685 results about "Rehabilitation robot" patented technology

The invention discloses a gait rehabilitation body-equipped robot time series data modeling optimization method based on frequency domain learning, and the method comprises the steps: obtaining multi-modal sensor data, carrying out the preprocessing, calculating gait parameters, and generating a patient state vector; enhanced features are obtained by applying multi-scale convolution and frequency domain processing; performing block processing and position coding to obtain a coding enhancement feature, and performing noise suppression processing to obtain a residual fusion feature; a final fusion feature is obtained by applying a self-attention mechanism and frequency domain alignment fusion processing; performing frequency domain decomposition and multi-scale memory filtering to obtain space-time fusion features; evaluating the rehabilitation progress, generating a gait correction vector and a correction time sequence curve, and outputting a smooth safety control sequence. According to the method, the influence of high-frequency noise is reduced, so that the robustness and accuracy of the model are improved; stable characterization of key gait features is realized, and the precision and consistency of the model in processing complex time sequence data are enhanced; and the control performance and the simulation precision of the gait rehabilitation robot are improved.

The invention discloses an upper limb exoskeleton rehabilitation robot control system and method, and relates to the technical field of rehabilitation robot upper limb control, and the method comprises the steps: collecting multi-modal data such as multi-channel myoelectricity, joint angles, angular velocity and interactive torque; constructing a sliding window feature vector, predicting a next joint angle increment through TCN and Bi-LSTM deep networks, and outputting intention confidence; and based on the confidence coefficient and the muscle fatigue factor, dynamically adjusting an impedance parameter, and combining an impedance control model to generate a power-assisted torque in real time so as to realize power-assisted torque output. By introducing an impedance adjustment mechanism of multi-modal perception, deep time sequence learning and myoelectricity driving, the bottleneck of a traditional upper limb rehabilitation robot in the aspects of intention prediction, parameter adjustment and training individuation is broken through, and the accuracy, safety and active participation degree in the rehabilitation training process are remarkably improved; wide clinical application prospects and industrial transformation values are realized.

The invention discloses a lower limb exoskeleton rehabilitation robot joint torque prediction method and device based on an electromyographic signal, and the method comprises the steps: firstly collecting the electromyographic, joint angle, angular velocity and joint torque data of a user, and carrying out the modeling of four muscles related to a knee joint through a neuromusculoskeletal model; and identifying physiological parameters used in the model by using a sparrow optimization algorithm, and outputting an optimal solution to obtain an estimated torque. Then, the real torque and the estimated torque are endowed with the weights of # imgabs0 # respectively, and a fusion torque is obtained; and training a neural network model by using the collected data and the fusion moment, and meanwhile, enabling the neuromusculoskeletal model to participate in the training process of the neural network model by adopting a method of bringing the neuromusculoskeletal model into a loss function, so that the constraint of the model is increased. And finally, storing the trained model for subsequent calling. The method has the advantages of high precision and strong generalization, and model training can converge more quickly under the condition of less data volume.

The invention belongs to the technical field of digital twinning, and discloses a digital twinning rehabilitation robot state monitoring and control system based on the Internet of Things. Multi-dimensional state data such as joint angles, torque output, electromyographic signals and environment temperature of the rehabilitation robot are obtained in real time through an Internet of Things sensor network, a digital twin model is constructed, and hierarchical updating is carried out based on a self-adaptive updating frequency adjustment mechanism. And the system dynamically adjusts the updating periods of the high-frequency updating layer and the low-frequency updating layer according to the change rate of the state parameters and the deviation trend of the prediction response vector, so that accurate synchronization of the digital twin model and the physical system is realized. The state prediction module predicts a future response behavior, the error compensation module generates an error compensation vector adjustment model update and control instruction, and the control module generates an optimization control instruction meeting multiple constraints. The control precision and safety of the rehabilitation robot are remarkably improved, personalized precise rehabilitation is supported, and rapid changes and irregular motions can be effectively coped with.

