Upper limb cranial nerve rehabilitation training system and training method

Abstract

The present invention relates to the field of medical rehabilitation, and specifically discloses an upper limb cranial nerve rehabilitation training system and training method. The upper limb training control system includes: an information acquisition module that collects historical trainee upper limb motion trajectory information and upper limb training parameters based on a multidimensional sensor; a virtual reality and human-computer interaction module that displays a training virtual reality scene based on a trainee's head-mounted display, and controls a robot to drive the trainee's upper limb to perform motion based on the training virtual reality scene and controls the trainee's head-mounted speaker to feed back the robot training information; a trajectory tracking module that obtains the trainee's upper limb training feedback after human-computer interaction in real time, and tracks and follows the upper limb training movement trajectory in real time; a warning analysis module that analyzes the tracking results of the upper limb training movement trajectory and receives the trainee's upper limb training feedback and warnings; and a feedback module that adjusts the trainee's upper limb motion trajectory information based on the upper limb training feedback and updates a three-dimensional trajectory model.

Description

Technical Field

[0001] The present invention relates to the field of medical rehabilitation, and in particular to an upper limb cranial nerve rehabilitation training system and a training method. Background Art

[0002] Stroke is one of the main causes of adult disability, especially upper limb dysfunction, which seriously affects patients' daily living ability and quality of life. With the aging of the population and the increasing incidence of related diseases, the demand for upper limb neurological rehabilitation is increasing.

[0003] While effective, traditional rehabilitation therapies are limited by a shortage of therapists and limited treatment efficiency. In recent years, the development of rehabilitation robotics has provided a new approach to addressing this issue. Rehabilitation robots can provide standardized, repetitive training, while intelligent control systems can adjust training parameters based on the patient's specific needs, enhancing rehabilitation outcomes.

[0004] In existing technologies, combining non-invasive brain stimulation techniques (such as transcranial direct current stimulation (tDCS)) with rehabilitation robotic training has been shown in clinical studies to further promote upper limb motor function recovery in stroke patients. This combination of technologies leverages the principles of neuroplasticity, enhancing the reorganization of neural circuits and functional recovery by stimulating the cerebral cortex.

[0005] Although clinical sports rehabilitation training robot control technology has achieved certain upper limb rehabilitation treatment effects, there are still some problems in the process of applying it to actual operations to restore patients' upper limb abilities: due to the influence of the rehabilitation model required by the trainee, the degree of the trainee's condition and their physical fitness factors, in actual rehabilitation training, it still relies on the rehabilitation techniques and actual operation experience of medical staff, and the effect of using robots to achieve rehabilitation training is less than ideal. Summary of the Invention

[0006] The purpose of the present invention is to provide an upper limb cranial nerve rehabilitation training system and training method to solve the following technical problems:

[0007] How to improve the intelligent interactive training effect of upper limb cranial nerve rehabilitation training and optimize the rehabilitation experience of trainees.

[0008] The purpose of the present invention can be achieved through the following technical solutions:

[0009] An upper limb cranial nerve rehabilitation training system includes a robot capable of driving a trainee's upper limb movements, a trainee's head-mounted display, an upper limb training control system, and a trainee's head-mounted loudspeaker;

[0010] Upper limb training control system includes:

[0011] An information collection module is used to collect historical trainees' upper limb motion trajectory information and upper limb training parameters based on multi-dimensional sensors;

[0012] The upper limb motion trajectory information is achieved by simulating a preset three-dimensional trajectory model through machine training. The upper limb motion trajectory information includes a three-dimensional motion trajectory image and trajectory image data information;

[0013] Upper limb training parameters include upper limb training sites, upper limb training intensity levels, and upper limb training frequency;

[0014] A virtual reality and human-computer interaction module, configured to display a training virtual reality scene on the trainee's head-mounted display, control the robot to move the trainee's upper limbs according to the training virtual reality scene, and control the trainee's head-mounted speakers to feed back training information to the robot;

[0015] The trajectory tracking module is used to obtain real-time upper limb training feedback from the trainee after human-computer interaction and track the upper limb training movement trajectory in real time;

[0016] The early warning analysis module is used to perform standard analysis based on the tracking results of upper limb training movement trajectories:

[0017] When the standard analysis meets the requirements, early warning analysis is carried out;

[0018] When the standard analysis does not meet the requirements, the first warning signal is issued;

[0019] When the early warning analysis meets the requirements, receive upper limb training feedback from the trainer;

[0020] When the early warning analysis does not meet the requirements, a second early warning signal is issued;

[0021] The feedback module is used to adjust the trainee's upper limb motion trajectory information according to the upper limb training feedback and update the three-dimensional trajectory model.

