Rehabilitation robot control method based on brain-computer interface and related products

Abstract

The invention provides a control method of a rehabilitation robot based on a brain-computer interface and a related product. The method comprises the steps that a preset upper arm movement mode is obtained, and a mechanical arm of the rehabilitation robot is controlled to start to move according to the preset upper arm movement mode; an electromyographic signal and an electroencephalogram signal of a user are detected, and the joint angle of the mechanical arm is obtained; a trajectory prediction model is adopted, and according to the electromyographic signal, the electroencephalogram signal and the joint angle, a predicted moving trajectory of target equipment in the rehabilitation robot is obtained; acquiring a preset running track of the target equipment, and calculating running deviation between the predicted running track and the preset running track; and controlling a driving motor of the target equipment according to the running deviation so as to drive the target equipment to work according to the preset running track. According to the technical scheme, the consistency of the actual moving track and the preset moving track of the mechanical arm of the rehabilitation robot can be improved.

Description

Technical Field

[0001] The present invention relates to the field of rehabilitation robot control technology, and in particular to a control method for a rehabilitation robot based on a brain-computer interface and related products. Background Art

[0002] In 2019, an estimated 12.2 million stroke cases occurred worldwide, affecting 101 million people and resulting in 143 million disability-adjusted life years (DALYs), making stroke the second leading cause of death worldwide. According to WHO statistics, stroke was the leading cause of death and DALYs in China by 2019. The "China Cardiovascular Health and Disease Report 2020" revealed that in 2018, 1,853 tertiary hospitals reported a total of 3,010,204 stroke admissions, of which 81.9% were caused by stroke. The total cost of hospitalization for stroke was 65.432 billion yuan. Patients often experience limb movement disorders, requiring long-term rehabilitation training and treatment, which imposes significant mental distress and a heavy economic burden on individuals, their families, and society.

[0003] Research has shown that during rehabilitation training and the process of relearning limb movement, the central nervous system undergoes positive plastic changes, altering the functional connectivity and structure of brain regions to adapt to the demands of movement. Active rehabilitation training, driven by the patient's subjective desire to exercise, is particularly effective in inducing and promoting these plastic changes in the central nervous system, achieving significant therapeutic benefits.

[0004] Research on exoskeleton robotics initially began in the military field, aiming to improve human function and reduce operator fatigue and physical injury through wearable mechanical devices. Currently, exoskeleton robotic systems are used in stroke rehabilitation to provide external mechanical support to body parts, such as the hemiplegic limb, to help patients train corresponding limb movements while also providing passive motion for passive rehabilitation training. However, existing rehabilitation robots can only control the movement of the robotic arm according to a preset control strategy to assist the user's rehabilitation training. This cannot guarantee that the actual movement trajectory of the robotic arm matches the preset trajectory, resulting in reduced rehabilitation effectiveness for the user. Summary of the Invention

[0005] The present invention provides a control method and related products for a rehabilitation robot based on a brain-computer interface, which are used to improve the consistency between the actual running trajectory of the rehabilitation robot's mechanical arm and the preset running trajectory, thereby achieving the purpose of improving the user's rehabilitation training effect.

[0006] Specifically, in a first aspect, the present invention provides a control method for a rehabilitation robot based on a brain-computer interface, comprising:

[0007] Acquiring a preset upper arm motion pattern, and controlling the mechanical arm of the rehabilitation robot to start moving according to the preset upper arm motion pattern;

[0008] Detecting the user's electromyographic signals and electroencephalographic signals, and obtaining the joint angles of the robotic arm;

[0009] Obtaining a trajectory prediction model based on a grey prediction algorithm, and using the trajectory prediction model to obtain a predicted running trajectory of a target device in the rehabilitation robot according to the electromyographic signal, the electroencephalographic signal, and the joint angle;

[0010] Obtaining a preset running trajectory of the target device, and calculating a running deviation between the predicted running trajectory and the preset running trajectory;

[0011] The driving motor of the target device is controlled according to the operation deviation to drive the target device to operate according to the preset operation trajectory.

[0012] Furthermore, the step of controlling the drive motor of the target device according to the operation deviation includes:

[0013] Obtaining a preset PID model, and using the preset PID model to generate control information of the drive motor according to the operation deviation;

[0014] The drive motor is controlled using the control information.

