An intelligent control method and system for an upper and lower limb active and passive trainer

Abstract

The present invention discloses an intelligent control method and system for upper and lower limb active and passive trainers. By acquiring user information data and usage characteristic data of the upper and lower limb active and passive trainers, an AI big model is used to call the user's information data and usage characteristic data in the target data set to perform an intelligent analysis of the user's functional requirements, generate and output the user's AI analysis result data, and form personalized recommended rehabilitation information to push to the user, that is, the trainer can evaluate and analyze the user's rehabilitation effect and perform correlation analysis through the server side; the present invention generates the user's AI analysis result based on control parameter information and in combination with the AI big model, which not only can intelligently evaluate the rehabilitation training results and has a machine intelligent evaluation function, but also adds and improves the personalized recommendation rehabilitation function, thereby improving the use effect, efficiency and function richness of the trainer equipment.

Description

Technical Field

[0001] The present invention relates to the technical field of medical equipment, and in particular to an intelligent control method and system for upper and lower limb active and passive training devices. Background Art

[0002] The upper and lower limb rehabilitation trainer is suitable for limb dysfunction caused by a variety of diseases and injuries, including but not limited to sequelae of stroke, spinal cord injury, multiple sclerosis, Parkinson's disease, postoperative recovery of fractures, muscle atrophy, and postoperative rehabilitation of joint replacements.

[0003] However, the evaluation of the training effects of existing upper and lower limb active and passive trainers often relies on manual questionnaires, which are difficult to associate and match with the control parameters, functional modes and training categories of upper and lower limb rehabilitation trainers. The intelligence is relatively general, there is no intelligent analysis of user information and usage characteristics, and it is unable to generate training effect evaluation. In addition, there is still a lack of intelligent control of upper and lower limb active and passive trainers. At the same time, it is difficult to provide users with diversified personalized rehabilitation needs. Therefore, in view of the shortcomings of its relatively simple functions at this stage and the difficulty in intelligent evaluation and selection of training categories, intelligent control modes and parameters, we propose an intelligent control method and system for upper and lower limb active and passive trainers. Summary of the Invention

[0004] In view of the above-mentioned problems existing in the existing upper and lower limb active and passive training devices, the present invention is proposed.

[0005] Therefore, one of the objects of the present invention is to provide an intelligent control method and system for upper and lower limb active and passive trainers, which utilizes the trainer device to obtain the user's information data and usage characteristic data, and generates the user's AI analysis results based on the control parameter information and combined with the AI large model. It can not only intelligently evaluate the rehabilitation training results and has the machine intelligent evaluation function, but also adds and improves the personalized recommendation rehabilitation function, thereby improving the use effect, efficiency and functional richness of the trainer device.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In one aspect, the present invention provides an intelligent control method for an upper and lower limb active and passive training device, comprising the following steps:

[0008] Obtaining user information data and usage feature data of upper and lower limb active and passive training devices, and performing data clustering to form a target data set of upper and lower limb active and passive training devices;

[0009] Through the AI big model and calling the user's information data and usage feature data in the target data set, an intelligent analysis of the user's functional requirements is performed, and after matching the user, an intelligent control mode and training strategy data of the upper and lower limb active and passive trainer are generated. According to the intelligent control mode and training strategy data, the control parameter information of the upper and lower limb active and passive trainer is matched, and the user's AI analysis result data is generated and output according to the control parameter information;

[0010] The upper and lower limb active and passive training device receives and responds to the user's AI analysis result data, recommends the intelligent control mode and training strategy data in the AI analysis result data to the user for selection to form personalized recommended rehabilitation information, and visualizes the personalized recommended rehabilitation information to present the detailed functions of the intelligent control mode, the detailed content of the training strategy, and the training function parameters;

[0011] The upper and lower limb active and passive trainers complete the electrical control signal matching of the main control drive system according to the control parameter information, and wait for the user to confirm the start.

[0012] As a preferred solution of the present invention, the intelligent control mode includes an active mode, a passive mode, and an active-passive mode, and the intelligent control mode switching for different users and usage characteristics is completed through the intelligent control mode;

[0013] The passive mode is the default startup mode of the upper and lower limb active and passive training device. The passive mode is configured based on the user's usage characteristic data, after AI intelligent analysis and processing, matching the commonly used training mode function selection; at the same time, when the sensor of the upper and lower limb active and passive training device detects that the motor speed is 2r / min higher than the preset speed value for 3 consecutive seconds during operation, the upper and lower limb active and passive training device automatically switches from passive mode to active mode;

[0014] The active mode is a training mode function selection configuration performed based on the user's current output information by matching and determining the user's current output information; at the same time, when the training mode is active mode, the upper and lower limb active and passive training device automatically switches from active mode to passive mode when the sensor detects that the motor speed is less than 9r / min for 2 consecutive seconds;

[0015] The active-passive mode is used for intelligent switching between the active mode and the passive mode.