[0022] Preferably, the process of standard analysis is:

[0023] Design a tracking controller to obtain the dynamic equations of multiple motion joints of the robot;

[0024] Set the position tracking error range threshold according to the actual position, movement speed and acceleration of each moving joint in the dynamic equation;

[0025] The historical trajectory image data information and dynamic equations are input into the neural network model for training to output the actual position tracking error.

[0026] Preferably, the actual position tracking error is compared with a position tracking error range threshold:

[0027] If the actual position tracking error is less than the position tracking error range threshold, it is determined that the current position standard analysis meets the requirements; otherwise, the current position standard analysis does not meet the requirements.

[0028] Preferably, the process of early warning analysis is:

[0029] Obtain the curve E of the actual position tracking error of each point of the trainee's upper limb training over time i ;

[0030] Collect the change curve E of the same upper limb training frequency at all upper limb training sites i The set A∈{E1,E2,…,E n}, n is the total number of upper limb training sites, and i∈[1,n];

[0031] The standard change curve E of the same upper limb training frequency at the same upper limb training site in the history thr The set X of

[0032] Determine the number of elements x in set A that fall into set X:

[0033] If B is greater than the preset standard quantity threshold B ith , then it is judged that the current early warning analysis meets the requirements;

[0034] If B is less than the preset standard quantity threshold B ith , then it is judged that the current early warning analysis does not meet the requirements.

[0035] Preferably, upper limb training feedback includes:

[0036] The trainer receives the robot's training information and selects a training mode; the training modes include: passive training, active training, and assisted training;

[0037] Collect upper limb training parameters based on the training mode selected by the trainee to perform robot training stability analysis and determine whether the training stability analysis results meet the requirements:

[0038] If so, continue with the current mode and generate the control strategy for the current mode;

[0039] If not, switch to other modes automatically.

[0040] Preferably, the training stability analysis method is:

[0041] By formula Calculate and obtain the stability coefficient Sta;

[0042] Among them, V i is the robot joint position parameter of the i-th upper limb training site; is the robot movement speed parameter of the i-th upper limb training site; is the robot movement acceleration parameter of the i-th upper limb training site; V i0 is the standard joint position parameter of the robot at the i-th upper limb training site; is the standard moving speed parameter of the robot at the i-th upper limb training site; is the standard movement acceleration parameter of the robot at the i-th upper limb training site; ΔV is the deviation value of the preset joint position parameter; is the preset moving speed parameter deviation value; is the preset movement acceleration parameter deviation value; F is the upper limb training frequency; S is the upper limb training intensity; θ(F, S) is the upper limb training mode factor influence function of the joint position parameter; ∈(F, S) is the upper limb training mode factor influence function of the movement speed parameter; δ(F, S) is the upper limb training mode factor influence function of the movement acceleration parameter;

[0043] The stability coefficient Sta is compared with the preset stability coefficient threshold interval [Sta X , Sta Y ] for comparison:

[0044] If Sta∈[Sta X , Sta Y ], it is judged that the robot is in a stable state when executing the current training mode;

[0045] like It is determined that the robot is in an unstable state when executing the current training mode.

[0046] Preferably, the system further comprises:

[0047] The trainee evaluation module is used to evaluate the effectiveness of rehabilitation training based on the trainee's comprehensive sensitivity to each upper limb training point. Sensitivity is a parameter selected by the trainee's level of sustained pain, soreness, and tolerance time at the current upper limb training point. Within the set tolerance time level, the greater the sensitivity, the better the patient's rehabilitation effect.

[0048] Preferably, the trainer evaluation module includes:

[0049] By formula Calculate the recovery coefficient Re;

[0050] Among them, Per i is the sensitivity parameter of the i-th upper limb training site; Per i0 is the standard sensitivity parameter of the i-th upper limb training site; σ i Set the rehabilitation influence function of the tolerance time level for the i-th upper limb training site.

[0051] Preferably, the method for obtaining the evaluation of the effect of this rehabilitation training is:

[0052] The recovery coefficient Re is compared with the standard recovery coefficient Re ith To compare:

[0053] If Re≥Re ith , it is judged that the current rehabilitation effect is good.