[0015] Furthermore, after the step of controlling the drive motor of the target device according to the operation deviation, the method further includes:

[0016] Obtaining an actual motion trajectory of the target device, and calculating an actual error between the actual motion trajectory and the preset motion trajectory;

[0017] Determining whether the actual error meets a preset condition;

[0018] If not, the tuning parameters of the preset PID model are updated.

[0019] Furthermore, the step of obtaining the predicted running trajectory of the target device in the rehabilitation robot includes:

[0020] Obtain a predicted running trajectory of the mechanical vibration module and / or the mechanical arm in the rehabilitation robot.

[0021] Furthermore, the step of obtaining the joint angle of the robotic arm includes:

[0022] An incremental signal detected by an incremental encoder on a driving motor of the robotic arm and an absolute signal detected by an absolute encoder are obtained, and the angle information is obtained according to the incremental signal and the absolute signal.

[0023] Furthermore, before the step of detecting the user's electromyographic signal and electroencephalographic signal, the method further includes:

[0024] The audio and video playback device of the rehabilitation robot is controlled to play preset audio and video information to induce the user to generate the brain electrical signal.

[0025] Furthermore, before the step of controlling the mechanical arm of the rehabilitation robot to start moving according to the preset upper arm movement pattern, the method further includes:

[0026] A length feedback signal of the robotic arm is obtained, and the arm length of the robotic arm is adjusted according to the length feedback signal.

[0027] Furthermore, the step of adjusting the arm length of the robotic arm according to the length feedback signal includes:

[0028] Determining whether a limit signal of the robotic arm is received;

[0029] If yes, stop adjusting the arm length of the robotic arm.

[0030] In a second aspect, the present invention further provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the control method of the rehabilitation robot based on the brain-computer interface described in any one of the above items are implemented.

[0031] In a third aspect, the present invention further provides a computer program product, comprising a computer program, which, when executed by a processor, implements the steps of any of the above-mentioned methods for controlling a rehabilitation robot based on a brain-computer interface.

[0032] In the technical solution of the present invention, during the process of controlling a rehabilitation robot according to a preset upper arm motion pattern, the predicted operating trajectory of a target device in the rehabilitation robot is obtained based on the user's electromyographic and electroencephalographic signals and the joint angles of the robotic arm. The target device's drive motor is then controlled based on the operating deviation between the predicted operating trajectory and the preset operating trajectory of the target device, thereby controlling the target device to operate according to its preset operating trajectory. Because the present invention can detect the operating trajectory of the target device in the rehabilitation robot in real time and adjust the control method of the target device's drive motor based on the detection results, the reliability of the rehabilitation robot's operation can be guaranteed, ensuring the effectiveness of rehabilitation training for the user.

[0033] Based on the following detailed description of specific embodiments of the present invention in conjunction with the accompanying drawings, those skilled in the art will become more aware of the above and other objects, advantages and features of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Hereinafter, some specific embodiments of the present invention will be described in detail in an exemplary and non-limiting manner with reference to the accompanying drawings. The same reference numerals in the accompanying drawings indicate the same or similar components or parts. It should be understood by those skilled in the art that these drawings are not necessarily drawn to scale. In the accompanying drawings:

[0035] Figure 1 is a schematic structural diagram of a rehabilitation robot according to an embodiment of the present invention;

[0036] Figure 2 is a schematic structural diagram of a control system of a rehabilitation robot according to an embodiment of the present invention;

[0037] Figure 3 is a schematic structural diagram of an electromyography acquisition module according to an embodiment of the present invention;

[0038] Figure 4 is a schematic structural diagram of an EEG acquisition module according to an embodiment of the present invention;

[0039] Figure 5 is a schematic flow chart of a control method for a rehabilitation robot based on a brain-computer interface according to an embodiment of the present invention;

[0040] Figure 6 is a schematic diagram of a computer program product according to one embodiment of the present invention; and

[0041] Figure 7 is a schematic diagram of a computer-readable storage medium according to one embodiment of the present invention. DETAILED DESCRIPTION

[0042] Refer to the following Figures 1 to 7 To describe a control method and related products of a rehabilitation robot based on a brain-computer interface according to an embodiment of the present invention. In the description of this embodiment, it should be understood that the terms "first" and "second" are used for descriptive purposes only, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features, that is, include one or more of the features. In the description of the present invention, the meaning of "multiple" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined. When a feature "includes or contains" one or some of the features it covers, unless otherwise specifically described, this indicates that other features are not excluded and may further include other features.

