Intelligent control method for rehabilitation training device robot

Abstract

The invention relates to an intelligent control method for a rehabilitation training device robot, and belongs to the technical field of rehabilitation training device robot control. The motion intention feature data information of each joint position node is acquired based on the joint intention motion model, so that motion risk assessment is performed on the motion intention feature data information of each joint position node, a risk assessment result is acquired, and a rehabilitation scheme of the rehabilitation trainer robot is reconfigured according to the risk assessment result. And finally performing motion control on the rehabilitation training device robot based on the rehabilitation scheme of the rehabilitation training device robot. According to the method, the movement risk assessment is carried out on the movement intention characteristic data information of each joint position node, so that the rehabilitation scheme of the rehabilitation trainer robot is reconfigured, the recovery condition of a user is fully considered to formulate more reasonable rehabilitation training parameters, and injuries in the recovery training process are avoided.

Description

Technical Field

[0001] The invention relates to the technical field of rehabilitation training robot, and in particular to an intelligent control method of a rehabilitation training robot. Background Art

[0002] Rehabilitation training plays a great role in the early rehabilitation of hemiplegic stroke patients and seriously injured patients. In the past, walking ability training and standing balance training were mostly used. Conventional rehabilitation training can improve the patient's limb motor function and balance ability to a certain extent by training the patient's gait and balance function, but the effect is not obvious, and the lack of necessary feedback exercises can easily lead to poor gait. The rehabilitation trainer robot is an intelligent lower limb feedback rehabilitation training system that can combine movement patterns and visual feedback to trigger lower limb motor function, so that patients can regain walking function and has high application value. Today's rehabilitation trainer robots often combine the user's movement intention to achieve assisted rehabilitation. However, if rehabilitation exercises are performed only according to the user's movement consciousness, it is easy to cause injuries during the rehabilitation process, which in turn causes the user's rehabilitation effect to fail to achieve the expected effect and delays the rehabilitation period. Summary of the invention

[0003] The present invention overcomes the deficiencies of the prior art and provides an intelligent control method for a rehabilitation training robot.

[0004] To achieve the above object, the technical solution adopted by the present invention is: A first aspect of the present invention provides a rehabilitation training robot intelligent control method, comprising the following steps: Acquire sensor data information of the user at each rehabilitation joint through the wearable rehabilitation training robot, and obtain the sensor data information after noise reduction by performing noise reduction processing on the sensor data information of the user at each rehabilitation joint; Constructing a joint intention motion model according to the sensor data information of the user at each rehabilitation joint, and acquiring motion intention feature data information of each joint position node based on the joint intention motion model; By performing motion risk assessment on the motion intention feature data information of each joint position node, a risk assessment result is obtained, and a rehabilitation program of the rehabilitation trainer robot is reconfigured according to the risk assessment result; The rehabilitation trainer robot is motion-controlled based on the rehabilitation program of the rehabilitation trainer robot.

[0005] Furthermore, in this method, the sensor data information of the user at each rehabilitation joint is obtained by a wearable rehabilitation training robot, and the sensor data information of the user at each rehabilitation joint is subjected to noise reduction processing to obtain the sensor data information after noise reduction processing, specifically: By installing sensors on the wearable rehabilitation training robot, the sensor data information of the user at each rehabilitation joint is obtained through the sensors, and a discrete wavelet transform algorithm is introduced to perform noise reduction processing on the sensor data information of the user at each rehabilitation joint; Obtaining noise characteristics and original signal characteristics in sensor data information through the discrete wavelet transform algorithm; Selecting a wavelet basis for the noise characteristics and the original signal characteristics in the sensor data information, determining the number of layers, and performing hierarchical decomposition on the original signal according to the number of layers; Through hierarchical decomposition, low-frequency signals and high-frequency signals are obtained, and an empirical threshold is selected for the high-frequency signal of each layer. The low-frequency signal and the high-frequency signal are reconstructed according to the empirical threshold to obtain sensor data information after noise reduction processing.

