Elbow joint rope exoskeleton robot system and active rehabilitation training method

Abstract

According to the elbow joint rope exoskeleton robot system based on electroencephalogram signal control and the active rehabilitation training method, an electroencephalogram signal and motion control correlation model is constructed, and rehabilitation training and daily activity assistance are achieved in combination with a motion adjusting mechanism. Motion intentions are captured through electroencephalogram signals, personalized rehabilitation schemes are customized, training parameters are accurately adjusted, rehabilitation accuracy and efficiency are improved, and active participation of patients and neuromuscular function recovery are promoted. Meanwhile, the electroencephalogram signals are sensed in real time to provide assistance for daily activities, and the life self-care ability of the patient is improved. The technology can also reduce the dependence on nursing personnel, reduce the labor cost, and collect exercise and electroencephalogram data to provide an objective basis for the adjustment of a rehabilitation scheme. In addition, the method is expected to be applied to the technical fields of other limb movement function assistance and man-machine interaction.

Description

Technical Field

[0001] This invention relates to the field of elbow joint cable exoskeleton robot technology, and particularly to an elbow joint cable exoskeleton robot system and an active rehabilitation training method. Background Technology

[0002] With the increasing aging of society and the frequent occurrence of various accidents, the number of patients with elbow joint movement disorders is on the rise. These patients face many inconveniences in daily life, such as difficulty in performing basic actions like dressing, eating, and washing, which seriously affects their quality of life.

[0003] Traditional elbow joint rehabilitation and training mainly rely on one-on-one manual assisted training by therapists. This method not only consumes a lot of manpower and time, but the training effect also varies depending on the therapist's professional level and experience. At the same time, manual training makes it difficult to ensure precise control of training intensity and frequency, and cannot meet the individualized rehabilitation needs of patients.

[0004] Currently, some elbow joint rehabilitation devices have appeared on the market, but most of them are passive training devices. Patients are in a passive state of receiving movement during training, lacking active participation, which is not conducive to stimulating the patient's own neuromuscular function recovery. In addition, these devices often cannot flexibly adjust according to the patient's real-time physiological state and movement intentions, resulting in insufficient targeting and effectiveness of training.

[0005] In terms of daily living assistance, existing assistive devices such as simple elbow braces and splints can only provide limited support and cannot provide real-time assistance based on the patient's movement needs, making it difficult to truly help patients achieve independent daily activities. However, electroencephalography (EEG) technology has developed rapidly in recent years. It can reflect neural activity in the brain, offering new possibilities for human-computer interaction. Combining EEG signals with elbow joint rehabilitation training and daily living assistance holds promise for overcoming the shortcomings of existing technologies and providing more efficient and personalized rehabilitation and daily living assistance solutions for patients with elbow joint movement disorders. Summary of the Invention

[0006] To achieve the above-mentioned objectives and other advantages of the present invention, a first objective of the present invention is to provide an active rehabilitation training method for an elbow joint cable exoskeleton robot, comprising the following steps:

[0007] Acquire multimodal signals; wherein the multimodal signals include at least electroencephalogram (EEG) signals;

[0008] An elbow joint rope exoskeleton robot controlled by the aforementioned electroencephalogram (EEG) signals is used to assist patients with elbow joint movement disorders in rehabilitation training and daily activities.

[0009] Furthermore, the EEG signal is configured to be a μ / β wave associated with motor imagery captured in the motor cortex region.

[0010] Furthermore, the multimodal signals also include electromyographic signals of the target forearm muscle group, elbow joint angle, and exoskeleton assist force.

[0011] Furthermore, the step of controlling the elbow joint rope exoskeleton robot based on the EEG signals includes:

[0012] The force-position dual closed-loop control based on the PID algorithm uses the real-time angle of the encoder as the feedback quantity in the position loop and the data of the tension sensor as the input in the force loop. The influence of rope elastic deformation is offset by the feedforward compensation algorithm.

[0013] Furthermore, it also includes the following steps:

[0014] The training should be flexibly adjusted according to the patient's real-time physiological state and exercise intentions to improve its relevance and effectiveness.

[0015] Furthermore, the step of flexibly adjusting the training based on the patient's real-time physiological state and movement intentions to enhance the targeting and effectiveness of the training includes:

[0016] When patients exhibit motor imagery and hand muscle activation via EEG, personalized assistance can be provided to those unable to achieve motor training by using rope tension feedback and measuring elbow joint angles, adapting to rehabilitation needs at different stages and in different situations.

