Rehabilitation training system, rehabilitation training method, electronic device and medium

Abstract

The application provides a rehabilitation training system, a rehabilitation training method, an electronic device and a medium, which are applied to the technical field of rehabilitation training and include: a rehabilitation robot connected with a robot controller, the rehabilitation robot being used for guiding a rehabilitation object to move; a measurement module being used for acquiring a measurement signal of the rehabilitation object; and a control module connected with the robot controller and the measurement module, the control module being used for: adjusting a spring coefficient and an applied force coefficient of the robot controller based on a preset rehabilitation training mode; determining a training state of the rehabilitation object based on the measurement signal; and in the case that the rehabilitation training mode is a challenge mode, adding force noise to the robot controller and adjusting a noise gain parameter of the force noise based on the training state, so as to adjust the force disturbance amplitude of the rehabilitation robot to the rehabilitation object through the noise gain parameter. The embodiment of the application can realize continuous adjustment of the rehabilitation training task and improve the rehabilitation training effect.

Description

Technical Field

[0001] This application relates to the field of rehabilitation training technology, and in particular to a rehabilitation training system, rehabilitation training method, electronic device and medium. Background Technology

[0002] Stroke has become the leading cause of adult disability worldwide. Approximately half of stroke patients face long-term arm dysfunction, severely impacting their quality of life. Current rehabilitation robot systems primarily adjust task difficulty discretely by changing the nature of the task. For example, if the current task is point-to-point training, increasing the difficulty might change it to picking fruit. This lack of continuous adjustment results in insufficient adaptability, requiring patients to readjust to the new training task, increasing rehabilitation time, and leading to unsatisfactory results. Summary of the Invention

[0003] The main objective of this application is to provide a rehabilitation training system, rehabilitation training method, electronic device, and medium, which aims to achieve continuous adjustment of rehabilitation training tasks and improve the effectiveness of rehabilitation training.

[0004] To achieve the above objectives, a first aspect of this application provides a rehabilitation training system, comprising:

[0005] A rehabilitation robot, connected to a robot controller, is used to guide the rehabilitation subject to move.

[0006] Measurement module, the measurement module being used to acquire measurement signals from the rehabilitation subject;

[0007] The control module is connected to both the robot controller and the measurement module, and is used for:

[0008] Based on a preset rehabilitation training mode, the spring coefficient and applied force coefficient of the robot controller are adjusted;

[0009] Based on the measurement signals, the training status of the rehabilitation subject is determined;

[0010] When the rehabilitation training mode is challenge mode, force noise is added to the robot controller, and the noise gain parameter of the force noise is adjusted based on the training state, so as to adjust the force disturbance amplitude of the rehabilitation robot on the rehabilitation object through the noise gain parameter.

[0011] In some embodiments, the control module is further configured to:

[0012] The training force applied to the rehabilitation subject is obtained from the measurement signal;

[0013] The dynamic equations of the robot controller are determined based on the training applied force, the spring coefficient, the applied force coefficient, and the noise gain parameter of the force noise.

[0014] Discretize the dynamic equations to determine the target end effector velocity of the rehabilitation robot;

[0015] The target end-effector velocity is inversely calculated using the inverse kinematics model of the rehabilitation robot to determine the operating parameters of the rehabilitation robot, so that the robot controller can control the rehabilitation robot to operate with the operating parameters.

[0016] In some embodiments, the rehabilitation training system further includes a virtual reality device, and the control module is further configured to:

[0017] Acquire multiple input graphics;

[0018] The input graphics are preprocessed separately to determine the graphic trajectory corresponding to each input graphic.

[0019] For each of the aforementioned graphic trajectories, the minimum abrupt trajectory method is used to determine the corresponding optimized trajectory, and the graphic complexity of the optimized trajectory is determined.

[0020] The target difficulty is determined based on the current display trajectory of the virtual reality device and the training state.

[0021] Determine an optimized trajectory that matches the graphics complexity with the target difficulty, and use it as the target display trajectory;

[0022] Control the virtual reality device to display the target display trajectory.

[0023] In some embodiments, the control module is further configured to:

[0024] Based on the optimized trajectory, determine the time series corresponding to the optimized trajectory and the trajectory length of the optimized trajectory;

[0025] Obtain the drawing speed function and drawing duration of the optimized trajectory from the time series;

[0026] Calculate the second derivative of the plotted velocity function with respect to time;

[0027] Based on the drawing time, the trajectory length, and the second derivative, the logarithmic dimensionless jerk of the optimized trajectory is determined;

[0028] The graphical complexity of the optimized trajectory is determined based on the logarithmic dimensionless jerk.

[0029] In some embodiments, the measurement module includes an eye-tracking module, which is mounted on the virtual reality device;

[0030] The control module is also used for:

[0031] Eye movement signals are obtained from the measurement signals;

[0032] The eye movement signals are analyzed using an intent detection model to obtain the gaze position of the rehabilitation subject;

[0033] The training status of the rehabilitation subject is determined based on the line of sight position.

[0034] In some embodiments, the virtual reality device for displaying the measurement module further includes a position detection module;

[0035] The control module is also used for:

[0036] The position detection signal is obtained from the measurement signal;

[0037] Based on the position detection signal, the object position of the rehabilitation object in the virtual reality device is determined;

[0038] When the rehabilitation training mode is in auxiliary mode, if the distance between the object's position and the current displayed trajectory is greater than a preset distance threshold, a first target position is determined based on the line of sight position and the current displayed trajectory, and the spring coefficient is adjusted so that the rehabilitation robot can be controlled by the robot controller to move the rehabilitation object toward the first target position.

[0039] In some embodiments, the control module is further configured to:

[0040] When the rehabilitation training mode is passive mode, the applied force coefficient is set to 0;

[0041] The spring coefficient is set based on a preset coefficient threshold;

[0042] The second target position is determined based on the tangent direction of the object position along the current display trajectory;

[0043] The robot controller controls the rehabilitation robot to move the rehabilitation subject toward the second target position.

[0044] In some embodiments, the control module is further configured to:

[0045] The current display trajectory of the virtual reality device is compared with the gaze position to obtain a comparison result;

[0046] If the comparison result indicates that the current displayed trajectory and the line of sight position remain in a non-overlapping state for a period of time longer than a preset time threshold, the rehabilitation robot is controlled to stop operating.

[0047] In some embodiments, the control module is further configured to:

[0048] Based on the measurement signal and the current displayed trajectory, the assessment parameters of the rehabilitation subject are determined;

[0049] The virtual reality device is controlled to display the evaluation parameters.