[0006] Furthermore, in the present method, a joint intention movement model is constructed according to the sensor data information of the user at each rehabilitation joint, specifically including: Based on the sensor data information of the user at each rehabilitation joint, a human joint motion torque feature corresponding to each sensor data is obtained, and a global human joint feature is constructed based on the human joint motion torque feature corresponding to each sensor data; Parameterizing the three-dimensional posture of human joints based on the global human joint features to obtain three-dimensional posture parameters, and estimating the joints and three-dimensional postures based on the three-dimensional posture parameters to obtain intentional features of human posture joints; A three-dimensional model is constructed based on the joint intention features of the human body posture, and through the construction, a joint intention motion model in the motion perception time stage is obtained, and the joint intention motion model is output.

[0007] Further, in the present method, obtaining the motion intention feature data information of each joint position node based on the joint intention motion model specifically includes: By performing dimensionality reduction processing on the joint intention motion model, a two-dimensional graph of the motion model of each joint position node is obtained, and motion features are extracted from the two-dimensional graph of the motion model of each joint position node; By extracting motion features, motion intention feature data information of each joint position node is obtained, and the motion intention feature data information of each joint position node is output.

[0008] Furthermore, in the present method, a risk assessment result is obtained by performing a motion risk assessment on the motion intention feature data information of each joint position node, specifically including: Obtaining injury risk assessment feature data caused by different exercise states used by users under various joint disease states through big data, and constructing a sports risk knowledge graph, and inputting the injury risk assessment feature data caused by different exercise states used by the users under various joint disease states into the sports risk knowledge graph for storage; Obtaining the condition status information of each joint of the user, and inputting the condition status information of each joint of the user and the motion intention feature data information of each joint position node into the knowledge graph for data search; Through data search, the injury risk assessment feature data caused by exercise is obtained according to the motion intention feature data information of the joint position nodes under the disease status information of each joint of the user; A risk assessment result is generated based on the injury risk assessment feature data caused by exercising according to the movement intention feature data information of the joint position nodes under the disease status information of each joint of the user, and the risk assessment result is output.

[0009] Furthermore, in this method, the rehabilitation program of the rehabilitation trainer robot is reconfigured according to the risk assessment result, specifically: Obtaining motion intention feature data information of joint position nodes corresponding to injury risk assessment feature data greater than preset risk assessment indicators, and reinitializing motion parameters of joint position nodes corresponding to injury risk assessment feature data greater than preset risk assessment indicators, thereby reducing motion parameters of the rehabilitation trainer robot; Obtaining the motion parameters of the rehabilitation training robot after initialization, and determining whether the motion parameters of the rehabilitation training robot after initialization still result in a situation where the injury risk assessment characteristic data is greater than a preset risk assessment index; When the motion parameters of the rehabilitation training robot after the initialization still result in the existence of the injury risk assessment characteristic data being greater than the preset risk assessment index, reconfiguring and reducing the motion parameters of the rehabilitation training robot; When there is no injury risk assessment characteristic data of the rehabilitation trainer robot motion parameters after initialization that is greater than the preset risk assessment index, the rehabilitation trainer robot motion parameters after initialization are output, and a rehabilitation plan for the rehabilitation trainer robot is generated.

[0010] Furthermore, in the present method, the rehabilitation training robot is subjected to motion control based on the rehabilitation program of the rehabilitation training robot, specifically: Determining whether a motion parameter in a rehabilitation program of the rehabilitation trainer robot is greater than a maximum motion parameter of the rehabilitation trainer robot; When the motion parameter in the rehabilitation program of the rehabilitation trainer robot is greater than the maximum motion parameter of the rehabilitation trainer robot, the motion control is performed according to the maximum motion parameter of the rehabilitation trainer robot; When the motion parameters in the rehabilitation program of the rehabilitation trainer robot are not greater than the maximum motion parameters of the rehabilitation trainer robot, motion control is performed according to the motion parameters in the current rehabilitation program.

