CN113730190A - Upper limb rehabilitation robot system with three-dimensional space motion - Google Patents
Upper limb rehabilitation robot system with three-dimensional space motion Download PDFInfo
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61H—PHYSICAL THERAPY APPARATUS, e.g. DEVICES FOR LOCATING OR STIMULATING REFLEX POINTS IN THE BODY; ARTIFICIAL RESPIRATION; MASSAGE; BATHING DEVICES FOR SPECIAL THERAPEUTIC OR HYGIENIC PURPOSES OR SPECIFIC PARTS OF THE BODY
- A61H1/00—Apparatus for passive exercising; Vibrating apparatus; Chiropractic devices, e.g. body impacting devices, external devices for briefly extending or aligning unbroken bones
- A61H1/02—Stretching or bending or torsioning apparatus for exercising
- A61H1/0274—Stretching or bending or torsioning apparatus for exercising for the upper limbs
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- A—HUMAN NECESSITIES
- A63—SPORTS; GAMES; AMUSEMENTS
- A63B—APPARATUS FOR PHYSICAL TRAINING, GYMNASTICS, SWIMMING, CLIMBING, OR FENCING; BALL GAMES; TRAINING EQUIPMENT
- A63B23/00—Exercising apparatus specially adapted for particular parts of the body
- A63B23/035—Exercising apparatus specially adapted for particular parts of the body for limbs, i.e. upper or lower limbs, e.g. simultaneously
- A63B23/12—Exercising apparatus specially adapted for particular parts of the body for limbs, i.e. upper or lower limbs, e.g. simultaneously for upper limbs or related muscles, e.g. chest, upper back or shoulder muscles
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- A—HUMAN NECESSITIES
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- A—HUMAN NECESSITIES
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- A61H2201/00—Characteristics of apparatus not provided for in the preceding codes
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- A61H2201/1207—Driving means with electric or magnetic drive
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61H—PHYSICAL THERAPY APPARATUS, e.g. DEVICES FOR LOCATING OR STIMULATING REFLEX POINTS IN THE BODY; ARTIFICIAL RESPIRATION; MASSAGE; BATHING DEVICES FOR SPECIAL THERAPEUTIC OR HYGIENIC PURPOSES OR SPECIFIC PARTS OF THE BODY
- A61H2201/00—Characteristics of apparatus not provided for in the preceding codes
- A61H2201/16—Physical interface with patient
- A61H2201/1657—Movement of interface, i.e. force application means
- A61H2201/1659—Free spatial automatic movement of interface within a working area, e.g. Robot
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61H—PHYSICAL THERAPY APPARATUS, e.g. DEVICES FOR LOCATING OR STIMULATING REFLEX POINTS IN THE BODY; ARTIFICIAL RESPIRATION; MASSAGE; BATHING DEVICES FOR SPECIAL THERAPEUTIC OR HYGIENIC PURPOSES OR SPECIFIC PARTS OF THE BODY
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61H—PHYSICAL THERAPY APPARATUS, e.g. DEVICES FOR LOCATING OR STIMULATING REFLEX POINTS IN THE BODY; ARTIFICIAL RESPIRATION; MASSAGE; BATHING DEVICES FOR SPECIAL THERAPEUTIC OR HYGIENIC PURPOSES OR SPECIFIC PARTS OF THE BODY
- A61H2230/00—Measuring physical parameters of the user
- A61H2230/08—Other bio-electrical signals
- A61H2230/085—Other bio-electrical signals used as a control parameter for the apparatus
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Abstract
The invention discloses an upper limb rehabilitation robot system with three-dimensional space motion, which relates to the field of rehabilitation robots and comprises a software module, a myoelectricity module, a control module and an interaction module, wherein the software module runs on an upper computer and is configured to realize user information interaction and real-time motion parameter simulation; the myoelectric module is configured to collect and process surface myoelectric signals, extract features and establish a correlation model with action intentions; the control module receives an online control decision from the myoelectric module and/or a motion parameter set value from the software module, controls the mechanical arm to perform safe upper limb rehabilitation training in a three-dimensional space, and simultaneously returns the real-time motion parameter of the mechanical arm to the software module to update data; the interaction module comprises a fixing device for connecting the tail end of the upper limb of the patient with the tail end of the mechanical arm. The invention adopts the cooperative robot, can carry out upper limb rehabilitation training on patients in each rehabilitation period, reduces the repetitive working intensity and improves the interestingness of the rehabilitation training.