[0050] In some embodiments, the control module is further configured to:

[0051] Obtain the optimization parameters corresponding to the currently displayed trajectory;

[0052] Based on the measurement signals, the training parameters of the rehabilitation subject are determined;

[0053] The evaluation parameters for the rehabilitation subject are determined based on the ratio of the training parameters to the optimization parameters.

[0054] To achieve the above objectives, a second aspect of this application proposes a rehabilitation training method applied to a control module of a rehabilitation training system. The rehabilitation training system further includes a rehabilitation robot and a measurement module. The rehabilitation robot is connected to a robot controller. The rehabilitation training method includes:

[0055] Obtain preset rehabilitation training modes;

[0056] Based on the rehabilitation training mode, adjust the spring coefficient and applied force coefficient of the robot controller;

[0057] The training status of the rehabilitation subject is determined based on the measurement signals from the measurement module.

[0058] When the rehabilitation training mode is challenge mode, force noise is added to the robot controller, and the noise gain parameter of the force noise is adjusted based on the training state, so as to adjust the force disturbance amplitude of the rehabilitation robot on the rehabilitation object through the noise gain parameter.

[0059] To achieve the above objectives, a third aspect of this application provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the rehabilitation training method described in the second aspect above.

[0060] To achieve the above objectives, a fourth aspect of the present application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the rehabilitation training method described in the second aspect above.

[0061] The rehabilitation training system, method, electronic device, and medium proposed in this application include a rehabilitation robot, a measurement module, and a control module. The rehabilitation robot is connected to a robot controller. By setting the spring coefficient and applied force coefficient of the robot controller, the rehabilitation training mode can be adjusted to meet the training needs of stroke patients at different stages. Furthermore, in the challenge mode of rehabilitation training, force noise is added to the robot controller to increase force interference for the rehabilitation subject. The noise gain parameter of the force noise is adjusted according to the training status to regulate the amplitude of force disturbance exerted by the rehabilitation robot on the rehabilitation subject, thereby achieving adaptive adjustment of the training task difficulty. This adjustment method can continuously adjust the difficulty of the training task according to the training status of the rehabilitation subject and can be applied to the same training task. The rehabilitation subject does not need to readjust to new training tasks, reducing rehabilitation training time and improving rehabilitation training effectiveness. Attached Figure Description

[0062] Figure 1 This is a structural block diagram of the rehabilitation training system provided in an embodiment of this application;

[0063] Figure 2 This is a schematic diagram of the rehabilitation robot provided in an embodiment of this application;

[0064] Figure 3 This is a schematic diagram of force noise under different noise gain parameters provided in the embodiments of this application;

[0065] Figure 4 This is a schematic diagram of the damping-spring-mass system corresponding to the robot controller provided in the embodiments of this application;

[0066] Figure 5 This is a schematic diagram of a rehabilitation robot controlled by a robot controller according to an embodiment of this application;

[0067] Figure 6 This is a schematic diagram illustrating the determination of image complexity provided in an embodiment of this application;

[0068] Figure 7 This is a schematic diagram illustrating the overall difficulty of the rehabilitation training task provided in the embodiments of this application;

[0069] Figure 8 This is a schematic diagram of the assisted training mode provided in an embodiment of this application;

[0070] Figure 9 This is a schematic diagram of the first target location provided in an embodiment of this application;

[0071] Figure 10 This is a flowchart of a rehabilitation training method provided in an embodiment of this application;

[0072] Figure 11 This is the hardware structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0073] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0074] It should be noted that although functional modules are divided in the device schematic diagram and a logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the device or the order in the flowchart. The terms "first," "second," etc., in the specification, claims, and the aforementioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.

[0075] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit the scope of this application.

[0076] Stroke has become the leading cause of adult disability worldwide. Approximately half of stroke patients face long-term arm dysfunction, severely impacting their quality of life. Current rehabilitation robot systems primarily adjust task difficulty discretely by changing the nature of the task. For example, if the current task is point-to-point training, increasing the difficulty might change it to picking fruit. This lack of continuous adjustment results in insufficient adaptability, requiring patients to readjust to the new training task, increasing rehabilitation time, and leading to unsatisfactory results.

[0077] Based on this, this application provides a rehabilitation training system, a rehabilitation training method, an electronic device, and a medium. The rehabilitation training system provided in the embodiments of this application can realize continuous adjustment of rehabilitation training tasks and improve the rehabilitation training effect.

[0078] The present application will now be described in further detail with reference to the accompanying drawings.

[0079] Reference Figure 1 The first aspect of this application proposes a rehabilitation training system, including a rehabilitation robot, a measurement module, and a control module.

[0080] The rehabilitation robot is connected to a robot controller and is used to guide the rehabilitation patient to move.

[0081] The measurement module is used to acquire measurement signals from the rehabilitation subjects.

[0082] The control module is connected to the robot controller and the measurement module respectively.

[0083] The control module is specifically used for:

[0084] Based on the preset rehabilitation training mode, adjust the spring coefficient and applied force coefficient of the robot controller.

[0085] Based on the measurement signals, the training status of the rehabilitation subject is determined.

[0086] When the rehabilitation training mode is challenge mode, force noise is added to the robot controller, and the noise gain parameter of the force noise is adjusted based on the training state, so as to adjust the force disturbance amplitude of the rehabilitation robot on the rehabilitation object through the noise gain parameter.

[0087] It should be noted that the rehabilitation target is the individual who needs rehabilitation training, typically a stroke patient. (See reference...) Figure 2 The rehabilitation robot provided in this application is a planar rehabilitation robot. The planar rehabilitation robot includes a wrist support. During rehabilitation training, the patient can place their wrist on the wrist support to perform the training tasks. The position of the wrist support is controlled by an H-shaped drive mechanism, which includes two stepper motor pulleys, two driven wheels, and a belt passing through these pulleys. The belt is fixed to a central platform, which is the platform on which the patient's wrist rests. When the two motors rotate in the same direction, the wrist support moves left and right, i.e., along... Figure 2 The movement is along the x-axis; when the two motors rotate in opposite directions, the wrist brace moves up and down, i.e., along... Figure 2 The movement is indicated by the y-axis. The rehabilitation robot is used to guide the rehabilitation patient's movement. Specifically, the patient can move under the robot's guidance or independently, thus moving the robot.