[0011] A second aspect of the present invention provides a rehabilitation trainer robot intelligent control system, comprising a memory and a processor, wherein the memory comprises a rehabilitation trainer robot intelligent control method program, and when the rehabilitation trainer robot intelligent control method program is executed by the processor, the steps of any one of the rehabilitation trainer robot intelligent control methods are implemented.

[0012] A third aspect of the present invention provides a computer-readable storage medium, comprising a rehabilitation trainer robot intelligent control method program, which, when executed by a processor, implements any one of the steps of the rehabilitation trainer robot intelligent control method.

[0013] The present invention solves the defects existing in the background technology and has the following beneficial effects: The present invention obtains sensor data information of the user at each rehabilitation joint through a wearable rehabilitation training robot, performs noise reduction processing on the sensor data information of the user at each rehabilitation joint, obtains the sensor data information after noise reduction processing, and then builds a joint intention motion model according to the sensor data information of the user at each rehabilitation joint, obtains the motion intention feature data information of each joint position node based on the joint intention motion model, and then performs motion risk assessment on the motion intention feature data information of each joint position node to obtain the risk assessment result, reconfigures the rehabilitation program of the rehabilitation trainer robot according to the risk assessment result, and finally controls the rehabilitation trainer robot based on the rehabilitation program of the rehabilitation trainer robot. The present invention reconfigures the rehabilitation program of the rehabilitation trainer robot by performing motion risk assessment on the motion intention feature data information of each joint position node, fully considers the user's recovery situation to formulate more reasonable rehabilitation training parameters, and avoids injuries during the recovery process. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, drawings of other embodiments can be obtained based on these drawings without paying creative work.

[0015] Figure 1 The overall flow chart of the intelligent control method of the rehabilitation training robot is shown; Figure 2 A partial method flow chart of the intelligent control method of the rehabilitation training robot is shown; Figure 3 The system block diagram of the intelligent control system of the rehabilitation trainer robot is shown. DETAILED DESCRIPTION

[0016] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0017] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0018] like Figure 1 As shown, the first aspect of the present invention provides a rehabilitation training robot intelligent control method, comprising the following steps: S102: obtaining sensor data information of the user at each rehabilitation joint through the wearable rehabilitation training robot, and performing noise reduction processing on the sensor data information of the user at each rehabilitation joint to obtain the sensor data information after noise reduction processing; S104: constructing a joint intention motion model according to the sensor data information of the user at each rehabilitation joint, and obtaining motion intention feature data information of each joint position node based on the joint intention motion model; S106: performing motion risk assessment on the motion intention feature data information of each joint position node, obtaining a risk assessment result, and reconfiguring a rehabilitation program of the rehabilitation training robot according to the risk assessment result; S108: Perform motion control on the rehabilitation trainer robot based on the rehabilitation plan of the rehabilitation trainer robot.

[0019] It should be noted that the present invention reconfigures the rehabilitation plan of the rehabilitation trainer robot by performing motion risk assessment on the motion intention feature data information of each joint position node, fully considers the user's recovery situation to formulate more reasonable rehabilitation training parameters, and avoids injuries during the recovery process.

[0020] Furthermore, in this method, the sensor data information of the user at each rehabilitation joint is obtained by a wearable rehabilitation training robot, and the sensor data information of the user at each rehabilitation joint is subjected to noise reduction processing to obtain the sensor data information after noise reduction processing, specifically: By installing sensors on the wearable rehabilitation training robot, the sensor data information of the user at each rehabilitation joint is obtained through the sensors, and the discrete wavelet transform algorithm is introduced to perform noise reduction processing on the sensor data information of the user at each rehabilitation joint; Obtain the noise characteristics and original signal characteristics in the sensor data information, and introduce the discrete wavelet transform algorithm; Select wavelet basis for the noise characteristics in the sensor data information and the original signal characteristics, determine the number of layers, and decompose the original signal hierarchically according to the number of layers; Through hierarchical decomposition, low-frequency signals and high-frequency signals are obtained, and an empirical threshold is selected for the high-frequency signal of each layer. The low-frequency signals and high-frequency signals are reconstructed according to the empirical threshold to obtain the sensor data information after noise reduction processing.

