Rehabilitation training exoskeleton robot multi-motion-mode control system and method

Abstract

The invention provides a multi-motion-mode control system and method for a lower limb rehabilitation training exoskeleton robot. Surface electromyographic signals are obtained through an electromyographic sensor of the robot; inputting the surface electric muscle signal into a neuromuscular activation model to generate a muscle activation degree; a man-machine interaction torque is obtained through a pressure sensor; inputting the surface electromyogram signal characteristic value RMS, the muscle activation degree and the man-machine interaction torque into a fuzzy adaptive switching signal regulator to obtain a training mode switching signal; the training modes comprise a passive training mode, a power-assisted training mode, an active training mode and an anti-resistance training mode; and according to the training mode switching signal, switching to a corresponding training mode so as to perform training control under the corresponding training mode on the lower limb exoskeleton rehabilitation robot. The motion state of the patient is evaluated according to the physiology and force signals of the patient, fuzzy rule reasoning is carried out to achieve self-adaptive grading switching, the rehabilitation effect is enhanced, and rehabilitation training better meets the individualized requirement of the patient.

Description

Technical Field

[0001] The present invention relates to the technical field of rehabilitation training, and in particular to a multi-motion mode control system and method for a rehabilitation training exoskeleton robot. Background Art

[0002] Stroke is an acute cerebrovascular event closely related to brain tissue damage and is one of the leading causes of death and disability worldwide. Statistics show that in the past 30 years, the incidence of stroke has increased by 70%, and the overall disability rate has exceeded 80%. Stroke often leads to severe motor dysfunction, especially limited lower limb movement, which significantly affects the patient's ability to walk independently and the quality of daily life.

[0003] Studies have shown that systematic rehabilitation training helps improve muscle strength, promote nerve remodeling and accelerate blood circulation. However, traditional rehabilitation methods mainly rely on patients' self-training or the assistance of rehabilitation therapists. There are problems such as low patient participation, high training repetition, and limited human resources, which make it difficult to ensure the rehabilitation effect. Therefore, rehabilitation robots that can accurately and promptly respond to patients' movement intentions and provide accurate and efficient training plans have gradually become a research hotspot.

[0004] However, the existing rehabilitation robot training methods have the following problems: 1) Training mode switching relies on manual intervention and lacks intelligent adjustment: Traditional rehabilitation robots usually rely on rehabilitation therapists to manually adjust the training mode according to the patient's rehabilitation progress. The mode switching lacks intelligent adjustment, which affects the consistency and adaptability of the training effect. 2) The lack of real-time evaluation mechanism leads to insufficient basis for training mode switching, that is, the existing on-bed lower limb exoskeleton rehabilitation system has limited monitoring of the patient's movement state, lacks real-time evaluation of multi-source information of physiological and mechanical parameters such as surface electromyographic signals (sEMG) and human-machine interaction force, resulting in unscientific training mode switching. 3) Although the existing on-bed lower limb exoskeleton rehabilitation robots can provide auxiliary training, there is a general lack of rich training modes, and lack of multi-mode coordination and adaptive adjustment for different rehabilitation stages, which makes it difficult to meet the needs of different individuals and rehabilitation stages. There are few common rehabilitation robot training modes, such as single passive training or active training, which cannot cover the different needs of the entire rehabilitation cycle. At different stages of patient recovery, relying only on a single mode may lead to insufficient or excessive training intensity, affecting the rehabilitation effect. 4) Traditional rehabilitation systems usually run various training modes according to fixed rules. The modes are independent of each other and lack the ability to integrate and dynamically adjust the modes. The modes run independently and lack collaborative optimization.

[0005] The control strategy of the present invention comprehensively considers the patient's motor ability, electromyographic signals, human-computer interaction and other multi-source information, ensures the reasonable switching of training modes, and dynamically adjusts training parameters to optimize the rehabilitation effect. At the same time, the control method is applied to the in-bed lower limb exoskeleton rehabilitation robot, enabling the robot to provide accurate and personalized training plans and the most appropriate training intensity at different rehabilitation stages, improve the intelligence level and adaptability of training, and accelerate the recovery of patients' lower limb functions. Summary of the invention

[0006] The purpose of the present invention is to provide a rehabilitation robot multi-motion mode control system or method that can adapt to changes in the patient's motion state in real time, adaptively switch to the corresponding training mode, and enhance the rehabilitation effect, thereby improving the current problems of the lower limb exoskeleton rehabilitation robot on bed, such as the single training mode, fixed control strategy, lack of adaptive adjustment to different rehabilitation stages, and difficulty in meeting the needs of different individuals and rehabilitation stages.