[0088] The robot controller is a damped-spring-mass system, and the robot controller follows the dynamics of the damped-spring-mass system along each axis, that is:

[0089]

[0090] Where M is the mass parameter, x(t) is the wrist position of the rehabilitation subject, D is the damping parameter, K is the spring constant, f(t) is the force applied by the rehabilitation subject (i.e., the applied force), which can be measured by the measurement module, and H is the switching coefficient for controlling whether the applied force is included (i.e., the applied force coefficient). The robot controller follows the damping-spring-mass system dynamics along both the x and y axes. The spring constant corresponds to the spring and can pull the rehabilitation subject to move. The damping parameter represents the damping applied by the rehabilitation subject's movement. The damping parameter can be used to simulate tasks such as drawing and painting to increase the immersion of rehabilitation training tasks. When the applied force coefficient is 0, the rehabilitation subject has no applied force, and the control module controls the rehabilitation robot to pull the rehabilitation subject to move through the robot controller. When the applied force coefficient is 1, the rehabilitation subject moves actively, and the movement of the rehabilitation subject in turn drives the movement of the rehabilitation robot.

[0091] The robot controller is connected to the control module, which can control the movement of the rehabilitation subject through the robot controller.

[0092] The specific type of robot controller can be set according to requirements, as long as its system is a damped-spring-mass system. This application embodiment uses an admittance controller as the robot controller. An admittance controller is a controller used to control the interaction between the robot's end effector and the environment; it achieves compliant control by adjusting the robot's dynamic properties.

[0093] It should be noted that the measurement module is used to measure the behavioral and physiological signals of the rehabilitation subject. Behavioral signals can include eye movement fixation points, blinking, and applied force, while physiological signals can include electrocardiogram (ECG), skin conductance, respiration, body temperature, and pupil size. These two types of signals can be analyzed to quantify the rehabilitation subject's focus and emotional state, thereby determining the subject's training status and using it as feedback input for adjusting the difficulty of rehabilitation training tasks.

[0094] The measurement signals are obtained by the measurement module and include behavioral and physiological signals of the rehabilitation subject. Based on behavioral signals, the subject's level of focus and gaze position during rehabilitation training tasks can be determined, facilitating the assessment of the difficulty level of the current task. Based on physiological signals, the subject's emotional state and physical health status can be determined, facilitating the assessment of their ability to perform the rehabilitation training task and ensuring their health status during the training.

[0095] It should be noted that rehabilitation training modes include various types, such as passive mode, assisted mode, active mode, and challenge mode. These modes can be set according to the needs of the individual undergoing rehabilitation. The control module sets the spring coefficient and applied force coefficient based on the preset rehabilitation training modes. Stroke patients at different stages have varying upper limb strength, requiring different rehabilitation training modes. The rehabilitation training modes can be configured to meet the training needs of stroke patients at different stages and with different symptoms.

[0096] It should be noted that when the rehabilitation subject's upper limb function has largely recovered and they enter the chronic phase, the difficulty of the training tasks can be appropriately increased. This allows the subject to train at a difficulty level that matches their skill level, thus accelerating the recovery process. In this case, the spring coefficient should be set to 0, and the applied force coefficient to 1.

[0097] To adjust the difficulty of the task, this application introduces perlin noise into the damping-spring-mass system:

[0098]

[0099] Where n(t) represents the introduced force noise, and s is the noise gain parameter. The force disturbance amplitude refers to the perturbation force set for the rehabilitation training task during the rehabilitation training process, which can also be understood as resistance. The larger the noise gain parameter, the larger the force disturbance amplitude on the rehabilitation subject. Therefore, if the rehabilitation subject wants to move the same distance, more force is required, i.e., a larger applied force is needed, thus adjusting the difficulty of the rehabilitation training task.

[0100] This application uses Perlin noise as force noise. Perlin noise is an algorithm widely used in computer graphics to generate smooth, natural-like random noise. Perlin noise is used to generate random force perturbations to alter the difficulty of completing rehabilitation training tasks.

[0101] Reference Figure 3 , Figure 3 The diagram illustrates the Burmester noise, or force noise, under different noise gain parameters provided in the embodiments of this application. It can be seen that the larger the noise gain parameter, the greater the force noise introduced by the damping-spring-mass system, resulting in a greater force disturbance amplitude on the rehabilitation subject and a greater difficulty in completing the training.

[0102] The rehabilitation training system proposed in this application includes a rehabilitation robot, a measurement module, and a control module. The rehabilitation robot is connected to a robot controller. By setting the spring coefficient and applied force coefficient of the robot controller, the rehabilitation training mode can be adjusted to adapt to the training needs of stroke patients at different stages. Furthermore, in the challenge mode of rehabilitation training, the control module adds force noise to the robot controller to increase force interference for the rehabilitation subject. The noise gain parameter of the force noise is adjusted according to the training status to regulate the amplitude of force disturbance exerted by the rehabilitation robot on the rehabilitation subject, thereby achieving adaptive adjustment of the training task difficulty. This adjustment method can continuously adjust the difficulty of the training task according to the training status of the rehabilitation subject and can be applied to the same training task. The rehabilitation subject does not need to readjust to new training tasks, reducing rehabilitation training time and improving rehabilitation training effectiveness.

[0103] Understandably, the control module is also used for:

[0104] The training force applied to the rehabilitation subject is obtained from the measurement signals.

[0105] The dynamic equations of the robot controller are determined based on the noise gain parameters of the applied force, spring constant, applied force constant, and force noise.

[0106] Discretize the dynamic equations to determine the target end effector velocity of the rehabilitation robot.

[0107] By using the inverse kinematics model of the rehabilitation robot to perform inverse calculations on the target end velocity, the operating parameters of the rehabilitation robot are determined, so that the robot controller can control the rehabilitation robot to operate according to the operating parameters.

[0108] It should be noted that, referring to Figure 2 The measurement module includes a force sensor mounted on the wrist brace, which measures the magnitude of the force applied by the rehabilitation subject during training. Therefore, the training force applied by the rehabilitation subject can be obtained from the measurement signal.

[0109] The force applied during training refers to the magnitude of the force exerted by the rehabilitation subject on the rehabilitation robot, which is also the robot controller, during the training process.

[0110] Reference Figure 4 The robot controller is a damped spring-mass system. Based on the applied force, spring constant, applied force constant, and noise gain parameters of force noise, the dynamic equation of the robot controller is determined. The dynamic equation of the robot controller can be expressed as:

[0111]

[0112] Where M is the mass parameter, x(t) is the wrist position of the rehabilitation subject, D is the damping parameter, K is the spring constant, f(t) is the applied force during training, H is the applied force coefficient, n(t) is the force noise, and s is the noise gain parameter.