[0016] As a preferred solution of the present invention, wherein: the active and passive modes are intelligently switched between active and passive modes after analyzing the user's information data and the matching usage feature data;

[0017] The intelligent switching between the active mode and the passive mode is specifically that when the user exerts more force than a preset threshold matched by the intelligent switching, the upper and lower limb active and passive training device enters the active mode; when the user does not exert force and does not exceed the preset threshold matched by the intelligent switching, the upper and lower limb active and passive training device automatically switches to the passive mode;

[0018] The parameter values of the passive mode, active passive mode and active passive mode are all set as default values. During the training process, the user's current usage feature data is obtained and the generated AI analysis results are responded to and recommended to the user for the user to select and modify.

[0019] As a preferred embodiment of the present invention, the training strategy data includes lower limb training control strategy data, upper limb training control strategy data, and spasticity function training control strategy data;

[0020] The lower limb training control includes: forward rotation training control and reverse rotation training control with changing direction;

[0021] The upper limb training control includes: horizontal training, vertical cross and vertical parallel upper limb training control.

[0022] As a preferred embodiment of the present invention, the strategy data for the lower limb training control and the upper limb training control are specifically the training parameters of the lower limbs and upper limbs, and the training parameters of the lower limbs and upper limbs include duration, control resistance and running speed parameter information.

[0023] As a preferred embodiment of the present invention, the spasticity function training control is set to be on by default. After being turned on, the spasticity function training control intelligently identifies the user's characteristic data of spasticity, automatically reverses the movement direction after identifying the spasticity, and slowly returns the upper and lower limb active and passive training device to the original training direction and speed after deceleration and stopping.

[0024] If the upper and lower limb active and passive training device detects spasm again after reversal, reactivate the spasm protection. The number of consecutive reactivations of the spasm protection should not exceed 5 times, otherwise the upper and lower limb active and passive training device will stop output.

[0025] After the spasm protection is turned on for the upper and lower limb active and passive training devices, the spasm recognition sensitivity is divided into low, medium and high, and has a sensitivity adjustment function. Among them, the low sensitivity corresponds to a trigger time of 4.5s; the medium sensitivity corresponds to a trigger time of 2.5s; the high sensitivity corresponds to a trigger time of 1.5s, with an error of ±0.5s. When the spasm recognition sensitivity conditions are met, it is identified as the corresponding degree of spasm.

[0026] As a preferred embodiment of the present invention, the present invention further includes providing at least one graphical interface after the user completes rehabilitation training using the upper and lower limb active and passive training device, and providing a current rehabilitation training effect based on the currently completed rehabilitation training through a pop-up questionnaire and a machine score of the upper and lower limb active and passive training device, performing a scoring operation of 1-10 points for different categories of rehabilitation training effects, and performing statistics on the rehabilitation training results. After matching the user's information data and usage feature data, the user's rehabilitation training result data is generated;

[0027] The rehabilitation training effects include functional assessment, activity ability assessment, quality of life assessment, electromyographic signal analysis, spasticity level assessment, training data record assessment, and training trend change assessment.

[0028] As a preferred embodiment of the present invention, the AI analysis results of the user are generated by obtaining the user's control parameter information and establishing a training control evaluation expert library for upper and lower limb active and passive trainers based on the AI large model. By constructing a large model expert group, the user's control parameters and the user's information data in the training control evaluation expert library are evaluated and analyzed to evaluate the user's rehabilitation effect.

[0029] At the same time, LSTM long short-term memory network analysis is used to generate corresponding rehabilitation training program information, and association analysis is performed based on the rehabilitation training program information. Then, user information data belonging to the upper and lower limb active and passive trainers and weight data using feature data are generated. According to the control parameter information of the upper and lower limb active and passive trainers, the rehabilitation training program information currently associated with the user is determined and output as the rehabilitation training information output result of the deep learning network of the upper and lower limb active and passive trainers.