[0007] To achieve the above-mentioned object, the present invention proposes a multi-motion mode control system for a lower limb rehabilitation training exoskeleton robot, including an on-bed lower limb exoskeleton rehabilitation robot body, a neuromuscular activation model, a motion state monitoring module and a training module: The robot body obtains the human-machine interaction torque of the patient's trained lower limbs through a pressure sensor, and obtains the surface electromyographic signal of the trained lower limbs through an electromyographic sensor; The neuromuscular activation model is used to convert surface electromyographic signals into muscle activation degrees; The motion state monitoring module introduces an adaptive training mode switching method based on fuzzy rule reasoning, and uses a fuzzy adaptive switching signal regulator to generate a training mode switching signal from the surface electromyography signal characteristic value, muscle activation degree and human-computer interaction torque; The training module is used to receive a training mode switching signal and generate a corresponding training mode; The training modes include a passive training mode, an assisted training mode, an active training mode and a resistance training mode; the hierarchical switching mechanism of the training modes is gradually increasing or decreasing.

[0008] Furthermore, the robot training control method in the passive training mode is: 1.1: Subtract the expected trajectory of the training task from the actual trajectory fed back by the robot encoder to get the trajectory error. The calculation formula is: ; in, is the trajectory error, is the expected trajectory angle of the training task, The actual angle of the motor of the lower limb exoskeleton robot joint in bed; 1.2: Input the trajectory error into the sliding mode controller to obtain the expected auxiliary torque of the robot's joints. The calculation formula is: ; in, denote the trajectory error and trajectory error change rate respectively, represents the sliding surface function, represents the modeling error of the system, represents the positive definite inertia matrix of the system, represents the centrifugal and Coriolis force matrices of the system, represents the gravity matrix of the system, Respectively represent the joint motor angle, angular velocity, and angular acceleration, is the controller parameter, Represents the desired assist torque of the joints for robot motion.

[0009] 1.3: The in-bed lower limb exoskeleton rehabilitation robot is passively trained and controlled according to the expected joint assisting torque. The specific control method is as follows: 1) If the expected joint assisting torque is positive, the movement trend of the in-bed lower limb exoskeleton is in the same direction as the gravity; 2) If the expected joint assisting torque is negative, the movement trend of the in-bed lower limb exoskeleton is opposite to the gravity.

[0010] Furthermore, the robot training control method of the power-assisted training mode is: 2.1: Input the expected trajectory of the training task into the robot inverse dynamics model to obtain the expected joint torque of the in-bed lower limb exoskeleton rehabilitation robot. The calculation formula is: + ; in, is the desired joint torque, represent the kinetic energy and potential energy of the robot joints under the Lagrangian method, are the angles and angular velocities in the expected trajectory for the training task.

[0011] 2.2: Obtain the human-machine interaction torque through the robot's pressure sensor; 2.3: Input the joint desired torque and human-machine interaction torque into the motion ability assessment module to calculate and generate auxiliary factors; 2.4: Subtract the expected trajectory of the training task from the actual trajectory fed back by the robot encoder to obtain the trajectory error, which is input into the sliding mode controller to generate the expected auxiliary torque of the joints of the robot motion; 2.5: Input the acquired joint desired torque, joint desired auxiliary torque and auxiliary factor into the power training controller to obtain the joint auxiliary torque of the robot motion; 2.6: Perform assisted training control on the in-bed lower limb exoskeleton rehabilitation robot based on the joint assist torque.

[0012] Furthermore, the method for the sports ability assessment module to obtain auxiliary factors through joint expected torque and human-computer interaction torque calculation includes the following steps: 2.31: Calculate the patient's state fluctuation rate through the human-computer interaction torque. The calculation formula is as follows: ; 2.32: Calculate the patient torque deviation rate through the human-computer interaction torque. The calculation formula is as follows: ; 2.33: The auxiliary factor is calculated by the following formula using the patient state fluctuation rate and moment deviation rate; ; in, represents the patient's state fluctuation rate, represents the patient torque deviation rate, is the window period, For the current moment, For human-computer interaction torque, is the desired moment of the joint; represents the auxiliary factor, is the weighting factor; In step 2.5, the joint desired torque, joint desired auxiliary torque and auxiliary factor are input into the power training controller to obtain the joint auxiliary torque of the robot motion. The calculation formula is: ; in, represents the joint auxiliary torque, is the desired auxiliary torque of the joint, is the desired joint torque, As auxiliary factor.

[0013] Furthermore, the robot training control method in the active training mode is: 3.1: Input the actual trajectory fed back by the robot encoder into the active training controller to obtain the joint auxiliary torque. The formula is as follows: ; in, is the joint assist torque, are the inertia, damping and stiffness parameter matrices of the robot, The actual trajectory of the robot encoder feedback includes actual angle, actual angular velocity and actual angular acceleration respectively.

