a plurality of initial rehabilitation instructions are generated, the plurality of initial rehabilitation instructions corresponding to a plurality of rehabilitation training phases of the user. In some embodiments, step 320 may be performed by the second generation module 570.

The number and specific content of the rehabilitation training stages can be set by doctors, rehabilitation teachers, and the wearable device according to the physiological data, disease information and rehabilitation targets of the user.

In some embodiments, the rehabilitation training phase may be established according to the total time of rehabilitation training, for example, for a rehabilitation training regimen of 10 days, the first 5 days of rehabilitation training performed by the user may be established as the first rehabilitation training phase, and the last 5 days as the second rehabilitation training phase. The rehabilitation training stage may also be formulated according to the rehabilitation training effect of the user, for example, the first rehabilitation training stage may be defined as a stage in which the completion degree of the user executing the rehabilitation instruction reaches 50%, and the next rehabilitation training stage may be defined as a stage in which the completion degree of the user executing the rehabilitation instruction is 51% to 80%.

In some embodiments, the second generation module 570 may generate initial rehabilitation instructions corresponding to a plurality of rehabilitation training sessions. For example only, rehabilitation instructions of different difficulty may be generated for different rehabilitation training sessions. For example, the difficulty of the rehabilitation instruction corresponding to the rehabilitation training stage at the front may be lower than the difficulty of the rehabilitation instruction corresponding to the rehabilitation training stage at the rear. For example, the initial rehabilitation instruction for the first rehabilitation training phase may be "raise the right foot by 30cm", and accordingly, the initial rehabilitation instruction for the second rehabilitation training phase may be "raise the right foot by 50cm".

Step 330, determining the completion of the user executing the plurality of initial rehabilitation instructions. In some embodiments, step 330 may be performed by the third determination module 580.

In some embodiments, the third determination module 580 may determine the completion of the user's execution of the plurality of initial rehabilitation instructions and based thereon determine the rehabilitation training effect corresponding to the user at different rehabilitation training stages. For relevant content of determining the completion degree of the user executing the rehabilitation instruction, see step 230 and the related description thereof.

Step 340, updating the plurality of initial rehabilitation instructions based on the physiological data and/or the completion of the user executing the plurality of initial rehabilitation instructions. In some embodiments, step 340 may be performed by the update module 590.

In some embodiments, the update module 590 may update all or a portion of the plurality of initial rehabilitation instructions based on the physiological data of the user. For example only, the updating module 590 may obtain the current heart rate of the user through the sensing component and analyze the current heart rate, if the current heart rate of the user is too high, it indicates that the user is difficult to execute the initial rehabilitation instruction, and the difficulty of the initial rehabilitation instruction is high, and the updating module 590 may down-regulate the initial rehabilitation instruction, or down-regulate a plurality of initial rehabilitation instructions close to the difficulty of the initial rehabilitation instruction, so that the user obtains a rehabilitation training scheme with a suitable difficulty.

In some embodiments, the update module 590 can update all or a portion of the plurality of initial rehabilitation instructions based on the completion of the user's execution of the plurality of initial rehabilitation instructions. In some embodiments, the updating module 590 may obtain a plurality of degrees of completion of the user performing the same initial rehabilitation instruction in different rehabilitation training stages, and analyze the plurality of degrees of completion. For example, the obtained completion degrees are 40%, 60%, and 80%, it can be analyzed that the progress of the user in executing the initial rehabilitation instruction is obvious, and the updating module 590 may increase the difficulty of the initial rehabilitation instruction, so that the user obtains a better rehabilitation training scheme.