The invention discloses a self-adaptive position constraint exoskeleton rehabilitation robot on-demand auxiliary control system and method, and relates to the technical field of rehabilitation robot control. Comprising the following steps: collecting historical motion time sequence data of a patient, and recording a corresponding motion intention of the patient; establishing a deep learning network model, taking the historical motion time sequence data as an input sample, taking the corresponding motion intention as a label, and training the model; collecting real-time motion time sequence data of the patient, inputting the real-time motion time sequence data into the trained model to obtain real-time motion intention data of the patient, and calculating and generating target state parameters based on the real-time motion intention data; establishing a state parameter adaptive adjustment model, collecting a current state parameter, and analyzing and generating control output of the motion joint based on the generated target state parameter; and collecting real-time heart rate data of the patient, correcting the data in the control output, and sending a control instruction to the exoskeleton rehabilitation robot based on the corrected data.

The invention discloses a control method, system and equipment of a remote rehabilitation robot, a medium and a product, and belongs to the technical field of robots. The method comprises the steps of obtaining multi-modal data of a patient end, obtaining a time delay prediction value through prediction of a long short-term memory network model, dynamically adjusting admittance parameters of an admittance model according to the multi-modal data and the time delay prediction value, and finally controlling the remote rehabilitation robot according to the dynamically adjusted admittance parameters. According to the method, the admittance parameters of the admittance model are dynamically adjusted in real time based on the time delay predicted value predicted by the long-short-term memory network model and the electromyographic signals of the patient in the multi-modal number, dual adaptation of the network state and the physiological features of the patient is achieved, the admittance parameters are dynamically adjusted through the electromyographic signals, and the change requirement of the complete rehabilitation cycle of the patient is met; admittance parameters are dynamically adjusted through the time delay prediction value reflecting the network time delay state, and the time delay disturbance requirement in the remote communication environment is met.

The invention provides a motion capture method and equipment of a rehabilitation robot. An IMU sensor in a passive glove can obtain sensor information such as a gyroscope, an accelerometer and a magnetometer in real time. The IMU sensors are placed on the palm and the fingertips of the five fingers of the passive glove, sensor data of all positions can be collected in real time, and the data are transmitted to the MCU to be processed. The MCU estimates the spatial attitude of each sensor through an algorithm and calculates the actual bending angle of each finger. According to the method, the posture data of each finger can be compared with the target rehabilitation action, feedback control is formed, the bending angle of the finger can be accurately captured, errors caused by sensor position deviation are effectively avoided, and higher posture estimation precision is achieved. The miniature IMU sensor is used, a flexible potentiometer or an external optical device is not needed, the environment interference resistance is high, use in a mobile scene is facilitated, and the requirements of the rehabilitation robot for comfort, portability and low cost are met.

The invention provides an active-disturbance-rejection sliding mode compound control system of a wrist rehabilitation robot, and relates to the technical field of rehabilitation robot control, the wrist rehabilitation robot adopts a folding air bag as a driving element, and the control system comprises a tracking differentiator used for tracking and differentiating an input target angle signal; the extended state observer is used for estimating the system state and the total disturbance in real time; the sliding mode controller is used for designing a nonlinear control rate; the dead-zone nonlinear inverse function control module is used for compensating the nonlinear control rate of the sliding mode controller and eliminating dead-zone nonlinearity of the high-speed switch valve; and the self-adaptive combination valve comprises two high-speed switch valves, a control unit and a pressure sensor and is used for executing the control instruction and monitoring the pressure in the folding air bag. The problems of compressibility of air in the air bag, dead zone nonlinearity of a gas control element, nonlinearity of gas flow and the like can be solved, and high-precision trajectory tracking control over the wrist rehabilitation robot is achieved.

The invention discloses a brain-computer interface three-dimensional space motion rehabilitation training system and method. The system comprises a naked eye 3D stimulation display module, a space motion training scene module, an electroencephalogram signal acquisition and analysis module and a rehabilitation robot module. A stimulation interface is presented based on a naked eye 3D technology, a user is supported to operate a target indication point on a naked eye 3D display screen to perform three-dimensional motion through a steady-state visual evoked potential brain control method, and then a rehabilitation robot in an actual environment follows the indication point to drive an affected limb to move in a three-dimensional space, so that virtual-real combined training is achieved; and the immersive three-dimensional scene is provided for the user, three-dimensional space motion rehabilitation training is completed comfortably and efficiently, and the effects of being good in interaction experience, low in dizziness and more stable are achieved.

