VR chronic neck pain rehabilitation training system based on CPPC technology

Abstract

The invention discloses a VR chronic neck pain rehabilitation training system based on a CPPC technology, relates to the technical field of intelligent medical treatment, and solves the problem that an existing rehabilitation system combined with VR cannot play effective help in the process of improving the neck posture muscle control ability of a patient. The system comprises a data acquisition assembly used for acquiring motion and posture data of a patient in an evaluation period and a training period; the data processing module is used for analyzing and processing the action posture data to obtain an analysis result; the control center is used for pushing the analysis result to the medical care terminal, and then receiving guidance suggestions and adjustment suggestions from the medical care terminal; the plan making module is used for generating a training plan according to the guidance opinions and adjusting the training plan according to the adjustment opinions; the VR equipment is used for building a virtual scene according to the training plan and guiding the patient to execute the training plan in the virtual scene; the database is used for storing data related to rehabilitation training; according to the invention, the pain caused by the reduction of the neck muscle posture control ability of the patient is fundamentally solved.

Description

Technical Field

[0001] The present invention relates to the field of smart medical technology, and in particular to a VR chronic neck pain rehabilitation training system based on CPPC technology. Background Art

[0002] Chronic neck pain is a common global health problem, with a reported prevalence of 10% to 24%. Studies have shown that the main cause of chronic neck pain is the decline in neck muscle strength and posture control, which causes cervical segmental instability, resulting in additional pressure on the cervical structure, thereby limiting the patient's neck movement and causing pain. According to the latest clinical research, enhancing the neck muscle posture control ability of patients with chronic neck pain is considered to be an effective way to improve pain in patients with chronic neck pain.

[0003] At present, the existing VR devices or systems for rehabilitation training of patients with neck pain mainly focus on improving patients' neck mobility and strengthening neck muscles. Some patients have short-term symptom relief, but the long-term effect is not good. This is because the patients' neck posture muscle control ability has not been significantly improved.

[0004] For example, the Chinese patent "Cervical Rehabilitation Training System" (patent application number: 202010211003.5, publication number: CN111359158A). During training, this patent monitors the training posture of the rehabilitator in real time and gives reminders to improve the standardization of exercise training; the exercise plan generated by the rehabilitator's cervical spine range of motion ensures targeted training and improves the safety of training; at the same time, it combines game guidance to divert the rehabilitator's attention, reduce the pain of the rehabilitator during training, increase the fun of training, and improve the compliance of the rehabilitator.

[0005] However, the main purpose of this program is to design training programs mainly for cervical spine mobility, that is, in the process of treating neck pain, it only focuses on the patient's strength, range of motion and other superficial aspects, but does not consider the patient's posture control and neuromuscular regulation.

[0006] For example, the Chinese patent "Rehabilitation training auxiliary method and system based on brain-computer interface and virtual reality" (patent application number: 202411414791.2, publication number: CN118919020A). This patent realizes a comprehensive and integrated training experience through the effective linkage of virtual devices and rehabilitation equipment. This linkage can not only improve the interactivity of training, but also enhance the user's sense of participation and enthusiasm.

[0007] However, the main purpose of this program is to increase the fun of training and help patients actively participate in training, but it does not involve designing a training program that is more conducive to the patient's recovery based on the patient's postural control and neuromuscular regulation.

[0008] It can be seen that the existing technology mainly sets training plans based on superficial conditions such as patient strength and range of motion, and promotes patients' active participation in training by making training more interesting. However, the assessment of the patient's body is still not comprehensive enough, resulting in the problem that the training cannot effectively help improve the patient's neck posture muscle control ability. Summary of the invention

[0009] In order to solve the problems existing in the above-mentioned prior art, the present invention provides a VR chronic neck pain rehabilitation training system based on CPPC technology to solve the problem that the existing rehabilitation system combined with VR cannot effectively help improve the patient's neck posture muscle control ability.

[0010] A VR (virtual reality) chronic neck pain rehabilitation training system based on CPPC (centra l pathway and posture control) technology, including a data acquisition component, a data processing module, a plan making module, VR equipment, a control center, a database, and a medical terminal;

[0011] The data processing module is used to analyze and process the action posture data to obtain analysis results;

[0012] The control center is used to push the analysis results to the medical and nursing end, and then receive guidance and adjustment opinions from the medical and nursing end;

[0013] The plan making module is used to generate a training plan according to the guidance opinions and adjust the training plan according to the adjustment opinions;

[0014] The VR device is used to build a virtual scene according to the training plan and guide the patient to execute the training plan in the virtual scene;

[0015] The database is used to store data related to rehabilitation training.