In some embodiments, the update module 590 may obtain a plurality of degrees of completion of the plurality of initial rehabilitation instructions and analyze the plurality of degrees of completion. For example, the completion degree of obtaining the initial rehabilitation instruction a is 60%, the completion degree of obtaining the initial rehabilitation instruction B is 70%, and the completion degree of obtaining the initial rehabilitation instruction C is 100%, and it can be analyzed that the completion degree of executing the initial rehabilitation instruction C is the best, and the completion degrees of executing the initial rehabilitation instruction a and the initial rehabilitation instruction B are general, so the updating module 590 can adjust the difficulty of the initial rehabilitation instruction a and the initial rehabilitation instruction B down, and increase the difficulty of the initial rehabilitation instruction C.

In some embodiments, the update module 590 can update the plurality of initial rehabilitation instructions based on the physiological data and the completion of the user's execution of the plurality of initial rehabilitation instructions. For example only, the update module 590 may perform a comprehensive analysis combining the physiological data of the user and the completion of the plurality of initial rehabilitation instructions. For example, the updating module 590 may obtain that the initial rehabilitation instruction D is completed by 95%, but if the heart rate of the user is too large during the execution of the initial rehabilitation instruction D, it indicates that the difficulty of the user in executing the initial rehabilitation instruction D is large, and although the completion degree of the initial rehabilitation instruction D is high, the updating module 590 may also adjust the difficulty of the initial rehabilitation instruction D down to ensure that the user obtains a more appropriate rehabilitation instruction.

It should be noted that the above description of the process 300 is for illustration and description only and is not intended to limit the scope of the present disclosure. Various modifications and changes to flow 300 will be apparent to those skilled in the art in light of this description. However, such modifications and variations are intended to be within the scope of the present description.

FIG. 4 is a schematic diagram illustrating generation of an updated recommendation of an initial rehabilitation instruction by a rehabilitation training model according to some embodiments of the present description.

In some embodiments, the plurality of initial rehabilitation instructions, the physiological data, and the rehabilitation data for the plurality of rehabilitation training phases may be processed based on the trained rehabilitation training model to determine whether the initial rehabilitation instructions of the user in the current rehabilitation training phase need to be updated. Wherein the rehabilitation data reflects at least a movement of a body part of the user. For example, the rehabilitation data may include the completion of the user's execution of the rehabilitation instructions, the magnitude of the body part movements, and the like.

In some embodiments, the rehabilitation training model may be a machine learning model. In some embodiments, the machine learning model may include, but is not limited to, a neural network model (e.g., CNN model, DNN model, RNN model), a support vector machine model, a lambdatarank model, a LambdaMart model, a GBDT + LR model, and the like.

In some embodiments, the input to the trained model of the rehabilitation training may be a plurality of initial rehabilitation instructions, physiological data, rehabilitation data for a plurality of rehabilitation training sessions, and the output may be whether the user's initial rehabilitation instructions at the current or next rehabilitation training session need to be updated.

In some embodiments, the rehabilitation training model may include a recurrent neural network layer (RNN layer) and a fully-connected layer. The recurrent neural network layer may be configured to encode the input data, acquire a plurality of sequence data corresponding to the input data, and output an association relationship between the plurality of sequence data. The fully-connected layer may output a corresponding classification result based on the association relationship of the plurality of sequence data, for example, the classification result may be yes or no, yes indicates that the current initial rehabilitation instruction needs to be updated, and no indicates that the current initial rehabilitation instruction does not need to be updated. For the related contents of updating the initial rehabilitation instruction, refer to fig. 3 and the related description thereof.

In some embodiments, the updated recommendation for the initial rehabilitation instruction may also be determined by a trained rehabilitation training model. For example, after the initial rehabilitation instruction is determined to need to be updated, the rehabilitation training model may analyze the initial rehabilitation instruction according to the degree of completion and difficulty of the initial rehabilitation instruction, and physiological data and rehabilitation data of the user, for example, if the difficulty of the initial rehabilitation instruction is identified to be too high by the rehabilitation training model, the corresponding update suggestion may be to down-regulate the initial rehabilitation instruction in the current rehabilitation training stage and the next rehabilitation training stage.