[0017] Furthermore, the steps for adapting to rehabilitation needs at different stages and in different situations include:

[0018] In the early stages of rehabilitation, the elbow joint rope exoskeleton robot is used as the main driving force to guide the elbow joint to complete passive movements according to the preset joint movement trajectory.

[0019] As patients' brain-muscle coordination improves, the system gradually transitions to an assistive mode driven by the patient's movement intentions. The elbow joint rope exoskeleton robot dynamically adjusts the rope tension to provide adaptive assistance based on the movement direction identified by EEG and EMG signals and the active force ratio calculated by EMG signals, combined with encoder and tension sensor control.

[0020] A second objective of this invention is to provide an elbow joint cable exoskeleton robot system, employing the aforementioned method, comprising an elbow joint cable exoskeleton robot, a multi-dimensional data acquisition module, and a main controller; wherein,

[0021] The elbow joint cable exoskeleton robot is used to simulate the flexion and extension motion trajectory of the elbow joint.

[0022] The multi-dimensional data acquisition module includes an electroencephalogram (EEG) sensor, a surface electromyography (SMG) electrode array, a high-precision encoder, and a tension sensor. The EEG sensor is used to acquire EEG signals, the SMG electrode array is used to acquire SMG signals of the target muscle group in the forearm, the high-precision encoder is used to acquire the elbow joint angle, and the tension sensor is used to acquire the exoskeleton-assisted force.

[0023] The main controller is used to acquire the multimodal signals collected by the multidimensional data acquisition module, and control the elbow joint rope exoskeleton robot based on the electroencephalogram (EEG) signals to assist patients with elbow joint movement disorders in rehabilitation training and daily activities.

[0024] Furthermore, the range of motion of the elbow joint rope exoskeleton robot is limited to 0° to 150°, and it is fixed to the upper arm and forearm by adjustable straps.

[0025] A third object of the present invention is to provide a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the above-described method.

[0026] Compared with the prior art, the beneficial effects of the present invention are:

[0027] This invention provides an elbow joint rope exoskeleton robot system and an active rehabilitation training method based on electroencephalogram (EEG) signal control. It constructs an association model between EEG signals and motor control, combining this with motor regulation mechanisms to achieve rehabilitation training and assistance with daily activities. By capturing motor intentions through EEG signals, personalized rehabilitation plans can be customized, training parameters can be precisely adjusted, and the accuracy and efficiency of rehabilitation can be improved, promoting active patient participation and neuromuscular function recovery. Simultaneously, real-time sensing of EEG signals provides assistance for daily activities, improving patients' self-care abilities. This technology can also reduce reliance on caregivers, lower labor costs, and collect motor and EEG data to provide objective evidence for adjusting rehabilitation plans. Furthermore, it is expected to be applied to other limb motor function assistance and human-computer interaction technologies.

[0028] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it according to the contents of the specification, the preferred embodiments of the present invention are described in detail below with reference to the accompanying drawings. Specific embodiments of the present invention are given in detail below with reference to the accompanying drawings. Attached Figure Description

[0029] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0030] Figure 1Flowchart of active rehabilitation training method using a cable exoskeleton robot for elbow joint;

[0031] Figure 2 A schematic diagram illustrating elbow joint motion data collection and control.

[0032] Figure 3 Flowchart of different support strategies for different stages of rehabilitation;

[0033] Figure 4 Illustration of an elbow joint cable exoskeleton robot Figure 1 ;

[0034] Figure 5 Illustration of an elbow joint cable exoskeleton robot Figure 2 ;

[0035] Figure 6 This is a schematic diagram of a computer device.

[0036] Figure 7 This is a schematic diagram of a computer-readable storage medium. Detailed Implementation

[0037] The present invention will now be further described with reference to the accompanying drawings and specific embodiments. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. It should be noted that, without conflict, the various embodiments or technical features described below can be arbitrarily combined to form new embodiments.

[0038] Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without inventive effort are within the scope of protection of this invention.

[0039] The drawing numbers in this application are only used to distinguish the steps in the scheme and are not used to limit the execution order of the steps. The specific execution order is as described in the specification.

[0040] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used in this specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention.