[0043] The control method of the rehabilitation robot based on the brain-computer interface provided in this embodiment is applied to Figure 1 The rehabilitation robot shown. Figure 1 The rehabilitation robot shown includes a fixed unit 10 and a robotic arm, which includes an upper arm support frame 11 and a lower arm support frame 12. The arm support frame 11 and the lower arm support frame 12 are both made of magnesium alloy or aluminum alloy. The first end of the upper arm support frame 11 is movably connected to the fixed unit 10 through a shoulder joint module 13. The shoulder joint module 13 includes a shoulder joint drive motor. The shoulder joint drive motor can drive the upper arm support frame 11 to move relative to the fixed unit 10 to assist the user's shoulder joint movement during rehabilitation training. The second end is movably connected to the first end of the lower arm support frame 12 through an elbow joint module 14. The elbow joint module 14 includes an elbow joint drive motor. The elbow joint drive motor can drive the lower arm support frame 12 to move relative to the upper arm support frame 11 to assist the user's elbow joint movement during rehabilitation training.

[0044] An upper arm length adjustment unit 111 and an upper arm fixing unit 112 are provided on the upper arm support frame 11, wherein the upper arm length adjustment unit 111 includes an upper arm drive motor, which is connected to the upper arm support frame 11 via a chain for adjusting the length of the upper arm support frame 11; the upper arm fixing unit 112 is used to fix the user's upper arm. A lower arm length adjustment unit 112 is provided on the lower arm support frame 12, and a lower arm fixing unit 122 is provided at the second end of the lower arm support frame 122, wherein the lower arm length adjustment unit 112 includes a lower arm drive motor, which is connected to the lower arm support frame 12 for adjusting the length of the lower arm support frame 12; the lower arm fixing unit 122 is used to fix the user's lower arm to the lower arm support frame 12.

[0045] When using the rehabilitation robot, the user can use the upper arm fixing unit 112 to fix the user's upper arm on the upper arm support frame 11, use the lower arm fixing unit 122 to fix the user's lower arm on the lower arm support frame 12, and adjust the length of the upper arm support frame 11 through the upper arm length adjustment unit 111 to match the length of the upper arm support frame 11 with the length of the user's upper arm, and use the lower arm length adjustment unit 121 to adjust the length of the lower arm support frame 12 to match the length of the lower arm support frame 12 with the length of the user's lower arm.

[0046] Figure 1 The control system of the rehabilitation robot shown is as follows Figure 2As shown, the control module 201 is connected to the motion drive module 211, the human-computer interaction module 212, the length adjustment module 213, the mechanical vibration module 214, the electromyography acquisition module 215, and the electroencephalography acquisition module 216. The motion drive module 211 includes the shoulder joint drive motor and the elbow joint drive motor mentioned above, and the length adjustment module 213 includes the upper arm drive motor in the upper arm length adjustment unit 111 and the lower arm drive motor in the lower arm length adjustment unit 122. The mechanical vibration module 214 is used to provide mechanical vibration to the user to stimulate the user's body surface and assist the user in rehabilitation training.

[0047] The structure of the myoelectric acquisition module 215 is as follows: Figure 3 As shown, it includes an electromyographic electrode 311, an electromyographic signal amplifying unit 312, an electromyographic signal filtering unit 313 and an electromyographic analog-to-digital conversion unit 314, wherein the electromyographic electrode 311 is used to collect the user's electromyographic signal, the electromyographic signal amplifying unit 312 is used to amplify the electromyographic signal by increasing the impedance and improving the common mode rejection ratio, the electromyographic signal filtering unit 313 is used to perform power frequency filtering on the electromyographic signal, and the electromyographic analog-to-digital conversion unit 314 is used to convert the electromyographic signal from an analog signal to a digital signal and transmit it to the control module 201.