In some embodiments, the rehabilitation training model may be trained based on a plurality of labeled training samples, specifically, the labeled training samples are input into the initial rehabilitation training model and trained by a common method (such as a gradient descent method) to update the relevant parameters of the rehabilitation training model, and obtain a trained rehabilitation training model. For example only, each training sample may include physiological data of the user, current rehabilitation data, initial rehabilitation instructions of the current rehabilitation training stage, historical rehabilitation data of the user performing rehabilitation training in the historical rehabilitation training stage, historical initial rehabilitation instructions, initial rehabilitation instructions of the user performing training next time, and the like. The manner in which the training samples are obtained may call historical data in a memory or database 130 connected to the server.

In some embodiments, the identification of the sample may be whether the initial rehabilitation instructions need to be updated. If so, it is labeled "1", otherwise it is labeled "0". For example only, the user a performs three rehabilitation training stages, and if the physiological data of the user in the second rehabilitation training stage is within a normal preset range, for example, the heart rate of the user is 60 to 100 times/minute, the historical data of the first stage and the initial rehabilitation instruction of the second stage are used as positive samples, which indicate that the positive samples do not need to be updated and are marked as "0"; if the physiological data of the user in the second rehabilitation training stage is not in the normal preset range and is a value higher than the range, for example, the heart rate of the user is more than 100 times/minute, the historical data of the first stage and the initial rehabilitation instruction of the second stage are used as negative samples, the negative samples are required to be updated, and the corresponding updating suggestion is the difficulty of adjusting the initial rehabilitation instruction downwards.

In some embodiments, the obtaining manner of the identifier may be manual marking, or may also be machine automatic marking or other manners, which is not limited in this embodiment.

FIG. 5 is a block diagram of a rehabilitation training system according to some embodiments of the present disclosure. As shown in fig. 5, the system 500 may include a first generating module 510, a first obtaining module 520, a second obtaining module 530, a first determining module 540, a second determining module 550, a third obtaining module 560, a second generating module 570, a third determining module 580, and an updating module 590.

In some embodiments, the first generation module 510 may be configured to generate rehabilitation instructions for instructing the user to move the body part to the first position, and to display the rehabilitation instructions on a display component of the wearable device for viewing and execution by the user.

In some embodiments, the first obtaining module 520 may be configured to obtain head movement information of the user during the user executing the rehabilitation instruction, perform real-time correction on the first position, and instruct the body part of the user to move to a second position; and

in some embodiments, the second obtaining module 530 may be configured to obtain at least one rehabilitation image of the user executing the rehabilitation instruction, and determine the completion of the user executing the rehabilitation instruction based on the at least one rehabilitation image.

In some embodiments, the second obtaining module 530 may be further configured to: acquiring a standard image, wherein the standard image corresponds to the standard completion degree of the rehabilitation instruction; and determining the completion degree of the user executing the rehabilitation instruction based on the comparison result of the standard image and the at least one rehabilitation image.

In some embodiments, the rehabilitation instructions are a set of instructions including at least one instruction for instructing the user's body part to move to a first position.

In some embodiments, the first determination module 540 may be configured to determine a degree of completion of each instruction of the set of instructions based on the at least one rehabilitation image and the each instruction.

In some embodiments, the second determination module 550 may be configured to determine the completion of the set of instructions based on the completion of each of the instructions.

In some embodiments, the third acquisition module 560 may be used to acquire physiological data of the user.

In some embodiments, the second generation module 570 may be configured to generate a plurality of initial rehabilitation instructions corresponding to a plurality of rehabilitation training sessions of the user.

In some embodiments, the third determination module 580 may be for determining a degree of completion of the user's execution of the plurality of initial rehabilitation instructions.

In some embodiments, the update module 590 may be configured to update the plurality of initial rehabilitation instructions based on the physiological data and/or the completion of the user's execution of the plurality of initial rehabilitation instructions.

