Training method and device, electronic equipment and storage medium

Abstract

The embodiment of the invention provides a training method and device, electronic equipment and a storage medium, and belongs to the technical field of medical treatment. The method comprises the steps of collecting training data according to a target prescription; calculating the training data to obtain a training index; generating an analysis result according to the training data, the training index and the training target; and in response to the first instruction, displaying the analysis result on the evaluation interface. According to the embodiment of the invention, the pertinence and effectiveness of training can be improved, and the user experience is improved through a clear and comprehensive analysis result.

Description

Technical Field

[0001] The present application relates to the field of medical technology, and in particular to a training method and device, an electronic device and a storage medium. Background Art

[0002] In related technologies, traditional training methods usually lack clear training objectives, resulting in random data collection and training processes, lack of targeted training, affecting training effects, and difficulty in intuitively obtaining training results, thus affecting user experience.

[0003] In summary, the technical problems existing in the relevant technologies need to be improved. Summary of the invention

[0004] The main purpose of the embodiments of the present application is to propose a training method and device, an electronic device and a storage medium, aiming to improve the pertinence and effectiveness of training and enhance user experience.

[0005] To achieve the above object, an embodiment of the present application provides a training method in one aspect, the method comprising the following steps:

[0006] Collecting training data according to a target prescription, wherein the target prescription includes a training target;

[0007] Calculating the training data to obtain a training index;

[0008] Generate an analysis result according to the training data, the training indicator and the training goal;

[0009] In response to the first instruction, the analysis result is displayed on the evaluation interface.

[0010] In some embodiments, the method further comprises:

[0011] Get a preset prescription;

[0012] A target prescription is determined in the preset prescription according to the evaluation result.

[0013] In some embodiments, the method further comprises:

[0014] In response to the second instruction, a target joint is determined in a joint selection area of ​​the evaluation page.

[0015] In some embodiments, the method further comprises:

[0016] Collect the real-time angle and range of motion of the target joint;

[0017] An evaluation result is generated according to the real-time angle and range of motion of the target joint.

[0018] In some embodiments, the method further comprises:

[0019] In response to the third instruction, the evaluation result is displayed on the evaluation data viewing interface.

[0020] In some embodiments, the target prescription includes a target joint, and collecting training data according to the target prescription includes at least one of the following:

[0021] Collecting activity data of the target joint;

[0022] Collect muscle strength data;

[0023] Collecting motion trajectory data of the target joint;

[0024] Collection of spasticity data;

[0025] Collect training time and frequency data.

[0026] In some embodiments, generating analysis results according to the training data, the training indicators and the training goals includes:

[0027] The training data, the training indicators and the training objectives are identified through a machine learning algorithm to generate analysis results.

[0028] To achieve the above object, another aspect of the embodiment of the present application provides a training device, the device comprising:

[0029] A collection module, used for collecting training data according to a target prescription, wherein the target prescription includes a training target;

[0030] A calculation module, used to calculate the training data to obtain a training index;

[0031] An analysis module, used to generate an analysis result according to the training data, the training indicators and the training objectives;

[0032] The first display module is used to display the analysis result on the evaluation interface in response to the first instruction.

[0033] To achieve the above-mentioned purpose, another aspect of an embodiment of the present application provides an electronic device, the electronic device comprising a memory and a processor, the memory storing a computer program, and the processor implementing the above-mentioned method when executing the computer program.

[0034] To achieve the above objective, another aspect of an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method described above is implemented.