[0113] Reference Figure 5 After determining the dynamic equations of the robot controller, the target end-effector velocity of the rehabilitation robot can be determined by discretizing these equations. Specifically, the target end-effector velocity of the rehabilitation robot is determined by discretizing the dynamic equations using the implicit Euler method, and can be expressed as:

[0114]

[0115] Where v(t) is the target end-effector velocity, which is the velocity that the rehabilitation robot's end effector wants to achieve, and is also the end-effector velocity at time t. v(t-1) is the end-effector velocity of the rehabilitation robot at time t-1, and Δt is the sampling period.

[0116] Assuming the training task in this embodiment is drawing, sketching, or similar tasks, and this task is implemented using a virtual reality device, this embodiment typically sets the mass parameter M to 0.1 kg, the damping coefficient D to 0.1 k / s, and the spring constant K to 0 kg / s. 2 This kind of movement is neither too smooth nor too viscous, and it is closer to the feeling of drawing on paper with a pen, which improves the realism and immersion of training tasks performed through virtual reality devices.

[0117] What needs to be determined is, referring to Figure 5 After determining the target end velocity, the inverse kinematics model of the rehabilitation robot is used to perform inverse calculations on the end velocity to determine the operating parameters of the rehabilitation robot. The robot controller is then used to control the rehabilitation robot to operate according to the operating parameters.

[0118] The rehabilitation robot includes two stepper motors, so the specific operating parameters are the rotation angles of the two stepper motors.

[0119] The kinematic model of a rehabilitation robot is a model that determines the robot's end-effector velocity by the rotation angle of the stepper motor. In contrast, the inverse kinematic model of a rehabilitation robot is a model that determines the rotation angle of the stepper motor by the robot's end-effector velocity.

[0120] It should be noted that, in this embodiment of the application, the dynamic equation of the robot controller is determined by determining the training applied force, spring coefficient, applied force coefficient, and noise gain parameter of force noise, thereby determining the target end velocity and operating parameters of the rehabilitation robot, and controlling the rehabilitation robot to run according to the operating parameters through the robot controller, thereby achieving precise control of the rehabilitation robot and improving the accuracy of rehabilitation training.

[0121] Understandably, the rehabilitation training system also includes a virtual reality device, and the control module is also used for:

[0122] Obtain multiple input graphics.

[0123] Multiple input graphics are preprocessed separately to determine the graphic trajectory corresponding to each input graphic.

[0124] For each graphic trajectory, the minimum abrupt trajectory method is used to determine the corresponding optimized trajectory, and the graphic complexity of the optimized trajectory is determined.

[0125] The target difficulty is determined based on the current display trajectory and training status of the virtual reality device.

[0126] Determine an optimized trajectory that matches the graphics complexity with the target difficulty, and display it as the target trajectory.

[0127] Control the virtual reality device to display the target's trajectory.

[0128] It should be noted that, referring to Figure 1 The rehabilitation training system also includes a virtual reality device, which can be used to display rehabilitation training tasks. Compared with traditional training tasks, the virtual reality device is more convenient.

[0129] To facilitate rehabilitation training, this embodiment uses line drawing as a rehabilitation training task. The virtual reality device includes a head-mounted device and a virtual reality controller. The head-mounted device is worn on the head of the rehabilitation subject and can display the image of the rehabilitation training task. The virtual reality controller is held by the rehabilitation subject, who can perform the line drawing task on the image displayed on the head-mounted device through the virtual reality controller.

[0130] It should be noted that the input graphic can be a graphic obtained from a database or any line drawing image uploaded by the object.

[0131] It should be noted that the graphic trajectory is the trajectory corresponding to the input image, which includes multiple trajectory lines.

[0132] Since the rehabilitation training task is a line drawing task, and the input graphic cannot meet the requirements for line drawing, it is necessary to preprocess the input image to obtain the graphic trajectory corresponding to the input image.

[0133] Reference Figure 6In the preprocessing stage, the input image is first binarized to convert it into black and white. Then, connectivity analysis is performed to identify connected regions in the input image. Next, skeleton extraction is performed, iteratively removing pixels outside the trajectory until only the center line remains. Based on this, fitting and sampling are used to reconstruct a smoother curve or shape that better matches the input image, thus obtaining the image trajectory.

[0134] It should be noted that the purpose of the minimum abrupt trajectory method is to minimize the rate of change of acceleration during motion, i.e., the jerkiness. Therefore, by processing the graphic trajectory using the minimum abrupt trajectory method, we can obtain the time trajectory with the minimum jerkiness under the constraints of the graphic trajectory. The minimum abrupt trajectory method can be expressed as:

[0135]

[0136] Where J represents the jerkiness, r represents the graphic trajectory, s(t) represents the optimized trajectory corresponding to the graphic trajectory (which has the minimum jerkiness), and T represents the drawing time corresponding to the optimized trajectory. Typically, the drawing time corresponding to the optimized trajectory is the shortest time required to draw the graphic trajectory.

[0137] It should be noted that, referring to Figure 6 After determining the optimized trajectory, the graphical complexity of the optimized trajectory is determined. Graphical complexity also represents the difficulty of the rehabilitation training task; the higher the graphical complexity, the greater the difficulty of the rehabilitation training task. Figure 6 The shapes of snails and pigs in the diagram are significantly more complex than those of straight lines and circles, so the corresponding rehabilitation training tasks are more difficult.

[0138] It should be noted that the currently displayed trajectory corresponds to the rehabilitation training task that the rehabilitation subject is currently undertaking, and the target difficulty is the difficulty that needs to be adjusted for the rehabilitation training task.

[0139] The target difficulty is determined based on the current display trajectory of the virtual reality device and the training status. If the training status is poor, the target difficulty is lower than the difficulty of the rehabilitation training task corresponding to the current display trajectory. If the training status is good, the target difficulty is higher than the difficulty of the rehabilitation training task corresponding to the current display trajectory.

[0140] Then, an optimized trajectory that matches the graphic complexity with the target difficulty is determined as the target display trajectory, and the virtual reality device is controlled to display the target display trajectory.

[0141] Reference Figure 7Assuming the current displayed trajectory is an apple, its image complexity is 5.72. If the training status is poor, meaning the rehabilitation subject takes too long or has low accuracy in completing the current displayed trajectory, then adjust the target displayed trajectory to a straight line with an image complexity of 2.68, or a circle with an image complexity of 3.94. If the training status is good, meaning the rehabilitation subject completes the current displayed trajectory in the same time as the minimum speed trajectory with perfect accuracy, then adjust the target displayed trajectory to a snail with an image complexity of 7.25, or a bird with an image complexity of 7.73. If the training status is acceptable, the target difficulty can still be set to 5.72, or the target displayed trajectory can be adjusted to a banana with an image complexity of 5.69.