It should be understood that the system and its modules shown in FIG. 5 may be implemented in a variety of ways. For example, in some embodiments, the system and its modules may be implemented in hardware, software, or a combination of software and hardware. Wherein the hardware portion may be implemented using dedicated logic; the software portions may be stored in a memory for execution by a suitable instruction execution system, such as a microprocessor or specially designed hardware. Those skilled in the art will appreciate that the methods and systems described above may be implemented using computer executable instructions and/or embodied in processor control code, such code being provided, for example, on a carrier medium such as a diskette, CD-or DVD-ROM, a programmable memory such as read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The system and its modules in this specification may be implemented not only by hardware circuits such as very large scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, etc., or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., but also by software executed by various types of processors, for example, or by a combination of hardware circuits and software (e.g., firmware).

It should be noted that the above description of the rehabilitation training system 500 and the modules thereof is for convenience only and should not limit the present disclosure to the scope of the illustrated embodiments. It will be appreciated by those skilled in the art that, given the teachings of the present system, any combination of modules or sub-system configurations may be used to connect to other modules without departing from such teachings. For example, the first obtaining module 520 and the second obtaining module 530 in fig. 5 may be different modules in a system, or may be a module that implements the functions of the two modules. For another example, the modules in the rehabilitation training system 500 may share one memory module, and each of the modules may have its own memory module. Such variations are within the scope of the present disclosure.

Fig. 6 is a schematic structural diagram of a wearable device for rehabilitation training according to some embodiments of the present description. As shown in fig. 6, the wearable device 600 for training may include a head support structure 601, a display assembly 602, a sensing assembly 603, and a processor.

In some embodiments, the head support structure 601 may be used to be worn on the head of a user performing rehabilitation exercises. In some embodiments, the head support structure 601 may be a fully enclosed structure, a semi enclosed structure, or an open structure. A fully enclosed structure is understood to mean that the head support structure 601 is capable of integrally wrapping around the head of a user. A semi-enclosed structure may be understood as a head support structure 601 that wraps around a partial area of a user's head (e.g., a scalp area) but does not wrap around another partial area of the user's head (e.g., a face area). By way of example only, both the fully enclosed and semi-enclosed structures may be enclosed by the cradle head support structure shown in FIG. 6 and a transparent plastic panel provided to the cradle head support structure. An open configuration may be understood as a configuration in which the head support structure 601 does not wrap around the user's head, but merely snaps around the user's head, e.g., an open configuration may comprise only a cradle-type head support structure as shown in fig. 6. In some embodiments, the material of the head support structure 601 may be metal, plastic, and various composite materials. Exemplary metallic materials may include iron, stainless steel, aluminum, and the like. Exemplary plastic materials may include PET, PVC, PP, and the like.

In some embodiments, a display component 602 may be disposed on the head support structure 601 for displaying the rehabilitation instructions and the rehabilitation data. In some embodiments, the display component 602 may be understood as a device capable of playing video or pictures. Preferably, when the head support structure 101 is worn in place (e.g., the head support structure 101 is clipped to the head and does not easily shift), the display assembly 602 may be positioned directly in front of the eyes of the user, so that the user can view the content played by the display assembly 602. In some embodiments, the display component 602 may be an augmented reality display. The augmented reality display can make the user see outside environment, can show again simultaneously, can avoid being used for in the rehabilitation training by the display shelters from the sight to prevent that the patient from bumping or falling in the in-process of carrying out the rehabilitation training. In other embodiments, the display component 602 may be a virtual reality display. In still other embodiments, the display assembly 602 may also be a liquid crystal display, an LED display, or the like.

In some embodiments, the sensing component 603 may be used to acquire head data and the rehabilitation data of the user. In some embodiments, the sensing component 603 may include one or more sensors. In some embodiments, each sensor is capable of sensing movement of one hand or one foot to acquire hand or foot rehabilitation data. The sensor may be a gravity sensor, a gyro sensor, an acceleration sensor, or the like. The signal connection mode of each sensor and the processor can be a wired connection or a wireless connection (such as a network connection, a bluetooth connection and the like).