[0021] It should be noted that the sensor includes data such as electromyographic signal sensors and torque sensors. The sensor data information includes pressure data, torque data, electromyographic signal data, etc. The sensor data is denoised by a discrete wavelet transform algorithm to obtain a more reliable signal.

[0022] Furthermore, in this method, a joint intention movement model is constructed based on the sensor data information of the user at each rehabilitation joint, specifically including: Based on the sensor data information of the user at each rehabilitation joint, the human joint motion torque characteristics corresponding to each sensor data are obtained, and the global human joint characteristics are constructed based on the human joint motion torque characteristics corresponding to each sensor data; Parameterize the 3D posture of human joints based on the global human joint features, obtain the 3D posture parameters, and estimate the joints and 3D posture based on the 3D posture parameters to obtain the intention features of human posture joints; A three-dimensional model is constructed based on the joint intention features of the human body posture. Through the construction, the joint intention motion model in the motion perception time stage is obtained, and the joint intention motion model is output.

[0023] It should be noted that a three-dimensional model is constructed based on the intention features of human body posture joints through three-dimensional modeling software or virtual reality technology, so as to obtain the joint intention motion model in the motion perception time stage to form a joint motion intention estimate.

[0024] Furthermore, in this method, the motion intention feature data information of each joint position node is obtained based on the joint intention motion model, specifically including: By performing dimensionality reduction processing on the joint intention motion model, a two-dimensional graph of the motion model of each joint position node is obtained, and motion features are extracted from the two-dimensional graph of the motion model of each joint position node; By extracting motion features, the motion intention feature data information of each joint position node is obtained, and the motion intention feature data information of each joint position node is output.

[0025] Furthermore, in this method, the motion risk assessment is performed on the motion intention feature data information of each joint position node to obtain the risk assessment result, which specifically includes: The characteristic data of injury risk assessment caused by different exercise states of users under different joint disease states are obtained through big data, and a sports risk knowledge graph is constructed, and the characteristic data of injury risk assessment caused by different exercise states of users under different joint disease states are input into the sports risk knowledge graph for storage; Obtain the condition status information of each joint of the user, and input the condition status information of each joint of the user and the motion intention feature data information of each joint position node into the knowledge graph for data search; Through data search, the injury risk assessment feature data caused by exercise is obtained according to the motion intention feature data information of the joint position nodes under the disease status information of each joint of the user; Based on the condition status information of each joint of the user, the risk assessment feature data of the injury risk caused by exercise is used according to the movement intention feature data information of the joint position nodes to generate a risk assessment result, and the risk assessment result is output.

[0026] It should be noted that the joint condition state includes the condition assessment state of each joint of the user, such as low injury state, moderate injury state, high injury state, etc., and the injury risks (such as low sealing risk, moderate risk, high risk, etc.) caused by the user using different motion states (motion intention characteristics, such as torque characteristics) under each joint condition are different. This method can obtain the risk assessment results, thereby optimizing the control parameters.