The invention provides a multifunctional rehabilitation robot real-time monitoring and intelligent regulation and control system for ICU-AW patients. Comprising a trial operation data transmission self-inspection module, an initial data acquisition module, a rehabilitation data real-time monitoring module and a rehabilitation training parameter real-time regulation and control module. According to the method, the basic physiological data of the user is collected and analyzed to obtain the personalized rehabilitation characteristic coefficient, so that the training parameters of the robot are initialized, the suitability of the training parameters and the requirements of the patient is ensured, the rehabilitation efficiency is improved, and training injuries are prevented. In addition, the system also performs pilot run self-inspection, so that the data transmission quality is ensured, and the analysis efficiency and accuracy are improved. In addition, the system monitors physiological and training data in rehabilitation training in real time, provides immediate feedback, dynamically adjusts training intensity and rhythm, optimizes the training effect and reduces the training risk.

The invention relates to the technical field of rehabilitation robot control, and particularly discloses a gait prediction compensation admittance control method and system for lower limb rehabilitation exoskeleton, and the method comprises the steps: inputting a multi-dimensional motion vector into a time sequence prediction model, and outputting a prediction result of a gait phase at a future moment; if the maximum probability output by the time sequence prediction model is lower than a preset threshold value, automatically switching to a pre-constructed rule criterion to carry out gait phase recognition; an admittance control parameter matched with the gait phase prediction result is called from an admittance parameter mapping table, and a main control torque is calculated based on the called admittance control parameter; calculating a compensation torque based on the deviation between the actual human-computer interaction force and the expected interaction force; and the main control torque and the compensation torque are superposed to serve as a final torque driving instruction of the exoskeleton motor. Real-time gait prediction, admittance parameter self-adaptive adjustment and interaction force error dynamic compensation can be fused, and high-flexibility and high-safety man-machine collaborative rehabilitation training is achieved.

The invention discloses a control method of a lower limb rehabilitation robot system based on personalized gait generation and an active compliance control strategy. The control method comprises the steps that S1, lower limb gait data of a plurality of volunteers are collected and preprocessed; s2, constructing a personalized gait generation model, and generating a personalized gait mode according to the preprocessed lower limb gait data; s3, based on a personalized gait mode, establishing an inner ring-position controller based on active disturbance compensation control (ADRC) to perform expected trajectory tracking; an outer ring-trajectory planner is established, and a preset trajectory is adjusted in real time according to the man-machine interaction torque and the predicted system state; and S4, realizing active gait rehabilitation training through a personalized gait generation model and a double-loop active compliance control strategy. The gait track designed and generated by the method is highly matched with a normal gait mode, the track loss is small, a safe and personalized rehabilitation training scheme is provided for a patient, and the applicability and effect of gait training are remarkably improved.

The invention discloses a rehabilitation training anti-loosening method and device based on AAN challenge degree adjustment. The method comprises the steps that the challenge degree is determined according to the main power of a person and the driving force of a front man-machine hybrid system; a virtual force field is constructed, so that the virtual auxiliary force is stably controlled within a certain range, and the virtual auxiliary force is determined; determining the non-model robust auxiliary force of the robot through a time delay estimation method; determining a current man-machine hybrid system driven force according to the human active force, the challenge degree, the virtual auxiliary force and the robot non-model robust auxiliary force; and based on the kinetic model of the rope-driven rehabilitation robot, determining the action of the rehabilitation robot according to the current driving force applied to the man-machine hybrid system. Machine assistance is reasonably provided according to the motion control ability of the patient, training looseness is prevented, active participation is encouraged, and the rehabilitation training effect is improved.