In some embodiments, a processor may be in signal communication with both the display component and the sensing component; the processor is configured to: the display component is controlled to display and/or play the rehabilitation instruction, the rehabilitation data of the user acquired by the sensing component are received, and the display component is controlled to display the rehabilitation data and/or rehabilitation information corresponding to the rehabilitation data.

In some embodiments, the wearable device 600 further comprises a massage assembly 604, the massage assembly 604 being provided on a side of the head support structure 601 proximate to the head of the user to massage the head of the user. The massage assembly 604 is in signal communication with a processor configured to control the massage assembly 604 to massage the head of the user. In some embodiments, the massage assembly 604 may include a number of telescoping raised structures, a number of ball structures capable of vibrating, a number of rollers, and/or a number of microcurrent stimulation devices. The micro-current massage device is a device for stimulating the acupuncture points of the human body by micro-current. The massage positions of the massage assembly 604 may be acupuncture points of the user's head. The acupuncture points of the head of the user can comprise one or more acupuncture points of Baihui acupuncture point, shenting acupuncture point, right Chengling acupuncture point, left Chengling acupuncture point, right TouWei acupuncture point, left TouWei acupuncture point, right Fengchi acupuncture point, left Fengchi acupuncture point, fengfu acupuncture point and the like.

Fig. 7 and 8 are display interface schematic diagrams of a display assembly of a wearable device for rehabilitation training according to some embodiments of the present description.

Fig. 7 shows a display interface in which the display component 602 plays a rehabilitation instruction indicating that the left hand of the user is doing exercise, a marker region corresponding to the first position, rehabilitation data of the user, and a corresponding degree of completion of the user's execution of the rehabilitation instruction.

Fig. 8 illustrates a display interface in which the display component 602 plays rehabilitation instructions instructing the user's left foot to perform a movement, rehabilitation data, and the user's corresponding degree of completion to execute the rehabilitation instructions.

The beneficial effects that may be brought by the embodiments of the present specification include, but are not limited to: (1) The display component of the wearable device can display and play the rehabilitation instruction, so that the user can conveniently perform rehabilitation training, and meanwhile, the display component can display rehabilitation data and rehabilitation information, so that the user can more clearly know the rehabilitation condition of the user; (2) The wearable device can update and correct the rehabilitation instruction in real time by acquiring the head movement information of the user, so that the accuracy of executing the rehabilitation instruction by the user is improved; (3) The wearable device can update the plurality of initial rehabilitation instructions and provide corresponding update suggestions based on physiological data and rehabilitation data of the user and/or the completion degree of the user executing the plurality of initial rehabilitation instructions, so that a better rehabilitation training scheme can be determined for the user; (4) The wearable device is provided with the head support frame and the display component arranged on the head support frame, so that the display component can change the position along with the head movement of a user in the movement process of the user for rehabilitation training, and the user can more conveniently watch the instruction of the rehabilitation training; (5) The wearable device integrates multiple functions (such as limb training function, massage function and the like), is convenient to operate, compact in structure and convenient to transport and carry. It is to be noted that different embodiments may produce different advantages, and in different embodiments, any one or combination of the above advantages may be produced, or any other advantages may be obtained.

Having thus described the basic concept, it will be apparent to those skilled in the art that the foregoing detailed disclosure is to be regarded as illustrative only and not as limiting the present specification. Various modifications, improvements and adaptations to the present description may occur to those skilled in the art, although not explicitly described herein. Such modifications, improvements and adaptations are proposed in the present specification and thus fall within the spirit and scope of the exemplary embodiments of the present specification.

Also, the description uses specific words to describe embodiments of the specification. Reference throughout this specification to "one embodiment," "an embodiment," and/or "some embodiments" means that a particular feature, structure, or characteristic described in connection with at least one embodiment of the specification is included. Therefore, it is emphasized and should be appreciated that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, some features, structures, or characteristics of one or more embodiments of the specification may be combined as appropriate.