[0016] Furthermore, the VR device includes a scene construction module and a training guidance module. The scene construction module is used to generate a specified training scene according to a training plan, and the training guidance module is used to generate suggestive text or images in the training scene according to the training plan, and give corresponding feedback after the patient completes the operation.

[0017] Furthermore, the data acquisition component includes an image acquisition unit and a sensor unit. The image acquisition unit is used to acquire the patient's motion images according to the instructions issued by the control center, and the sensor unit is used to obtain the patient's head and neck motion data in real time.

[0018] Furthermore, the control center sends an evaluation instruction to the VR device, and the VR device executes the evaluation instruction and broadcasts a voice prompt; the evaluation instruction includes a static evaluation instruction and a dynamic evaluation instruction, and the voice prompts corresponding to the static evaluation instruction include "evaluation starts", "sit upright", "keep still for n seconds", and "evaluation ends"; the voice prompts corresponding to the dynamic evaluation instruction include "evaluation starts", "please make specified actions", "keep still for n minutes", and "evaluation ends". When the VR device broadcasts "keep still for n seconds", the action posture data collected by the data acquisition component is used as static evaluation data, and when the VR device broadcasts "keep still for n minutes", the action posture data collected by the data acquisition component is used as dynamic evaluation data.

[0019] Furthermore, the designated actions include sitting upright, turning the head to the left, turning the head to the right, raising the head, and tucking the chin.

[0020] Furthermore, the data processing module processes the static assessment data to obtain static assessment results, and processes the dynamic assessment data to obtain dynamic assessment results, and then inputs the static assessment results and the dynamic assessment results into the analysis model to obtain the comprehensive assessment results of the patient's neck, and finally sends the static assessment results, the dynamic assessment results and the comprehensive neck assessment results to the medical end.

[0021] Furthermore, the data processing module analyzes the static assessment data including: the static assessment data is the movement posture data of the patient when he feels his body sitting upright, the control center retrieves the normal movement posture data for comparison, and at the same time analyzes the patient's head position inclination angle based on the three-dimensional coordinates to analyze the patient's head static neck posture perception and control ability as the static assessment result.

[0022] Furthermore, the data processing module analyzes the dynamic assessment data: the dynamic assessment data is divided into multiple data segments according to different designated actions, and then each sub-data segment is divided into multiple time series one by one, and the data corresponding to each time series in a single sub-data segment is compared and analyzed to obtain the changes of the patient's single designated action over time, and the changes over time corresponding to multiple designated actions are integrated and analyzed to evaluate whether the patient has cervical movement disorders or posture control disorders, and obtain dynamic assessment results.

[0023] Furthermore, a large language model is deployed in the plan making module, and the large language model is used to understand and analyze the guidance opinions, combine with the database to clarify the training direction and generate a complete action instruction sequence, and then convert the complete action instruction sequence into a number of VR device game instructions and voice prompt groups as a training plan; the large language model is used to understand and analyze the adjustment opinions, combine with the database to clarify the action instructions that need to be adjusted to adjust the complete action instruction sequence, and then convert the adjusted complete action instruction sequence into a new training plan.

[0024] Furthermore, the training plan includes:

[0025] Posture control training: After the patient wears the VR device, he controls the head position and aligns the crosshairs with the crosshairs. The system will also evaluate the patient's swing amplitude.

[0026] Neck muscle activation: The muscles at the back of the neck are contracted to retract the head so as to select the control crosshair. If the head is extended forward, the crosshair cannot be selected. After the crosshair is selected, the training begins.

[0027] APA training: Based on the APA principle, the patient's observation controls the head to make timely movements without prompting the patient;

[0028] CPA training: Based on the CPA principle, when prompted in advance, the patient anticipates the direction of the movement and performs the action to improve CPA control ability.

[0029] The beneficial effects of the present invention include:

[0030] 1. Based on the principle of posture control and combined with virtual reality technology, the existing VR rehabilitation training has been improved, changing from simple strength and speed training to cervical neuromuscular posture control training, which can fundamentally solve the pain caused by the patient's decreased neck muscle posture control ability.

[0031] 2. It can provide an immersive training environment for patients with neck pain, which can enhance the fun of training and make it easier for patients to get involved.