[0027] like Figure 2 As shown, further, in this method, the rehabilitation program of the rehabilitation trainer robot is reconfigured according to the risk assessment result, specifically: S202: obtaining motion intention feature data information of the joint position nodes corresponding to the injury risk assessment feature data being greater than the preset risk assessment index, and reinitializing the motion parameters of the joint position nodes corresponding to the injury risk assessment feature data being greater than the preset risk assessment index, thereby reducing the motion parameters of the rehabilitation training robot; S204: Obtaining the motion parameters of the rehabilitation training robot after initialization, and determining whether the motion parameters of the rehabilitation training robot after initialization still result in a situation where the injury risk assessment characteristic data is greater than a preset risk assessment index; S206: when the motion parameters of the rehabilitation training robot after initialization still result in the presence of injury risk assessment characteristic data greater than the preset risk assessment index, reconfigure and reduce the motion parameters of the rehabilitation training robot; S208: When there is no injury risk assessment characteristic data of the rehabilitation trainer robot motion parameters after initialization that is greater than the preset risk assessment index, the rehabilitation trainer robot motion parameters after initialization are output, and a rehabilitation plan for the rehabilitation trainer robot is generated.

[0028] It should be noted that the motion parameters of the rehabilitation trainer robot include the rotation speed of the motor, the number of rotations of the motor, the swing angle of the motor, etc. When the motion parameters of the rehabilitation trainer robot after initialization still result in the situation that the injury risk assessment characteristic data is greater than the preset risk assessment index, the motion parameters of the rehabilitation trainer robot are reconfigured and reduced. When the motion parameters of the rehabilitation trainer robot after initialization do not result in the situation that the injury risk assessment characteristic data is greater than the preset risk assessment index, the motion parameters of the rehabilitation trainer robot after initialization are output, and the rationality of the user's recovery exercise is improved. Furthermore, in the present method, the rehabilitation training robot is motion controlled based on the rehabilitation program of the rehabilitation training robot, specifically: Determining whether a motion parameter in a rehabilitation program of the rehabilitation trainer robot is greater than a maximum motion parameter of the rehabilitation trainer robot; When the motion parameter in the rehabilitation program of the rehabilitation trainer robot is greater than the maximum motion parameter of the rehabilitation trainer robot, the motion control is performed according to the maximum motion parameter of the rehabilitation trainer robot; When the motion parameters in the rehabilitation program of the rehabilitation trainer robot are not greater than the maximum motion parameters of the rehabilitation trainer robot, motion control is performed according to the motion parameters in the current rehabilitation program.

[0029] It should be noted that this method can further optimize the rehabilitation program of the rehabilitation trainer robot and improve the rationality of control.

[0030] like Figure 3 The second aspect of the present invention provides a rehabilitation training robot intelligent control system 4, including a memory 41 and a processor 42. The memory 41 includes a rehabilitation training robot intelligent control method program. When the rehabilitation training robot intelligent control method program is executed by the processor 42, the following steps are implemented: The wearable rehabilitation training robot is used to obtain sensor data information of the user at each rehabilitation joint, and the sensor data information of the user at each rehabilitation joint is subjected to noise reduction processing to obtain the sensor data information after noise reduction processing; Construct a joint intention motion model based on the sensor data information of the user at each rehabilitation joint, and obtain the motion intention feature data information of each joint position node based on the joint intention motion model; By performing motion risk assessment on the motion intention feature data information of each joint position node, a risk assessment result is obtained, and a rehabilitation program of the rehabilitation trainer robot is reconfigured according to the risk assessment result; The motion of the rehabilitation trainer robot is controlled based on the rehabilitation program of the rehabilitation trainer robot.

[0031] Furthermore, in this system, the sensor data information of the user at each rehabilitation joint is obtained through the wearable rehabilitation training robot, and the sensor data information of the user at each rehabilitation joint is subjected to noise reduction processing to obtain the sensor data information after noise reduction processing, specifically: By installing sensors on the wearable rehabilitation training robot, the sensor data information of the user at each rehabilitation joint is obtained through the sensors, and the discrete wavelet transform algorithm is introduced to perform noise reduction processing on the sensor data information of the user at each rehabilitation joint; Obtain the noise characteristics and original signal characteristics in the sensor data information through the discrete wavelet transform algorithm; Select wavelet basis for the noise characteristics in the sensor data information and the original signal characteristics, determine the number of layers, and decompose the original signal hierarchically according to the number of layers; Through hierarchical decomposition, low-frequency signals and high-frequency signals are obtained, and an empirical threshold is selected for the high-frequency signal of each layer. The low-frequency signals and high-frequency signals are reconstructed according to the empirical threshold to obtain the sensor data information after noise reduction processing.