[0142] It should be noted that, in addition to force noise, the embodiments of this application further adjust the difficulty of the rehabilitation training task through image complexity. On the basis of the training task being a descriptive task, the diversity of the task is enriched, and the difficulty of the task can be accurately adjusted through force noise and image complexity.

[0143] Reference Figure 7 The overall difficulty of the rehabilitation training task is determined based on image complexity and force perturbation amplitude, i.e., the noise gain parameter. The overall difficulty of the rehabilitation training task can be expressed as:

[0144] G=b1η LDJ +b2σ noise ,

[0145] Where G represents the overall difficulty of the rehabilitation training task, and η LDJ For image complexity, σ noise Let b1 be the force perturbation amplitude, b2 be the influence factor of image complexity on the overall difficulty of the rehabilitation training task, and b3 be the influence factor of force perturbation amplitude on the overall difficulty of the rehabilitation training task. The larger the influence factor, the greater its impact on the overall difficulty of the rehabilitation training task.

[0146] It should be noted that adjusting the graphic complexity is suitable for any rehabilitation training mode, while adjusting the force noise is only suitable for the challenge mode. In other modes, the rehabilitation effect of the rehabilitation object is not good, and it is not appropriate to increase the force noise. However, adjusting the graphic complexity can adjust the difficulty of the training task, thus achieving continuous adjustment of the difficulty.

[0147] Understandably, the control module is also used for:

[0148] Based on the optimized trajectory, the time series corresponding to the optimized trajectory and the trajectory length of the optimized trajectory are determined.

[0149] Obtain the plotting speed function and plotting duration of the optimized trajectory from the time series.

[0150] Calculate and plot the second derivative of the velocity function with respect to time.

[0151] Based on the drawing time, trajectory length, and second derivative, the logarithmic dimensionless jerk of the optimized trajectory is determined.

[0152] The graphical complexity of the optimized trajectory is determined based on the logarithmic dimensionless jerk.

[0153] It should be noted that the time series corresponds to the optimized trajectory, and it includes the motion information of the optimized trajectory at each time point. Therefore, based on the time series, the drawing speed function and drawing duration of the optimized trajectory can be determined.

[0154] The velocity function refers to the function of the velocity at each time point of the optimized trajectory. The plotting duration is the time required to plot the optimized trajectory.

[0155] In addition, the trajectory length is the total length of the optimized trajectory. Assuming the optimized trajectory is a circle with a radius of 2cm, then the trajectory length is 4πcm.

[0156] The formula for calculating the logarithmic dimensionless jerk of the optimized trajectory is:

[0157]

[0158] Where, η LDJ To optimize the logarithmic dimensionless jerkness of the trajectory, D represents the plotting time of the optimized trajectory, S represents the trajectory length of the optimized trajectory, and v represents the plotting speed function of the optimized trajectory. To plot the second derivative of the velocity function with respect to time.

[0159] Logarithmic dimensionless jerk is positively correlated with the curvature of the optimized trajectory and is independent of the trajectory length. Therefore, logarithmic dimensionless jerk can be used to measure the complexity of the trajectory. For example... Figure 7 The complexity of the images shown increases sequentially from straight lines, circles, bananas, apples, snails, birds, etc.

[0160] In addition, logarithmic dimensionless jerk is often used to measure the smoothness of a movement.

[0161] It should be noted that the embodiments of this application measure the graphical complexity of the optimized trajectory by using the dimensionless jerkiness of the object, thereby quantifying the graphical complexity and making it easier to adjust the difficulty of the rehabilitation training task through the graphical complexity.

[0162] Understandably, the measurement module includes an eye-tracking module, which is mounted on the virtual reality device.

[0163] The control module is also used for:

[0164] Eye movement signals are obtained from the measurement signals.

[0165] An intent detection model was used to analyze eye movement signals to obtain the gaze position of the rehabilitation subject.

[0166] Determine the training status of the rehabilitation subject based on their line of sight.

[0167] It should be noted that the eye-tracking module is installed on the virtual reality device. The eye-tracking module can be used to measure the eye movement signals and blink frequency of the rehabilitation subject.

[0168] The intent detection model is used to determine the gaze position of a rehabilitation subject based on their eye movement signals. By inputting the eye movement signals into the intent detection model, the gaze position of the rehabilitation subject can be obtained, thereby determining the subject's training status.

[0169] It should be noted that the embodiments of this application utilize eye-tracking signals to analyze the intentions of the rehabilitation subject, which can more accurately determine the gaze position of the rehabilitation subject and the training status of the rehabilitation subject.

[0170] Understandably, the virtual reality device used to display the measurement module also includes a position detection module.

[0171] The control module is also used for:

[0172] The position detection signal is obtained from the measurement signal.

[0173] Based on position detection signals, the object position of the rehabilitation subject in the virtual reality device is determined.

[0174] When the rehabilitation training mode is in auxiliary mode, if the distance between the object's position and the currently displayed trajectory is greater than a preset distance threshold, the first target position is determined based on the line of sight position and the currently displayed trajectory, and the spring coefficient is adjusted so that the rehabilitation robot can be controlled by the robot controller to move the rehabilitation object toward the first target position.

[0175] It should be noted that the position detection module is used to detect the position of the rehabilitation subject in the virtual reality device, that is, the position of the pen tip when the rehabilitation subject is performing a drawing task. The position of the pen tip is the position of the object.

[0176] The assisted training mode is suitable for mid-stage stroke patients. At this stage, the patient can perform active training because their upper limbs have regained some function. However, due to the susceptibility of upper limb spasticity, the hand may deviate from the trajectory, meaning the patient's position may deviate from the currently displayed trajectory corresponding to the rehabilitation training task. When the patient moves voluntarily, the spring coefficient and noise gain parameter are both set to 0, and the applied force coefficient is set to 1.

[0177] Reference Figure 8 and Figure 9If the distance between the detected object position x(t) and the current displayed trajectory is greater than a preset distance threshold, the first target position x is determined based on the line-of-sight position and the current displayed trajectory. d (t). First target position x d (t) is the point that the rehabilitation subject expects to travel to, the first target location x. d x(t) is located on the current display trajectory. First, determine the point on the current display trajectory that is closest to the object's position x(t), but the closest point is not necessarily the first target position x. d (t), which may be a point that has already been depicted. Therefore, based on the gaze position determined by the eye movement signal, the point closest to the gaze position can be selected from among several nearest points as the first target position x. d (t). If multiple points are far from the line of sight, then the point in the currently displayed trajectory that is closest to the line of sight will be taken as the first target position x. d (t).

[0178] To correct the position of the rehabilitation subject, the spring coefficient is adjusted so that the rehabilitation robot can be controlled by the robot controller to move the rehabilitation subject toward the first target position.