Description
Technical Field
The invention relates to the field of rehabilitation robots, in particular to an upper limb rehabilitation robot system with three-dimensional space motion.
Background
In recent years, the incidence of stroke is on the rise, and stroke leaves hemiplegia at the upper limbs, so that the light people are inconvenient to move and the life cannot be managed by themselves. The robot can continuously and controllably perform repeated tasks, and can be provided with a plurality of sensors which can be used for recording data such as motion tracks, speeds, forces and the like of patients so as to objectively evaluate the motion performance of the patients. Based on the advantages, the auxiliary rehabilitation robot becomes an effective and promising treatment mode for the hemiplegic patients. Most of the existing rehabilitation robots only carry out completely passive traction training on a patient or carry out feedback control on a motion system based on mechanical signals. A completely passive traction ignores the autonomous participation of the patient and only feedback control of the robot by force sensors is ineffective for patients with no residual motion. Some current researches show that for severely injured patients, although the specified actions cannot be completed, the movement trend of muscles can be identified through myoelectric signals and used for triggering the robot to perform auxiliary training. Therefore, the acquisition of the bioelectrical signals, the reflection of the neural activity and the acquisition of the motor intention can promote the activation of the damaged brain area or muscle, and is a better control mode.
The mainstream products of the existing upper limb rehabilitation equipment based on the bioelectricity signals are wearable exoskeleton equipment and two-dimensional traction equipment. The exoskeleton equipment is complex to wear, and the motion planning is easy to conflict with the motion mode of the stroke patient; the motion range of the two-dimensional traction equipment can not cover the three-dimensional motion space of the upper limb of the human body, so that the motion range, the rehabilitation action and the number of trainable muscles of a patient are limited, and the training requirement of the actual rehabilitation process is difficult to meet. For the above reasons, the clinical use rate of active rehabilitation devices based on neuroelectrical signals is low. In addition, clinically, the patients are divided into different stages according to different motor function recovery of the patients, and the motor function disorder expression forms of the patients in each stage are different, so different rehabilitation treatment strategies need to be adopted for the patients in different periods.
Therefore, those skilled in the art are dedicated to develop a multi-mode three-dimensional upper limb rehabilitation robot system with myoelectric signals to meet the training requirements of patients in different rehabilitation stages.
Disclosure of Invention
In view of the above defects in the prior art, the technical problem to be solved by the present invention is to develop a multi-mode three-dimensional upper limb rehabilitation robot system with myoelectric signals, so as to meet the training requirements of patients in different rehabilitation stages.
In order to achieve the above object, the present invention provides an upper limb rehabilitation robot system with three-dimensional space motion, which comprises a software module, a myoelectric module, a control module and an interaction module, and is characterized in that:
the software module runs on the upper computer and is configured to realize the interaction of user information and update the simulation of real-time motion parameters of the upper limbs and the mechanical arms of the human body on the basis of the terminal pose data and the inverse kinematics solution data of the upper limbs of the patient, which are transmitted by the control module;
the myoelectric module is configured to collect and process surface myoelectric signals, extract features and establish a correlation model with action intentions;
the control module receives an online control decision from the myoelectricity module and/or a motion parameter set value from the software module, controls the mechanical arm to perform safe upper limb rehabilitation training in a three-dimensional space according to different modes, and simultaneously returns the real-time motion parameters of the mechanical arm to the software module to update data;
the interactive module comprises a fixing device for connecting the tail end of the upper limb of the patient with the tail end of the mechanical arm.