[0048] The structure of the EEG acquisition module 216 is as follows: Figure 4 As shown, it includes an EEG electrode 411, an EEG amplifying unit 412, an EEG signal filtering unit 413 and an EEG analog-to-digital conversion unit 414, wherein the EEG electrode 411 is used to collect the user's EEG signal, the EEG signal amplifying unit 412 is used to amplify the EEG signal by increasing the impedance and improving the common mode rejection ratio, the EEG signal filtering unit 413 is used to perform power frequency filtering processing on the EEG signal, and the EEG analog-to-digital conversion unit 414 is used to convert the EEG signal from an analog signal to a digital signal and transmit it to the control module 201.

[0049] The rehabilitation robot of this embodiment also includes a battery management module, which includes a power management unit and a mobile power supply, wherein the power management is used to manage the charging and discharging of the mobile power supply and provide battery warnings, such as power warnings and temperature warnings; the mobile power supply is used to power the above-mentioned motion drive module 211, human-computer interaction module 212, length adjustment module 213, mechanical vibration module 214, electromyography acquisition module 215 and electroencephalography acquisition module 216.

[0050] The control method of the rehabilitation robot based on the brain-computer interface of this embodiment has the following process: Figure 5 As shown, the specific steps include:

[0051] Step S101: obtaining a preset upper arm motion pattern, and controlling the mechanical arm of the rehabilitation robot to start moving according to the preset upper arm motion pattern;

[0052] Step S102: Acquire the user's electromyographic signals and electroencephalographic signals, as well as the joint angles of the rehabilitation robot's robotic arm;

[0053] Step S103: obtaining a trajectory prediction model based on a grey prediction algorithm, and using the trajectory prediction model to obtain a predicted running trajectory of a target device in the rehabilitation robot according to the user's electromyographic signals, electroencephalographic signals, and joint angles of the robotic arm;

[0054] Step S104: obtaining a preset running trajectory of the target device, and calculating a running deviation between the predicted running trajectory of the target device and the preset running trajectory;

[0055] Step S105: controlling a drive motor of the target device according to an operation deviation between the predicted operation trajectory of the target device and the preset operation trajectory, so as to drive the target device to operate according to the preset operation trajectory.

[0056] In the above step S101, since the rehabilitation robot in this embodiment is a robot used to perform rehabilitation training on the user's upper limbs, before training the rehabilitation robot, it is necessary to first determine the rehabilitation training actions that the rehabilitation training robot needs to perform, such as raising arms, swinging arms, bending arms, etc., and obtain a preset upper arm movement pattern based on these rehabilitation training actions to control the mechanical arm of the rehabilitation robot to perform rehabilitation training on the user.

[0057] In the above step S102 , the EEG acquisition module 216 may be used to acquire the user's EEG signals, the EMG acquisition module 215 may be used to acquire the user's EMG signals, and the joint angles of the rehabilitation robot's mechanical arms may be acquired according to the motion driving module 211 .

[0058] In the above step S103, the method of obtaining the predicted running trajectory of the target device in the rehabilitation robot by using the trajectory prediction model based on the grey prediction algorithm includes:

[0059] Assume that the collected user's electromyographic signal is x(t), the EEG signal is z(t), and the joint angle of the rehabilitation robot's manipulator is c(t), where t represents the acquisition time. Assume that there are n1 signals in the electromyographic signal x(t), n2 signals in the EEG signal z(t), and n3 signals in the joint angle c(t). Then, by fusing the electromyographic signal x(t), the EEG signal z(t), and the joint angle c(t), the user's fused signal M(t) can be obtained as:

[0060] M(t)=[x(t),z(t),c(t)]=[m1(t),m2(t),……,mN (t)]

[0061] Among them, m1(t) to is the electromyographic signal x(t), arrive is the EEG signal z(t), to m N (t) is the joint angle c(t) of the robotic arm.