The invention provides a rehabilitation training plan processing method, a brain-computer interface device and a storage medium, and relates to the technical field of brain-computer interfaces. According to the method, the motor imagery ability evaluation result of the target object is obtained mainly based on the electroencephalogram signal of the target object, that is, the motor imagery ability of different patients can be quantitatively evaluated according to the scheme; then, aiming at the two indexes of the motor imagery ability evaluation result and the dyskinesia level of the patient, a rehabilitation training plan is generated for the patient in a personalized manner, namely, the rehabilitation plan has the characteristics of pertinence, scientificity, reasonability and the like, so that the rehabilitation efficiency of the patient is greatly improved, and the rehabilitation time of the patient is saved; meanwhile, the use efficiency of the rehabilitation robot is improved, that is, the rehabilitation effect of the motor imagery therapy is better than that of a traditional rehabilitation method, and the problem that in the prior art, rehabilitation training is unreasonable in formulation is solved.

The invention provides a control method and system of a rehabilitation robot, a readable storage medium and a computer. The method comprises the steps that a motion control sketch is constructed according to motion data, collected in real time, of the rehabilitation robot and upper limbs of a patient; calculating displacement data of an end effector according to the motion control sketch and a kinematics model of the rehabilitation robot; according to the kinematics model of the equalization mechanism of the rehabilitation robot, the displacement data of the end effector and the kinematics model, the maximum motion space of the end effector is established; the effective working space of the rehabilitation robot is calculated through the end effector and the maximum movement space of the upper limb hemiplegic patient, and the rehabilitation robot is controlled through the effective working space. According to the method, the displacement data of the end effector is calculated by utilizing the kinematic model and the motion control sketch, and the mechanical model of the rehabilitation robot is established by utilizing the kinetic model and the displacement data, so that the rehabilitation robot is controlled.

The invention relates to a lower limb motion intention recognition method based on surface electromyogram (sEMG) fusion features. An sEMG feature fusion model and a genetic radial neural network model are included. The sEMG is widely applied to gait analysis and robot control, and due to the fact that the accuracy rate of intention recognition is low and development of an exoskeleton robot is limited, a genetic radial neural network algorithm based on sEMG multi-feature fusion is provided to achieve accurate recognition of eight lower limb actions. An original sEMG signal is collected through a gait experiment, and time domain, frequency domain and wavelet decomposition signal characteristic parameters of the original sEMG signal are extracted and fused. And using a principal component analysis method to carry out dimension reduction on fusion features, and inputting sEMG fusion feature vectors into a radial basis function neural network optimized by a genetic algorithm to carry out training and identification. According to the method provided by the invention, the sEMG fusion features are utilized to identify the human body lower limb actions, the accurate identification of the lower limb actions is realized in combination with a high-performance neural network algorithm, and support is provided for the application of sEMG signals to an intelligent rehabilitation robot.

The invention discloses a design method of a barrier function rehabilitation robot safety tracking control system, and relates to the technical field of medical robots. The method comprises the steps that on the basis of an existing double-joint single-arm robot mathematical model, a mathematical model with uncertain model parameters, uncertain external disturbance and uncertain external force borne by the tail end is established, and the established model is closer to a real model of a rehabilitation robot; the uncertainty in the model is classified into one class, and a disturbance observer is used for estimating and compensating the uncertainty; a logarithmic obstacle Lyapunov function is adopted to constrain the joint angle; and designing an adaptive trajectory tracking controller based on the disturbance observer and the obstacle Lyapunov function method, and tracking a set trajectory. The high-precision tracking of the target trajectory is ensured, the actual motion trajectory always moves within a certain range on the two sides of the target trajectory, and the secondary injury of the patient in the rehabilitation process is avoided.