[0035] The embodiments of the present application include at least the following beneficial effects: the present application provides a training method and device, an electronic device and a storage medium, which collects training data according to the target prescription, facilitates the personalized formulation of the target prescription, and improves the pertinence and effectiveness of the training; calculates the training data to obtain the training indicators; generates analysis results based on the training data, training indicators and training goals, and provides clear and comprehensive analysis results; and displays the analysis results on the evaluation interface in response to the first instruction to improve the user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 is a flow chart of the training method provided in an embodiment of the present application;

[0037] Figure 2 is a flow chart of the step of determining the target prescription of the training method provided in the embodiment of the present application;

[0038] Figure 3 is another flow chart of the training method provided in the embodiment of the present application;

[0039] Figure 4 This is a schematic diagram of an evaluation page provided in an embodiment of the present application;

[0040] Figure 5 is a schematic diagram of an angle evaluation data viewing interface provided in an embodiment of the present application;

[0041] Figure 6 is a schematic diagram of an angle assessment report provided in an embodiment of the present application;

[0042] Figure 7 It is a schematic diagram of a prescription training page provided in an embodiment of the present application;

[0043] Figure 8 is a schematic diagram of the analysis results of the evaluation interface provided in the embodiment of the present application;

[0044] Fig. 9 It is a schematic diagram of a training report provided in an embodiment of the present application;

[0045] Fig.10 is a schematic diagram of the structure of the training device provided in an embodiment of the present application;

[0046] Fig.11 It is a schematic diagram of the hardware structure of the electronic device provided in the embodiment of the present application. DETAILED DESCRIPTION

[0047] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application is further described in detail below in conjunction with the accompanying drawings and examples. It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the embodiments of the present application. They are only examples of devices and methods consistent with some aspects of the embodiments of the present application as detailed in the attached claims.

[0048] It is understood that the terms "first", "second", etc. used in this application can be used to describe various concepts in this article, but unless otherwise specified, these concepts are not limited by these terms. These terms are only used to distinguish one concept from another concept. For example, without departing from the scope of the embodiment of the present application, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the words "if" and "if" as used herein can be interpreted as "at the time of" or "when" or "in response to determination".

[0049] The terms "at least one", "multiple", "each", "any", etc. used in this application, at least one includes one, two or more, multiple includes two or more, each refers to each of the corresponding multiple, and any refers to any one of the multiple.

[0050] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0051] Before describing the embodiments of the present application in detail, some nouns and terms involved in the embodiments of the present application are first described. The nouns and terms involved in the embodiments of the present application are subject to the following explanations.

[0052] 1) Spasm sensitivity refers to the term used to describe the degree and susceptibility of muscle spasm in clinical assessment or rehabilitation treatment.

[0053] In related technologies, traditional training methods usually lack clear training objectives, resulting in random data collection and training processes, lack of targeted training, affecting training effects, and difficulty in intuitively obtaining training results, thus affecting user experience.

[0054] In summary, the technical problems existing in the relevant technologies need to be improved.

[0055] In view of this, a training method, apparatus, device and medium are provided in an embodiment of the present application. The scheme collects training data according to the target prescription, which is conducive to the personalized formulation of the target prescription and improves the pertinence and effectiveness of the training; the training data is calculated to obtain the training indicators; the analysis results are generated according to the training data, the training indicators and the training goals, and clear and comprehensive analysis results are provided; in response to the first instruction, the analysis results are displayed on the evaluation interface to improve the user experience.

[0056] The training method provided in the embodiment of the present application relates to the field of medical technology. The training method provided in the embodiment of the present application can be applied to a terminal, can be applied to a server, or can be software running in a terminal or a server. In some embodiments, the terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart speaker, a smart watch, and a car terminal, etc., but is not limited to this; the server side can be configured as an independent physical server, or it can be configured as a server cluster or distributed system composed of multiple physical servers, and can also be configured to provide cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms and other basic cloud computing services. The server can also be a node server in the blockchain network; the software can be an application that implements the training method, etc., but is not limited to the above forms.

[0057] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc. The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application can also be practiced in distributed computing environments, in which tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0058] It should be noted that in each specific implementation of the present application, when it comes to the need to perform relevant processing based on data related to user identity or characteristics such as user information, user behavior data, user historical data, and user location information, the user's permission or consent will be obtained first, and the collection, use, and processing of these data will comply with relevant laws, regulations, and standards. In addition, when the embodiment of the present application needs to obtain the user's sensitive personal information, the user's separate permission or consent will be obtained through a pop-up window or by jumping to a confirmation page. After clearly obtaining the user's separate permission or consent, the necessary user-related data for the normal operation of the embodiment of the present application will be obtained.