[0062] In this embodiment, the user's observation signal is constructed based on the user's continuous n fusion signals M(t), and the jth one is but

[0063]

[0064] in, Then, the cumulative sum of each observation signal can be obtained, where the jth cumulative term is but

[0065]

[0066] Then the trajectory prediction model based on the grey prediction algorithm is obtained as:

[0067]

[0068] Among them, a is the development coefficient, h1(t-1) is the linear correction term, h2 is the gray action, g1 (1) (t) is a sequence of neighbor values, and

[0069]

[0070] According to the above prediction trajectory parameters are listed as but and

[0071]

[0072] in

[0073]

[0074] The approximate time response formula of the trajectory prediction model based on the grey prediction algorithm is:

[0075]

[0076] Where,

[0077] Assume that in the predicted trajectory of the target device, the position of the target device at time t is but

[0078]

[0079] Then, a preset operating trajectory is obtained, which is the operating trajectory selected by the user, and the operating deviation between the preset operating trajectory and the predicted operating trajectory is calculated. Finally, the control strategy of the drive motor of the target device is adjusted according to the operating deviation so that the target device can operate according to the preset operating trajectory, thereby achieving the purpose of improving the reliability of the control of the rehabilitation robot.

[0080] As can be seen from the foregoing, in this embodiment, when controlling the rehabilitation robot according to a preset upper arm motion pattern, the predicted trajectory of the target device in the rehabilitation robot is obtained based on the user's electromyographic and electroencephalographic signals and the joint angles of the robotic arm. The target device's drive motor is then controlled based on the deviation between the predicted trajectory and the preset trajectory of the target device, thereby controlling the target device to operate according to its preset trajectory. Because this embodiment can detect the trajectory of the target device in the rehabilitation robot in real time and adjust the control method of the target device's drive motor based on the detection results, it can ensure the reliability of the rehabilitation robot's operation and the effectiveness of rehabilitation training for the user.

[0081] In some embodiments of the present invention, the method of controlling the drive motor of the target device according to the running deviation between the predicted running trajectory of the target device and the preset running trajectory in step S105 includes:

[0082] Obtaining a preset PID model, and using the preset PID model to generate control information for a drive motor of the target device based on a calculated operating deviation between a predicted operating trajectory of the target device and a preset operating trajectory;

[0083] The control information of the drive motor of the target device is used to control the drive motor of the target device.

[0084] In this embodiment, the running deviation between the predicted running trajectory of the target device and the preset running trajectory is set to e t , then the preset PID model is used to obtain

[0085]

[0086] Among them, k p is the proportionality coefficient, k i is the integration coefficient, k d is the differential coefficient, and c(t) is the angle that the target device needs to rotate at time t.

[0087] In this embodiment, a preset PID model is used to obtain control information of the drive motor of the target device based on the operating deviation between the predicted operating trajectory of the target device and the preset operating trajectory. Since the PID model is easy to adjust and has good stability, the reliability of the control of the target device can be improved.

[0088] In some embodiments of the present invention, after controlling the drive motor of the target device using the control information of the drive motor of the target device, the method further includes:

[0089] Obtaining an actual motion trajectory of the target device and obtaining an actual error between the actual motion trajectory and a preset motion trajectory of the target device;

[0090] Determine whether the actual error mentioned above meets the preset conditions;

[0091] If not, the tuning parameters of the preset PID model are updated.

[0092] In this embodiment, it is assumed that when the target device is controlled for the jth time, the actual error between the actual motion trajectory of the target device and the preset motion trajectory is r j , then if The actual error r j Satisfy the preset conditions; otherwise, it is determined to be the actual error r j The preset conditions are not met.

[0093] In this embodiment, it is assumed that when the target device is controlled for the jth time, the proportional coefficient of the preset PID model is k p,j , the integral coefficient is k i,j , the differential coefficient is k d,j ,but

[0094] k p,j =k p,j-1 +Δk p,j

[0095] k i,j =k i,j-1 +Δk i,j

[0096] k d,j =k d,j-1 +Δk d,j

[0097] In the above formulas

[0098] Δk p,j =k×r j ×r j

[0099] Δk i,j =k×rj ×(r j +r j-1 )

[0100] Δk d,j =k×r j ×(r j -r j-1 )

[0101] Wherein, k is a preset step coefficient, and the preferred value of k in this embodiment is 0.001.

[0102] In this embodiment, the tuning parameters of the preset PID model can be dynamically updated according to the actual error between the actual motion trajectory of the target device and the preset operation trajectory, thereby ensuring the accuracy of the preset PID model and improving the reliability of the control of the target device.