[0030] In one aspect, the present invention provides an intelligent control system for an upper and lower limb active and passive training device, comprising:

[0031] The trainer acquisition module is used to obtain user information data and usage feature data of upper and lower limb active and passive trainers, and to form a target data set of upper and lower limb active and passive trainers after data clustering;

[0032] The server data processing module is used to perform intelligent analysis of user functional requirements through the AI large model and call the user's information data and usage characteristic data in the target data set, generate intelligent control modes and training strategy data of the upper and lower limb active and passive trainers after matching the users, match the control parameter information of the upper and lower limb active and passive trainers according to the intelligent control mode and training strategy data, and generate and output the user's AI analysis result data based on the control parameter information;

[0033] The trainer data receiving module is used for the upper and lower limb active and passive trainers to receive and respond to the user's AI analysis result data, recommend the intelligent control mode and training strategy data in the AI analysis result data to the user to select to form personalized recommended rehabilitation information, and visualize the personalized recommended rehabilitation information to present the detailed functions of the intelligent control mode, the detailed content of the training strategy, and the training function parameters;

[0034] The trainer electronic control module is used for the upper and lower limb active and passive trainers to complete the electronic control signal matching of the main control drive system according to the control parameter information, and wait for the user to confirm the start;

[0035] The rehabilitation training result statistics module is used to give the current rehabilitation training effect based on the currently completed rehabilitation training through pop-up questionnaire information, and perform scoring operations on different categories of rehabilitation training effects with a score of 1-10. After matching the user's information data and usage feature data, the user's rehabilitation training result data is generated.

[0036] As a preferred solution of the present invention, it also includes a main control drive system for controlling the upper and lower limb active and passive trainers with electrical signals, and the main control drive system includes: a touch display unit, an upper limb Hall detection unit, a lower limb Hall detection unit, an emergency switch, a rehabilitation analysis unit, an upper limb motor encoder, a lower limb motor encoder, an upper limb DC brush motor, an upper limb and lower limb switching circuit, and a lower limb DC brush motor.

[0037] Compared with the prior art, the beneficial effects of the present invention are as follows: the intelligent control method of the upper and lower limb active and passive trainers of the present invention utilizes the trainer equipment to obtain the user's information data and usage characteristic data, and generates the user's AI analysis results based on the control parameter information and combined with the AI large model. It can not only intelligently evaluate the rehabilitation training results with machine intelligent evaluation functions, but also increase and improve the personalized recommendation rehabilitation function, thereby improving the use effect, efficiency and functional richness of the trainer equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. Those skilled in the art can also derive other drawings based on these drawings without inventive effort. Among them:

[0039] Figure 1 Flowchart of the intelligent control method of the upper and lower limb active and passive training device in an embodiment of the present invention;

[0040] Figure 2Schematic diagram of the lower limb training operation interface of the intelligently controlled upper and lower limb active and passive training device in an embodiment of the present invention;

[0041] Figure 3 Schematic diagram of the upper limb training function selection interface of the intelligent control of the upper and lower limb active and passive training device in an embodiment of the present invention;

[0042] Figure 4 This is a schematic diagram of the active mode parameter setting operation interface of the upper and lower limb active and passive training device in an embodiment of the present invention;

[0043] Figure 5 Schematic diagram of the modular structure of the intelligent control system of the upper and lower limb active and passive training device in an embodiment of the present invention;

[0044] Figure 6 Schematic diagram of the modular structure of the main control drive system in the intelligent control system of the upper and lower limb active and passive training device in an embodiment of the present invention;

[0045] Reference numerals in the figure: 100, trainer acquisition module; 101, server data processing module; 102, trainer data receiving module; 103, trainer electronic control module; 104, rehabilitation training result statistics module. DETAILED DESCRIPTION

[0046] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the described embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field are within the scope of protection of the present invention.

[0047] Reference Figure 1-Figure 4 , is an embodiment of the present invention, which provides an intelligent control method for upper and lower limb active and passive training devices, such as Figure 1 As shown, the following steps are included:

[0048] Step 1: Obtain user information data and usage feature data of upper and lower limb active and passive training devices, and perform data clustering to form a target data set of upper and lower limb active and passive training devices;

[0049] Step 2: Use the AI big model and call the user's information data and usage feature data in the target dataset to perform intelligent analysis of the user's functional requirements. After matching the user, generate the intelligent control mode and training strategy data of the upper and lower limb active and passive trainers. Match the control parameter information of the upper and lower limb active and passive trainers based on the intelligent control mode and training strategy data. Generate the user's AI analysis result data based on the control parameter information and output it.