[0032] 3. Through various interactive devices, patients can interact with the virtual environment, truly perceive their own progress, and improve their enthusiasm for training.

[0033] 4. Through real-time feedback information and multi-sensory stimulation such as vision and hearing, timely feedback can be provided to adjust the coordination and control of the patient's neck muscles during training, thereby improving the accuracy and effectiveness of training.

[0034] 5. Through data analysis and processing, real-time monitoring of the patient's recovery process can help doctors make scientific decisions and improve recovery efficiency.

[0035] 6. Develop individualized training plans based on the patient's specific situation to make rehabilitation training more targeted, improve training effects, and accelerate the rehabilitation process. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 This is a structural schematic diagram of a VR chronic neck pain rehabilitation training system based on CPPC technology involved in an embodiment of the present application.

[0037] Figure 2This is a schematic diagram of the use of a VR chronic neck pain rehabilitation training system based on CPPC technology involved in an embodiment of the present application.

[0038] Figure 3 An interface for running APA training on the VR device involved in the embodiment of the present application.

[0039] Figure 4 It is the interface for the VR device involved in the embodiment of the present application to run CPA training. DETAILED DESCRIPTION

[0040] In order to make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the technical scheme in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present application.

[0041] A VR chronic neck pain rehabilitation training system based on CPPC technology, such as Figure 1 As shown, it includes data acquisition components, data processing modules, plan making modules, VR equipment, control center, database, and medical terminal;

[0042] The data acquisition component is used to collect the patient's action and posture data during the evaluation period and the training period;

[0043] The data processing module is used to analyze and process the action posture data to obtain analysis results;

[0044] The control center is used to push the analysis results to the medical and nursing end, and then receive guidance and adjustment opinions from the medical and nursing end;

[0045] The plan making module is used to generate a training plan according to the guidance opinions and adjust the training plan according to the adjustment opinions;

[0046] The VR device is used to build a virtual scene according to the training plan and guide the patient to execute the training plan in the virtual scene;

[0047] The database is used to store data related to rehabilitation training.

[0048] The VR device includes a scene construction module and a training guidance module. The scene construction module is used to generate a specified training scene according to a training plan, and the training guidance module is used to generate suggestive text or images in the training scene according to the training plan, and give corresponding feedback after the patient completes the operation.

[0049] In another embodiment, the data acquisition component includes an image acquisition unit and a sensor unit. The image acquisition unit is used to acquire the patient's motion images according to the instructions issued by the control center, and the sensor unit is used to obtain the patient's head and neck motion data in real time.

[0050] In another embodiment, the control center sends an evaluation instruction to the VR device, and the VR device executes the evaluation instruction and broadcasts a voice prompt; the evaluation instruction includes a static evaluation instruction and a dynamic evaluation instruction, and the voice prompts corresponding to the static evaluation instruction include "evaluation starts", "sit upright", "keep still for n seconds", and "evaluation ends";

[0051] The voice prompts corresponding to the dynamic evaluation instructions include "Evaluation starts", "Please make specified actions", "Keep still for n minutes", and "Evaluation ends". When the VR device announces "Keep still for n seconds", the action posture data collected by the data acquisition component is used as static evaluation data. When the VR device announces "Keep still for n minutes", the action posture data collected by the data acquisition component is used as dynamic evaluation data.

[0052] In another embodiment, the specified actions include sitting upright, turning the head to the left, turning the head to the right, raising the head, and tucking the chin.

[0053] In another embodiment, the data processing module processes the static assessment data to obtain static assessment results, and processes the dynamic assessment data to obtain dynamic assessment results, and then inputs the static assessment results and the dynamic assessment results into the analysis model to obtain the comprehensive assessment results of the patient's neck, and finally sends the static assessment results, the dynamic assessment results and the comprehensive neck assessment results to the medical end.

[0054] In another embodiment, the data processing module analyzes the static assessment data including: the static assessment data is the movement posture data when the patient feels his body sitting upright, the control center retrieves the normal movement posture data for comparison, and at the same time analyzes the patient's head position inclination angle based on the three-dimensional coordinates to analyze the patient's head static neck posture perception and control ability as the static assessment result.