Furthermore, the software module comprises a user interface module and a simulation module, the user interface module receives medical experience provided by a rehabilitation teacher, starts an execution process of the upper limb rehabilitation robot system, transmits training requirement parameters of the rehabilitation teacher as the motion parameter set values to the control module of the mechanical arm, provides interesting game interaction for a patient, performs rehabilitation training visual and/or auditory feedback, and refreshes the state of a game interface man-machine coupling model in real time; the simulation module receives the pose data of the tail end of the mechanical arm in real time, updates the data of the mechanical arm in the man-machine coupling model, and displays the coupling relation between the patient and the mechanical arm in the actual process on the simulation platform.
Furthermore, the electromyography module comprises a surface electromyography signal acquisition and transmission module and a signal processing module, the surface electromyography signal acquisition and transmission module acquires the surface electromyography signal of the patient, the surface electromyography signal is amplified, preliminarily filtered and subjected to A/D conversion to obtain a raw electromyography signal, and the raw electromyography signal is transmitted to the upper computer through a transmission link for processing and analysis.
Furthermore, the signal processing module comprises a preprocessing module, a feature extraction module, a pattern recognition module and a decision module, wherein the preprocessing module is used for preprocessing the original electromyographic signals, including filtering and sliding window processing, extracting features, sending the extracted multidimensional features to the pattern recognition module, predicting the action intention of the patient, then smoothing the prediction result by using a maximum voting method, and providing the online control decision to the control module.
Furthermore, the control module comprises a path planning module, a safety guarantee module and a mechanical arm motion parameter feedback module, the mechanical arm is enabled to carry out rehabilitation training through the path planning module according to the online control decision from the myoelectricity module and/or the motion parameter set value from the software module, the safety guarantee module is operated all the time in the training to protect the safety of the patient, and the mechanical arm motion parameter feedback module is in real-time communication with the software module to update the data of the software module.
Further, the rehabilitation training comprises an active mode, a passive mode and an assistance mode;
the active mode comprises a fixed track training mode and an indefinite track training mode, and the fixed track training mode comprises a trigger motion mode and a continuous motion mode; under the trigger motion mode, acquiring the initial surface electromyographic signals of the patient as the active intention of the patient, and carrying out complete trajectory traction according to the active intention; under the continuous motion mode, acquiring the surface electromyographic signals of the patient in real time, and enabling the patient to move on the track according to the active intention of the patient;
in the passive mode, the mechanical arm pulls the tail end of the upper limb of the patient to perform three-dimensional motion, the motion track comes from a fixed track determined by the upper limb rehabilitation robot system, and the fixed track comes from elbow bending, shoulder stretching, shoulder abduction and introduction of a rehabilitation teacher through teaching;
the power-assisted mode provides a soft and smooth touch interface by adopting an admittance control method and provides auxiliary force required by the patient to finish the rehabilitation training.
Still further, the fixation device couples a portion of the patient's upper limb with the robotic arm, the fixation device meeting ergonomic requirements.
Further, the fixing device comprises a spindle-shaped holding rod with an inclination angle and a fixing bracket.
Furthermore, the top of the holding rod is provided with a screw hole, the screw hole is designed according to a threaded hole of a flange of the mechanical arm end effector, and the screw hole is used for connecting the fixing device and the mechanical arm.
Furthermore, the bottom surface of the fixing bracket is provided with a radian, a bandage hole is formed in the fixing bracket, and the bandage is an elastic bandage.
In a preferred embodiment of the invention, the upper limb rehabilitation training can be performed on the patient under the guidance of a rehabilitation teacher. The invention adopts the cooperative robot, the rehabilitation teacher inputs the training requirement parameters on the user interface of the software module, and the patient in each rehabilitation period can be treated in a targeted manner according to the parameters and the patient movement intention identified by the myoelectricity module, so that the repetitive working intensity of the rehabilitation teacher is reduced; the game provided by the software module can improve the interest of rehabilitation training.