[0059] Figure 1 is an optional flowchart of the training method provided in the embodiment of the present application. Figure 1 The method may include but is not limited to steps S101 to S104.

[0060] Step S101, collecting training data according to the target prescription.

[0061] Specifically, the training data includes target joint range of motion data, muscle strength data, target joint motion trajectory data, spasm data, training time and frequency data. The target joint is determined according to the target prescription, which includes guiding force, target joint and training mode.

[0062] The present application does not make any specific limitation on the target prescription, and it can be flexibly selected according to actual needs. For example, the target prescription can be determined by the user, or it can be determined in a preset prescription according to the evaluation result.

[0063] In some embodiments, training data is collected based on the spasticity sensitivity, preset time and target prescription, wherein the spasticity sensitivity and preset time can be determined by the user to determine the target prescription, or can be determined based on the evaluation results.

[0064] In some embodiments, the patient performs training according to the target prescription and collects training data during the training process.

[0065] Exemplarily, if the target prescription is a wrist flexion prescription, wrist joint training data during the training process is collected.

[0066] In some embodiments, the activity data of the target joint is collected; the muscle strength data is collected; the motion trajectory data of the target joint is collected; the spasm data is collected; and the training time and frequency data are collected.

[0067] In this embodiment, collecting training data according to the target prescription is conducive to personalizing the target prescription and improving the pertinence and effectiveness of the training.

[0068] Step S102, calculating training data to obtain training indicators.

[0069] In some embodiments, statistical methods are used to process the training data to calculate training indicators such as mean, standard deviation, and rate of change to evaluate the patient's rehabilitation progress and training effect.

[0070] Optionally, the training data can be calculated using data analysis tools or custom algorithms to improve the accuracy of the training metrics.

[0071] In this embodiment, the training data is calculated to obtain the training indicators, which is helpful for relevant personnel (patients, the above and therapists) to understand the training effect and physical condition of the patients, and provide a basis for the subsequent generation of analysis results.

[0072] Step S103, generating analysis results according to the training data, training indicators and training goals.

[0073] Specifically, the target prescription includes a training target.

[0074] In some embodiments, the training data, calculated metrics, and target prescriptions are combined to generate analysis results.

[0075] Optionally, algorithms such as machine learning are used to identify the patient's movement patterns and rehabilitation trends based on training data, training indicators and training goals to generate analysis results.

[0076] In some embodiments, historical training data corresponding to the target prescription is obtained, the training data is compared with the historical training data, and an analysis result is generated.

[0077] Specifically, the training data and training indicators are compared with the training goals of the target prescription through a machine learning algorithm to obtain a training score, wherein the training score indicates the degree of completion of this training.

[0078] In this embodiment, analysis results are generated based on training data, training indicators and training goals, which is conducive to providing clear and comprehensive analysis results, helping patients and related personnel understand the training effects and optimize training plans and goals.

[0079] Step S104: in response to the first instruction, display the analysis result on the evaluation interface.

[0080] Specifically, the first instruction is an instruction for displaying the analysis results. The first instruction is triggered after the analysis results are generated. The analysis results include target prescription, target joint, training mode, training time, speed level, spasm sensitivity, grip strength, training score and training date.

[0081] In some embodiments, the analysis results are displayed on the evaluation interface in the form of charts, animations, etc., which are convenient for doctors or patients to view and understand.

[0082] The present application does not specifically limit the display mode of the analysis results, and can be flexibly selected according to actual needs. For example, the analysis results can be displayed in the form of a chart, in the form of an animation, or in the form of an interactive visual element.

[0083] Furthermore, the analysis results can be exported as a training report, allowing the user to save or share the training report.