[0041] This invention addresses the problem of autonomous elbow joint movement training by using an electroencephalogram (EEG) signal-controlled elbow joint cable exoskeleton robot to assist patients with elbow joint movement disorders in rehabilitation training and daily activities.

[0042] Example 1

[0043] An active rehabilitation training method using an elbow joint cable exoskeleton robot, such as Figure 1-Figure 2As shown, it includes the following steps:

[0044] S100. Acquire the collected multimodal signals; wherein, the multimodal signals include at least electroencephalogram (EEG) signals;

[0045] Furthermore, the EEG signal is configured to be a μ / β wave associated with motor imagery captured in the motor cortex region.

[0046] Furthermore, the multimodal signals also include electromyographic signals of the target forearm muscle group, elbow joint angle, and exoskeleton assist force.

[0047] like Figure 4 As shown, the multimodal signal acquisition and control system in this embodiment is constructed as follows: a multi-dimensional data acquisition network is built using a 32-lead EEG sensor, a surface electromyography (EMG) electrode array, a high-precision encoder, and a force sensor. The EEG sensor is attached to the motor cortex region to capture μ / β waves related to motor imagery, which can directly reflect the brain's motor intention; the EMG electrode array covers the target muscle group of the forearm to collect muscle activation signals, which serve as a direct representation of neuromuscular activation, and multi-channel synchronous acquisition can help determine the trend of active force exertion; the encoder and force sensor at the elbow joint track the joint angle dynamics and monitor the exoskeleton assistive force in real time, respectively. The combination of the two forms an "intention-action-force feedback" closed loop, ensuring that the assistive strategy is accurately matched with the patient's physiological state.

[0048] S200: An elbow joint rope exoskeleton robot controlled by the aforementioned electroencephalogram (EEG) signals to assist patients with elbow joint movement disorders in rehabilitation training and daily activities.

[0049] In this embodiment, the elbow joint rope exoskeleton robot adopts a lightweight and modular design. The mechanical arm structure simulates the flexion and extension motion trajectory of the elbow joint, and the range of motion of the joint is limited to 0° to 150°. It is fixed to the upper arm and forearm by adjustable straps (nylon Velcro).

[0050] Furthermore, the step of controlling the elbow joint rope exoskeleton robot based on the EEG signals includes:

[0051] In rehabilitation training, adaptive adjustment of assisting parameters is achieved through a force-position dual closed-loop control based on a PID algorithm. The position loop uses the real-time angle of the encoder as feedback, while the force loop uses the data from the tension sensor as input. The influence of rope elastic deformation is offset by a feedforward compensation algorithm.

[0052] To employ different supportive strategies at different stages of rehabilitation, some embodiments further include the step of:

[0053] The training should be flexibly adjusted according to the patient's real-time physiological state and exercise intentions to improve its relevance and effectiveness.

[0054] Furthermore, the step of flexibly adjusting the training based on the patient's real-time physiological state and movement intentions to enhance the targeting and effectiveness of the training includes:

[0055] When patients exhibit motor imagery and hand muscle activation via EEG, personalized assistance can be provided to those unable to achieve motor training by using rope tension feedback and measuring elbow joint angles, adapting to rehabilitation needs at different stages and in different situations.

[0056] Furthermore, such as Figure 3 As shown, the steps for adapting to rehabilitation needs at different stages and in different situations include:

[0057] S210. In the early stage of rehabilitation, in view of the patient's insufficient active neuromuscular control ability, the elbow joint rope exoskeleton robot is used as the main driving force for muscle training, and the elbow joint is guided to complete passive movement according to the preset joint movement trajectory.

[0058] S220. As the patient's brain-muscle coordination improves, the system gradually transitions to an assistive mode driven by the patient's movement intentions. The elbow joint cable exoskeleton robot dynamically adjusts the cable tension based on the movement direction (elbow flexion / extension) identified by EEG and EMG signals and the active force ratio calculated from EMG signals, combined with encoder and tension sensor control, to provide adaptive assistance. Ultimately, this assists the patient in learning the correct force application method.

[0059] This embodiment provides an active rehabilitation training method for an elbow joint cable exoskeleton robot based on EEG signal control. It constructs a correlation model between EEG signals and motor control, combining this with motor regulation mechanisms to achieve rehabilitation training and assistance with daily activities. By capturing motor intentions through EEG signals, personalized rehabilitation plans can be customized, training parameters can be precisely adjusted, and the accuracy and efficiency of rehabilitation can be improved, promoting patient participation and neuromuscular function recovery. Simultaneously, real-time sensing of EEG signals provides assistance for daily activities, improving patients' self-care abilities. This technology can also reduce reliance on caregivers, lower labor costs, and collect motor and EEG data to provide objective evidence for adjusting rehabilitation plans. Furthermore, it is expected to be applied to other limb motor function assistance and human-computer interaction technologies.