Compared with the prior art, the invention has the following beneficial effects:
1. according to different periods of cerebral apoplexy of patients, developing multiple modes to adapt to training requirements of different periods, and providing rehabilitation treatment for patients in each rehabilitation period;
2. by introducing the bioelectricity signals, the active intention of the patient is really recognized, the patient is helped to establish a neural closed loop, and targeted help is provided for rehabilitation training;
3. through the user interface module in the software module, the rehabilitation teacher can conveniently input the training requirement parameters, and the repetitive work intensity of the rehabilitation teacher is reduced;
4. through the simulation module in the software module, the patient and the rehabilitation teacher have psychological expectation on the movement of the upper limbs of the person in the rehabilitation process, certain guidance is provided for the later training, and the psychological burden of the patient can be relieved;
5. the participation enthusiasm of the patient is improved through the interesting game in the software module.
The conception, the specific structure and the technical effects of the present invention will be further described with reference to the accompanying drawings to fully understand the objects, the features and the effects of the present invention.
Drawings
FIG. 1 is a block diagram of a system architecture in accordance with a preferred embodiment of the present invention;
FIG. 2 is a block diagram of the present invention in various rehabilitation modes;
FIG. 3 is a schematic diagram of an admittance control method of the present invention;
FIG. 4 is a software module flow diagram of the present invention;
FIG. 5 is a flow chart of the myoelectric module of the present invention;
FIG. 6 is a flow chart of a control module of the present invention;
fig. 7 is a schematic structural view of a fixing device according to a preferred embodiment of the invention.
Detailed Description
The technical contents of the preferred embodiments of the present invention will be more clearly and easily understood by referring to the drawings attached to the specification. The present invention may be embodied in many different forms of embodiments and the scope of the invention is not limited to the embodiments set forth herein.
In the drawings, structurally identical elements are represented by like reference numerals, and structurally or functionally similar elements are represented by like reference numerals throughout the several views. The size and thickness of each component shown in the drawings are arbitrarily illustrated, and the present invention is not limited to the size and thickness of each component. The thickness of the components may be exaggerated where appropriate in the figures to improve clarity.
As shown in fig. 1, the upper limb rehabilitation robot system with three-dimensional space motion provided by the invention mainly comprises the following parts: the system comprises a software module, a myoelectricity module, a control module and an interaction module. The software module runs on the upper computer, realizes the interaction of user information and the simulation of updating real-time motion parameters of the upper limbs and the mechanical arm of the human body based on the terminal pose data and the inverse kinematics solution data of the upper limbs of the patient transmitted by the control module, and comprises a user interface module and a simulation module which are respectively displayed in the two display screens; the electromyography module is used for realizing the acquisition and processing of surface electromyography signals, extracting characteristics and establishing a correlation model with action intentions; the control module receives an online control decision from the myoelectricity module and/or a motion parameter set value from the software module, controls the mechanical arm to perform safe upper limb rehabilitation training in a three-dimensional space according to different modes, and simultaneously returns real-time data of the mechanical arm to the software module to update the data; the interactive module comprises a fixing device for connecting the tail end of the upper limb of the patient with the tail end of the mechanical arm, and the mechanical arm drives the patient to carry out upper limb rehabilitation training.
As shown in fig. 2, the present invention provides a plurality of different training modes, which mainly include an active mode, a passive mode and a power-assisted mode, and the interaction among the modules in the system in each mode is shown in fig. 2.
The active mode is divided into a fixed track training mode and an indefinite track training mode, and the fixed track training mode comprises a trigger motion mode and a continuous motion mode. The rehabilitee makes a mode selection at the user interface which feeds back the game task to the patient visually and/or audibly. At the moment, the patient activates muscles to generate surface electromyographic signals, the electromyographic module decodes the surface electromyographic signals into action information and transmits the action information to the control module, and the interaction module converts the tail end movement into the movement of the upper limbs of the patient after controlling the movement of the mechanical arms. And continuously feeding back the task execution condition to the software module in the motion process of the mechanical arm, and updating the characters in the user interface module and the simulation module or the postures of the characters and the mechanical arm. The triggering movement only collects the initial myoelectric signal of the patient as the active intention of the patient, and then complete trajectory traction is carried out according to the active intention information decoded by myoelectric; the myoelectric signals of the patient are collected in real time through continuous movement, so that the patient can move on the track according to the own initiative intention, the continuous requirement on the muscle group force is increased, and the training difficulty is improved.