The invention discloses a knee joint stroking and pressing manipulator of a rehabilitation robot, and relates to the technical field of rehabilitation manipulators, the knee joint stroking and pressing manipulator comprises a double-shaft mechanical arm, one end of the double-shaft mechanical arm is provided with an upper clamping structure, and the bottom of the upper clamping structure is provided with a lower clamping structure; the upper clamping structure comprises an upper supporting frame connected with the double-shaft mechanical arm, a sliding assembly installed on the upper supporting frame in a sliding mode, a massager installed at the bottom end of the sliding assembly, two upper arc-shaped air guide frames arranged on the two sides of the massager and a position control assembly arranged between the two upper arc-shaped air guide frames and the upper supporting frame. Through cooperation of the lower expansion piece, the upper expansion piece, the air supply set, the lower arc-shaped air guide frame and other structures, the requirement for fixing different positions of the legs of the patient can be met, the different requirements for leg rehabilitation of the patient are met, and the fixing process is convenient and rapid.

The invention discloses a high-compatibility self-calibration lower limb exoskeleton rehabilitation robot which comprises a first lower limb rehabilitation device, a second lower limb rehabilitation device and a hip joint adjusting device. The hip joint exoskeleton assembly comprises three active driving units, and the active driving units with the rotating surfaces corresponding to the horizontal plane are aligned with the rotating center of the hip joint through the remote motion center mechanism with the virtual axis. According to the high-compatibility self-calibration lower limb exoskeleton rehabilitation robot, rotation of a horizontal plane, a coronal plane and a sagittal plane can be completed at the position corresponding to the hip joint of a user, three degrees of freedom of forward flexion / backward extension, adduction / abduction and inward rotation / outward rotation of the hip joint of the human body are achieved respectively, and the exoskeleton robot is aligned to the rotation center of the hip joint of the human body; therefore, the rehabilitation effect of the high-compatibility self-calibration lower limb exoskeleton rehabilitation robot can be improved.

The invention relates to the technical field of rehabilitation training, in particular to a rehabilitation training method and system integrating a rehabilitation robot and a cloud platform. Comprising the following steps: constructing a standardized motion track data set; joint time sequence features are extracted, coordination deviation in the multi-joint coordination process is analyzed, time sequence coordination deviation distribution is determined, and when deviation exceeds a preset range, an abnormal deviation track and a compensation behavior mode are recognized; generating a personalized training protocol scheme in combination with the compensatory behavior pattern and patient personalized data, and obtaining an optimized motion guidance path through multi-joint collaborative path simulation and parameter correction; training feedback indexes are extracted based on the motion guidance path, the training effect is judged, rehabilitation robot control parameters are dynamically updated, and a continuously optimized function recovery path is formed. The problems that in existing rehabilitation training, multi-joint collaborative evaluation is insufficient, and a training scheme is difficult to dynamically adapt are solved, and intelligentization, individualization and continuous optimization of the rehabilitation training process are achieved.

An embodiment rehabilitation robot control apparatus includes a brainwave signal measuring device configured to measure a brainwave signal of a user, a preprocessing device configured to preprocess the measured brainwave signal, a classification device configured to classify a motor intention of the user based on the brainwave signal preprocessed by the preprocessing device, and a controller configured to reflect the motor intention of the user in real time to control an operation or a stop of a rehabilitation robot.

The invention addresses the problem of providing a pipe rehabilitation robot consisting of a device which can be advanced into a pipe and has a pivotable milling head (8; 38) with a forwardly-oriented milling tool (14) driven by a milling motor (9; 39) and with a camera group (10; 57) having a forwardly-oriented camera (11; 41 ), and has supply lines required for same, wherein said pipe rehabilitation robot is optimised, flexibly usable and multifunctional, can be moved through tight pipe elbows and the tool of which is able to be observed, said tool achieving a high service life, which is achieved in that the pipe rehabilitation robot has supply lines for compressed water, compressed air and electrical connection lines for the transmission of information and power and an external control panel (44), and at least one radially expandable bellows (4; 36) is arranged on the device, by means of which bellows, in the expanded state, the device can be clamped in a pipe, and that a telescopic advancement cylinder (7; 37) for the milling head (8; 38) is arranged between the bellows (4; 36) and milling head (8; 38).