[0014] 3.2: Active training control of the in-bed lower limb exoskeleton rehabilitation robot based on joint assist torque.

[0015] Furthermore, the robot training control method of the resistance training mode is: 4.1: Obtaining human-machine interaction torque through robot pressure sensor; 4.2: Input the human-machine interaction torque into the virtual impedance model to obtain the trajectory correction for adjusting the desired trajectory of the robot; 4.3: Subtract the expected trajectory of the training task from the trajectory correction to obtain the adjusted expected trajectory; 4.4: Subtract the adjusted desired trajectory from the actual trajectory fed back by the robot encoder to obtain the trajectory error; 4.5: Input the obtained trajectory error into the sliding mode controller to obtain the expected auxiliary torque of the joints of the robot motion; 4.6: Perform resistance training control on the in-bed lower limb exoskeleton rehabilitation robot according to the desired auxiliary torque of the joints.

[0016] Furthermore, the virtual impedance model is: ; in, They represent joint mass, virtual damping coefficient, and virtual stiffness coefficient respectively. Represents human-computer interaction, They represent trajectory correction, joint motor angular velocity, and joint motor angular acceleration respectively.

[0017] Further, the neuromuscular activation model includes a neural activation function and a muscle activation function; The neural activation function is: ; The muscle activation function is: ; in, Indicates the current time The neural activation function is is the electromyographic signal, are the neural activation coefficients, For time delay, is the sampling time window period; Indicates the current time The muscle activation function, is the muscle activation coefficient.

[0018] Furthermore, the fuzzy adaptive switching signal regulator includes input variables, output variables and fuzzy rules. By establishing corresponding fuzzy rules, the patient's movement state is evaluated according to the patient's physiological and mechanical signals, and the fuzzy rule reasoning output is switched to the most appropriate training mode under the corresponding movement state.

[0019] The input variables include three key physiological and mechanical parameters: surface electromyographic signal characteristic value RMS, muscle activation degree and human-computer interaction torque; The output variable includes a training mode switching signal; In the fuzzy rules, the input variable fuzzy subsets include low L, medium M, and high H; the output variable fuzzy subsets include passive, power, active, and impedance; The passive training mode, assisted training mode, active training mode, and resistance training mode are integrated into a control architecture to achieve collaborative optimization among modes. Among them, the passive training, assisted training, and resistance training modes all adopt a trajectory tracking control method based on sliding mode control. By combining multi-channel data such as lower limb surface electromyography (sEMG), human-computer interaction force, and motion trajectory, the training mode can be optimized in real time according to the patient's status, so that the overall control model has dynamic adjustment capabilities, and can effectively reduce the robot's computational complexity and power consumption.

[0020] The passive training mode, assisted training mode, active training mode and resistance training mode correspond to the acute phase, early recovery phase, functional recovery phase and strengthening phase of lower limb rehabilitation of patients, respectively. According to the four phases of lower limb rehabilitation (acute phase, early recovery phase, functional recovery phase and strengthening phase), corresponding rehabilitation training modes and control strategies are designed respectively, namely passive training, assisted training, active training and resistance training. By integrating passive training, assisted training, active training and resistance training, a training system covering the four phases of lower limb rehabilitation (acute phase, early recovery phase, functional recovery phase and strengthening phase) is formed. By constructing a multi-mode fusion framework, full-cycle rehabilitation coverage is achieved, so that patients can smoothly transition between different modes, ensuring that the training effect is more scientific and reasonable.

[0021] According to the characteristics of physiological signals and force signals under different training task trajectories, the corresponding physiological parameter trapezoidal membership functions are set respectively, and the training mode switching strategy is dynamically adjusted. The formula is as follows: ; in, The input variables include three key physiological and mechanical parameters: surface electromyographic signal characteristic value RMS, muscle activation degree and human-computer interaction torque. The output variables include a training mode switching signal.

[0022] The training mode switching adopts a hierarchical switching mechanism and can only be switched by increasing or decreasing step by step. That is, the training mode can only be switched step by step according to "passive training mode→power-assisted training mode→active training mode→resistance training mode" or "resistance training mode→active training mode→power-assisted training mode→passive training mode" to prevent mode "jumping" and avoid injuries caused by muscle failure to adapt.