[0103] In some embodiments of the present invention, the target device includes a vibration module and a mechanical arm. Accordingly, the driving motor of the target device includes a vibration motor for driving the mechanical vibration module 214, and an elbow joint driving motor and a shoulder joint driving motor for driving the mechanical arm.

[0104] Various somatosensory stimulation methods have been used clinically to improve post-stroke rehabilitation outcomes. These include electroacupuncture, repetitive passive movements, and mechanical vibration. Because electroacupuncture, electrical stimulation, and magnetic stimulation severely interfere with myoelectric signals and affect the collection of myoelectric information, this embodiment employs a mechanical vibration module 214 to provide mechanical vibrations to stimulate the user's body surface. Controlling the mechanical vibration module 214 can improve the accuracy of stimulation to the user's body surface, thereby improving the effectiveness of assisting user training. A robotic arm can assist a user's arm in extending and flexing, so controlling the robotic arm can improve the reliability of assisting the user's arm movements.

[0105] In some embodiments of the present invention, an incremental encoder and an absolute encoder are provided on the driving motor of the robotic arm in the rehabilitation robot, wherein the incremental encoder is used to detect the speed loop and position loop of the driving motor of the robotic arm, and the absolute encoder is used to sense the posture information of the robotic arm.

[0106] In this embodiment, the drive motor of the robotic arm includes a shoulder joint drive motor and an elbow joint drive motor. Therefore, a first incremental encoder and a first absolute encoder are provided on the shoulder joint drive motor, and a second incremental encoder and a second absolute encoder are provided on the elbow joint drive motor. The first incremental encoder is used to detect incremental coding information of the shoulder joint drive motor, and the first absolute encoder is used to detect absolute coding information of the shoulder joint drive motor. The second incremental encoder is used to detect incremental coding information of the elbow joint drive motor, and the second absolute encoder is used to detect absolute coding information of the elbow joint drive motor.

[0107] The control module 201 can obtain the shoulder joint angle of the rehabilitation robot based on the incremental coding signal detected by the first incremental encoder and the absolute coding signal detected by the first absolute encoder, and obtain the elbow joint angle of the rehabilitation robot based on the incremental coding signal detected by the second incremental encoder and the absolute coding signal detected by the second absolute encoder.

[0108] Taking the example of obtaining the shoulder joint angle of a rehabilitation robot based on the incremental coded signal detected by the first incremental encoder and the absolute coded signal detected by the first absolute encoder, in this embodiment, the rotation angle increment and position increment of the shoulder joint motor can be obtained based on the incremental coded signal detected by the first incremental encoder. The first shoulder joint angle of the rehabilitation robot can then be obtained based on the rotation angle increment and position increment. Then, the second shoulder joint angle of the rehabilitation robot can be obtained based on the absolute coded information detected by the first absolute encoder. Finally, the average of the first and second shoulder joint angles is calculated as the shoulder joint angle of the rehabilitation robot.

[0109] In this embodiment, both the elbow joint drive motor and the shoulder joint drive motor of the robotic arm adopt the form of dual encoders, thereby improving the accuracy and reliability of the angle signal detection of the robotic arm.

[0110] In some embodiments of the present invention, before obtaining the user's electromyographic signal and electroencephalographic signal in step S102, the method further includes:

[0111] The audio and video playback device of the rehabilitation robot is controlled to play preset audio and video information to induce the user to generate brain electrical signals.

[0112] In this embodiment, a human-computer interaction module 212 is provided in the rehabilitation robot. The human-computer interaction module 212 can adopt an integrated touch display. The control module 201 of the rehabilitation robot can control the human-computer interaction module 212 to play preset audio and video information according to the user's operation, so as to achieve the purpose of inducing the user to generate EEG signals.

[0113] Because the body's motor function relies on the input and transmission of somatosensory information, and stroke patients experience reduced or lost proprioception in their peripheral nervous system due to reduced limb motor function, their somatosensory impairment can affect the patient's motor function recovery. Applying somatosensory input can affect the excitability of the motor cortex on the stimulated side, thereby inducing changes in brain central plasticity and improving the effectiveness of motor rehabilitation.

[0114] In this embodiment, the user is induced to generate EEG signals by playing preset audio and video information, so as to improve the reliability of the user's brain rehabilitation training.