[0054] An upper limb cranial nerve rehabilitation training method is applied to an upper limb cranial nerve rehabilitation training system. The method is implemented by a robot capable of driving a trainee's upper limb movements, a trainee's head-mounted display, an upper limb training control system, and a trainee's head-mounted speaker, including:

[0055] S1. Collecting historical upper limb motion trajectory information and upper limb training parameters of trainees using a multi-dimensional sensor; the upper limb motion trajectory information is simulated by a preset three-dimensional trajectory model through machine training, and the upper limb motion trajectory information includes a three-dimensional motion trajectory image and trajectory image data information; the upper limb training parameters include upper limb training site, upper limb training intensity level, and upper limb training frequency;

[0056] S2, displaying a training virtual reality scene on the trainee's head-mounted display, controlling the robot to move the trainee's upper limbs according to the training virtual reality scene, and controlling the trainee's head-mounted speakers to feed back training information to the robot;

[0057] S3, real-time acquisition of upper limb training feedback from the trainee after human-computer interaction, and real-time tracking of upper limb training movement trajectory;

[0058] S4. Conduct standard analysis based on the tracking results of upper limb training movement trajectories:

[0059] When the standard analysis meets the requirements, early warning analysis is carried out;

[0060] When the standard analysis does not meet the requirements, the first warning signal is issued;

[0061] When the early warning analysis meets the requirements, receive upper limb training feedback from the trainer;

[0062] When the early warning analysis does not meet the requirements, a second early warning signal is issued;

[0063] S5. Adjust the trainee's upper limb motion trajectory information according to upper limb training feedback and update the three-dimensional trajectory model.

[0064] Beneficial effects of the present invention:

[0065] (1) The present invention realizes the systematization process of the rehabilitation training of the trainee's upper limb cranial nerves by setting up four parts: a robot that can drive the trainee's upper limb movement, a trainee's head-mounted display, an upper limb training control system and a trainee's head-mounted speaker. The training items are displayed on the trainee's head-mounted display, the trainee selects the training item mode, and the robot starts the corresponding upper limb movement training instructions according to the training item mode; the training results are also fed back through the trainee's head-mounted speaker, and the trainee can also issue corresponding feedback on the rehabilitation training effect, so that medical staff and management personnel can directly receive the trainee's rehabilitation training information, and the entire rehabilitation training is completed through interactive training between the trainee and the robot.

[0066] (2) The present invention uses an upper limb training control system to issue control instructions and transmit upper limb movement training instructions; specifically, by setting a trajectory tracking module, it is ensured that the trainee's upper limb training feedback after human-computer interaction is obtained in real time, and the upper limb training movement trajectory is tracked in real time, so as to ensure further monitoring of the trajectory data during the training and rehabilitation process of the robot, and ensure the robot's good trajectory tracking ability; by setting an early warning analysis module, it is ensured that the acquired upper limb training data is subjected to a progressive analysis process of standard analysis and early warning analysis, and finally feedback on the trainee's upper limb training situation is achieved and the trainee's trajectory information is further adjusted in time according to the analysis results; by setting a feedback module, the trainee's upper limb movement trajectory information is adjusted according to the upper limb training feedback to achieve an update process of a three-dimensional data model such as a three-dimensional trajectory model, so as to ensure further optimization of the robot's human-computer interaction rehabilitation training effect.

[0067] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0069] Figure 1 This is a structural diagram of an upper limb cranial nerve rehabilitation training system of the present invention;

[0070] Figure 2 This is a step diagram of an upper limb cranial nerve rehabilitation training method of the present invention. DETAILED DESCRIPTION

[0071] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0072] Although clinical sports rehabilitation training robot control technology has achieved certain upper limb rehabilitation treatment effects, there are still some problems in the process of applying it to actual operations to restore patients' upper limb abilities: due to the influence of the rehabilitation model required by the trainee, the degree of the trainee's condition and their physical fitness factors, in actual rehabilitation training, it still relies on the rehabilitation techniques and actual operation experience of medical staff, and the effect of using robots to achieve rehabilitation training is less than ideal.

[0073] To change this, see Figure 1-2 As shown, by designing an upper limb cranial nerve rehabilitation training system and training method, it is used to achieve real-time feedback on the rehabilitation training effect and enrich the interaction effect between the trainer and the machine, thereby optimizing the trainer's rehabilitation experience.

[0074] The present invention is an upper limb cranial nerve rehabilitation training system, comprising a robot capable of driving a trainee's upper limbs to move, a trainee's head-mounted display, an upper limb training control system, and a trainee's head-mounted loudspeaker;

[0075] Upper limb training control system includes:

[0076] An information collection module is used to collect historical trainees' upper limb motion trajectory information and upper limb training parameters based on multi-dimensional sensors;

[0077] The upper limb motion trajectory information is achieved by simulating a preset three-dimensional trajectory model through machine training. The upper limb motion trajectory information includes a three-dimensional motion trajectory image and trajectory image data information;

[0078] Upper limb training parameters include upper limb training sites, upper limb training intensity levels, and upper limb training frequency;

[0079] A virtual reality and human-computer interaction module, configured to display a training virtual reality scene on the trainee's head-mounted display, control the robot to move the trainee's upper limbs according to the training virtual reality scene, and control the trainee's head-mounted speakers to feed back training information to the robot;

[0080] The trajectory tracking module is used to obtain real-time upper limb training feedback from the trainee after human-computer interaction and track the upper limb training movement trajectory in real time;

[0081] The early warning analysis module is used to perform standard analysis based on the tracking results of upper limb training movement trajectories:

[0082] When the standard analysis meets the requirements, early warning analysis is carried out;

[0083] When the standard analysis does not meet the requirements, the first warning signal is issued;

[0084] When the early warning analysis meets the requirements, receive upper limb training feedback from the trainer;

[0085] When the early warning analysis does not meet the requirements, a second early warning signal is issued;

[0086] The feedback module is used to adjust the trainee's upper limb motion trajectory information according to the upper limb training feedback and update the three-dimensional trajectory model.