In the passive mode, the mechanical arm pulls the upper limb end of the patient to perform three-dimensional motion, and the source of the motion track comprises default fixed tracks of the system, such as elbow bending, shoulder stretching and shoulder abduction, and fixed tracks introduced by a rehabilitation technician through teaching. The rehabilitation teacher inputs the running times and running speed multiplying power of each track in the user interface according to the actual situation of the patient, the user interface sends a command to the mechanical arm control module to execute track planning, and the motion of the tail end of the mechanical arm drives the upper limb of the patient to carry out the motion with the specified running speed multiplying power and times through the interaction module. Meanwhile, the control module and the software module are communicated in real time to send the terminal pose of the mechanical arm (upper limb of the patient), and the states of the figure and the mechanical arm model in the simulation module are updated.
In the power-assisted mode, an admittance control method is adopted to provide a soft and smooth tactile interface, and auxiliary force as small as possible is provided to help a patient to finish movement so as to avoid power assistance instead of active force. The user interface module informs the position information of a target point of a patient through visual prompt of a game, the patient actively moves, interaction is generated between the patient and the mechanical arm through the interaction module, the control module can also feed back the task execution condition to the software module in real time, and the user interface module and the simulation module in the software module respectively refresh the states of the game interface and the human-computer coupling model of the virtual environment in real time.
In one embodiment, as shown in FIG. 3, the admittance control method operates as follows. And obtaining the target track through teaching of a rehabilitation teacher. Setting a maximum distance threshold value D, setting the direction from P to the nearest point on the track as an expected radial speed when the distance between the current point P at the tail end of the mechanical arm and the nearest point on the track is smaller than D, and setting the expected tangential speed as the tangential direction of the nearest point on the track to synthesize a planned expected speed; when P is a distance greater than D from the closest point on the trajectory, the patient is deemed to have insufficient muscle strength to complete the portion of the motion, and the robotic arm needs to be controlled to provide additional assistance F to bring the tip back into the velocity field. In the velocity field, according to the distance between the current point P and the nearest point on the track, the impedance parameters are adjusted in real time, namely the inertia force, the damping force and the resilience force are adjusted, so that the patient can participate in the rehabilitation process more actively and comfortably according to the principle that the robot system assists as required, namely the intervention of a small number of robots is reduced when the error is small.
As shown in fig. 4, in a specific embodiment, the user interface is classified in parallel according to the options of the rehabilitee, that is, a main interface menu has a series of buttons, each button corresponds to a sub-menu composed of a series of short series-connected processes, and the main page of the system is divided into five sub-menus according to the operation of the rehabilitee. The user management interface integrates the functions of adding, deleting, modifying and checking account information. After logging in, a rehabilitation teacher can acquire the electromyographic signals on an electromyographic signal acquisition interface to train a model, can add or manage teaching tracks on the teaching interface, can select a training mode and set parameters for a patient on the training interface to enter training, can check the signals on a checking interface, and can check the real-time electromyographic signals and the characteristic radar chart in a popped new window. The training mode interface integrates three types of training mode options: the system comprises an active mode, a passive mode and an assistance mode, wherein two three-dimensional game task scenes are respectively set in the active mode and the assistance mode, audiovisual feedback is provided for a patient in real time, and results and training assessment including action completion rate, completion time, stagnation point proportion and path efficiency are displayed.
The simulation module runs an open-source robot operating system ROS, firstly uses SolidWorks three-dimensional modeling software to establish a three-dimensional model of human-computer interaction rehabilitation training, and derives a URDF file based on the model. And opening a model file in the Gazebo, and updating the angles of all joints of the upper limbs and the mechanical arms of the human body in real time based on the tail end pose data and the inverse kinematics transmitted by the control module.