[0060] Example 2

[0061] An elbow joint cable exoskeleton robot system, applying the above-described method, and a detailed description of the active rehabilitation training method for the elbow joint cable exoskeleton robot can be found in the corresponding description in the above-described embodiments of the active rehabilitation training method for the elbow joint cable exoskeleton robot, and will not be repeated here. Figure 2 , Figures 4-5 As shown, the method includes an elbow-joint rope exoskeleton robot, a multi-dimensional data acquisition module, and a main controller; wherein,

[0062] The elbow joint cable exoskeleton robot is used to simulate the flexion and extension motion trajectory of the elbow joint.

[0063] The multi-dimensional data acquisition module includes an electroencephalogram (EEG) sensor, a surface electromyography (SMG) electrode array, a high-precision encoder, and a tension sensor. The EEG sensor is used to acquire EEG signals, the SMG electrode array is used to acquire SMG signals of the target muscle group in the forearm, the high-precision encoder is used to acquire the elbow joint angle, and the tension sensor is used to acquire the exoskeleton-assisted force.

[0064] like Figure 4 As shown, the multimodal signal acquisition and control system in this embodiment is constructed as follows: a multi-dimensional data acquisition network is built using a 32-lead EEG sensor, a surface electromyography (EMG) electrode array, a high-precision encoder, and a force sensor. The EEG sensor is attached to the motor cortex region to capture μ / β waves related to motor imagery, which can directly reflect the brain's motor intention; the EMG electrode array covers the target muscle group of the forearm to collect muscle activation signals, which serve as a direct representation of neuromuscular activation, and multi-channel synchronous acquisition can help determine the trend of active force exertion; the encoder and force sensor at the elbow joint track the joint angle dynamics and monitor the exoskeleton assistive force in real time, respectively. The combination of the two forms an "intention-action-force feedback" closed loop, ensuring that the assistive strategy is accurately matched with the patient's physiological state.

[0065] The main controller is used to acquire the multimodal signals collected by the multidimensional data acquisition module, and control the elbow joint rope exoskeleton robot based on the electroencephalogram (EEG) signals to assist patients with elbow joint movement disorders in rehabilitation training and daily activities.

[0066] In this embodiment, the elbow joint rope exoskeleton robot adopts a lightweight and modular design. The mechanical arm structure simulates the flexion and extension motion trajectory of the elbow joint, and the range of motion of the joint is limited to 0° to 150°. It is fixed to the upper arm and forearm by adjustable straps (nylon Velcro).

[0067] Based on the technical solution of the above embodiments, optionally, the EEG signal is configured as a μ / β wave related to motor imagery captured in the motor cortex region.

[0068] Based on the technical solutions of the above embodiments, optionally, the step of controlling the elbow joint rope exoskeleton robot based on the electroencephalogram (EEG) signal includes:

[0069] In rehabilitation training, adaptive adjustment of assisting parameters is achieved through a force-position dual closed-loop control based on a PID algorithm. The position loop uses the real-time angle of the encoder as feedback, while the force loop uses the data from the tension sensor as input. The influence of rope elastic deformation is offset by a feedforward compensation algorithm.

[0070] In order to employ different supportive strategies at different stages of rehabilitation, the method optionally includes the following steps, based on the technical solution of the above embodiments:

[0071] The training should be flexibly adjusted according to the patient's real-time physiological state and exercise intentions to improve its relevance and effectiveness.

[0072] Based on the technical solutions of the above embodiments, optionally, the step of flexibly adjusting according to the patient's real-time physiological state and movement intention to improve the pertinence and effectiveness of training includes:

[0073] When patients exhibit motor imagery and hand muscle activation via EEG, personalized assistance can be provided to those unable to achieve motor training by using rope tension feedback and measuring elbow joint angles, adapting to rehabilitation needs at different stages and in different situations.