[0087] In the above technical solution, this embodiment implements a systematic process for upper limb cranial nerve rehabilitation training by arranging four components: a robot capable of driving the trainee's upper limb movements, a head-mounted display for the trainee, an upper limb training control system, and a head-mounted speaker for the trainee. The training program is displayed on the head-mounted display, the trainee selects a training program mode, and the robot initiates the corresponding upper limb movement training instructions based on the training program mode. These instructions are obtained by the upper limb training control system, which issues and transmits control commands. Furthermore, training results are fed back through the head-mounted speaker, and the trainee can also provide feedback on the effectiveness of the rehabilitation training, allowing medical staff and management personnel to directly receive information about the trainee's rehabilitation training. The entire rehabilitation training process is completed through interactive training between the trainee and the robot. The specific software and hardware interaction of this design also utilizes big data processing technology, intelligent model analysis, training, and verification processes, allowing for real-time adjustments to the robot's rehabilitation training trajectory to ensure an enhanced rehabilitation experience for the trainee.

[0088] Specifically, the software design modules of the upper limb training control system involved in this design mainly include information acquisition module, virtual reality and human-computer interaction module, trajectory tracking module, early warning analysis module, and feedback module. The above five modules realize the optimization process of the trainee's upper limb training data, specifically:

[0089] First, by setting up an information collection module, the upper limb motion trajectory information and upper limb training parameters of historical trainees are collected based on multi-dimensional sensors, ensuring the acquisition of real-time training data and the collection process of historical data.

[0090] Among them, the acquisition of upper limb motion trajectory information is achieved through simulation of a preset three-dimensional trajectory model through machine training, and the upper limb motion trajectory information mainly includes three-dimensional motion trajectory images and trajectory image data information; the upper limb motion trajectory data can intuitively and digitally reflect the current trainee's training method; the upper limb training parameters set in this embodiment include upper limb training sites, upper limb training intensity levels, and upper limb training frequencies; through the analysis of different training sites of the patient's upper limbs, the selection of key training sites, upper limb training intensity levels, and upper limb training frequencies for rehabilitation training of different trainees is realized.

[0091] Secondly, by setting up a virtual reality and human-computer interaction module, the robot and the trainer can interact through the interactive equipment according to the virtual reality and human-computer interaction module, ensuring the accurate transmission of rehabilitation training information and improving the interaction effect. Specifically, this design mainly includes a head-mounted display and head-mounted speakers; the training virtual reality scene is displayed on the trainer's head-mounted display, and the robot can be controlled in real time to move the trainer's upper limbs according to the training virtual reality scene; and the trainer's head-mounted speakers are controlled to feedback the robot's training information.

[0092] Third, by setting up a trajectory tracking module, we can ensure that we can obtain real-time upper limb training feedback from the trainee after human-computer interaction, track and follow the upper limb training movement trajectory in real time, and ensure that the trajectory data of the robot is further monitored during training and rehabilitation, and ensure the robot's good trajectory tracking capability. The main equipment involved includes a trajectory tracking controller.

[0093] Fourthly, by setting up an early warning analysis module, a progressive analysis process of standard analysis and early warning analysis is ensured for the acquired upper limb training data, ultimately achieving feedback on the trainee's upper limb training situation and further timely adjustment of the trainee's trajectory information based on the analysis results.

[0094] Fifth, by setting up a feedback module to adjust the trainee's upper limb motion trajectory information according to the upper limb training feedback, the three-dimensional data model, such as the three-dimensional trajectory model, is updated to ensure further optimization of the robot's human-computer interaction rehabilitation training effect.

[0095] As an embodiment of the present invention, the process of standard analysis is:

[0096] Design a tracking controller to obtain the dynamic equations of multiple motion joints of the robot;

[0097] Set the position tracking error range threshold according to the actual position, movement speed and acceleration of each moving joint in the dynamic equation;

[0098] The historical trajectory image data information and dynamic equations are input into the neural network model for training to output the actual position tracking error.