As shown in fig. 5, the electromyography module includes a signal acquisition and transmission module and a signal processing module. The signal acquisition and transmission module is used for acquiring surface electromyographic signals of a patient by using AgCl electrodes which can be conveniently pasted on target muscle groups, amplifying the signals, primarily filtering the signals and carrying out A/D conversion to obtain original electromyographic signals, and transmitting the signals to an upper computer for signal processing through a transmission link such as wireless communication. The signal processing module comprises a preprocessing module, a feature extraction module, a mode identification module and a decision-making module. The upper computer performs preprocessing including filtering and sliding window segmentation on the received original electromyographic signals, and extracts the action characteristics of each channel electromyographic signal: average absolute value, waveform length and Willison amplitude; the extracted multi-dimensional features and action intentions are transmitted to a mode recognition module to train a classifier according to requirements, a corresponding model is obtained, newly extracted feature information is classified through the model in actual use, and the action intentions of the patient are predicted; and smoothing the prediction result by using a maximum voting method, and transmitting the on-line control decision to a control module of the mechanical arm. Optionally, for the trigger motion with a fixed track, four classifications are made for four actions of rest, elbow bending, shoulder forward bending and shoulder abduction only before the rehabilitation motion starts; for the continuous motion of the fixed track, the four classifications are still carried out before the motion starts, and the motion or rest state is judged in real time in the motion process; for an indefinite trajectory, it is simplified and discretized into motions in six directions in space: up, down, left, right, forward, backward, each direction representing a fixed distance of movement into that direction.
As shown in fig. 6, in one embodiment, after the system is turned on, the control module is also turned on to first detect whether the mechanical arm operating condition is good; then, the rehabilitee takes the patient to complete a plurality of fixed actions, and the patient upper limb parameters are intelligently obtained by assisting the position sensor and the force sensor of the mechanical arm, so that a coupling model of the patient and the mechanical arm is established, and the conversion between a mechanical arm coordinate system and a patient coordinate system is determined; in different modes, the rehabilitation training can be uniformly scheduled through subjective training requirement parameters of a rehabilitation teacher transmitted by a software module and the movement intention of a patient identified by an electromyography module, and a rehabilitation training track suitable for the patient is designed and executed according to the conversion between the previously obtained mechanical arm coordinate system and the patient coordinate system; in the execution process, the safety guarantee module and the motion parameter feedback module are also in execution, the safety guarantee module guarantees the safety of a patient through measures of avoiding singular points, limiting the interaction force between the patient and the mechanical arm and the like, and the motion parameter feedback module feeds back the motion parameters of the mechanical arm to the software module, so that games in a user interface and a man-machine coupling simulation model in the simulation module are updated in real time.
As shown in fig. 7, in one embodiment, the fixing device mainly comprises: the spindle-shaped holding rod with the inclination angle is provided with a fixing screw hole which is designed according to a flange threaded hole of the mechanical arm end effector and is used for connecting a fixing device and the mechanical arm at the top and a fixing bracket with a radian at the bottom surface so as to ensure the comfort of the forearm. The fixing bracket is provided with a bandage hole for fixing an elastic bandage, and comfortable and quick wearing and taking off can be realized.
The foregoing detailed description of the preferred embodiments of the invention has been presented. It should be understood that numerous modifications and variations could be devised by those skilled in the art in light of the present teachings without departing from the inventive concepts. Therefore, the technical solutions available to those skilled in the art through logic analysis, reasoning and limited experiments based on the prior art according to the concept of the present invention should be within the scope of protection defined by the claims.
Claims (10)
1. The utility model provides an upper limbs rehabilitation robot system of three-dimensional space motion, includes software module, flesh electricity module, control module and interactive module, its characterized in that:
the software module runs on the upper computer and is configured to realize the interaction of user information and update the simulation of real-time motion parameters of the upper limbs and the mechanical arms of the human body on the basis of the terminal pose data and the inverse kinematics solution data of the upper limbs of the patient, which are transmitted by the control module;
the myoelectric module is configured to collect and process surface myoelectric signals, extract features and establish a correlation model with action intentions;
the control module receives an online control decision from the myoelectricity module and/or a motion parameter set value from the software module, controls the mechanical arm to perform safe upper limb rehabilitation training in a three-dimensional space according to different modes, and simultaneously returns the real-time motion parameters of the mechanical arm to the software module to update data;
the interactive module comprises a fixing device for connecting the tail end of the upper limb of the patient with the tail end of the mechanical arm.