[0023] The present invention also proposes a multi-motion mode control method for a lower limb rehabilitation training exoskeleton robot. Based on the multi-motion mode control system of the lower limb rehabilitation training exoskeleton robot, the method comprises the following steps: S1: Surface electromyographic signals are obtained through the electromyographic sensors of the lower limb exoskeleton robot under the bed; S2: Extract the RMS of surface electromyography signal feature value; S3: inputting the surface electromyographic signal into a neuromuscular activation model to generate a muscle activation degree; S4: Use the robot’s pressure sensor to obtain the human-machine interaction torque; S5: inputting the surface electromyography signal characteristic value RMS, muscle activation degree and human-computer interaction torque into the motion state monitoring module, and obtaining the training mode switching signal by using the fuzzy adaptive switching signal regulator; S6: The training modes include passive training mode, assisted training mode, active training mode and resistance training mode; according to the training mode switching signal, switch to the corresponding training mode, so as to perform training control on the in-bed lower limb exoskeleton rehabilitation robot under the corresponding training mode.

[0024] Compared with the prior art, the advantages of the present invention are: 1. The control strategy of the present invention introduces an adaptive training mode switching method based on fuzzy rule reasoning, combines the three key physiological and mechanical parameters of the lower limb surface electromyographic signal characteristic value (RMS), muscle activation degree and human-computer interaction torque, establishes corresponding fuzzy rules, evaluates the patient's movement state according to the patient's physiological and force signals, performs fuzzy rule reasoning and outputs to switch to the most appropriate training mode under the corresponding movement state, thereby adapting to the patient's movement state changes in real time, realizing adaptive switching to the corresponding training mode, enhancing the rehabilitation effect, making rehabilitation training more in line with the individual needs of patients, enabling the robot to provide accurate and personalized training plans and the most suitable training intensity at different rehabilitation stages, improving the intelligence level and adaptability of training, and accelerating the recovery of patients' lower limb function.

[0025] 2. The method of the present invention adopts a rule-based hierarchical switching mechanism, and the training mode can only increase or decrease step by step, preventing mode "jumping" and avoiding the occurrence of skipping levels, thereby avoiding injuries caused by muscle failure to adapt and improving the safety of the control method.

[0026] 3. According to the four stages of lower limb rehabilitation for bedridden patients, namely the acute stage, the initial recovery stage, the functional recovery stage and the strengthening stage, the present invention designs four rehabilitation exercise modes, namely passive training, assisted training, active training and resistance training, and integrates them into one, constructs a multi-mode fusion framework, realizes full-cycle rehabilitation coverage, enables patients to smoothly transition between different modes, and ensures that the training effect is more scientific and reasonable.

[0027] 4. The control strategy of the present invention comprehensively considers the patient's motor ability, electromyographic signals, human-computer interaction and other multi-source information to ensure the reasonable switching of training modes and dynamically adjust the training parameters to optimize the rehabilitation effect. At the same time, the control method is applied to the lower limb exoskeleton rehabilitation robot in bed, so that the robot can provide accurate and personalized training programs and the most suitable training intensity at different rehabilitation stages, improve the intelligence level and adaptability of training, and accelerate the recovery of the patient's lower limb function.

[0028] 5. The present invention sets corresponding membership functions according to the characteristics of physiological signals and force signals under different training task trajectories, and dynamically adjusts the training mode switching strategy, thereby ensuring the accuracy of judging the patient's motion state under different training tasks and the rationality of training mode switching.

[0029] 6. The four training modes in the present invention are integrated into a control architecture, using a unified basic control loop and a unified input and output channel to achieve collaborative optimization between modes, so that the overall control model has dynamic adjustment capabilities and can effectively reduce the computational complexity and power consumption of the robot. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 This is the overall architecture diagram of the multi-motion mode control system of the rehabilitation training exoskeleton robot of the present invention; Figure 2 4 is a schematic diagram of an adaptive hierarchical mode switching method based on multiple motion modes in an embodiment of the present invention. DETAILED DESCRIPTION

[0031] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be further described below.

[0032] Aiming at the rehabilitation training needs of bedridden elderly people, this embodiment proposes a multi-motion mode control system for a rehabilitation training exoskeleton robot, such as Figure 1 and Figure 2 As shown, the system includes an on-bed extremity exoskeleton rehabilitation robot body, a neuromuscular activation model, a motion state monitoring module and a training module.

[0033] Among them, the in-bed lower limb exoskeleton rehabilitation robot body obtains the human-machine interaction torque of the patient's trained lower limbs through a pressure sensor, and obtains the surface electromyography signal of the trained lower limbs through an electromyography sensor; the neuromuscular activation model is used to convert the surface electromyography signal into muscle activation degree; in this embodiment, the neuromuscular activation model includes a neural activation function and a muscle activation function, specifically: The neural activation function is: ; The muscle activation function is: ; in, Indicates the current time The neural activation function is is the electromyographic signal, are the neural activation coefficients, For time delay, is the sampling time window period, Indicates the current time The muscle activation function, is the muscle activation coefficient.