[0099] In the above technical solution, the standard analysis process in this embodiment ensures the acquisition of actual data errors in position tracking, and ensures that the dynamic equations of multiple motion joints of the robot are first obtained according to the designed trajectory tracking controller; then the position tracking error range threshold is set according to the actual position, motion speed and acceleration of each motion joint in the dynamic equation, and the accurate acquisition of the robot rehabilitation training trajectory is ensured by obtaining data information of the robot's motion situation; finally, by introducing models related to machine algorithms, such as neural network models, the historical trajectory image data information and dynamic equations are taken as input, and the actual position tracking error is output through multiple machine training.

[0100] As an embodiment of the present invention, the actual position tracking error is compared with the position tracking error range threshold:

[0101] If the actual position tracking error is less than the position tracking error range threshold, it is determined that the current position standard analysis meets the requirements; otherwise, the current position standard analysis does not meet the requirements.

[0102] In the above technical solution, this embodiment also determines whether the corresponding position meets the normal deviation range based on the output actual position tracking error, and compares the actual position tracking error with the position tracking error range threshold. For objects that meet the standard analysis, the tracking error range threshold is used as the maximum critical value standard. If it is less than the current standard, it meets the standard analysis requirements. Otherwise, it is judged that it does not meet the standard analysis requirements.

[0103] As an embodiment of the present invention, the process of early warning analysis is as follows:

[0104] Obtain the curve E of the actual position tracking error of each point of the trainee's upper limb training over time i ;

[0105] Collect the change curve E of the same upper limb training frequency at all upper limb training sites i The set A∈{E1,E2,…,E n}, n is the total number of upper limb training sites, and i∈[1,n];

[0106] The standard change curve E of the same upper limb training frequency at the same upper limb training site in the history thr The set X of

[0107] Determine the number of elements x in set A that fall into set X:

[0108] If B is greater than the preset standard quantity threshold B ith , then it is judged that the current early warning analysis meets the requirements;

[0109] If B is less than the preset standard quantity threshold Bith , then it is judged that the current early warning analysis does not meet the requirements.

[0110] In the above technical solution, this embodiment performs a warning analysis for situations that meet the preliminary standard analysis. Since different upper limb training points require different upper limb training frequencies and intensities, this embodiment uses the variation of the upper limb training frequency as a reference for the warning analysis. Of course, in actual operation, the training intensity of different upper limb training points can also be used as a judgment, and the decision is made based on the actual specific operation. Therefore, the judgment process of the warning analysis includes: first, obtaining a curve E of the actual position tracking error of each upper limb training point of the trainee over time i Then, the change curve E of the same upper limb training frequency of all upper limb training sites is collected. i The set A∈{E1,E2,…,E n}, n is the total number of upper limb training sites, and i∈[1,n]; then, the standard change curve E of the same upper limb training frequency at the same upper limb training site in history is collected thr Finally, determine the number B of elements x in set A that fall into set X: if B is greater than the preset standard number threshold B ith , then the current warning analysis is judged to meet the requirements; if B is less than the preset standard quantity threshold B ith , then it is judged that the current early warning analysis does not meet the requirements.

[0111] As an embodiment of the present invention, upper limb training feedback includes:

[0112] The trainer receives the robot's training information and selects a training mode; the training modes include: passive training, active training, and assisted training;

[0113] Collect upper limb training parameters based on the training mode selected by the trainee to perform robot training stability analysis and determine whether the training stability analysis results meet the requirements:

[0114] If so, continue with the current mode and generate the control strategy for the current mode;

[0115] If not, switch to other modes automatically.

[0116] In the above technical solution, the main content of the upper limb training feedback in this embodiment is to select the robot training information mode received by the trainee and further judge whether the current mode meets the stability requirements of the training based on the selected training mode. For the trainee's upper limb rehabilitation training, a relatively stable training mode is more in line with the current upper limb rehabilitation status. Therefore, the trainee ensures that the training cycle of upper limb rehabilitation is shortened and the process of rehabilitation training is accelerated through multiple training controls under the stable mode; and for training situations that do not meet the current mode, the mode is automatically or manually changed. The automatic change method is adopted in this embodiment to achieve intelligent needs.

[0117] It should also be noted that the training modes in this embodiment mainly include three modes: passive training, active training, and assisted training. Different modes have different requirements for patients' upper limb training. In actual training, the current training mode can be further refined to ensure that the diverse needs of upper limb rehabilitation of a wider range of trainees are met.