2. The upper limb rehabilitation robot system according to claim 1, wherein the software module comprises a user interface module and a simulation module, the user interface module receives medical experience provided by a rehabilitation teacher, starts an execution process of the upper limb rehabilitation robot system, transmits training requirement parameters of the rehabilitation teacher as the motion parameter setting values to the control module of the mechanical arm, provides interesting game interaction for a patient, performs rehabilitation training visual and/or auditory feedback, and refreshes the state of a game interface man-machine coupling model in real time; the simulation module receives the pose data of the tail end of the mechanical arm in real time, updates the data of the mechanical arm in the man-machine coupling model, and displays the coupling relation between the patient and the mechanical arm in the actual process on the simulation platform.
3. The upper limb rehabilitation robot system according to claim 1, wherein the electromyography module comprises a surface electromyography signal acquisition and transmission module and a signal processing module, the surface electromyography signal acquisition and transmission module acquires the surface electromyography signal of the patient, amplifies, primarily filters and A/D converts the surface electromyography signal to obtain a raw electromyography signal, and transmits the raw electromyography signal to the upper computer through a transmission link for processing and analysis.
4. The upper limb rehabilitation robot system according to claim 3, wherein the signal processing module comprises a preprocessing module, a feature extraction module, a pattern recognition module and a decision module, the preprocessing including filtering and sliding window processing is performed on the raw electromyographic signals, the feature extraction is performed, the extracted multidimensional features are sent to the pattern recognition module, the action intention of the patient is predicted, then the predicted result is smoothed by using a maximum voting method, and the online control decision is provided to the control module.
5. The upper limb rehabilitation robot system according to claim 1, wherein the control module comprises a path planning module, a safety guarantee module and a mechanical arm motion parameter feedback module, the mechanical arm is enabled to perform rehabilitation training through the path planning module according to the online control decision from the myoelectric module and/or the motion parameter set value from the software module, the safety guarantee module is operated all the time during training to protect the safety of the patient, and the data of the software module is updated through real-time communication between the mechanical arm motion parameter feedback module and the software module.
6. The upper extremity rehabilitation robot system of claim 5, wherein the rehabilitation training includes an active mode, a passive mode, and a power-assisted mode;
the active mode comprises a fixed track training mode and an indefinite track training mode, and the fixed track training mode comprises a trigger motion mode and a continuous motion mode; under the trigger motion mode, acquiring the initial surface electromyographic signals of the patient as the active intention of the patient, and carrying out complete trajectory traction according to the active intention; under the continuous motion mode, acquiring the surface electromyographic signals of the patient in real time, and enabling the patient to move on the track according to the active intention of the patient;
in the passive mode, the mechanical arm pulls the upper limb tail end of the patient to perform three-dimensional motion, the motion track is from a fixed track determined by the upper limb rehabilitation robot system, and the fixed track is from elbow bending, shoulder stretching, shoulder abduction and introduction of a rehabilitation teacher through teaching;
and under the assistance mode, an admittance control method is adopted to provide a flexible tactile interface and provide auxiliary force required by the patient to finish the rehabilitation training.
7. The upper extremity rehabilitation robot system of claim 1, wherein the fixture couples a portion of the patient's upper extremity with the robotic arm, the fixture meeting ergonomic requirements.
8. The upper extremity rehabilitation robot system of claim 7, wherein the securement device includes a spindle-shaped holding rod with an inclination angle and a securement bracket.
9. The upper limb rehabilitation robot system according to claim 8, wherein the top of the holding rod has screw holes designed according to the threaded holes of the flange of the end effector of the mechanical arm, and the screw holes are used for connecting the fixing device and the mechanical arm.
10. The upper limb rehabilitation robot system according to claim 8, wherein the bottom surface of the fixing bracket is provided with a radian, a bandage hole is formed in the fixing bracket, and the bandage is an elastic bandage.
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