[0034] The human-computer interaction torque, surface electromyographic signal characteristic value RMS and muscle activation are input into the motion state monitoring module. The motion state monitoring module introduces an adaptive training mode switching method based on fuzzy rule reasoning. The fuzzy adaptive switching signal regulator is used to generate a training mode switching signal from the surface electromyographic signal characteristic value, muscle activation and human-computer interaction torque to complete the adaptive hierarchical switching of the training mode. The training module is used to receive the training mode switching signal and generate the corresponding training mode. According to the four stages of lower limb rehabilitation of bedridden patients, namely the acute stage, the initial recovery stage, the functional recovery stage and the strengthening stage, the control strategy of the training mode is designed as a passive training mode, a power training mode, an active training mode and a resistance training mode. At the same time, the hierarchical switching mechanism of the training mode can only be gradually increased or decreased, that is, the training mode can only be switched step by step according to "passive training mode → power training mode → active training mode → resistance training mode" or "resistance training mode → active training mode → power training mode → passive training mode" step by step to prevent mode "jumping" and avoid injuries caused by muscle failure. By integrating passive training, assisted training, active training and resistance training, a training system covering the four stages of lower limb rehabilitation (acute phase, initial recovery phase, functional recovery phase and strengthening phase) is formed. By constructing a multimodal fusion framework, full-cycle rehabilitation coverage is achieved, allowing patients to smoothly transition between different modes and ensuring that the training effect is more scientific and reasonable.

[0035] At the same time, in this embodiment, the passive training mode, the assisted training mode, the active training mode, and the resistance training mode all use a unified basic control loop and a unified input and output channel to achieve collaborative optimization between modes. Among them, the passive training, assisted training, and resistance training modes all use a trajectory tracking control method based on sliding mode control. By combining multi-channel data such as lower limb surface electromyography (sEMG), human-computer interaction force, and motion trajectory, the training mode can be optimized in real time according to the patient's status, so that the overall control model has dynamic adjustment capabilities, and can effectively reduce the robot's computational complexity and power consumption.

[0036] In this embodiment, Figure 1 As shown, the robot training control methods of the four training modes, namely, passive training mode, power training mode, active training mode, and resistance training mode, are as follows: (1) The robot training control method in passive training mode is: 1.1: Subtract the expected trajectory of the training task from the actual trajectory fed back by the robot encoder to get the trajectory error, where the expected trajectory includes the expected trajectory angle , expected trajectory angular velocity and the desired trajectory angular acceleration ; The trajectory error calculation formula is: ; in, is the trajectory error, is the expected trajectory angle of the training task, The actual angle of the motor of the lower limb exoskeleton robot joint in bed; 1.2: Input the trajectory error into the sliding mode controller to obtain the expected auxiliary torque of the robot's joints. The specific calculation formula is as follows: ; in, denote the trajectory error and trajectory error change rate respectively, represents the sliding surface function, represents the modeling error of the system, represents the positive definite inertia matrix of the system, represents the centrifugal and Coriolis force matrices of the system, represents the gravity matrix of the system, Respectively represent the joint motor angle, angular velocity, and angular acceleration, is the controller parameter, Represents the desired assist torque of the joints for robot motion.

[0037] 1.3: Passive training control of the in-bed lower limb exoskeleton rehabilitation robot is performed according to the expected joint assisting torque. The specific control method is as follows: 1) If the expected joint assisting torque obtained is positive, the movement trend of the in-bed lower limb exoskeleton is in the same direction as the gravity; 2) If the expected joint assisting torque obtained is negative, the movement trend of the in-bed lower limb exoskeleton is opposite to the gravity.

[0038] (2) The robot training control method of the power-assisted training mode is: 2.1: Input the expected trajectory of the training task into the robot inverse dynamics model to obtain the expected joint torque of the in-bed lower limb exoskeleton rehabilitation robot , the calculation formula is: + ; in, is the desired joint torque, represent the kinetic energy and potential energy of the robot joints under the Lagrangian method, are the angles and angular velocities in the expected trajectory for the training task.

[0039] 2.2: Obtaining human-machine interaction torque through the robot's pressure sensor ; 2.3: The desired joint torque Human-computer interaction torque Input into the sports performance assessment module to calculate and generate auxiliary factors , the specific calculation method is as follows: 2.31: Calculate the patient's state fluctuation rate through human-computer interaction torque , the calculation formula is as follows: ; in, represents the patient's state fluctuation rate, is the window period, For the current moment, It is the human-computer interaction torque; 2.32: Calculate the patient torque deviation rate through human-computer interaction torque , the calculation formula is as follows: ; in, represents the patient torque deviation rate, is the window period, For the current moment, For human-computer interaction torque, is the desired moment of the joint; 2.33: Through patient status fluctuation rate and moment deviation rate , calculate the cofactor , the formula is as follows; ; in, represents the auxiliary factor, is the weighting factor, is the patient status fluctuation rate, is the patient torque deviation rate.