[0050] Step 3: The upper and lower limb active and passive training device receives and responds to the user's AI analysis result data, recommends the intelligent control mode and training strategy data in the AI analysis result data to the user to select and form personalized recommended rehabilitation information, and visualizes the personalized recommended rehabilitation information to present the detailed functions of the intelligent control mode, the detailed content of the training strategy, and the training function parameters;

[0051] Step 4: The upper and lower limb active and passive training device completes the electronic control signal matching of the main control drive system according to the control parameter information, and waits for the user to confirm the start;

[0052] Step five also includes providing at least one graphical interface after the user completes rehabilitation training using the upper and lower limb active and passive trainers, and giving the current rehabilitation training effect based on the currently completed rehabilitation training through pop-up questionnaire messages and machine scoring of the upper and lower limb active and passive trainers, and scoring different categories of rehabilitation training effects with a score of 1-10 points, and performing statistics on the rehabilitation training results. After matching the user's information data and usage feature data, the user's rehabilitation training result data is generated; wherein the rehabilitation training effects include functional assessment, activity ability assessment, quality of life assessment, electromyographic signal analysis, spasticity level assessment, training data record assessment, and training trend change assessment.

[0053] This embodiment further illustrates that functional assessment: during the rehabilitation training process, the patient's physical strength, flexibility, balance ability and other functions are assessed to understand the changes before and after the rehabilitation training.

[0054] Activity Assessment: This assesses the patient's ability to perform daily activities, including walking, climbing stairs, and sitting-to-standing transfers. The effectiveness of rehabilitation training is assessed by recording changes in the patient's activity before and after rehabilitation training.

[0055] Quality of life assessment: The goal of rehabilitation training is to improve the patient's quality of life, so it is also very important to assess the patient's quality of life. Some quality of life questionnaires, such as the SF-36, can be used to understand the changes in the patient's quality of life before and after rehabilitation training.

[0056] EMG signal analysis: By collecting surface EMG signals from designated parts of the human body during rehabilitation training, and performing denoising and effective signal extraction, the effective EMG signals corresponding to the rehabilitation movements of the human body can be extracted from the collected EMG signals.

[0057] Spasm level assessment: Assess the spasm level, adjustable from 1 to 6 levels. The higher the level, the higher the spasm sensitivity and the easier it is to trigger spasm protection.

[0058] Training Data Recording: After training, all patient training records are automatically saved and a training report is generated. The training report includes basic information (name, ID, gender, age, height, weight, etiology), training parameters (game name, training mode, spasm level, training area, game difficulty), and training status (game score, average power, average speed, energy consumption, number of spasms, training distance, training duration, and symmetry ratio).

[0059] Training trend changes: The training report must reflect changes in training trends, including power change trend graphs and resistance / speed change trend graphs, so that the clinician can have a clear and comprehensive understanding of the patient's training situation each time.

[0060] It should be emphasized in this embodiment that when generating the AI analysis results of the user, the user's control parameter information is obtained, and a training control evaluation expert library of upper and lower limb active and passive trainers is established based on the AI big model. By building a big model expert group, the user's control parameters and user information data in the training control evaluation expert library are evaluated and analyzed to evaluate the user's rehabilitation effect; at the same time, the LSTM long short-term memory network analysis is used to generate corresponding rehabilitation training program information, and association analysis is performed based on the rehabilitation training program information. Then, the user information data of the upper and lower limb active and passive trainers and the weight data of the feature data are generated, and according to the control parameter information of the upper and lower limb active and passive trainers, the rehabilitation training program information currently associated with the user is determined, and output as the rehabilitation training information output result of the deep learning network of the upper and lower limb active and passive trainers.

[0061] Specifically, in this embodiment, the intelligent control modes include: active mode, passive mode, and active-passive mode, and the intelligent control mode switching for different users and usage characteristics is completed through the intelligent control mode;

[0062] Passive mode is the default startup mode for the upper and lower limb active and passive trainer. Passive mode is configured based on the user's usage characteristics, after AI intelligent analysis and processing, matching the commonly used training mode functions. At the same time, when the upper and lower limb active and passive trainer's sensor detects that the motor speed is 2r / min higher than the preset speed value for 3 consecutive seconds during operation, the upper and lower limb active and passive trainer automatically switches from passive mode to active mode.

[0063] Active mode is a training mode function selection configuration based on the user's current output information, which is performed by matching and determining the user's current output information. At the same time, when the training mode is active mode, if the sensor of the upper and lower limb active and passive trainer detects that the motor speed is less than 9r / min for 2 consecutive seconds, it will automatically switch from active mode to passive mode.

[0064] Active-passive mode is used for intelligent switching between active mode and passive mode.