The invention provides a rigid-flexible coupling wrist rehabilitation robot which comprises an arm supporting unit and a wrist supporting unit, the arm supporting unit is matched with the forearm of a patient, the wrist supporting unit is matched with the palm of the patient, and an upper / lower elastic rope support and a left / right elastic rope support are installed on a connecting plate of the forearm supporting unit. An elastic rope connecting anchor point at the front end of the upper elastic rope support and an elastic rope connecting anchor point at the front end of the lower elastic rope support are connected with corresponding anchor points of the wrist supporting unit through three elastic ropes to form an integral tensioning structure, and four driving flexible ropes penetrate through Bowden wire pipes installed in the elastic rope supports to be connected to the corresponding anchor points of the wrist supporting unit. And the flexion / extension and radial deflection / ulnar deflection of the wrist joint are realized by traction and driving of the flexible cable. The open type wearable structure is convenient for a patient to wear, and the problems that a rigid wrist rehabilitation robot is not matched with the instant movement center when the wrist of the patient moves and the joint torque of a flexible wrist rehabilitation robot is insufficient are solved, so that the treatment effect of rehabilitation training on the patient is improved.

The invention discloses a pathology adaptive prediction control method for a lower limb rehabilitation exoskeleton robot, and belongs to the technical field of rehabilitation robot control. The method comprises the steps that the joint angle, the angular speed and the man-machine interaction torque of the lower limb rehabilitation exoskeleton robot are collected in real time, and a kinetic equation of the lower limb rehabilitation exoskeleton robot is constructed; on the basis of the kinetic equation of the lower limb rehabilitation exoskeleton robot, parameter vectors representing the current pathological features of the patient are estimated online, so that a prediction model in the lower limb rehabilitation exoskeleton robot is updated, and the optimal auxiliary torque is solved. The intelligent brain of the exoskeleton robot can be deeply fused with the real-time state of the body of the patient, and rehabilitation training which is really personalized and self-adaptive and can effectively promote neural function recovery is provided for the patient.

The invention discloses a rehabilitation manipulator based on myoelectricity feedback and an electrical stimulation self-adaptive control method. The method comprises the following steps: S1, constructing a myoelectricity acquisition-rehabilitation manipulator-neuromuscular electrical stimulation combined rehabilitation system; s2, performing data preprocessing on the surface electromyogram signals; s3, performing loop iteration on the long and short term memory network parameters by using a frost ice optimization algorithm, and constructing a long and short term memory network model of the optimal parameters; s4, predicting the surface electromyogram signals by using the constructed long and short-term memory network model; s5, constructing a three-dimensional myoelectricity adaptive control system according to the prediction signal; s6, generating a fuzzy control rule based on the adaptive fuzzy control system; and S7, controlling the combined rehabilitation system based on the control strategy. According to the rehabilitation manipulator based on myoelectricity feedback and the electrical stimulation self-adaptive control method, secondary injury caused by non-timeliness of subjective parameter regulation and control in the existing rehabilitation process can be solved, and the rehabilitation efficiency and the rehabilitation effect can be improved.

The invention relates to the technical field of rehabilitation robots, in particular to a rigid-flexible coupling trunk rehabilitation robot and a control method thereof. The invention comprises: a flexible wearing garment; the upper-layer protector is mounted at the upper part of the flexible wearing clothes; the lower-layer protector is mounted at the lower part of the flexible wearing clothes; two lumbar vertebra branch mechanisms; the URS branch chain mechanism is used for adjusting the rehabilitation center to be consistent with the lumbar vertebra rotation center of the human body; the thoracic vertebra branch mechanism is arranged on the inner wall of the upper-layer protector; during wearing, the upper-layer protector is worn at the thoracic vertebra position of a human body, and the lower-layer protector corresponds to the hip joint of the human body. The thoracic vertebra branch mechanism is arranged on the upper-layer protector, the lumbar vertebra branch mechanism is arranged between the upper-layer protector and the lower-layer protector, and the thoracic vertebra branch mechanism and the lumbar vertebra branch mechanism act on the thoracic vertebra and the lumbar vertebra in a synergistic mode, so that partitioned targeted rehabilitation is achieved, namely independent stretching, forward flexion, backward stretching and lateral flexion rehabilitation training of the thoracic vertebra is achieved; therefore, the rehabilitation action of the human trunk is more targeted, the rehabilitation effect is good, and the applicability and the rehabilitation efficiency in the rehabilitation process are greatly improved.