Additionally, the order in which the elements and sequences of the process are recited in the specification, the use of alphanumeric characters, or other designations, is not intended to limit the order in which the processes and methods of the specification occur, unless otherwise specified in the claims. While various presently contemplated embodiments of the invention have been discussed in the foregoing disclosure by way of example, it is to be understood that such detail is solely for that purpose and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover all modifications and equivalent arrangements that are within the spirit and scope of the embodiments herein. For example, although the system components described above may be implemented by hardware devices, they may also be implemented by software-only solutions, such as installing the described system on an existing server or mobile device.

Similarly, it should be noted that in the preceding description of embodiments of the present specification, various features are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure aiding in the understanding of one or more of the embodiments. This method of disclosure, however, is not intended to imply that more features than are expressly recited in a claim. Indeed, the embodiments may be characterized as having less than all of the features of a single disclosed embodiment.

Numerals describing the number of components, attributes, etc. are used in some embodiments, it being understood that such numerals used in the description of the embodiments are modified in some instances by the use of the modifier "about", "approximately" or "substantially". Unless otherwise indicated, "about", "approximately" or "substantially" indicates that the number allows a variation of ± 20%. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximations that may vary depending upon the desired properties of the individual embodiments. In some embodiments, the numerical parameter should take into account the specified significant digits and employ a general digit preserving approach. Notwithstanding that the numerical ranges and parameters setting forth the broad scope of the range in some embodiments of the specification are approximations, in specific embodiments, such numerical values are set forth as precisely as possible within the practical range.

For each patent, patent application publication, and other material, such as articles, books, specifications, publications, documents, etc., cited in this specification, the entire contents of each are hereby incorporated by reference into this specification. Except where the application history document does not conform to or conflict with the contents of the present specification, it is to be understood that the application history document, as used herein in the present specification or appended claims, is intended to define the broadest scope of the present specification (whether presently or later in the specification) rather than the broadest scope of the present specification. It is to be understood that the descriptions, definitions and/or uses of terms in the accompanying materials of this specification shall control if they are inconsistent or contrary to the descriptions and/or uses of terms in this specification.

Finally, it should be understood that the embodiments described herein are merely illustrative of the principles of the embodiments described herein. Other variations are also possible within the scope of the present description. Thus, by way of example, and not limitation, alternative configurations of the embodiments of the specification can be considered consistent with the teachings of the specification. Accordingly, the embodiments of the present description are not limited to only those embodiments explicitly described and depicted herein.

Claims

1. A rehabilitation training method, the method performed by a wearable device for rehabilitation training, comprising:

generating rehabilitation instructions, and displaying the rehabilitation instructions on a display component of the wearable device for a user to view and execute, wherein the rehabilitation instructions are used for indicating the body part of the user to move to a first position, and the rehabilitation instructions comprise an image marking the first position; during the period that the user executes the rehabilitation instruction, head movement information of the user is obtained, the first position is corrected in real time, the body part of the user is indicated to move to the second position, and the rehabilitation training effect that the body part of the user moves to the second position after the head of the user moves is the same as the rehabilitation training effect that the body part of the user moves to the first position when the head of the user does not move; wherein the acquiring of the head motion information of the user comprises:

generating a virtual image comprising two marker points, wherein only one marker point changes position with the head movement of the user;

acquiring the relative position between the two mark points at the two moments when the user starts and finishes executing the rehabilitation instruction, and determining the head movement information of the user based on the relative position;

acquiring at least one rehabilitation image, wherein the rehabilitation image is used for displaying that the body part of the user is at a first position and/or a second position, and determining the completion degree of the user for executing the rehabilitation instruction based on the at least one rehabilitation image;