[0032] Furthermore, in this system, a joint intention movement model is constructed based on the sensor data information of the user at each rehabilitation joint, specifically including: Based on the sensor data information of the user at each rehabilitation joint, the human joint motion torque characteristics corresponding to each sensor data are obtained, and the global human joint characteristics are constructed based on the human joint motion torque characteristics corresponding to each sensor data; Parameterize the 3D posture of human joints based on the global human joint features, obtain the 3D posture parameters, and estimate the joints and 3D posture based on the 3D posture parameters to obtain the intention features of human posture joints; A three-dimensional model is constructed based on the joint intention features of the human body posture. Through the construction, the joint intention motion model in the motion perception time stage is obtained, and the joint intention motion model is output.

[0033] Furthermore, in this system, the motion intention feature data information of each joint position node is obtained based on the joint intention motion model, specifically including: By performing dimensionality reduction processing on the joint intention motion model, a two-dimensional graph of the motion model of each joint position node is obtained, and motion features are extracted from the two-dimensional graph of the motion model of each joint position node; By extracting motion features, the motion intention feature data information of each joint position node is obtained, and the motion intention feature data information of each joint position node is output.

[0034] Furthermore, in this system, the motion risk assessment is performed on the motion intention feature data information of each joint position node to obtain the risk assessment result, which specifically includes: The characteristic data of injury risk assessment caused by different exercise states of users under different joint disease states are obtained through big data, and a sports risk knowledge graph is constructed, and the characteristic data of injury risk assessment caused by different exercise states of users under different joint disease states are input into the sports risk knowledge graph for storage; Obtain the condition status information of each joint of the user, and input the condition status information of each joint of the user and the motion intention feature data information of each joint position node into the knowledge graph for data search; Through data search, the injury risk assessment feature data caused by exercise is obtained according to the motion intention feature data information of the joint position nodes under the disease status information of each joint of the user; Based on the condition status information of each joint of the user, the risk assessment feature data of the injury risk caused by exercise is used according to the movement intention feature data information of the joint position nodes to generate a risk assessment result, and the risk assessment result is output.

[0035] Furthermore, in this system, the rehabilitation program of the rehabilitation trainer robot is reconfigured according to the risk assessment results, specifically: Obtaining motion intention feature data information of joint position nodes corresponding to injury risk assessment feature data greater than preset risk assessment indicators, and reinitializing motion parameters of joint position nodes corresponding to injury risk assessment feature data greater than preset risk assessment indicators, thereby reducing motion parameters of the rehabilitation trainer robot; Obtaining the motion parameters of the rehabilitation training robot after initialization, and determining whether the motion parameters of the rehabilitation training robot after initialization still result in a situation where the injury risk assessment characteristic data is greater than a preset risk assessment index; When the motion parameters of the rehabilitation training robot after initialization still result in the existence of injury risk assessment characteristic data greater than the preset risk assessment index, reconfigure and reduce the motion parameters of the rehabilitation training robot; When there is no injury risk assessment characteristic data of the rehabilitation trainer robot motion parameters after initialization that is greater than the preset risk assessment index, the rehabilitation trainer robot motion parameters after initialization are output, and a rehabilitation plan for the rehabilitation trainer robot is generated.