[0065] Specifically, in this embodiment, the active and passive modes are intelligently switched between active and passive modes after analyzing the user's information data and the matching usage feature data;

[0066] The intelligent switching between active mode and passive mode is specifically that when the user's force exceeds the preset threshold matched by the intelligent switching, the upper and lower limb active and passive training device enters the active mode; when the user does not exert force and does not exceed the preset threshold matched by the intelligent switching, the upper and lower limb active and passive training device automatically switches to the passive mode;

[0067] The parameter values of passive mode, passive mode, and active-passive mode are all set to default values. During the training process, the user's current usage feature data is obtained and the generated AI analysis results are recommended to the user for the user to select and confirm before modification.

[0068] like Figure 2 and Figure 3 As shown, in this embodiment, when the mode and function are selected, the training strategy data of the functional training specifically includes: lower limb training control strategy data, upper limb training control strategy data, and spasticity function training control strategy data;

[0069] Lower limb training control includes: forward rotation training control and reverse training control with changing direction;

[0070] Upper limb training control includes: horizontal training, vertical cross and vertical parallel upper limb training control.

[0071] Specifically, in this embodiment, the strategy data for lower limb training control and upper limb training control are specifically the training parameters of the lower limbs and upper limbs. The training parameters of the lower limbs and upper limbs include: duration, control resistance and running speed parameter information.

[0072] like Figure 4 As shown in the figure, the parameter settings specifically include the sound setting button to turn the voice on and off. The training time setting can set the training time, up to 99 minutes. When the user reaches the training time, the power will automatically shut down and the device will be in standby mode. The spasm button can turn spasm monitoring on and off. The spasm level can be set to high, medium, and low. The speed setting is the default speed value in passive mode. The resistance setting is the default resistance value in active mode. The steering time is the switching time between forward and reverse in passive mode.

[0073] Upper limb training differs from lower limb training in that it offers three exercise modes, corresponding to "Horizontal Training," "Vertical Cross," and "Vertical Parallel" in Figure 5. In the upper limb horizontal training mode, the user's upper limbs undergo horizontal circular motion. This mode allows both upper limbs to perform vertical circular motions simultaneously on a single plane, while also enabling switching between two vertical training modes. The participant holds the upper limb training handles with both hands. Simply select the "Upper Limb Training" button, then choose "Horizontal Training," "Vertical Cross," or "Vertical Parallel" based on the desired training mode. Three training modes are available: Active / Passive, Active, and Passive. In Active / Passive mode, the product automatically detects the user's status and determines whether to enter active or passive mode. When the user exerts force, the product enters active mode; when the user is not exerting force, the device automatically switches to passive mode. The active and passive parameters are default values and can be manually modified during training.

[0074] Specifically, in this embodiment, the spasticity function training control is set to on by default. After being turned on, the user's characteristic data of spasticity is intelligently identified. After the spasticity is identified, the movement direction is automatically reversed to relieve the spasticity. After deceleration and stopping, the upper and lower limb active and passive training device slowly returns to the original training direction and speed.

[0075] If the upper and lower limb active and passive training device detects spasm again after reversal, reactivate the spasm protection. The number of consecutive reactivations of the spasm protection should not exceed 5 times, otherwise the upper and lower limb active and passive training device will stop output.

[0076] After the spasm protection is turned on for the upper and lower limb active and passive training devices, the spasm recognition sensitivity is divided into low, medium and high, and has a sensitivity adjustment function. Among them, the low sensitivity corresponds to a trigger time of 4.5s; the medium sensitivity corresponds to a trigger time of 2.5s; the high sensitivity corresponds to a trigger time of 1.5s, with an error of ±0.5s. When the spasm recognition sensitivity conditions are met, it is identified as the corresponding degree of spasm.

[0077] The intelligent control method and system of the upper and lower limb active and passive trainer of this embodiment are applied to the upper and lower limb active and passive trainer. The upper and lower limb active and passive trainer is a circular motion training device. Its working principle is: according to the set parameters, a given signal is output or the patient's own muscle strength is relied on to make the patient's limb extremities move in a circular motion along a fixed axis, driving the entire limbs of the unilateral or bilateral upper limbs and / or lower limbs (including shoulders, elbows, wrists, fingers, hips, knees, ankle joints and related muscle groups) to perform comprehensive exercise training to enhance the patient's joint mobility and muscle strength.