[0074] Based on the technical solutions of the above embodiments, optionally, the steps for adapting to rehabilitation needs at different stages and in different situations include:

[0075] In the early stage of rehabilitation, in response to the patient's insufficient neuromuscular control, muscle training is carried out using an elbow joint rope exoskeleton robot as the main driving force, guiding the elbow joint to complete passive movements according to a preset joint movement trajectory.

[0076] As the patient's brain-muscle coordination improves, the system gradually transitions to an assistive mode driven by the patient's movement intentions. The elbow joint cable exoskeleton robot dynamically adjusts the cable tension based on the movement direction (elbow flexion / extension) identified by EEG and EMG signals and the active force ratio calculated from EMG signals. This adjustment, combined with encoder and tension sensor control, provides adaptive assistance. Ultimately, it helps the patient learn the correct force application method.

[0077] This embodiment provides an elbow joint cable exoskeleton robot system based on EEG signal control. It constructs a correlation model between EEG signals and motor control, combining this with motor regulation mechanisms to achieve rehabilitation training and assistance with daily activities. By capturing motor intentions through EEG signals, personalized rehabilitation plans can be customized, training parameters can be precisely adjusted, improving rehabilitation accuracy and efficiency, and promoting patient participation and neuromuscular function recovery. Simultaneously, real-time sensing of EEG signals provides assistance for daily activities, improving patients' self-care abilities. This technology can also reduce reliance on caregivers, lower labor costs, and collect motor and EEG data to provide objective evidence for adjusting rehabilitation plans. Furthermore, it is expected to be applied to other limb motor function assistance and human-computer interaction technologies.

[0078] Example 3

[0079] A computer device 300, such as Figure 6As shown, the system includes a memory 310, a processor 320, and a computer program 330 stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the elbow joint cable exoskeleton robot active rehabilitation training method. For a detailed description of the method, please refer to the corresponding description in the above method embodiments; it will not be repeated here.

[0080] Example 4

[0081] A computer-readable storage medium, such as Figure 7 As shown, a computer program is stored thereon, which, when executed by a processor, implements the steps of the elbow joint rope exoskeleton robot active rehabilitation training method. For a detailed description of the method, please refer to the corresponding description in the above method embodiments, which will not be repeated here.

[0082] The number of devices and processing scale described herein are for the purpose of simplifying the description of the invention. Applications, modifications, and variations of the invention will be readily apparent to those skilled in the art.

[0083] Although the embodiments of the present invention have been disclosed above, they are not limited to the applications listed in the description and implementation methods. They can be fully applied to various fields suitable for the present invention. For those familiar with the art, additional modifications can be easily implemented. Therefore, without departing from the general concept defined by the claims and the scope of equivalents, the present invention is not limited to the specific details and illustrations shown and described herein.

[0084] The apparatus, computer device, and non-volatile computer storage medium and method provided in the embodiments of this specification are corresponding. Therefore, the apparatus, computer device, and non-volatile computer storage medium also have similar beneficial technical effects as the corresponding method. Since the beneficial technical effects of the method have been described in detail above, the beneficial technical effects of the corresponding apparatus, computer device, and non-volatile computer storage medium will not be repeated here.

[0085] Those skilled in the art will also know that, besides implementing the controller in the form of purely computer-readable program code, the same functions can be achieved by logically programming the method steps, making the controller take the form of logic gates, switches, application-specific integrated circuits (ASICs), programmable logic controllers (PLCs), and embedded microcontrollers. Therefore, such a controller can be considered a hardware component, and the devices included within it for implementing various functions can also be considered structures within that hardware component. Alternatively, the devices for implementing various functions can be considered as both software units implementing the method and structures within a hardware component.

[0086] The systems, apparatuses, or units described in the above embodiments can be implemented by computer chips or physical entities, or by products with certain functions. For ease of description, the above apparatuses are described separately as various units based on their functions. Of course, when implementing one or more embodiments of this specification, the functions of each unit can be implemented in one or more software and / or hardware.

[0087] Those skilled in the art will understand that the embodiments of this specification can be provided as methods, systems, or computer program products. Therefore, the embodiments of this specification can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the embodiments of this specification can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0088] This specification is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this specification. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a machine for implementing the flowchart illustrations and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0089] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0090] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0091] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0092] This specification may be described in the general context of computer-executable instructions, such as program units, that are executed by a computer. Generally, program units include routines, programs, objects, components, data structures, etc., that perform a specific task or implement a specific abstract data type. This specification may also be practiced in distributed computing environments, where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program units may reside in local and remote computer storage media, including storage devices.