[0040] 2.4: Subtract the expected trajectory of the training task from the actual trajectory fed back by the robot encoder to get the trajectory error , the trajectory error is input into the sliding mode controller to generate the desired auxiliary torque of the joint of the robot motion ; 2.5: Get the desired joint torque , joint expected auxiliary torque and cofactors Input into the power training controller to obtain the joint auxiliary torque of the robot movement ; The specific calculation formula is as follows: ; in, represents the joint auxiliary torque, is the desired auxiliary torque of the joint, is the desired joint torque, As auxiliary factor; 2.6: According to the joint auxiliary torque Provide assisted training control for the in-bed lower limb exoskeleton rehabilitation robot.

[0041] (3) The robot training control method in active training mode is: 3.1: The actual trajectory fed back by the robot encoder Input into the active training controller to obtain the joint auxiliary torque , the specific calculation formula is as follows: ; in, is the joint assist torque, are the inertia, damping and stiffness parameter matrices of the robot, They respectively represent the actual angle, actual angular velocity, and actual angular acceleration fed back by the robot encoder.

[0042] 3.2: Active training control of the in-bed lower limb exoskeleton rehabilitation robot based on joint assist torque; The robot training control method for the resistance training mode is: 4.1: Obtaining human-machine interaction torque through robot pressure sensor ; 4.2: Human-computer interaction torque Input into the virtual impedance model to obtain the trajectory correction value for adjusting the desired trajectory of the robot ; Among them, the virtual impedance model is: ; in, They represent joint mass, virtual damping coefficient, and virtual stiffness coefficient respectively. Represents human-computer interaction, They represent trajectory correction, joint motor angular velocity, and joint motor angular acceleration respectively; 4.3: Subtract the expected trajectory of the training task from the trajectory correction to obtain the adjusted expected trajectory, including the angle of the expected trajectory after the virtual impedance model is adjusted in the resistance training mode. and the angular velocity of the desired trajectory after adjustment ; 4.4: Subtract the adjusted desired trajectory from the actual trajectory fed back by the robot encoder to obtain the trajectory error , the formula is: ;

[0043] in, is the trajectory error, is the expected trajectory angle after adjustment, The actual angles of the motors in the lower limb exoskeleton robot joints.

[0044] 4.5: Input the obtained trajectory error into the sliding mode controller to obtain the expected auxiliary torque of the joint of the robot motion , so as to achieve the effect of resistance; 4.6: Perform resistance training control on the in-bed lower limb exoskeleton rehabilitation robot according to the desired auxiliary torque of the joints.

[0045] In this embodiment, the fuzzy adaptive switching signal regulator includes input variables, output variables and fuzzy rules. By establishing corresponding fuzzy rules, the patient's movement state is evaluated according to the patient's physiological and mechanical signals, and the fuzzy rule reasoning output is switched to the most appropriate training mode under the corresponding movement state.

[0046] Among them, the input variables include three key physiological and mechanical parameters: surface electromyographic signal characteristic value RMS, muscle activation degree and human-computer interaction torque; the output variables include training mode switching signal; In the fuzzy rules, the input variable fuzzy subsets include low L, medium M, and high H; the output variable fuzzy subsets include passive, power, active, and impedance; The input variable-output variable mapping relationship is shown in Table 1: Table 1 RMS value Muscle activation Human-computer interaction torque Training Mode L L L passive L L M Assist L L H Assist L M L passive L M M Assist L M H initiative L H L passive L H M Assist L H H initiative M L L passive M L M Assist M L H initiative M M L passive M M M Assist M M H initiative M H L passive M H M Assist M H H initiative H L L passive H L M Assist H L H initiative H M L passive H M M Assist H M H initiative H H L passive H H M Assist H H H Impedance The hierarchical switching rules of the training mode are shown in Table 2: Table 2 Current training mode Output training mode Switching rules Passive Mode Passive training Keep training passive Passive Mode Assist training Passive mode → Assisted training Assist mode Passive training Power training → Passive training Assist mode Assist training Maintain support training Assist mode Active Training Assisted training → Active training Active Mode Assist training Active Training → Assisted Training Active Mode Active Training Keep active training Active Mode Resistance training Active Training → Resistance Training Impedance Mode Active Training Resistance training → Active training Impedance Mode Resistance training Keep up resistance training According to the characteristics of physiological signals and force signals under different training task trajectories, the corresponding physiological parameter trapezoidal membership functions are set respectively, and the training mode switching strategy is dynamically adjusted. The formula is as follows: ; in, The input variables include three key physiological and mechanical parameters: surface electromyographic signal characteristic value RMS, muscle activation degree and human-computer interaction torque. The output variables include a training mode switching signal.