[0118] As an embodiment of the present invention, the training stability analysis method is:

[0119] By formula Calculate and obtain the stability coefficient Sta;

[0120] Among them, V i is the robot joint position parameter of the i-th upper limb training site; is the robot movement speed parameter of the i-th upper limb training site; is the robot movement acceleration parameter of the i-th upper limb training site; V i0 is the standard joint position parameter of the robot at the i-th upper limb training site; is the standard moving speed parameter of the robot at the i-th upper limb training site; is the standard movement acceleration parameter of the robot at the i-th upper limb training site; ΔV is the deviation value of the preset joint position parameter; is the preset moving speed parameter deviation value; is the preset movement acceleration parameter deviation value; F is the upper limb training frequency; S is the upper limb training intensity; θ(F, S) is the upper limb training mode factor influence function of the joint position parameter; ∈(F, S) is the upper limb training mode factor influence function of the movement speed parameter; δ(F, S) is the upper limb training mode factor influence function of the movement acceleration parameter;

[0121] The stability coefficient Sta is compared with the preset stability coefficient threshold interval [Sta X , Sta Y ] for comparison:

[0122] If Sta∈[Sta X , Sta Y ], it is judged that the robot is in a stable state when executing the current training mode;

[0123] like It is determined that the robot is in an unstable state when executing the current training mode.

[0124] In the above technical solution, the specific method of training stability analysis in this embodiment is realized by a calculation formula, mainly through the formula The stability coefficient Sta is calculated and obtained; by obtaining the stability coefficient and according to the influence of its size, the actual position and movement speed of each moving joint of the robot, the acceleration value and its change influence function are obtained, and the stability state of the robot's current training mode is judged.

[0125] The stability coefficient needs to be further explained. Since the robot needs to design joint movements during the rehabilitation training of the trainee, the change in the amplitude of joint movement further reflects whether the patient is adapting to this mode. When the patient is not adapting, he will struggle and work hard. At this time, the tension of the human body's struggle affects its stability. Another situation is that the patient's rehabilitation part training is not in place, and the amplitude of movement of each joint of the machine will also show continuous abnormal changes, such as the actual position of the joint will be offset.

[0126] It should be noted that V i0 、 All of them are obtained by simulation based on historical data and will not be described in detail here; ΔV, These parameters are selected based on empirical data and will not be detailed here. The relationship between θ(F, S), ∈(F, S), and δ(F, S) and the upper-limb training frequency F and upper-limb training intensity S is determined based on the average values ​​of these parameters under different upper-limb training modes. θ(F, S), ∈(F, S), and δ(F, S) are adjusted based on the current upper-limb training mode. Consequently, the stability coefficient Sta is adjusted accordingly under different upper-limb training modes, improving the accuracy of judgment under different upper-limb training modes.

[0127] As an embodiment of the present invention, the system further includes:

[0128] The trainee evaluation module is used to evaluate the effectiveness of rehabilitation training based on the trainee's comprehensive sensitivity to each upper limb training point. Sensitivity is a parameter selected by the trainee's level of sustained pain, soreness, and tolerance time at the current upper limb training point. Within the set tolerance time level, the greater the sensitivity, the better the patient's rehabilitation effect.

[0129] In the above technical solution, this system not only reflects the patient's interaction process through intuitive language information and expressions, but also detects the patient's sensitivity during the interaction process, reflects the comprehensive sensitivity of each upper limb training point to obtain the evaluation of the rehabilitation training effect, including the parameters selected by the current upper limb training point's continuous pain, soreness, and tolerance time level; this parameter is obtained through the evaluation option selection and setting during the patient's training interaction process. Of course, in the actual implementation process, it still needs to be accurately confirmed by medical staff.

[0130] As an embodiment of the present invention, the trainer evaluation module includes:

[0131] By formula Calculate the recovery coefficient Re;

[0132] Among them, Per i is the sensitivity parameter of the i-th upper limb training site; Per i0 is the standard sensitivity parameter of the i-th upper limb training site; σ i Set the rehabilitation influence function of the tolerance time level for the i-th upper limb training site.

[0133] In the above technical solution, the trainee evaluation module in this embodiment calculates the rehabilitation coefficient Re through a formula, which can reflect the effect of the current rehabilitation training. Specifically, it is determined by the trainee's perception and the influence of the trainee's current tolerance time level on the rehabilitation effect. And Per i0 It is obtained by machine simulation based on historical data; the rehabilitation influence function σ i It is an influence function set according to historical data to ensure that the result of the recovery coefficient is within a specific reasonable range.

[0134] As an embodiment of the present invention, a method for obtaining the evaluation of the effect of this rehabilitation training is as follows:

[0135] The recovery coefficient Re is compared with the standard recovery coefficient Re ith To compare:

[0136] If Re≥Re ith , it is judged that the current rehabilitation effect is good.

[0137] In the above technical solution, this embodiment determines the rehabilitation training effect by comparing the obtained rehabilitation coefficient with the set standard threshold, mainly by judging whether the rehabilitation coefficient Re is greater than the standard rehabilitation coefficient Re ith , if it is greater than or equal to the current standard recovery coefficient Re ith , it is judged that the current rehabilitation training meets the expected effect.