[0078] While sitting or lying down, the patient secures their upper and / or lower limbs to the product's motion platform. Active or passive training can be performed by setting the training mode and parameters via the touchscreen based on the patient's functional impairment. Throughout the training process, the product consistently performs circular motions, with a real-time monitoring feedback protection system identifying the patient's muscle strength (spasticity detection) and automatically switching between training modes. The intelligent control method and system for upper and lower limb active and passive training devices in this embodiment primarily include active mode, passive mode, and automatic switching between these modes.

[0079] refer to Figure 5 and Figure 6 The present invention provides an intelligent control system for upper and lower limb active and passive training devices, comprising:

[0080] The training device acquisition module 100 is used to obtain user information data and usage feature data of the upper and lower limb active and passive training devices, and to form a target data set of the upper and lower limb active and passive training devices after data clustering;

[0081] The server data processing module 101 is used to perform intelligent analysis of user functional requirements by using the AI large model and calling the user's information data and usage characteristic data in the target data set, generate intelligent control modes and training strategy data for the upper and lower limb active and passive trainers after matching the users, match the control parameter information of the upper and lower limb active and passive trainers according to the intelligent control mode and training strategy data, and generate and output the user's AI analysis result data based on the control parameter information;

[0082] The trainer data receiving module 102 is used for the upper and lower limb active and passive trainer to receive and respond to the user's AI analysis result data, recommend the intelligent control mode and training strategy data in the AI analysis result data to the user to select to form personalized recommended rehabilitation information, and visualize the personalized recommended rehabilitation information to present the detailed functions of the intelligent control mode, the detailed content of the training strategy, and the training function parameters;

[0083] The trainer electronic control module 103 is used for the upper and lower limb active and passive trainers to complete the electronic control signal matching of the main control drive system according to the control parameter information, and wait for the user to confirm the start;

[0084] The rehabilitation training result statistics module 104 is used to give the current rehabilitation training effect based on the currently completed rehabilitation training through a pop-up questionnaire message, and perform a scoring operation of 1-10 points for different categories of rehabilitation training effects, and to perform statistics on the rehabilitation training results. After matching the user's information data and usage feature data, the user's rehabilitation training result data is generated.

[0085] like Figure 6As shown, the system of this embodiment also includes a main control drive system for controlling the upper and lower limb active and passive trainers with electrical signals. The main control drive system includes: a touch display unit, an upper limb Hall detection unit, a lower limb Hall detection unit, an emergency switch, a rehabilitation analysis unit, an upper limb motor encoder, a lower limb motor encoder, an upper limb DC brush motor, an upper limb and lower limb switching circuit, and a lower limb DC brush motor.

[0086] Based on the above, the intelligent control method can monitor the user's training performance in real time and perform intelligent evaluation based on the preset rehabilitation goals. The system provides instant feedback to help users adjust the intensity and method of training to achieve the best rehabilitation effect. Based on the user's training data and AI analysis results, the intelligent control system can recommend personalized rehabilitation training plans. These recommended plans include exercise type, intensity, frequency and duration to suit the user's specific situation and rehabilitation needs. Through intelligent control methods, the trainer equipment can provide a more scientific and efficient training experience, thereby improving the user's rehabilitation effect. It can also dynamically adjust the training plan based on user feedback and progress to ensure the continuity and effectiveness of the training. The functional richness of the equipment has been improved, and it can provide users with more comprehensive and in-depth rehabilitation support.

[0087] To sum up, this embodiment can generate AI analysis results for users based on control parameter information and combined with the AI big model. It can not only intelligently evaluate the rehabilitation training results with machine intelligent evaluation functions, but also increase and improve the personalized recommendation rehabilitation function, thereby improving the use effect, efficiency and functional richness of the training equipment.

[0088] In the description of this specification, the reference terms "one embodiment," "some embodiments," "example," "specific example," or "some examples" mean that the specific features, structures, materials, or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. Moreover, the specific features, structures, materials, or characteristics described may be combined in any appropriate manner in any one or more embodiments or examples. In addition, those skilled in the art may combine and combine different embodiments or examples described in this specification, as well as features of different embodiments or examples, unless they are mutually inconsistent.

[0089] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features being referred to. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. Throughout the description of this application, "plurality" means two or more, unless otherwise specifically defined.

[0090] Any process or method description in a flow chart or otherwise described herein can be understood to represent a module, segment or portion of code comprising one or more executable instructions for implementing the steps of a specific logical function or process. The scope of the preferred embodiments of the present application includes additional implementations in which the functions may be performed in a different order than shown or discussed, including in a substantially simultaneous manner or in a reverse order depending on the functions involved.

[0091] The logic and / or steps represented in the flowchart or otherwise described herein may be considered, for example, as a sequenced list of executable instructions for implementing the logical functions, and may be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device).