[0047] Based on the above-mentioned lower limb rehabilitation training exoskeleton robot multi-motion mode control system, a lower limb rehabilitation training exoskeleton robot multi-motion mode control method is performed, and the method comprises the following steps: S1: Surface electromyographic signals are obtained through the electromyographic sensors of the lower limb exoskeleton robot under the bed; S2: Extract the RMS of surface electromyography signal feature value; S3: Input the surface electromyographic signal into the neuromuscular activation model to generate muscle activation degree; S4: Use the robot’s pressure sensor to obtain the human-machine interaction torque; S5: input the surface electromyography signal characteristic value RMS, muscle activation degree and human-computer interaction torque into the motion state monitoring module, and use the fuzzy adaptive switching signal regulator to obtain the training mode switching signal; S6: The training modes include passive training mode, assisted training mode, active training mode and resistance training mode; according to the training mode switching signal, switch to the corresponding training mode, so as to perform training control on the in-bed lower limb exoskeleton rehabilitation robot under the corresponding training mode.

[0048] The above is only a preferred embodiment of the present invention and does not limit the present invention in any way. Any technician in the relevant technical field, without departing from the scope of the technical solution of the present invention, makes any form of equivalent replacement or modification to the technical solution and technical content disclosed in the present invention, which does not depart from the content of the technical solution of the present invention and still falls within the protection scope of the present invention.

Claims

1. A multi-motion mode control system for a lower limb rehabilitation training exoskeleton robot, characterized in that: It includes the on-bed lower limb exoskeleton rehabilitation robot body, neuromuscular activation model, motion state monitoring module and training module: The robot body obtains the human-machine interaction torque of the patient's trained lower limbs through a pressure sensor, and obtains the surface electromyographic signal of the trained lower limbs through an electromyographic sensor; The neuromuscular activation model is used to convert surface electromyographic signals into muscle activation degrees; The motion state monitoring module introduces an adaptive training mode switching method based on fuzzy rule reasoning, and uses a fuzzy adaptive switching signal regulator to generate a training mode switching signal from the surface electromyography signal characteristic value, muscle activation degree and human-computer interaction torque; The training module is used to receive a training mode switching signal and generate a corresponding training mode; The training modes include passive training mode, power training mode, active training mode and resistance training mode; The hierarchical switching mechanism of the training mode is to increase or decrease step by step.

2. The multi-motion mode control system for lower limb rehabilitation training exoskeleton robot according to claim 1, characterized in that: The robot training control method in the passive training mode is: 1.1: Subtract the expected trajectory of the training task from the actual trajectory fed back by the robot encoder to obtain the trajectory error; 1.2: Input the trajectory error into the sliding mode controller to obtain the desired auxiliary torque of the joints of the robot motion; 1.3: The in-bed lower limb exoskeleton rehabilitation robot is passively trained and controlled according to the expected joint assisting torque. The specific control method is as follows: 1) If the expected joint assisting torque is positive, the movement trend of the in-bed lower limb exoskeleton is in the same direction as the gravity; 2) If the expected joint assisting torque is negative, the movement trend of the in-bed lower limb exoskeleton is opposite to the gravity.

3. The multi-motion mode control system for lower limb rehabilitation training exoskeleton robot according to claim 1, characterized in that: The robot training control method of the power-assisted training mode is: 2.1: Input the expected trajectory of the training task into the robot inverse dynamics model to obtain the expected joint torque of the in-bed lower limb exoskeleton rehabilitation robot; 2.2: Obtain the human-machine interaction torque through the robot's pressure sensor; 2.3: Input the joint desired torque and human-machine interaction torque into the motion ability assessment module to calculate and generate auxiliary factors; 2.4: Subtract the expected trajectory of the training task from the actual trajectory fed back by the robot encoder to obtain the trajectory error, which is input into the sliding mode controller to generate the expected auxiliary torque of the joints of the robot motion; 2.5: Input the acquired joint desired torque, joint desired auxiliary torque and auxiliary factor into the power training controller to obtain the joint auxiliary torque of the robot motion; 2.6: Perform assisted training control on the in-bed lower limb exoskeleton rehabilitation robot based on the joint assist torque.