[0084] In this embodiment, in response to the first instruction, the analysis results are displayed on the evaluation interface, which is conducive to improving the user experience, helping patients and related personnel to quickly obtain training information, and improving rehabilitation effects and training efficiency.

[0085] Steps S101 to S104 shown in the embodiment of the present application, by collecting training data according to the target prescription, are conducive to personalizing the target prescription and improving the pertinence and effectiveness of the training; the training data is calculated to obtain the training indicators; the analysis results are generated according to the training data, the training indicators and the training goals, and clear and comprehensive analysis results are provided; in response to the first instruction, the analysis results are displayed on the evaluation interface to improve the user experience.

[0086] See also Figure 2 In some embodiments, the training method provided in the embodiment of the present application further includes a step of determining a target prescription. The step of determining a target prescription may include but is not limited to steps S201 to S206:

[0087] Step S201, in response to the second instruction, determining a target joint in a joint selection area of ​​an evaluation page.

[0088] Specifically, the second instruction is a selection instruction of the target joint, and the second instruction is triggered when the joint control is clicked, checked, and dragged.

[0089] In step S201 of some embodiments, multiple joints that can be evaluated are displayed in a graphical form in a joint selection area of ​​the evaluation page.

[0090] The present application does not make any specific limitation on the target joint, and the target joint can be flexibly selected according to actual needs. For example, the target joint can include multiple joints or only a single joint.

[0091] In this embodiment, in response to the second instruction, the target joint is determined in the joint selection area of ​​the evaluation page, which is helpful for determining the target joint that needs to be evaluated and prepares for the subsequent joint range of motion evaluation.

[0092] Step S202, collecting the real-time angle and range of motion of the target joint.

[0093] In step S202 of some embodiments, after determining the target joint and clicking the "Start Assessment" button, the patient can move his arm to collect the real-time angle and range of motion of the target joint through the sensor.

[0094] Furthermore, the real-time angle and range of motion of the patient's target joint are displayed in real time through the joint motion table of the evaluation interface.

[0095] The real-time angle of the target joint is provided by an angle sensor that captures the angle changes of the patient's arm during real-time movement, and the range of motion of the target joint is calculated from the real-time angle by a software program.

[0096] It is understandable that the real-time angle and range of motion correspond to the target joint. Exemplarily, if the target joint includes shoulder adduction and abduction, the real-time angle includes shoulder adduction angle and shoulder abduction angle, and the range of motion includes shoulder adduction range of motion and shoulder abduction range of motion.

[0097] In this embodiment, the real-time angle and range of motion of the target joint are collected to prepare for the subsequent joint mobility assessment.

[0098] Step S203, generating an evaluation result according to the real-time angle and range of motion of the target joint.

[0099] In step S203 of some embodiments, the historical angle and historical range of motion of the target joint are obtained, and the real-time angle and range of motion are compared with the historical angle and range of motion to generate an evaluation result.

[0100] Step S204, in response to the third instruction, displaying the evaluation result on the evaluation data viewing interface.

[0101] Specifically, the third instruction is an instruction for displaying the evaluation result, and the third instruction is triggered after the evaluation result is generated.

[0102] In step S204 of some embodiments, the evaluation results are displayed on the evaluation interface in the form of charts, animations, etc., to facilitate user viewing and understanding.

[0103] The present application does not specifically limit the display mode of the evaluation results, and can be flexibly selected according to actual needs. For example, the evaluation results can be displayed in the form of a chart, in the form of an animation, or in the form of an interactive visual element.

[0104] Furthermore, the evaluation results can be exported as an evaluation report, allowing the user to save or share the evaluation report. The evaluation report includes an angle evaluation report and an activity range evaluation report.

[0105] In this embodiment, in response to the third instruction, the evaluation result is displayed on the evaluation data viewing interface, which is helpful for relevant personnel to view and understand the joint condition.

[0106] Step S205, obtaining a preset prescription.

[0107] In step S205 of some embodiments, preset prescriptions are set for different joints, illnesses, rehabilitation goals, physical conditions, and joint range of motion.