[0055] In another embodiment, the data processing module analyzes the dynamic assessment data: the dynamic assessment data is divided into multiple data segments according to different designated actions, and then the single sub-data segments are divided into multiple time series one by one, and the data corresponding to each time series in the single sub-data segment are compared and analyzed to obtain the changes of the patient's single designated action over time, and the changes over time corresponding to multiple designated actions are integrated and analyzed to evaluate whether the patient has cervical movement disorders or posture control disorders, and obtain dynamic assessment results.

[0056] The specific processing flow is as follows:

[0057] Data preprocessing and input: Collect and input the patient's dynamic assessment dataset, which contains posture data of the head or other parts at multiple time points.

[0058] Divide data segments by action: Divide the data into multiple sub-segments according to the action type (such as turning the head left, turning right, lowering the head, raising the head, etc.).

[0059] Time series division: Split each sub-data segment into multiple time series according to time, and analyze the changes of each action in the time dimension.

[0060] Comparative analysis of time series data: Compare the data of each time series one by one to analyze the stability and smoothness of the movement and whether there are any abnormalities.

[0061] Time-dependent motion analysis: Assess the time-dependent trend of each specified motion during execution to determine whether the patient is able to perform the motion stably.

[0062] Integrate the analysis results of multiple movements: Comprehensively analyze the analysis results of multiple movements to assess whether the patient has movement disorders or postural control disorders.

[0063] Generate dynamic assessment report: Finally, based on the evaluation results of the patient's various movements, a detailed dynamic assessment report is generated and provided to medical staff for further diagnosis and treatment.

[0064] Through multiple steps, the patient's dynamic assessment data is carefully analyzed to comprehensively evaluate the patient's posture control and movement performance when performing multiple specified actions. By dividing the dynamic data into multiple time series and performing comparative analysis, the patient's neck movement disorder or posture control disorder can be detected and the relevant dynamic assessment results can be output. The dynamic assessment results can also provide valuable references for medical staff to help formulate personalized treatment plans.

[0065] In another embodiment, a large language model is deployed in the plan making module, and the large language model is used to understand and analyze the guidance opinions, combine with the database to clarify the training direction and generate a complete action instruction sequence, and then convert the complete action instruction sequence into several VR device game instructions and voice prompt groups as a training plan; the large language model is used to understand and analyze the adjustment opinions, combine with the database to clarify the action instructions that need to be adjusted to adjust the complete action instruction sequence, and then convert the adjusted complete action instruction sequence into a new training plan. Specifically, the guidance opinions include exercise difficulty, intensity, frequency, etc.

[0066] Specifically, the design process of the large language model is as follows:

[0067] (1) Preparation for fine-tuning the large language model:

[0068] In this embodiment, fine-tuning technology is used to enable the large language model to convert natural language instructions (such as exercise difficulty, intensity, frequency, etc.) into executable training instruction codes. By using a special corpus containing patient rehabilitation training cases, medical expertise, and training plans, the pre-trained large language model is fine-tuned so that it can understand the medical terms, action sequences, motion control requirements, etc. in the instructions, and automatically generate corresponding instruction codes based on these inputs. During the fine-tuning process, the model maps natural language inputs (such as "moderate intensity head rotation training") to specific training instructions (such as "turn the head 10 degrees to the left for 5 seconds at a frequency of 30 times per minute").

[0069] (2) Fine-tuning the dataset and training process:

[0070] During the fine-tuning process, the data set includes a large number of actual training cases and their corresponding training instructions, covering various factors such as posture control, neck muscle activation, exercise intensity and difficulty. Each training case not only contains the training requirements described in natural language, but also includes precise training action parameters (such as angle, duration, frequency, etc.). With these data, the large language model can generate code that can be used directly for training after understanding the patient's needs. The model training process adopts a supervised learning method, using these labeled data for training, and optimizing the model by minimizing the error between the predicted instructions and the target instructions.

[0071] (3) Conversion mechanism from natural language to instruction code:

[0072] The fine-tuned large language model can map natural language instructions into structured training instruction codes. Specifically, the model semantically parses the input instructions, identifies key action parameters (such as "turn head left", "10 degrees", "last 5 seconds", etc.), and converts them into instruction formats that the system can understand. These instruction formats can be:

[0073] Action instructions: For example, "Turn head left: angle = 10 degrees, duration = 5 seconds"

[0074] Voice prompt instructions: For example, "Please turn your head left, maintain a 10-degree angle, and last for 5 seconds"

[0075] Subsequently, the generated instruction sequence is converted into executable commands for VR devices through a predefined interface, including image feedback, audio prompts, training task settings, etc. This ability to automatically generate instructions greatly improves the personalization and adaptability of training plans.