determining an update suggestion for the rehabilitation instruction based on the trained rehabilitation training model, wherein the update suggestion includes a difficulty in adjusting the rehabilitation instruction;

the determining an updated recommendation of the rehabilitation instruction comprises:

processing a plurality of initial rehabilitation instructions, physiological data and rehabilitation data of a plurality of rehabilitation training stages based on the rehabilitation training model, and determining whether the rehabilitation instructions need to be updated, wherein the plurality of initial rehabilitation instructions comprise the rehabilitation instructions;

the rehabilitation training model comprises a recurrent neural network layer and a full connection layer, wherein the recurrent neural network layer is used for coding the rehabilitation instructions, the physiological data and the rehabilitation data to obtain a plurality of corresponding sequence data and an incidence relation thereof, and the full connection layer is used for outputting a classification result of whether the rehabilitation instructions need to be updated or not based on the incidence relation;

the acquisition of the training samples of the rehabilitation training model comprises the following steps:

the sample user comprises a plurality of sample training stages, in response to the fact that sample physiological data of the sample user is in a preset range when the sample user trains in the second sample training stage, historical data of the first sample training stage and a rehabilitation instruction of the second sample training stage are used as a positive sample, the positive sample does not need to be updated, and the positive sample is marked as 0; responding to the fact that the sample physiological data of the sample user is higher than the preset range when the sample user trains in the second sample training stage, taking the historical data of the first sample training stage and the rehabilitation instruction of the second sample training stage as negative samples, wherein the negative samples need to be updated, and the corresponding updating suggestions are the difficulty of adjusting the rehabilitation instruction downwards; and

in response, an updated recommendation for the rehabilitation instruction is determined based on the completion of the rehabilitation instruction, the physiological data, and the rehabilitation data being processed by the rehabilitation training model.

2. The method of claim 1, the determining the completion of the user's execution of the rehabilitation instructions based on the at least one rehabilitation image, comprising:

acquiring a standard image, wherein the standard image corresponds to the standard completion degree of the rehabilitation instruction; and

and determining the completion degree of the user executing the rehabilitation instruction based on the comparison result of the standard image and the at least one rehabilitation image.

3. The method of claim 1, the rehabilitation instructions being a set of instructions including at least one instruction for instructing a body part of the user to move to a first position.

4. The method of claim 3, further comprising:

determining a degree of completion of each instruction of the set of instructions based on the at least one rehabilitation image and the each instruction; and

determining a completion of the set of instructions based on the completion of each instruction.

5. The method of claim 1, further comprising:

acquiring physiological data of the user;

generating a plurality of initial rehabilitation instructions corresponding to a plurality of rehabilitation training sessions of the user;

determining a degree of completion of the user's execution of the plurality of initial rehabilitation instructions; and

updating the plurality of initial rehabilitation instructions based on the physiological data and/or the completion of the user's execution of the plurality of initial rehabilitation instructions.

6. A rehabilitation training system, the system comprising:

the wearable device comprises a first generation module, a second generation module and a display module, wherein the first generation module is used for generating a rehabilitation instruction, displaying the rehabilitation instruction on a display assembly of the wearable device for a user to view and execute, the rehabilitation instruction is used for indicating a body part of the user to move to a first position, and the rehabilitation instruction comprises an image marked with the first position;

a first obtaining module, configured to obtain head movement information of the user during execution of the rehabilitation instruction by the user, perform real-time correction on the first position, instruct a body part of the user to move to a second position, and obtain a rehabilitation training effect that the body part of the user moves to the second position after the head of the user moves, where the rehabilitation training effect is the same as a rehabilitation training effect that the body part of the user moves to the first position when the head of the user does not generate head movement; wherein the acquiring of the head motion information of the user comprises:

the second acquisition module is used for acquiring at least one rehabilitation image of the user executing the rehabilitation instruction and determining the completion degree of the user executing the rehabilitation instruction based on the at least one rehabilitation image;