[0108] It is understandable that through preset prescriptions, therapists can ensure that each training is carried out according to the established plan, avoiding arbitrariness and subjectivity in the training process and improving the standardization of rehabilitation training.

[0109] Furthermore, the preset prescription can also serve as a basis for tracking the patient's rehabilitation progress. As rehabilitation progresses, the therapist can adjust the prescription content according to the patient's actual situation to gradually increase the difficulty and complexity of the training and promote further recovery of the patient's function.

[0110] It is understandable that the preset prescription includes a single joint training prescription and a multi-joint training prescription, wherein the multi-joint training prescription is used to train multiple joints.

[0111] Step S206: determine the target prescription in the preset prescription according to the evaluation result.

[0112] In step S206 of some embodiments, a target prescription is determined in a preset prescription according to the target joint and the evaluation result.

[0113] Figure 3 is another optional flow chart of the training method provided in the embodiment of the present application. Figure 3 The method may include but is not limited to steps S301 to S302.

[0114] Step S301, joint range of motion assessment.

[0115] In step S301 of some embodiments, before evaluation, the joint to be evaluated (ie, the target joint) must be selected on the evaluation page, and the check box in front of the joint name can be checked to evaluate the joint.

[0116] Furthermore, by clicking the "Start Evaluation" button on the evaluation page, the patient can move his arm. The joint motion table on the evaluation interface will display the real-time angle and range of motion of the patient's joint. The real-time angle of the joint motion is provided by the angle sensor that captures the angle changes when the patient's arm moves in real time, and the joint motion range is converted from the real-time angle by the software program.

[0117] For example, the evaluation page diagram is as follows: Figure 4As shown, ① in the evaluation joint selection area image corresponds to the "1. Shoulder joint adduction and abduction" option on the right, and ②③④⑤ also correspond to the optional options on the right.

[0118] Among them, sensor data processing and analysis includes the following steps:

[0119] 1) Raw data acquisition: The sensor first captures the original, unprocessed data (such as acceleration, angular velocity, position coordinates, etc.).

[0120] 2) Data processing: These data need to go through a series of processing steps, such as filtering (removing noise), calibration (improving data accuracy), conversion (converting raw data into understandable forms such as joint angles), etc., before they can become useful information.

[0121] 3) Data analysis: The processed data will be further analyzed to calculate parameters such as the range of motion, speed, acceleration, etc. of the joints, and may perform more complex movement pattern recognition or pathology diagnosis.

[0122] Optionally, after the evaluation is completed, click "Evaluation Data" to view the patient's current or historical evaluation data (i.e., evaluation results) on the evaluation data viewing interface. The evaluation data viewing interface includes an angle evaluation data viewing interface and a range of motion viewing interface.

[0123] Optionally, the data collected by the sensor includes left-hand data and right-hand data, and the evaluation data includes left-hand evaluation data and right-hand evaluation data.

[0124] Furthermore, the assessment data can be exported as an assessment report, allowing the user to save or share the assessment report. The assessment report includes an angle assessment report and an activity range assessment report.

[0125] For example, the angle evaluation data viewing interface is as follows: Figure 5 As shown, the visualization chart style can be changed through the column option and the line option, the left-hand evaluation data and the right-hand evaluation data can be switched through the left-hand option and the right-hand option, the evaluation data at different times can be displayed through the time option, and the evaluation report can be exported through the export report button.

[0126] Furthermore, the angle assessment report diagram is as follows: Figure 6 shown.

[0127] Step S302: prescription training.

[0128] In step S302 of some embodiments, training data is collected according to the spasticity sensitivity, the preset time and the target prescription.

[0129] It is understandable that the target prescription is selected from the preset prescriptions, and the preset prescriptions include the following functions:

[0130] 1) Personalized customization: The preset prescription is tailored according to the patient's specific condition, rehabilitation goals, physical condition, and joint range of motion assessment results. It improves the pertinence and effectiveness of the training content.