[0115] In some embodiments of the present invention, before controlling the mechanical arm of the rehabilitation robot to start moving according to the preset upper arm movement pattern in step S101, the method further includes:

[0116] A length feedback signal of the mechanical arm of the rehabilitation robot is obtained, and the arm length of the mechanical arm of the rehabilitation robot is obtained according to the length feedback signal.

[0117] In this embodiment, the rehabilitation robot is equipped with a distance feedback unit, which can be a laser rangefinder, for detecting the length of the rehabilitation robot's mechanical arm. Because the mechanical arm includes an upper arm support frame 11 and a lower arm support frame 12, the rehabilitation robot's distance feedback unit includes an upper arm distance feedback unit and a lower arm distance feedback unit. The upper arm distance feedback unit is used to detect the length of the upper arm support frame 11, and the lower arm distance feedback unit is used to detect the length of the lower arm support frame.

[0118] In this embodiment, the length of the robotic arm of the rehabilitation robot is detected to generate a driving signal for the driving motor according to the length of the robotic arm, and the length of the robotic arm is adjusted according to the driving signal to make the length of the robotic arm of the rehabilitation robot match the user, thereby improving the reliability of assisting the user's rehabilitation training.

[0119] In some embodiments of the present invention, a method for adjusting the length of a robotic arm of a rehabilitation robot according to a length feedback signal thereof includes:

[0120] During the process of adjusting the length of the robotic arm, it is determined whether the limit signal of the robotic arm is received;

[0121] If so, stop adjusting the arm length of the robotic arm of the rehabilitation robot.

[0122] In this embodiment, an upper arm limit unit is provided on the upper arm support frame 11 of the rehabilitation robot, and a lower arm limit unit is provided on the lower arm support frame 12, wherein the upper arm limit unit is used to generate a first limit signal when the length of the upper arm support frame 11 reaches a first set length, and the lower arm limit unit is used to generate a second limit signal when the length of the lower arm support frame 12 reaches a second set length.

[0123] When the control module 201 of the rehabilitation robot controls the extension of the upper arm support frame 11, if it receives a first limit signal from the upper arm limit unit, it determines that the length of the upper arm support frame has reached a first set length, and therefore controls the drive motor of the upper arm support frame 11 to stop operating to prevent the meshing chain of the upper arm support frame 11 from disengaging. Correspondingly, if it receives a second limit signal from the lower arm limit unit, it determines that the length of the lower arm support frame 12 has reached a second set length, and therefore controls the drive motor of the lower arm support frame 12 to stop operating to prevent the meshing chain of the lower arm support frame 12 from disengaging.

[0124] In this embodiment, when a limit signal from the robotic arm is received, it can be determined that the length of the robotic arm has reached the limit, and thus the adjustment of the length of the robotic arm is stopped to prevent the engagement chain mechanism of the robotic arm from disengaging, thereby improving the safety and reliability of the operation of the rehabilitation robot.

[0125] The flowcharts provided in this embodiment are not intended to indicate that the operations of the method will be performed in any particular order, or that all operations of the method are included in all cases. In addition, each of the above methods may include additional operations. Additional changes may be made to the above method within the scope of the technical ideas provided by the method of this embodiment.

[0126] It should be understood that in some embodiments, each part can be implemented by hardware, software, firmware or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system.

[0127] This embodiment also provides a computer program product 51 , a computer-readable storage medium 61 , and a computer device 30 . Figure 5 is a schematic diagram of a computer program product 51 according to one embodiment of the present invention, Figure 6 is a schematic diagram of a computer-readable storage medium 61 according to one embodiment of the present invention. The computer program product 51 includes a computer program 50. When executed by a processor 52, the computer program 50 implements the steps of any of the aforementioned methods for controlling a rehabilitation robot based on a brain-computer interface. The computer-readable storage medium 61 stores the computer program 50. When executed by the processor 52, the computer program 50 implements the steps of any of the aforementioned methods for controlling a rehabilitation robot based on a brain-computer interface.