[0138] See also Figure 2 As shown, an upper limb cranial nerve rehabilitation training method is also designed and applied to an upper limb cranial nerve rehabilitation training system. The method is implemented by a robot that can drive the trainee's upper limb movement, a trainee's head-mounted display, an upper limb training control system, and a trainee's head-mounted speaker, including:

[0139] S1. Collecting historical upper limb motion trajectory information and upper limb training parameters of trainees using a multi-dimensional sensor; the upper limb motion trajectory information is simulated by a preset three-dimensional trajectory model through machine training, and the upper limb motion trajectory information includes a three-dimensional motion trajectory image and trajectory image data information; the upper limb training parameters include upper limb training site, upper limb training intensity level, and upper limb training frequency;

[0140] S2, displaying a training virtual reality scene on the trainee's head-mounted display, controlling the robot to move the trainee's upper limbs according to the training virtual reality scene, and controlling the trainee's head-mounted speakers to feed back training information to the robot;

[0141] S3, real-time acquisition of upper limb training feedback from the trainee after human-computer interaction, and real-time tracking of upper limb training movement trajectory;

[0142] S4. Conduct standard analysis based on the tracking results of upper limb training movement trajectories:

[0143] When the standard analysis meets the requirements, early warning analysis is carried out;

[0144] When the standard analysis does not meet the requirements, the first warning signal is issued;

[0145] When the early warning analysis meets the requirements, receive upper limb training feedback from the trainer;

[0146] When the early warning analysis does not meet the requirements, a second early warning signal is issued;

[0147] S5. Adjust the trainee's upper limb motion trajectory information according to upper limb training feedback and update the three-dimensional trajectory model.

[0148] The various embodiments in this specification are described in a progressive manner. Similar portions between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from the other embodiments. In particular, the device, apparatus, and non-volatile computer storage medium embodiments are generally similar to the method embodiments, so their descriptions are relatively simplified. For relevant details, refer to the descriptions of the method embodiments.

[0149] The foregoing description is of specific embodiments of this specification. Other embodiments are within the scope of the accompanying documents. In some cases, the actions or steps described in this application can be performed in an order different from that shown in the embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order shown or the sequential order to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0150] The above content is merely an example and explanation of the concept of the present invention. Those skilled in the art may make various modifications or additions to the described specific embodiments or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined in this application, they should all fall within the scope of protection of the present invention.

Claims

1. An upper limb cranial nerve rehabilitation training system, characterized in that: It includes a robot capable of driving the trainee's upper limbs to move, a head-mounted display for the trainee, an upper limb training control system, and a head-mounted speaker for the trainee; The upper limb training control system comprises: An information collection module is used to collect historical trainees' upper limb motion trajectory information and upper limb training parameters based on multi-dimensional sensors; The upper limb motion trajectory information is achieved by simulating a preset three-dimensional trajectory model through machine training, and the upper limb motion trajectory information includes a three-dimensional motion trajectory image and trajectory image data information; The upper limb training parameters include upper limb training sites, upper limb training intensity levels, and upper limb training frequencies; A virtual reality and human-computer interaction module, configured to display a training virtual reality scene on a trainee's head-mounted display, and to control the robot to move the trainee's upper limbs and to control the trainee's head-mounted speakers to feed back training information to the robot based on the training virtual reality scene. The trajectory tracking module is used to obtain real-time upper limb training feedback from the trainee after human-computer interaction and track the upper limb training movement trajectory in real time; The early warning analysis module is used to perform standard analysis based on the tracking results of upper limb training movement trajectories: When the standard analysis meets the requirements, early warning analysis is carried out; When the standard analysis does not meet the requirements, the first warning signal is issued; When the early warning analysis meets the requirements, receive upper limb training feedback from the trainer; When the early warning analysis does not meet the requirements, a second early warning signal is issued; A feedback module is used to adjust the trainee's upper limb motion trajectory information according to upper limb training feedback and update the three-dimensional trajectory model; The upper limb training feedback includes: The trainer receives the robot training information and selects a training mode; the training modes include: passive training, active training, and power training; Collect upper limb training parameters based on the training mode selected by the trainee to perform robot training stability analysis and determine whether the training stability analysis results meet the requirements: If so, continue with the current mode and generate the control strategy for the current mode; If not, switch to other modes automatically; The training stability analysis method is: By formula Calculate the stability coefficient ; in, For the Robot joint position parameters for each upper limb training site; For the The robot movement speed parameters of the upper limb training sites; For the The robot movement acceleration parameters of the upper limb training sites; For the Standard joint position parameters of the robot for upper limb training sites; For the The robot's standard movement speed parameters for upper limb training sites; For the Standard movement acceleration parameters of the robot at each upper limb training site; is the preset joint position parameter deviation value; is the preset moving speed parameter deviation value; is the preset movement acceleration parameter deviation value; Frequency of upper limb training; For upper limb training intensity; The influence function of upper limb training mode factors on joint position parameters; The upper limb training mode factors influence function of movement speed parameter; The upper limb training mode factors influence function of movement acceleration parameter; The stability coefficient and the preset stability coefficient threshold range To compare: like , it is judged that the robot is in a stable state when executing the current training mode; like , it is judged that the robot is in an unstable state when executing the current training mode.