[0131] 2) Standardized execution: Through preset prescriptions, therapists can ensure that each training session is conducted according to the established plan, thus avoiding randomness and subjectivity in the training process and improving the standardization of rehabilitation training.

[0132] 3) Progress tracking: The preset prescription can also serve as a basis for tracking the patient's rehabilitation progress. As rehabilitation progresses, the therapist can adjust the prescription content according to the patient's actual situation to gradually increase the difficulty and complexity of the training and promote further recovery of the patient's function.

[0133] 4) Effect evaluation: After the training, the therapist can evaluate the effect of rehabilitation training by comparing the patient's performance in completing the preset prescription with the expected goals, and formulate the next rehabilitation plan accordingly.

[0134] The target prescription can be selected from the preset prescriptions according to the evaluation data (i.e., the evaluation results), or the user can select the target prescription from the preset prescriptions on the prescription training page and set the spasm sensitivity, preset time, and guiding force. Figure 7 shown.

[0135] Further, spasticity sensitivity includes the following effects:

[0136] 1) Safety protection: Spasticity is one of the common problems in upper limb rehabilitation training. If not handled in time, it may cause further harm to the patient. Setting the spasticity sensitivity can help the system detect the patient's spastic reaction in time and automatically stop or adjust the training action to avoid potential risks.

[0137] 2) Adaptive adjustment: The spasticity sensitivity can also be adaptively adjusted according to the actual situation of the patient. For patients with a higher risk of spasticity, a higher sensitivity can be set to improve safety; for patients with a lower risk of spasticity, the sensitivity can be appropriately reduced to improve the training effect.

[0138] 3) Feedback optimization: By monitoring and analyzing the occurrence of spasms, the system can continuously optimize the spasm sensitivity setting to more accurately reflect the patient's actual condition and provide therapists with more accurate feedback and reference.

[0139] It is understood that setting the time (i.e., presetting the time) includes the following functions:

[0140] 1) Control the intensity of training: Setting the time can control the duration and frequency of each training session, thereby controlling the intensity of training. Appropriate training intensity can stimulate the patient's muscles and joints and promote the recovery process; while excessive training may lead to adverse consequences such as muscle fatigue and joint damage.

[0141] 2) Arrange rehabilitation plan: Setting time can also help therapists arrange the patient's rehabilitation plan reasonably. By setting different training periods and cycles, therapists can ensure that patients receive appropriate types and intensities of training at different stages of rehabilitation.

[0142] 3) Improve patient participation: Setting a clear training time can remind patients to carry out rehabilitation training on time, improve patient participation and compliance. At the same time, fixed training time can also help patients form good rehabilitation habits and life rules.

[0143] In some embodiments, the preset prescriptions include single joint training prescriptions and multi-joint training prescriptions. Among them, there are 6 preset prescriptions for single joint training, namely, shoulder adduction and abduction, shoulder flexion, shoulder internal rotation and external rotation, elbow flexion, forearm pronation and supination, and wrist flexion. There are 3 preset prescriptions for multi-joint training, namely, eating training, hair combing training, and wooden peg board training.

[0144] It is understandable that during the prescription training process, the system will collect a variety of data to comprehensively evaluate the patient's training status and rehabilitation progress. The training data mainly includes the following data:

[0145] 1) Joint range of motion data: The built-in sensors (such as angle sensors, accelerometers, etc.) are used to collect data such as the angle and range of motion of each joint of the patient's upper limbs in real time.

[0146] 2) Muscle strength data: Assess the patient's muscle strength recovery by measuring the force or resistance required to complete specific movements.

[0147] 3) Motion trajectory data: records the movement trajectory of the patient's upper limbs in three-dimensional space to analyze their movement patterns and coordination.

[0148] 4) Spasticity: Monitor patients for any spasticity they experience during training, as well as the extent and duration of these reactions.

[0149] 5) Training time and frequency data: Record the duration, number of training sessions, and training intervals of each patient's training to assess their training intensity and compliance.