the updating module is used for determining an updating suggestion of the rehabilitation instruction based on a trained rehabilitation training model, wherein the updating suggestion comprises the difficulty of adjusting the rehabilitation instruction;

the rehabilitation training model comprises a recurrent neural network layer and a full connection layer, wherein the recurrent neural network layer is used for coding the rehabilitation instructions, the physiological data and the rehabilitation data to obtain a plurality of corresponding sequence data and incidence relations thereof, and the full connection layer is used for outputting classification results of whether the rehabilitation instructions need to be updated or not based on the incidence relations;

the sample user comprises a plurality of sample training stages, in response to the fact that sample physiological data of the sample user is in a preset range when the sample user trains in the second sample training stage, historical data of the first sample training stage and a rehabilitation instruction of the second sample training stage are used as a positive sample, the positive sample does not need to be updated, and the positive sample is marked as 0; in response to the fact that the sample physiological data of the sample user is higher than the preset range when the sample user trains in the second sample training stage, taking the historical data of the first sample training stage and the rehabilitation instruction of the second sample training stage as negative samples, wherein the negative samples need to be updated, and the corresponding updating suggestion is the difficulty of adjusting the rehabilitation instruction downwards; and

7. The system of claim 6, the second acquisition module further to:

8. The system of claim 6, the rehabilitation instructions being a set of instructions including at least one instruction for instructing a body part of the user to move to a first position.

9. The system of claim 8, further comprising:

a first determination module to determine a degree of completion of each instruction of the instruction set based on the at least one rehabilitation image and the each instruction; and

a second determination module to determine a completion of the set of instructions based on the completion of each instruction.

10. The system of claim 6, further comprising:

a third acquisition module for acquiring physiological data of the user;

a second generation module to generate a plurality of initial rehabilitation instructions corresponding to a plurality of rehabilitation training sessions of the user;

a third determination module for determining a degree of completion of the user's execution of the plurality of initial rehabilitation instructions; and

an updating module for updating the plurality of initial rehabilitation instructions based on the physiological data and/or the completion degree of the user executing the plurality of initial rehabilitation instructions.

11. A wearable device for rehabilitation training, the wearable device including AR glasses, comprising:

a head support structure for wearing on the head of a user performing rehabilitation training;

the display component is arranged on the head support structure and used for displaying rehabilitation instructions and rehabilitation data;

a sensing component for acquiring the head data and the rehabilitation data of the user; and

the processor is in signal connection with both the display assembly and the sensing assembly; the processor is configured to: the display component is controlled to display and/or play the rehabilitation instruction, the rehabilitation data of the user acquired by the sensing component is received, the display component is controlled to display the rehabilitation data and/or rehabilitation information corresponding to the rehabilitation data, and the processor is further used for:

generating a rehabilitation instruction, and displaying the rehabilitation instruction on a display component of a wearable device for a user to view and execute, wherein the rehabilitation instruction is used for indicating a body part of the user to move to a first position, and the rehabilitation instruction comprises an image marked with the first position;

during the period that the user executes the rehabilitation instruction, head movement information of the user is obtained, the first position is corrected in real time, the body part of the user is indicated to move to the second position, and the rehabilitation training effect that the body part of the user moves to the second position after the head of the user moves is the same as the rehabilitation training effect that the body part of the user moves to the first position when the head of the user does not move; wherein the acquiring of the head motion information of the user comprises:

acquiring at least one rehabilitation image, wherein the rehabilitation image is used for displaying that the body part of the user is at a first position and/or a second position, and determining the completion degree of the user executing the rehabilitation instruction based on the at least one rehabilitation image;

the acquisition of the training sample of the rehabilitation training model comprises the following steps:

12. A computer-readable storage medium storing computer instructions, the computer instructions when read by a computer executing a method comprising:

acquiring relative positions between the two mark points at two moments when the user starts and finishes executing the rehabilitation instruction respectively, and determining head movement information of the user based on the relative positions;