4. The multi-motion mode control system for lower limb rehabilitation training exoskeleton robot according to claim 3, characterized in that: The method for the sports ability assessment module to calculate and obtain auxiliary factors through joint expected torque and human-computer interaction torque includes the following steps: 2.31: Calculate the patient's state fluctuation rate through the human-computer interaction torque. The calculation formula is as follows: ； 2.32: Calculate the patient torque deviation rate through the human-computer interaction torque. The calculation formula is as follows: ； 2.33: The auxiliary factor is calculated by the following formula using the patient state fluctuation rate and moment deviation rate; ； in, represents the patient's state fluctuation rate, represents the patient torque deviation rate, is the window period, For the current moment, For human-computer interaction torque, is the desired moment of the joint; represents the auxiliary factor, is the weighting factor; In step 2.5, the joint desired torque, joint desired auxiliary torque and auxiliary factor are input into the power training controller to obtain the joint auxiliary torque of the robot motion. The calculation formula is: ； in, represents the joint auxiliary torque, is the desired auxiliary torque of the joint, is the desired joint torque, As auxiliary factor.

5. The multi-motion mode control system for lower limb rehabilitation training exoskeleton robot according to claim 1, characterized in that: The robot training control method of the active training mode is: 3.1: Input the actual trajectory fed back by the robot encoder into the active training controller to obtain the joint auxiliary torque; 3.2: Active training control of the in-bed lower limb exoskeleton rehabilitation robot based on joint assist torque.

6. The multi-motion mode control system for lower limb rehabilitation training exoskeleton robot according to claim 1, characterized in that: The robot training control method of the resistance training mode is: 4.1: Obtaining human-machine interaction torque through robot pressure sensor; 4.2: Input the human-machine interaction torque into the virtual impedance model to obtain the trajectory correction for adjusting the desired trajectory of the robot; 4.3: Subtract the expected trajectory of the training task from the trajectory correction to obtain the adjusted expected trajectory; 4.4: Subtract the adjusted desired trajectory from the actual trajectory fed back by the robot encoder to obtain the trajectory error; 4.5: Input the obtained trajectory error into the sliding mode controller to obtain the expected auxiliary torque of the joints of the robot motion; 4.6: Perform resistance training control on the in-bed lower limb exoskeleton rehabilitation robot according to the desired auxiliary torque of the joints.

7. The multi-motion mode control system for lower limb rehabilitation training exoskeleton robot according to claim 6, characterized in that: The virtual impedance model is: ； in, They represent joint mass, virtual damping coefficient, and virtual stiffness coefficient respectively. Represents human-computer interaction, They represent trajectory correction, joint motor angular velocity, and joint motor angular acceleration respectively.

8. The multi-motion mode control system for lower limb rehabilitation training exoskeleton robot according to claim 1, characterized in that: The neuromuscular activation model includes a neural activation function and a muscle activation function; The neural activation function is: ； The muscle activation function is: ； in, Indicates the current time The neural activation function is is the electromyographic signal, are the neural activation coefficients, For time delay, is the sampling time window period; Indicates the current time The muscle activation function, is the muscle activation coefficient.

9. The multi-motion mode control system for lower limb rehabilitation training exoskeleton robot according to claim 1, characterized in that: The fuzzy adaptive switching signal regulator includes input variables, output variables and fuzzy rules; The input variables include surface electromyographic signal characteristic value RMS, muscle activation degree, and human-computer interaction torque; The output variable includes a training mode switching signal; In the fuzzy rules, the input variable fuzzy subsets include low L, medium M, and high H; The fuzzy subsets of output variables include passive, assist, active, and impedance; The passive training mode, the power training mode, the active training mode, and the resistance training mode are integrated into a control architecture; The passive training mode, power training mode, active training mode, and resistance training mode correspond to the acute phase, early recovery phase, functional recovery phase, and strengthening phase of lower limb rehabilitation of patients, respectively; The training mode switching adopts a hierarchical switching mechanism and can only be switched by increasing or decreasing the levels step by step.

10. A method for controlling multiple motion modes of a lower limb rehabilitation training exoskeleton robot, based on the multiple motion mode control system of a lower limb rehabilitation training exoskeleton robot according to any one of claims 1 to 9, characterized in that: The steps include: S1: Surface electromyographic signals are obtained through the electromyographic sensors of the lower limb exoskeleton robot under the bed; S2: Extract the RMS of surface electromyography signal feature value; S3: inputting the surface electromyographic signal into a neuromuscular activation model to generate a muscle activation degree; S4: Use the robot’s pressure sensor to obtain the human-machine interaction torque; S5: inputting the surface electromyography signal characteristic value RMS, muscle activation degree and human-computer interaction torque into a fuzzy adaptive switching signal regulator to obtain a training mode switching signal; S6: The training modes include passive training mode, power training mode, active training mode and resistance training mode; According to the training mode switching signal, switch to the corresponding training mode, so as to perform training control on the lower limb exoskeleton rehabilitation robot in the bed under the corresponding training mode.

Images