[0036] Furthermore, in the present system, the rehabilitation training robot is motion controlled based on the rehabilitation program of the rehabilitation training robot, specifically: Determining whether a motion parameter in a rehabilitation program of the rehabilitation trainer robot is greater than a maximum motion parameter of the rehabilitation trainer robot; When the motion parameter in the rehabilitation program of the rehabilitation trainer robot is greater than the maximum motion parameter of the rehabilitation trainer robot, the motion control is performed according to the maximum motion parameter of the rehabilitation trainer robot; When the motion parameters in the rehabilitation program of the rehabilitation trainer robot are not greater than the maximum motion parameters of the rehabilitation trainer robot, motion control is performed according to the motion parameters in the current rehabilitation program.

[0037] The third aspect of the present invention provides a computer-readable storage medium, including a rehabilitation trainer robot intelligent control method program, which, when executed by a processor, implements any step of the rehabilitation trainer robot intelligent control method.

[0038] 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. The device embodiments described above are only schematic. For example, the division of the 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 can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0039] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; 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 present embodiment.

[0040] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0041] A person of ordinary skill in the art can understand that: all or part of the steps of implementing the above method embodiment can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above method embodiment; and the aforementioned storage medium includes: a mobile storage device, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and other media that can store program codes.

[0042] Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention can be essentially or partly reflected in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the methods of each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.

[0043] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art who is familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A rehabilitation training robot intelligent control method, characterized in that: The following steps are involved: Acquire sensor data information of the user at each rehabilitation joint through the wearable rehabilitation training robot, and obtain the sensor data information after noise reduction by performing noise reduction processing on the sensor data information of the user at each rehabilitation joint; Constructing a joint intention motion model according to the sensor data information of the user at each rehabilitation joint, and acquiring motion intention feature data information of each joint position node based on the joint intention motion model; By performing motion risk assessment on the motion intention feature data information of each joint position node, a risk assessment result is obtained, and a rehabilitation program of the rehabilitation trainer robot is reconfigured according to the risk assessment result; The rehabilitation trainer robot is motion-controlled based on the rehabilitation program of the rehabilitation trainer robot.

2. The intelligent control method of a rehabilitation training robot according to claim 1, characterized in that: The sensor data information of the user at each rehabilitation joint is obtained by using a wearable rehabilitation training robot, and the sensor data information of the user at each rehabilitation joint is subjected to noise reduction processing to obtain the sensor data information after noise reduction processing, specifically: By installing sensors on the wearable rehabilitation training robot, the sensor data information of the user at each rehabilitation joint is obtained through the sensors, and a discrete wavelet transform algorithm is introduced to perform noise reduction processing on the sensor data information of the user at each rehabilitation joint; Obtaining noise characteristics and original signal characteristics in sensor data information through the discrete wavelet transform algorithm; Selecting a wavelet basis for the noise characteristics and the original signal characteristics in the sensor data information, determining the number of layers, and performing hierarchical decomposition on the original signal according to the number of layers; Through hierarchical decomposition, low-frequency signals and high-frequency signals are obtained, and an empirical threshold is selected for the high-frequency signal of each layer. The low-frequency signal and the high-frequency signal are reconstructed according to the empirical threshold to obtain sensor data information after noise reduction processing.

3. The intelligent control method of a rehabilitation training robot according to claim 1, characterized in that: Constructing a joint intention movement model based on the sensor data information of the user at each rehabilitation joint, specifically including: Based on the sensor data information of the user at each rehabilitation joint, a human joint motion torque feature corresponding to each sensor data is obtained, and a global human joint feature is constructed based on the human joint motion torque feature corresponding to each sensor data; Parameterizing the three-dimensional posture of human joints based on the global human joint features to obtain three-dimensional posture parameters, and estimating the joints and three-dimensional postures based on the three-dimensional posture parameters to obtain intentional features of human posture joints; A three-dimensional model is constructed based on the joint intention features of the human body posture, and through the construction, a joint intention motion model in the motion perception time stage is obtained, and the joint intention motion model is output.