[0092] It should be understood that various parts of the present application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. All or part of the steps of the above embodiment method can be completed by instructing the relevant hardware through a program, which can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiment.

[0093] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing module, or each unit may exist physically separately, or two or more units may be integrated into a single module. The aforementioned integrated modules may be implemented in the form of hardware or in the form of software functional modules. If the aforementioned integrated modules are implemented in the form of software functional modules and sold or used as independent products, they may also be stored in a computer-readable storage medium. The storage medium may be a read-only memory, a magnetic disk, or an optical disk, etc.

[0094] The above are only specific embodiments of the present application, but the scope of protection of the present application is not limited thereto. Any person skilled in the art can easily conceive of various modifications or substitutions within the technical scope disclosed in this application, and such modifications or substitutions should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

Claims

1. An intelligent control method for upper and lower limb active and passive training devices, characterized by: The following steps are involved: Obtaining user information data and usage feature data of upper and lower limb active and passive training devices, and performing data clustering to form a target data set of upper and lower limb active and passive training devices; Through the AI big model and calling the user's information data and usage feature data in the target data set, an intelligent analysis of the user's functional requirements is performed, and after matching the user, an intelligent control mode and training strategy data of the upper and lower limb active and passive trainer are generated. According to the intelligent control mode and training strategy data, the control parameter information of the upper and lower limb active and passive trainer is matched, and the user's AI analysis result data is generated and output according to the control parameter information; The intelligent control modes include: active mode, passive mode, and active-passive mode, through which intelligent control mode switching for different users and usage characteristics is completed; the passive mode is the default startup mode of the upper and lower limb active and passive trainer, when the sensor of the upper and lower limb active and passive trainer detects that the motor speed is 2r / min higher than the preset speed value for 3 consecutive seconds during operation, the upper and lower limb active and passive trainer automatically switches from the passive mode to the active mode; when the training mode is the active mode, the sensor of the upper and lower limb active and passive trainer detects that the motor speed is less than 9r / min for 2 consecutive seconds, it automatically switches from the active mode to the passive mode; The training strategy data includes: lower limb training control strategy data, upper limb training control strategy data, and spasticity function training control strategy data; The spasticity function training control is on by default. When it is turned on, it intelligently identifies the user's characteristic data of spasticity, automatically reverses the direction of movement after identifying the spasticity, and slowly returns the upper and lower limb active and passive trainers to the original training direction and speed after deceleration and stopping. The upper and lower limb active and passive training device receives and responds to the user's AI analysis result data, recommends the intelligent control mode and training strategy data in the AI analysis result data to the user for selection to form personalized recommended rehabilitation information, and visualizes the personalized recommended rehabilitation information to present the detailed functions of the intelligent control mode, the detailed content of the training strategy, and the training function parameters; The upper and lower limb active and passive trainers complete the electrical control signal matching of the main control drive system according to the control parameter information, and wait for the user to confirm to start.

2. The intelligent control method for upper and lower limb active and passive training devices according to claim 1, characterized in that: The passive mode is based on the user's usage feature data, and is matched to the commonly used training mode function selection configuration after AI intelligent analysis and processing; The active mode is a training mode function selection configuration performed after matching and determining the user's current output information according to the user's current output information; The active-passive mode is used for intelligent switching between the active mode and the passive mode.

3. The intelligent control method for upper and lower limb active and passive training devices according to claim 2, characterized in that: The active and passive modes are intelligently switched between active and passive modes after analyzing the user's information data and matching usage feature data; The intelligent switching between the active mode and the passive mode is specifically that when the user exerts more force than a preset threshold matched by the intelligent switching, the upper and lower limb active and passive training device enters the active mode; when the user does not exert force and does not exceed the preset threshold matched by the intelligent switching, the upper and lower limb active and passive training device automatically switches to the passive mode; The parameter values of the passive mode, active passive mode and active passive mode are all set as default values. During the training process, the user's current usage feature data is obtained and the generated AI analysis results are responded to and recommended to the user for the user to select and modify.

4. The intelligent control method for upper and lower limb active and passive training devices according to claim 1, characterized in that: The lower limb training control includes: forward training control and reverse training control with changing direction; The upper limb training control includes: horizontal training, vertical cross and vertical parallel upper limb training control.

5. The intelligent control method for upper and lower limb active and passive training devices according to claim 4, characterized in that: The strategy data for the lower limb training control and the upper limb training control are specifically the training parameters of the lower limbs and upper limbs, and the training parameters of the lower limbs and upper limbs include: duration, control resistance and running speed parameter information.