[0179] The preset distance threshold can be set as needed, specifically based on the recovery status of the patient and the graphic complexity of the currently displayed trajectory.

[0180] It should be noted that the embodiments of this application, by setting an auxiliary mode, are suitable for subjects who are partially recovering upper limb function in the middle stage. When the subject deviates from the current display trajectory, the subject's position can be corrected, thereby improving the rehabilitation training effect.

[0181] Understandably, the control module is also used for:

[0182] When the rehabilitation training mode is passive, the applied force coefficient is set to 0.

[0183] Set the spring coefficient based on the preset coefficient threshold.

[0184] The location of the second target is determined based on the tangent direction of the object's position along the current displayed trajectory.

[0185] The robot controller controls the rehabilitation robot to move the patient toward the second target position.

[0186] It should be noted that the passive mode is suitable for individuals who have initially experienced complete loss of upper limb motor function. In this case, the individual can only undergo passive traction training. Therefore, the applied force coefficient is set to 0.

[0187] Based on a preset coefficient threshold, a spring coefficient is set to ensure that the spring coefficient is greater than the preset coefficient threshold. The preset coefficient threshold can be set according to the rehabilitation patient and the rehabilitation robot, and must ensure that the rehabilitation robot can move the rehabilitation patient within the preset coefficient threshold.

[0188] Based on the object's position and the current displayed trajectory, the current position of motion is determined, specifically the object's position along the tangent direction of the current displayed trajectory, thus defining the second target position. The second target position refers to the target location where the rehabilitation robot moves the rehabilitation subject. Assuming the current displayed trajectory is a circle, the second target position lies along the tangent direction of that circle. The tangent direction is simple and clear, essentially representing linear motion. This approach, while meeting the rehabilitation training needs of the subject, reduces the risk of injury caused by repeated turning movements due to overly complex patterns.

[0189] It should be noted that the embodiments of this application include a passive mode, which is suitable for individuals who have completely lost upper limb motor function in the early stages, thus expanding the applicability of the rehabilitation training system.

[0190] Understandably, the control module is also used for:

[0191] Compare the current display trajectory and gaze position of the virtual reality device to obtain the comparison results;

[0192] If the comparison results indicate that the current displayed trajectory and the line of sight position remain in a non-overlapping state for a period of time longer than a preset time threshold, the rehabilitation robot will be controlled to stop operating.

[0193] It should be noted that, in order to reduce the possibility of distraction or dangerous situations for rehabilitation subjects during training, if the rehabilitation subject's line of sight deviates from the current displayed trajectory, and the current displayed trajectory and line of sight remain in a non-overlapping state for a period of time exceeding a preset time threshold, the rehabilitation robot will be controlled to stop running and a warning will be issued.

[0194] The preset time threshold can be set as needed, specifically according to the condition of the person undergoing rehabilitation.

[0195] It should be noted that the embodiments of this application include an emergency stop mechanism, which controls the rehabilitation robot to stop running when the comparison result indicates that the current displayed trajectory and the line of sight position remain in a non-overlapping state for a period of time longer than a preset time threshold. This setting can improve the safety of rehabilitation subjects during rehabilitation training.

[0196] It should be noted that the rehabilitation training mode in this embodiment also includes an active mode. When the upper limb function of the rehabilitation subject recovers to a certain extent, unassisted active training can be performed, that is, self-training without any assistance. In this case, the spring coefficient is set to 0, the applied force coefficient is set to 1, and the noise gain parameter is set to 0.

[0197] Understandably, the control module is also used for:

[0198] Based on the measurement signals and the current displayed trajectory, the assessment parameters of the rehabilitation subject are determined.

[0199] Control the virtual reality device to display evaluation parameters.

[0200] It should be noted that after the rehabilitation training task is completed, that is, after the current display trajectory corresponding to the rehabilitation training task is drawn, the assessment parameters of the rehabilitation subject can be determined based on the measurement signal and the current display trajectory, so as to quantify the training effect of the rehabilitation subject.

[0201] Assessment parameters for rehabilitation subjects can include speed, trajectory accuracy, movement fluency, movement completion efficiency, and force control ability. Speed ​​refers to the speed at which the rehabilitation subject draws the currently displayed trajectory. Trajectory accuracy refers to the overlap rate between the trajectory drawn by the rehabilitation subject and the currently displayed trajectory. Movement fluency refers to the smoothness of the rehabilitation subject's movements when drawing the currently displayed trajectory. Movement completion efficiency refers to the efficiency with which the rehabilitation subject draws the currently displayed trajectory, which can be determined based on speed and accuracy. Force control ability refers to the rehabilitation subject's ability to control the force applied when drawing the currently displayed trajectory; excessive or insufficient force indicates poor force control.

[0202] In this embodiment of the application, after determining each evaluation parameter, a radar chart can be drawn based on the evaluation parameters, i.e., as shown below. Figure 1 The pentagonal diagram shown illustrates this. Therefore, the evaluation parameters need to be normalized so that their values ​​range from 0 to 1. The larger the area enclosed by the five points on the radar chart determined based on the evaluation parameters, the higher the training effect of the rehabilitation subject.

[0203] It should be noted that the embodiments of this application quantify the performance of rehabilitation subjects in rehabilitation training tasks by evaluating parameters, providing personalized feedback to rehabilitation subjects and enabling more accurate feedback on the training effect of rehabilitation subjects.

[0204] Understandably, the control module is also used for:

[0205] Obtain the optimization parameters corresponding to the currently displayed trajectory.

[0206] Based on the measurement signals, the training parameters of the rehabilitation subjects are determined.

[0207] The assessment parameters for rehabilitation subjects are determined based on the ratio of training parameters to optimization parameters.

[0208] It should be noted that the optimized parameters are the parameters corresponding to the currently displayed trajectory as the optimized trajectory. The training parameters are the parameters of the rehabilitation subject during the rehabilitation training process, and these parameters can be specifically determined based on the measurement signals. The evaluation parameters are determined based on the ratio of the training parameters and the optimized parameters, which can determine the training effect of the rehabilitation subject.

[0209] The speed of rehabilitation can be expressed as:

[0210]

[0211] Where Velocity is the velocity, a(i) is the object position at time i, a(i-1) is the object position at time i-1, I is the total number of points the object position passes through on the currently displayed trajectory, and Δt is the time step.

[0212] The normalized form of the rehabilitation speed can be expressed as:

[0213]

[0214] Among them, Velocity actual This refers to the actual speed, i.e., the speed specified in the training parameters. Velocity ref This is the reference speed, i.e., the speed in the optimization parameters.