4. The intelligent control method of a rehabilitation training robot according to claim 1, characterized in that: Based on the joint intention motion model, the motion intention feature data information of each joint position node is obtained, specifically including: By performing dimensionality reduction processing on the joint intention motion model, a two-dimensional graph of the motion model of each joint position node is obtained, and motion features are extracted from the two-dimensional graph of the motion model of each joint position node; By extracting motion features, motion intention feature data information of each joint position node is obtained, and the motion intention feature data information of each joint position node is output.

5. The intelligent control method of a rehabilitation training robot according to claim 1, characterized in that: By performing motion risk assessment on the motion intention feature data information of each joint position node, a risk assessment result is obtained, which specifically includes: Obtaining injury risk assessment feature data caused by different exercise states used by users under various joint disease states through big data, and constructing a sports risk knowledge graph, and inputting the injury risk assessment feature data caused by different exercise states used by the users under various joint disease states into the sports risk knowledge graph for storage; Obtaining the condition status information of each joint of the user, and inputting the condition status information of each joint of the user and the motion intention feature data information of each joint position node into the knowledge graph for data search; Through data search, the injury risk assessment feature data caused by exercise is obtained according to the motion intention feature data information of the joint position nodes under the disease status information of each joint of the user; A risk assessment result is generated based on the injury risk assessment feature data caused by exercising according to the movement intention feature data information of the joint position nodes under the disease status information of each joint of the user, and the risk assessment result is output.

6. The intelligent control method of a rehabilitation training robot according to claim 1, characterized in that: The rehabilitation program of the rehabilitation trainer robot is reconfigured according to the risk assessment results, specifically: Obtaining motion intention feature data information of joint position nodes corresponding to injury risk assessment feature data greater than preset risk assessment indicators, and reinitializing motion parameters of joint position nodes corresponding to injury risk assessment feature data greater than preset risk assessment indicators, thereby reducing motion parameters of the rehabilitation trainer robot; Obtaining the motion parameters of the rehabilitation training robot after initialization, and determining whether the motion parameters of the rehabilitation training robot after initialization still result in a situation where the injury risk assessment characteristic data is greater than a preset risk assessment index; When the motion parameters of the rehabilitation training robot after the initialization still result in the existence of the injury risk assessment characteristic data being greater than the preset risk assessment index, reconfiguring and reducing the motion parameters of the rehabilitation training robot; When there is no injury risk assessment characteristic data of the rehabilitation trainer robot motion parameters after initialization that is greater than the preset risk assessment index, the rehabilitation trainer robot motion parameters after initialization are output, and a rehabilitation plan for the rehabilitation trainer robot is generated.

7. The intelligent control method of a rehabilitation training robot according to claim 1, characterized in that: The rehabilitation training robot is controlled to move based on the rehabilitation program of the rehabilitation training robot, specifically: Determining whether a motion parameter in a rehabilitation program of the rehabilitation trainer robot is greater than a maximum motion parameter of the rehabilitation trainer robot; When the motion parameter in the rehabilitation program of the rehabilitation trainer robot is greater than the maximum motion parameter of the rehabilitation trainer robot, the motion control is performed according to the maximum motion parameter of the rehabilitation trainer robot; When the motion parameters in the rehabilitation program of the rehabilitation trainer robot are not greater than the maximum motion parameters of the rehabilitation trainer robot, motion control is performed according to the motion parameters in the current rehabilitation program.

8. A rehabilitation training robot intelligent control system, characterized in that: It comprises a memory and a processor, wherein the memory comprises a rehabilitation trainer robot intelligent control method program, and when the rehabilitation trainer robot intelligent control method program is executed by the processor, the steps of the rehabilitation trainer robot intelligent control method as described in any one of claims 1-7 are implemented.

9. A computer-readable storage medium, characterized in that: It comprises a rehabilitation trainer robot intelligent control method program, and when the rehabilitation trainer robot intelligent control method program is executed by a processor, the steps of the rehabilitation trainer robot intelligent control method as described in any one of claims 1 to 7 are implemented.

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