The invention relates to the technical field of rehabilitation robot control, in particular to an active-disturbance-rejection backstepping control system of a wrist rehabilitation robot. The wrist rehabilitation robot adopts two pneumatic artificial muscle systems as driving elements, and the control system comprises a tracking differentiator used for tracking and differentiating an input target angle signal; the extended state observer is used for estimating the system state and the total disturbance of the system in real time; the nonlinear error feedback controller is used for forming an error feedback signal and generating a control torque; the backstepping method controller is used for calculating and distributing the output pressure of the pneumatic artificial muscle system; the PID controller is used for dynamically correcting the output pressure; and the self-adaptive combination valve is used for controlling the pressure in the pneumatic artificial muscle system. According to the method, the inherent problems of high nonlinearity, uncertainty and hysteresis effect of a pneumatic artificial muscle system are solved, and high-precision trajectory tracking control over wrist joint movement is achieved.

The invention discloses a driving device for a rope-driven hand rehabilitation robot and a control method. The device comprises a fixing frame, a large spool, a small spool, a planetary gear motor, a buckling driving rope, a stretching driving rope, a buckling driving module and a stretching driving module. The buckling driving module and the stretching driving module each comprise a plurality of optical shafts, a thumb sliding block, an index finger and middle finger sliding block and a finger rope. Movement of the thumb, the index finger and the middle finger is controlled through a single planetary gear motor, self-adaptive grabbing of the hand rehabilitation robot is achieved through a differential structure of a pulley block, and the cost and complexity of the system are reduced; a spring is used for controlling the distance between the thumb sliding block and the index finger and middle finger sliding block, so that the hand rehabilitation robot can return to the initial hand posture after completing self-adaptive grasping; and through the design of different radiuses of the large thread spool and the small thread spool, the kinematics difference during finger bending and stretching is compensated, and the use comfort and the control efficiency of the hand rehabilitation robot are improved.

The invention discloses an electroencephalogram feature recognition method for motion intention prediction in a lower limb alternate stepping process. The electroencephalogram feature recognition method comprises the following steps: acquiring an electroencephalogram signal of a subject before the subject executes an action under the requirement of a lower limb alternate stepping motion execution normal form; preprocessing the electroencephalogram signal to obtain an initial signal, performing label labeling on the initial signal according to an action type, and forming a data set by a label and the initial signal; constructing a neural network model, wherein the neural network model comprises a multi-scale grouping space-time convolution module, a self-attention mechanism module for updating external memory based on Top-k gating and a grouping causal time convolution module; and training the neural network model in a supervised manner by using the data set to obtain a prediction model for predicting the action type. According to the method provided by the invention, the movement intention of the patient can be recognized by the movement brain-computer interface before the lower limb alternate movement, the control of the rehabilitation robot on the specific lower limb movement by the brain intention is realized, and the method has an important value on the clinical application of a brain-controlled rehabilitation robot system.

The invention relates to the technical field of rehabilitation robot control, and discloses a lower limb exoskeleton rehabilitation robot control system, which comprises a data acquisition module for acquiring biological data of a patient in real time by using a sensor; acquiring design parameters of the lower limb exoskeleton rehabilitation robot in real time; executing a biological parameter establishment strategy according to the biological data, and establishing biological parameters of the human body; according to the design parameters and the biological parameters, a kinetic model establishment strategy is executed, and the control torque of the lower limb exoskeleton rehabilitation robot is obtained; a self-adaptive control signal design strategy is executed, and the control torque of the lower limb exoskeleton rehabilitation robot is updated; the exoskeleton posture adjustment control module is used for executing a multi-target optimization control strategy and optimizing a plurality of adjustment targets; the stability evaluation strategy is executed, the stability of the lower limb exoskeleton rehabilitation robot control system is evaluated, an efficient and accurate control scheme is provided for the exoskeleton rehabilitation robot, rehabilitation of a patient can be effectively promoted, and safety of the patient can be effectively guaranteed.