[0215] The accuracy of a rehabilitation subject's trajectory first requires calculating the average trajectory error, which is used to evaluate the accuracy of the motion trajectory corresponding to the training parameters. The average trajectory error can be expressed as:

[0216]

[0217] Among them, Error ave It is the average error rate, N is the sequence length of the optimized trajectory corresponding to the currently displayed trajectory, and D is the average error rate. n It is a set, denoted as D. n ={p k |p k (0)=n}, that is, D n Includes all p at time point n k These points satisfy p k (0) = n. r(n) is the coordinate point of the minimum rapid trajectory at time n. a(p k (1) is the trajectory position obtained through dynamic time programming, where p k (1) is the corresponding time point after time planning. P is a set of paths, represented as P = {p k ∈N 2 ,pk (0)∈[1,N],p k (1)∈[1,I],k=1,2,K,K},p k It is a path point, p k (0) is the starting position of the path point in the time series, p k (1) is the end position of the path point in the time series, and K is the number of paths in the path set.

[0218] The normalized form of the trajectory accuracy of the rehabilitation subjects is:

[0219]

[0220] Among them, Error actual It is the average trajectory error in the training parameters. rref It is the average trajectory error in the optimization parameters.

[0221] The formula for calculating the fluidity of movement in rehabilitation subjects is:

[0222]

[0223] Where v is the pen tip speed when the object is being drawn, T is the total duration of the trajectory movement, and S is the total length of the currently displayed trajectory.

[0224] The normalized form of the motor fluency of the rehabilitation subjects is:

[0225]

[0226] Where, η ldj * represents the logarithmic dimensionless jerk of the optimized trajectory corresponding to the currently displayed trajectory, which is also the motion smoothness among the optimization parameters, η. ldj It is the logarithmic dimensionless jerkiness of the rehabilitation subject as they draw the currently displayed trajectory, and it is also the smoothness of movement in the training parameters.

[0227] The formula for calculating the efficiency of a rehabilitation subject's movement completion is:

[0228]

[0229] Where, PathLength actual For actual motion, i.e., the path length in the training parameters, PathLength theoretical To optimize the path length in the parameters, a(i) is the coordinate point of the actual movement, i.e., the path during rehabilitation training, and r(n) is the coordinate point of the optimized trajectory corresponding to the currently displayed trajectory.

[0230] The normalized form of the rehabilitation subject's motor completion efficiency is:

[0231]

[0232] The formula for calculating the force control ability of a rehabilitation subject is:

[0233]

[0234] FDE represents the force control error, F(i) is the applied force on the object at time i, and X(i) is the tangent direction of the object's position at time i along the current displayed trajectory.

[0235] The normalized form of the rehabilitation subject's force control ability is:

[0236]

[0237] It should be noted that the embodiments of this application evaluate the training status of the rehabilitation subject from five aspects: speed, trajectory accuracy, movement fluency, movement completion efficiency, and force control ability, and use the optimized parameters corresponding to the currently displayed trajectory as the measurement standard. This method can accurately reflect the training effect of the rehabilitation subject.

[0238] In addition, this application proposes a rehabilitation training method, which is applied to the measurement module of a rehabilitation training system. The rehabilitation training system also includes a rehabilitation robot and a measurement module. The rehabilitation robot is connected to a robot controller. The rehabilitation training method includes:

[0239] Step S100: Obtain the preset rehabilitation training mode.

[0240] Step S200: Based on the rehabilitation training mode, adjust the spring coefficient and applied force coefficient of the robot controller.

[0241] Step S300: Determine the training status of the rehabilitation subject based on the measurement signal from the measurement module.

[0242] Step S400: When the rehabilitation training mode is challenge mode, add force noise to the robot controller and adjust the noise gain parameter of the force noise based on the training state, so as to adjust the force disturbance amplitude of the rehabilitation robot on the rehabilitation object through the noise gain parameter.

[0243] The rehabilitation training method proposed in this application is applied to the measurement module of a rehabilitation training system. The system also includes a rehabilitation robot and the measurement module. The rehabilitation robot is connected to a robot controller. By setting the spring coefficient and applied force coefficient of the robot controller, the rehabilitation training mode can be adjusted to meet the training needs of stroke patients at different stages. Furthermore, in the challenge mode of the rehabilitation training, force noise is added to the robot controller to increase force interference on the rehabilitation subject. The noise gain parameter of the force noise is adjusted according to the training state to regulate the amplitude of force disturbance exerted by the rehabilitation robot on the rehabilitation subject. This achieves adaptive adjustment of the training task difficulty. This adjustment method can continuously adjust the difficulty of the training task according to the training state of the rehabilitation subject and is applicable to the same training task. The rehabilitation subject does not need to readjust to new training tasks, reducing rehabilitation training time and improving rehabilitation training effectiveness.

[0244] The specific implementation method of this rehabilitation training method is basically the same as the specific embodiment of the rehabilitation training device described above, and will not be repeated here.

[0245] This application also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the above-described rehabilitation training method. This electronic device can be any smart terminal, including a tablet computer, an in-vehicle computer, or similar device.

[0246] Please see Figure 11 , Figure 11 The hardware structure of an electronic device according to another embodiment is illustrated. The electronic device includes:

[0247] The processor 901 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application.

[0248] The memory 902 can be implemented as a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 902 can store the operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 902 and is called and executed by the processor 901 using the rehabilitation training method of the embodiments of this application.

[0249] The input / output interface 903 is used to implement information input and output.

[0250] The communication interface 904 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0251] Bus 905 transmits information between various components of the device (e.g., processor 901, memory 902, input / output interface 903, and communication interface 904).

[0252] The processor 901, memory 902, input / output interface 903, and communication interface 904 are connected to each other within the device via bus 905.

[0253] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described rehabilitation training method.

[0254] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0255] The rehabilitation training system, method, and related apparatus provided in this application include a rehabilitation robot and a measurement module. The rehabilitation robot is connected to a robot controller, which is a damped-spring-mass system. This system allows for adjustment of the rehabilitation training mode by setting the spring coefficient and applied force coefficient, adapting to the training needs of stroke patients at different stages. Furthermore, in challenge mode, force noise is added to the robot controller to introduce force interference to the rehabilitation subject. The noise gain parameter of the force noise is adjusted according to the training status, thereby achieving adaptive adjustment of the training task difficulty. This adjustment method can continuously adjust the training task difficulty according to the rehabilitation subject's training status and is applicable to the same training task. The rehabilitation subject does not need to readapt to new training tasks, reducing rehabilitation training time and improving rehabilitation training effectiveness.