[0128] The computer program 50 for performing the operations of the present invention may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, configuration data for an integrated circuit, or source code or object code written in any combination of one or more programming languages and procedural programming languages. The computer program 50 may be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the latter case, the remote computer may be connected to the user's computer via any type of network (including a local area network (LAN) or a wide area network (WAN)), or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, to perform various aspects of the present invention, an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), may execute computer-readable program instructions by utilizing state information of the computer-readable program instructions to personalize the electronic circuit.

[0129] In the description of this embodiment, the computer program product 51 is a related product including the computer program 50 .

[0130] For the purposes of the description of this embodiment, the computer-readable storage medium 61 is a tangible device capable of retaining and storing the computer program 50, and can be any device that can contain, store, communicate, propagate, or use the computer program 50 for an instruction execution system, apparatus, or device, or in conjunction with such an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable storage medium 61 include the following: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, and any suitable combination of the foregoing.

[0131] At this point, those skilled in the art will recognize that, although a number of exemplary embodiments of the present invention have been shown and described in detail herein, many other variations or modifications consistent with the principles of the present invention may be directly determined or derived from the disclosure of the present invention without departing from the spirit and scope of the present invention. Therefore, the scope of the present invention should be understood and deemed to cover all such other variations or modifications.

Claims

1. A control method for a rehabilitation robot based on a brain-computer interface, characterized in that: include: Acquiring a preset upper arm motion pattern, and controlling the mechanical arm of the rehabilitation robot to start moving according to the preset upper arm motion pattern; Detecting the user's electromyographic signals and electroencephalographic signals, and obtaining the joint angles of the robotic arm; Obtaining a trajectory prediction model based on a grey prediction algorithm, and using the trajectory prediction model to obtain a predicted running trajectory of a target device in the rehabilitation robot according to the electromyographic signal, the electroencephalographic signal, and the joint angle; Obtaining a preset running trajectory of the target device, and calculating a running deviation between the predicted running trajectory and the preset running trajectory; The driving motor of the target device is controlled according to the operation deviation to drive the target device to operate according to the preset operation trajectory.

2. The control method of the rehabilitation robot according to claim 1, characterized in that: The step of controlling the drive motor of the target device according to the operation deviation includes: Obtaining a preset PID model, and using the preset PID model to generate control information of the drive motor according to the operation deviation; The drive motor is controlled using the control information.

3. The control method of the rehabilitation robot according to claim 2, characterized in that: After the step of controlling the drive motor of the target device according to the operation deviation, the method further includes: Obtaining an actual motion trajectory of the target device, and calculating an actual error between the actual motion trajectory and the preset motion trajectory; Determining whether the actual error meets a preset condition; If not, the tuning parameters of the preset PID model are updated.

4. The control method of the rehabilitation robot according to claim 1, characterized in that: The step of obtaining the predicted running trajectory of the target device in the rehabilitation robot includes: Obtain a predicted running trajectory of the mechanical vibration module and / or the mechanical arm in the rehabilitation robot.

5. The control method of the rehabilitation robot according to claim 1, characterized in that: The step of obtaining the joint angle of the robotic arm includes: An incremental signal detected by an incremental encoder on a driving motor of the robotic arm and an absolute signal detected by an absolute encoder are obtained, and the angle information is obtained according to the incremental signal and the absolute signal.

6. The control method of the rehabilitation robot according to claim 1, characterized in that: Before the step of detecting the user's electromyographic signal and electroencephalographic signal, the method further includes: The audio and video playback device of the rehabilitation robot is controlled to play preset audio and video information to induce the user to generate the brain electrical signal.

7. The control method of the rehabilitation robot according to claim 1, characterized in that: Before the step of controlling the mechanical arm of the rehabilitation robot to start moving according to the preset upper arm movement pattern, the method further includes: A length feedback signal of the robotic arm is obtained, and the arm length of the robotic arm is adjusted according to the length feedback signal.

8. The control method of the rehabilitation robot according to claim 7, characterized in that: The step of adjusting the arm length of the robotic arm according to the length feedback signal comprises: Determining whether a limit signal of the robotic arm is received; If yes, stop adjusting the arm length of the robotic arm.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the control method of a rehabilitation robot based on a brain-computer interface according to any one of claims 1 to 8 are implemented.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the control method of a rehabilitation robot based on a brain-computer interface according to any one of claims 1 to 8 are implemented.

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