2. The upper limb cranial nerve rehabilitation training system according to claim 1, characterized in that: The process of the standard analysis is: Designing a tracking controller to obtain dynamic equations of multiple motion joints of the robot; Set the position tracking error range threshold according to the actual position, movement speed and acceleration of each moving joint in the dynamic equation; The historical trajectory image data information and dynamic equations are input into the neural network model for training to output the actual position tracking error.

3. The upper limb cranial nerve rehabilitation training system according to claim 2, characterized in that: Compare the actual position tracking error to the position tracking error range threshold: If the actual position tracking error is less than the position tracking error range threshold, it is determined that the current position standard analysis meets the requirements; otherwise, the current position standard analysis does not meet the requirements.

4. The upper limb cranial nerve rehabilitation training system according to claim 3, characterized in that: The process of the early warning analysis is as follows: Obtain the curve of the actual position tracking error of each point of the trainee's upper limb training over time ; Collect the change curve of the same upper limb training frequency at all upper limb training sites Collection ∈ , is the total number of upper limb training sites, and ∈ ; Collect the standard change curve of the same upper limb training frequency at the same upper limb training site in history Collection ; Judgment Set Fall into the collection The number of elements x in B: If B is greater than the preset standard quantity threshold , then it is judged that the current early warning analysis meets the requirements; If B is less than the preset standard quantity threshold , then it is judged that the current early warning analysis does not meet the requirements.

5. The upper limb cranial nerve rehabilitation training system according to claim 1, characterized in that: The system further comprises: The trainee evaluation module is used to obtain the evaluation of the rehabilitation training effect based on the trainee's comprehensive sensitivity to each upper limb training point; the sensitivity is a parameter selected by the trainee's continuous pain, soreness, and tolerance time level of the current upper limb training point; within the set tolerance time level range, the greater the sensitivity, the better the patient's rehabilitation effect.

6. The upper limb cranial nerve rehabilitation training system according to claim 5, characterized in that: The trainer evaluation module includes: By formula Calculate the recovery coefficient ; in, For the Sensitivity parameters of upper limb training sites; For the Standard sensitivity parameters for upper limb training sites; For the The rehabilitation impact function of each upper limb training site was set to the tolerance time level.

7. The upper limb cranial nerve rehabilitation training system according to claim 6, characterized in that: Method for obtaining the evaluation of the rehabilitation training effect: The recovery coefficient Standardized recovery coefficient To compare: like ≥ , it is judged that the current rehabilitation effect is good.

8. An upper limb cranial nerve rehabilitation training method, applied to an upper limb cranial nerve rehabilitation training system as claimed in any one of claims 1 to 7, wherein the method is implemented by a robot capable of driving the trainee's upper limb movements, a trainee's head-mounted display, an upper limb training control system and a trainee's head-mounted speakers, and characterized in that: The method comprises: S1. Collecting historical upper limb motion trajectory information and upper limb training parameters of a trainee using a multidimensional sensor; the upper limb motion trajectory information is simulated by a preset three-dimensional trajectory model for machine training, the upper limb motion trajectory information including a three-dimensional motion trajectory image and trajectory image data information; the upper limb training parameters including upper limb training sites, upper limb training intensity level, and upper limb training frequency; S2, displaying a training virtual reality scene on the trainee's head-mounted display, controlling the robot to move the trainee's upper limbs according to the training virtual reality scene, and controlling the trainee's head-mounted speakers to feed back the robot training information; S3, real-time acquisition of upper limb training feedback from the trainee after human-computer interaction, and real-time tracking of upper limb training movement trajectory; S4. Conduct standard analysis based on the tracking results of upper limb training movement trajectories: When the standard analysis meets the requirements, early warning analysis is carried out; When the standard analysis does not meet the requirements, the first warning signal is issued; When the early warning analysis meets the requirements, receive upper limb training feedback from the trainer; When the early warning analysis does not meet the requirements, a second early warning signal is issued; S5. Adjust the trainee's upper limb motion trajectory information according to upper limb training feedback and update the three-dimensional trajectory model.

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