[0093] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to interchangeably. Each embodiment focuses on describing the differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments.

[0094] The above description is merely an embodiment of this specification and is not intended to limit the scope of one or more embodiments of this specification. Various modifications and variations can be made to one or more embodiments of this specification by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of one or more embodiments of this specification should be included within the scope of the claims of one or more embodiments of this specification.

Claims

1. A method for active rehabilitation training using an elbow joint cable exoskeleton robot, characterized in that, Includes the following steps: Acquire multimodal signals; wherein the multimodal signals include at least electroencephalogram (EEG) signals; An elbow joint rope exoskeleton robot controlled by the aforementioned electroencephalogram (EEG) signals is used to assist patients with elbow joint movement disorders in rehabilitation training and daily activities.

2. The method for active rehabilitation training of an elbow joint cable exoskeleton robot as described in claim 1, characterized in that: The EEG signals were configured to capture μ / β waves associated with motor imagery in the motor cortex region.

3. The method for active rehabilitation training of an elbow joint rope exoskeleton robot as described in claim 2, characterized in that: The multimodal signals also include electromyographic signals of the target forearm muscle groups, elbow joint angle, and exoskeleton assist force.

4. The method for active rehabilitation training of an elbow joint cable exoskeleton robot as described in claim 3, characterized in that, The steps of controlling the elbow joint cable exoskeleton robot based on the electroencephalogram (EEG) signals include: The force-position dual closed-loop control based on the PID algorithm uses the real-time angle of the encoder as the feedback quantity in the position loop and the data of the tension sensor as the input in the force loop. The influence of rope elastic deformation is offset by the feedforward compensation algorithm.

5. The method for active rehabilitation training of an elbow joint cable exoskeleton robot as described in claim 3, characterized in that, It also includes the following steps: The training should be flexibly adjusted according to the patient's real-time physiological state and exercise intentions to improve its relevance and effectiveness.

6. The method for active rehabilitation training of an elbow joint cable exoskeleton robot as described in claim 5, characterized in that, The steps for flexibly adjusting the training based on the patient's real-time physiological state and movement intentions to enhance the relevance and effectiveness of the training include: When patients exhibit motor imagery and hand muscle activation via EEG, personalized assistance can be provided to those unable to achieve motor training by using rope tension feedback and measuring elbow joint angles, adapting to rehabilitation needs at different stages and in different situations.

7. The method for active rehabilitation training of an elbow joint cable exoskeleton robot as described in claim 6, characterized in that, The steps for adapting to rehabilitation needs at different stages and in different situations include: In the early stages of rehabilitation, the elbow joint rope exoskeleton robot is used as the main driving force to guide the elbow joint to complete passive movements according to the preset joint movement trajectory. As patients' brain-muscle coordination improves, the system gradually transitions to an assistive mode driven by the patient's movement intentions. The elbow joint rope exoskeleton robot dynamically adjusts the rope tension to provide adaptive assistance based on the movement direction identified by EEG and EMG signals and the active force ratio calculated by EMG signals, combined with encoder and tension sensor control.

8. An elbow joint cable exoskeleton robot system, using the method as described in any one of claims 1 to 7, characterized in that: This includes an elbow-joint rope exoskeleton robot, a multi-dimensional data acquisition module, and a main controller; among which, The elbow joint cable exoskeleton robot is used to simulate the flexion and extension motion trajectory of the elbow joint. The multi-dimensional data acquisition module includes an electroencephalogram (EEG) sensor, a surface electromyography (SMG) electrode array, a high-precision encoder, and a tension sensor. The EEG sensor is used to acquire EEG signals, the SMG electrode array is used to acquire SMG signals of the target muscle group in the forearm, the high-precision encoder is used to acquire the elbow joint angle, and the tension sensor is used to acquire the exoskeleton-assisted force. The main controller is used to acquire the multimodal signals collected by the multidimensional data acquisition module, and control the elbow joint rope exoskeleton robot based on the electroencephalogram (EEG) signals to assist patients with elbow joint movement disorders in rehabilitation training and daily activities.

9. The elbow joint rope exoskeleton robot system as described in claim 8, characterized in that: The elbow joint rope exoskeleton robot has a joint range of motion limited to 0° to 150° and is fixed to the upper arm and forearm by adjustable straps.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 7.

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