[0150] Furthermore, the collected data needs to go through a series of analysis and processing before it can be converted into useful information. Exemplarily, the analysis and processing steps include the following steps:

[0151] 1) Data filtering: remove interference factors such as noise and outliers to improve the accuracy and reliability of data.

[0152] 2) Data conversion: Convert raw data into an understandable format, such as converting sensor data into parameters such as joint angles and forces.

[0153] 3) Statistical analysis: Use statistical methods to process the data and calculate statistical indicators such as mean, standard deviation, and rate of change to evaluate the patient's rehabilitation progress and training effect.

[0154] 4) Pattern recognition: Identify the patient’s movement patterns and rehabilitation trends through machine learning and other algorithms, providing a basis for developing personalized rehabilitation plans.

[0155] 5) Visual display: The analysis results are displayed on the evaluation interface in the form of charts, animations, etc., which are convenient for doctors or patients to view and understand.

[0156] For example, the analysis result diagram of the evaluation interface is as follows: Figure 8 As shown, the arm represents the analysis result of the left hand training data or the analysis result of the right hand training data, the game name is the target prescription name, and the speed level can be set when determining the target prescription, or the preset speed of the preset prescription can be used.

[0157] Optionally, based on the above analysis and processing results, the system will generate a detailed training report. The training report usually contains the following contents:

[0158] 1) Basic information of the patient: including name, age, gender, diagnosis results and other basic information.

[0159] 2) Training Overview: Provide an overview of the patient’s training time, training content, and other basic information.

[0160] For example, the training report diagram is as follows: Fig. 9 shown.

[0161] The embodiment of the present application collects training data according to the target prescription, which is conducive to the personalized formulation of the target prescription and improves the pertinence and effectiveness of the training; calculates the training data to obtain the training indicators; generates analysis results based on the training data, training indicators and training goals, and provides clear and comprehensive analysis results; and displays the analysis results on the evaluation interface in response to the first instruction to improve the user experience.

[0162] See also Fig.10 The present application also provides a training device that can implement the above training method. The device includes:

[0163] A collection module 1001 is used to collect training data according to a target prescription, where the target prescription includes a training target;

[0164] A calculation module 1002 is used to calculate the training data to obtain the training index;

[0165] Analysis module 1003, used to generate analysis results according to training data, training indicators and training goals;

[0166] The first display module 1004 is used to display the analysis result on the evaluation interface in response to the first instruction.

[0167] It can be understood that the contents of the above method embodiments are all applicable to the present device embodiments, the functions specifically implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0168] The embodiment of the present application also provides an electronic device, the electronic device includes a memory and a processor, the memory stores a computer program, and the processor implements the above training method when executing the computer program. The electronic device can be any intelligent terminal including a tablet computer, a car computer, etc.

[0169] It can be understood that the contents of the above method embodiments are all applicable to the present device embodiments, the functions specifically implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0170] See also Fig.11 , Fig.11 The hardware structure of an electronic device of another embodiment is illustrated, and the electronic device includes:

[0171] The processor 1101 may be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present application;

[0172] The memory 1102 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 1102 can store an operating system and other application programs. When the technical solution provided in the embodiment of this specification is implemented by software or firmware, the relevant program code is stored in the memory 1102, and the processor 1101 is called to execute the training method of the embodiment of this application;

[0173] Input / output interface 1103, used to implement information input and output;

[0174] The communication interface 1104 is used to realize the communication interaction between the device and other devices. The communication can be realized through a wired manner (such as USB, network cable, etc.) or a wireless manner (such as mobile network, WI FI, Bluetooth, etc.);

[0175] A bus 1105 that transmits information between various components of the device (e.g., the processor 1101, the memory 1102, the input / output interface 1103, and the communication interface 1104);

[0176] The processor 1101 , the memory 1102 , the input / output interface 1103 and the communication interface 1104 are connected to each other in communication within the device via the bus 1105 .