[0256] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

[0257] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of this application, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0258] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0259] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0260] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0261] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0262] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0263] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0264] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0265] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes multiple instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0266] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.

Claims

1. A rehabilitation training system, characterized by, The application relates to a rehabilitation training system, comprising: a rehabilitation robot connected with a robot controller, the rehabilitation robot being used to guide a rehabilitation object to move; a measurement module used to acquire a measurement signal of the rehabilitation object; a control module connected with the robot controller and the measurement module respectively, the control module being used to: adjust a spring coefficient and an applied force coefficient of the robot controller based on a preset rehabilitation training mode; determine a training state of the rehabilitation object based on the measurement signal; in the case that the rehabilitation training mode is a challenge mode, add a force noise to the robot controller, and adjust a noise gain parameter of the force noise based on the training state, so as to adjust a force disturbance amplitude of the rehabilitation robot to the rehabilitation object through the noise gain parameter; the robot controller follows a formula along each axis: ， wherein is a mass parameter, is a wrist position of the rehabilitation subject, is a damping parameter, is a spring coefficient, is an exerted force of the rehabilitation subject, is an exerted force coefficient corresponding to the exerted force, is a force noise, and the force noise is a Perlin noise used to generate a random force disturbance, is a noise gain parameter.

2. The rehabilitation training system of claim 1, wherein, the control module is further used to: acquire a training applied force of the rehabilitation object from the measurement signal; determine a dynamic equation of the robot controller based on the training applied force, the spring coefficient, the applied force coefficient and the noise gain parameter of the force noise; perform discretization processing on the dynamic equation to determine a target end velocity of the rehabilitation robot; perform inverse operation on the target end velocity by using an inverse kinematics model of the rehabilitation robot to determine operation parameters of the rehabilitation robot, so that the rehabilitation robot is controlled to operate at the operation parameters by the robot controller.

3. The rehabilitation training system of claim 1, wherein, The rehabilitation training system further comprises a virtual reality device, and the control module is further used to: acquire a plurality of input graphics; perform preprocessing on the plurality of input graphics respectively to determine graphic trajectories corresponding to the input graphics; for each graphic trajectory, determine an optimized trajectory corresponding to the graphic trajectory by using a minimum jerk trajectory method, and determine a graphic complexity of the optimized trajectory; determine a target difficulty based on a current display trajectory of the virtual reality device and the training state; determine an optimized trajectory matched with the target difficulty as a target display trajectory according to the graphic complexity; control the virtual reality device to display the target display trajectory.

4. The rehabilitation training system of claim 3, wherein, The control module is further used to: determine a time sequence corresponding to the optimized trajectory and a trajectory length of the optimized trajectory based on the optimized trajectory; acquire a drawing speed function and a drawing time length of the optimized trajectory from the time sequence; calculate a second derivative of the drawing speed function with respect to time; determine a logarithmic dimensionless jerk of the optimized trajectory based on the drawing time length, the trajectory length and the second derivative; determine the graphic complexity of the optimized trajectory based on the logarithmic dimensionless jerk.

5. The rehabilitation training system of claim 3, wherein, The measurement module comprises an eye movement tracking module arranged on the virtual reality device; the control module is further used to: acquire an eye movement signal from the measurement signal; analyze the eye movement signal by using an intention detection model to obtain a line-of-sight position of the rehabilitation object; determine the training state of the rehabilitation object based on the line-of-sight position.

6. The rehabilitation training system of claim 5, wherein, The virtual reality device used to display the measurement module further comprises a position detection module; The control module is further configured to: acquire a position detection signal from the measurement signal; determine an object position of the rehabilitation subject in the virtual reality device based on the position detection signal; if the distance between the object position and the current display trajectory is greater than a preset distance threshold in the case that the rehabilitation training mode is an assisted mode, determine a first target position based on the line-of-sight position and the current display trajectory, and adjust the spring coefficient to control the rehabilitation robot to move the rehabilitation subject to the first target position through the robot controller.

7. The rehabilitation training system according to claim 6, characterized in that, The control module is further configured to: set the applied force coefficient to 0 in the case that the rehabilitation training mode is a passive mode; set the spring coefficient based on a preset coefficient threshold; determine a second target position based on the tangential direction of the current display trajectory along the object position; control the robot controller to control the rehabilitation robot to move the rehabilitation subject to the second target position.

8. The rehabilitation training system of claim 5, wherein, The control module is further configured to: compare the current display trajectory of the virtual reality device and the line-of-sight position to obtain a comparison result; control the rehabilitation robot to stop running in the case that the comparison result indicates that the time during which the current display trajectory and the line-of-sight position continuously do not coincide is greater than a preset time threshold.

9. The rehabilitation training system of claim 3, wherein, The control module is further configured to: determine an evaluation parameter of the rehabilitation subject based on the measurement signal and the current display trajectory; control the virtual reality device to display the evaluation parameter.

10. The rehabilitation training system of claim 9, wherein, The control module is further configured to: acquire an optimization parameter corresponding to the current display trajectory; determine a training parameter of the rehabilitation subject based on the measurement signal; determine an evaluation parameter of the rehabilitation subject based on the ratio of the training parameter and the optimization parameter.

11. A control method of rehabilitation training, characterized by, A control module applied to a rehabilitation training system, the rehabilitation training system further comprising a rehabilitation robot and a measurement module, the rehabilitation robot being connected with a robot controller, and a control method of the rehabilitation training comprising: acquiring a preset rehabilitation training mode; adjusting a spring coefficient and an applied force coefficient of the robot controller based on the rehabilitation training mode; determining a training state of the rehabilitation subject based on a measurement signal from the measurement module; in the case that the rehabilitation training mode is a challenge mode, adding a force noise to the robot controller, and adjusting a noise gain parameter of the force noise based on the training state to adjust the force disturbance amplitude of the rehabilitation robot to the rehabilitation subject through the noise gain parameter; the robot controller follows the formula along each axis: ， wherein is a mass parameter, is a wrist position of the rehabilitation subject, is a damping parameter, is a spring coefficient, is an exerted force of the rehabilitation subject, is an exerted force coefficient corresponding to the exerted force, is a force noise, and the force noise is a Berlin noise, the Berlin noise being used to generate a random force disturbance, is a noise gain parameter.

12. An electronic device, comprising: The electronic device comprises a memory and a processor, the memory stores a computer program, and the processor implements the control method of the rehabilitation training of claim 11 when executing the computer program.

13. A computer-readable storage medium, the computer-readable storage medium storing a computer program, characterized in that, The computer program is executed by the processor to implement the control method of the rehabilitation training of claim 11.

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