6. The intelligent control method for upper and lower limb active and passive training devices according to claim 4, characterized in that: If the upper and lower limb active and passive training device detects spasm again after reversal, reactivate the spasm protection. The number of consecutive reactivations of the spasm protection should not exceed 5 times, otherwise the upper and lower limb active and passive training device will stop output. After the spasm protection is turned on for the upper and lower limb active and passive training devices, the spasm recognition sensitivity is divided into low, medium and high, and has a sensitivity adjustment function. Among them, the low sensitivity corresponds to a trigger time of 4.5s; the medium sensitivity corresponds to a trigger time of 2.5s; the high sensitivity corresponds to a trigger time of 1.5s, with an error of ±0.5s. When the spasm recognition sensitivity conditions are met, it is identified as the corresponding degree of spasm.

7. The intelligent control method for upper and lower limb active and passive training devices according to claim 1, characterized in that: The system also includes providing at least one graphical interface after the user completes rehabilitation training using the upper and lower limb active and passive training device, and providing a current rehabilitation training effect based on the currently completed rehabilitation training through a pop-up questionnaire message and a machine score of the upper and lower limb active and passive training device, scoring different categories of rehabilitation training effects on a scale of 1-10, and performing statistics on the rehabilitation training results. After matching the user's information data and usage feature data, the system generates the user's rehabilitation training result data; The rehabilitation training effects include functional assessment, activity ability assessment, quality of life assessment, electromyographic signal analysis, spasticity level assessment, training data record assessment, and training trend change assessment.

8. The intelligent control method for upper and lower limb active and passive training devices according to claim 7, characterized in that: Generate AI analysis results for the user, specifically by obtaining the user's control parameter information and establishing a training control evaluation expert library for upper and lower limb active and passive trainers based on the AI big model. By building a big model expert group, the user's control parameters and information data in the training control evaluation expert library are evaluated and analyzed to determine the user's rehabilitation effect. At the same time, LSTM long short-term memory network analysis is used to generate corresponding rehabilitation training program information, and association analysis is performed based on the rehabilitation training program information. Then, user information data belonging to the upper and lower limb active and passive trainers and weight data using feature data are generated. According to the control parameter information of the upper and lower limb active and passive trainers, the rehabilitation training program information currently associated with the user is determined and output as the rehabilitation training information output result of the deep learning network of the upper and lower limb active and passive trainers.

9. An intelligent control system for an upper and lower limb active and passive training device, used to implement the intelligent control method for an upper and lower limb active and passive training device as claimed in claim 8, characterized in that: include: The trainer acquisition module is used to obtain user information data and usage feature data of upper and lower limb active and passive trainers, and to form a target data set of upper and lower limb active and passive trainers after data clustering; The server data processing module is used to perform intelligent analysis of user functional requirements through the AI large model and call the user's information data and usage characteristic data in the target data set, generate intelligent control modes and training strategy data of the upper and lower limb active and passive trainers after matching the users, match the control parameter information of the upper and lower limb active and passive trainers according to the intelligent control mode and training strategy data, and generate and output the user's AI analysis result data based on the control parameter information; The trainer data receiving module is used for the upper and lower limb active and passive trainers to receive and respond to the user's AI analysis result data, recommend the intelligent control mode and training strategy data in the AI analysis result data to the user to select to form personalized recommended rehabilitation information, and visualize the personalized recommended rehabilitation information to present the detailed functions of the intelligent control mode, the detailed content of the training strategy, and the training function parameters; The trainer electronic control module is used for the upper and lower limb active and passive trainers to complete the electronic control signal matching of the main control drive system according to the control parameter information, and wait for the user to confirm the start; The rehabilitation training result statistics module is used to give the current rehabilitation training effect based on the currently completed rehabilitation training through pop-up questionnaire information, and perform scoring operations on different categories of rehabilitation training effects with a score of 1-10. After matching the user's information data and usage feature data, the user's rehabilitation training result data is generated.

10. The intelligent control system for upper and lower limb active and passive training device according to claim 9, characterized in that: It also includes a main control drive system for controlling the upper and lower limb active and passive trainers with electrical signals, and the main control drive system includes: a touch display unit, an upper limb Hall detection unit, a lower limb Hall detection unit, an emergency switch, a rehabilitation analysis unit, an upper limb motor encoder, a lower limb motor encoder, an upper limb DC brush motor, an upper limb and lower limb switching circuit, and a lower limb DC brush motor.

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