[0177] An embodiment of the present application also provides a computer-readable storage medium, which stores a computer program, and the computer program implements the above-mentioned training method when executed by a processor.

[0178] It can be understood that the contents of the above method embodiments are all applicable to the present storage medium embodiments, the functions specifically implemented by the present storage medium embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0179] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely disposed relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0180] The training method, training device, electronic device and storage medium provided in the embodiments of the present application collect training data according to the target prescription, which is conducive to the personalized formulation of the target prescription and improves the pertinence and effectiveness of the training; calculates the training data to obtain the training indicators; generates analysis results based on the training data, training indicators and training goals, and provides clear and comprehensive analysis results; and displays the analysis results on the evaluation interface in response to the first instruction to improve the user experience.

[0181] The embodiments described in the embodiments of the present application are intended to more clearly illustrate the technical solutions of the embodiments of the present application and do not constitute a limitation on the technical solutions provided in the embodiments of the present application. Those skilled in the art will appreciate that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided in the embodiments of the present application are also applicable to similar technical problems.

[0182] Those skilled in the art will appreciate that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and may include more or fewer steps than shown in the figures, or a combination of certain steps, or different steps.

[0183] The device embodiments described above are merely illustrative, and the units described as separate components may or may not be physically separated, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0184] Those skilled in the art will appreciate that all or some of the steps in the methods disclosed above, and the functional modules / units in the systems and devices may be implemented as software, firmware, hardware, or a suitable combination thereof.

[0185] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can 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.

[0186] It should be understood that in the present application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the objects associated before and after are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0187] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the above units is only a logical function division. There may be other division methods in actual implementation, such as multiple units 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 units, which can be electrical, mechanical or other forms.

[0188] The units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0189] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0190] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can essentially or in other words, the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including multiple instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store programs.

[0191] The preferred embodiments of the present invention are described above with reference to the accompanying drawings, but the scope of the rights of the present invention is not limited thereto. Any modification, equivalent substitution and improvement made by a person skilled in the art without departing from the scope and essence of the present invention should be within the scope of the rights of the present invention.

Claims

1. A training method, characterized in that: The method comprises the following steps: Collecting training data according to a target prescription, wherein the target prescription includes a training target; Calculating the training data to obtain a training index; Generate an analysis result according to the training data, the training indicator and the training goal; In response to the first instruction, the analysis result is displayed on the evaluation interface.

2. The method according to claim 1, characterized in that The method further comprises: Get a preset prescription; A target prescription is determined in the preset prescription according to the evaluation result.

3. The method according to claim 1, characterized in that The method further comprises: In response to the second instruction, a target joint is determined in a joint selection area of ​​the evaluation page.

4. The method according to claim 3, characterized in that The method further comprises: Collect the real-time angle and range of motion of the target joint; An evaluation result is generated according to the real-time angle and range of motion of the target joint.

5. The method according to claim 4, characterized in that The method further comprises: In response to the third instruction, the evaluation result is displayed on the evaluation data viewing interface.

6. The method according to claim 1, characterized in that The target prescription includes a target joint, and the step of collecting training data according to the target prescription includes at least one of the following: Collecting activity data of the target joint; Collect muscle strength data; Collecting motion trajectory data of the target joint; Collection of spasticity data; Collect training time and frequency data.

7. The method according to claim 1, characterized in that The generating of the analysis result according to the training data, the training index and the training goal includes: The training data, the training indicators and the training objectives are identified through a machine learning algorithm to generate analysis results.

8. A training device, characterized in that: The device comprises: A collection module, used for collecting training data according to a target prescription, wherein the target prescription includes a training target; A calculation module, used to calculate the training data to obtain a training index; An analysis module, used to generate an analysis result according to the training data, the training indicators and the training objectives; The first display module is used to display the analysis result on the evaluation interface in response to the first instruction.

9. An electronic device, characterized in that: The electronic device comprises a memory and a processor, the memory stores a computer program, and the processor implements the method according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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