[0076] (4) Multi-level instruction generation and feedback mechanism:

[0077] The fine-tuned large language model supports multi-level training instruction generation. It can not only generate initial training instructions based on the patient's current state (such as exercise intensity, fatigue level, etc.), but also dynamically adjust the training plan through a real-time feedback mechanism. For example, if the system detects that the patient has difficulty performing a certain action, the model will automatically adjust the exercise intensity, frequency or other training parameters based on the patient's feedback and action execution to help the patient gradually improve his or her athletic ability. These adjustments will prompt the patient in natural language (such as "reduce exercise intensity and slow down"), and generate new instruction codes through the model, which will be converted into instructions for VR devices in real time.

[0078] (5) Model adjustment to match patient needs:

[0079] In practical applications, the large language model can be adjusted according to the individual needs of patients. For example, the patient's age, medical history, motor ability, rehabilitation progress, etc. will affect the setting of the training plan. Through fine-tuning, the large language model can analyze the patient's training progress based on the patient information and training records in the database, and adjust parameters such as exercise difficulty, intensity and frequency in the training plan. In addition, the model can also make real-time adjustments based on patient feedback to ensure that the training instructions can continue to meet the patient's rehabilitation needs.

[0080] (6) Technical effects and advantages:

[0081] The fine-tuned large language model can greatly improve the intelligence and automation of training plan generation, reducing the need for manual intervention. At the same time, the model can adapt to the personalized needs of different patients and flexibly adjust the training content and difficulty. Compared with the traditional method of manually writing training instructions, this technology not only improves the accuracy of training effects, but also speeds up the process of formulating training plans, ensuring that patients can get the best rehabilitation effect in the shortest time. In addition, due to the scalability of the model, new training plans can be quickly generated and adjusted in the future according to different disease types and different rehabilitation needs.

[0082] In another embodiment, the training program comprises:

[0083] (1) Posture control training: After the patient wears the VR device, he controls the head position and aligns the crosshairs with the crosshairs. The system will also evaluate the patient's swing amplitude. Figure 2 )

[0084] (2) Neck muscle activation: The head is retracted by contracting the muscles at the back of the neck to select the control crosshairs. If the head is extended forward, the crosshairs cannot be selected. After selecting the crosshairs, the patient will play the task game on the game screen for 30 minutes.

[0085] (3) APA training: Task game design is based on the APA principle. Without giving patients any prompts, game tasks such as avoiding thrown objects by controlling the head position are designed. Figure 3 )

[0086] (4) CPA training: The task game is designed based on the CPA principle. When a prompt (exclamation mark) is given in advance, the patient can predict the direction of the thrown object and improve the CPA control ability. Figure 4 )

[0087] Specifically, when the human body faces predictable or sudden internal and / or external posture disturbances, the central nervous system's response to the body's posture muscles mainly includes two regulatory mechanisms: anticipatory postural adjustment (APA) and compensatory postural adjustment (CPA). APA refers to connecting the body segments by establishing a stable kinematic chain to prevent mechanical disturbances caused by local movements. CPA refers to the postural adjustment produced after responding to unexpected posture disturbances.

[0088] The neural mechanism of APA may be that the perceptual and cognitive neural information from vision, hearing, proprioception and motor intention is transmitted to the brain, and the central nervous system integrates and sends out nerve impulses, generates movement prediction, pre-activates related muscle activities, and achieves optimal control of muscle activities. Studies have shown that the APA of the neck posture muscles of patients with chronic neck pain before exercise is significantly weakened, and the muscle activation speed is significantly reduced.

[0089] The purpose of CPA is to adjust the position of the body's center of gravity after a disturbance occurs. The motor commands from the motor cortex act on the motor system, changing its state and causing different sensory inputs for posture adjustment. CPA requires correct sensory input and motor output.

[0090] In summary, when faced with unpredictable interference, CPA is the main regulatory mechanism of the central nervous system for posture control; when faced with predictable interference, APA and CPA jointly control posture to maintain the stability of body posture.

[0091] In another embodiment, the VR device further includes:

[0092] (1) Virtual environment generation module: used to generate a three-dimensional training space environment. For example, the patient adjusts the head position to aim at the target and fires the gun. This requires the patient to constantly adjust the head position according to visual feedback during the process, and enhance the control ability of the neck posture muscles (such as Figure 2 ).

[0093] (2) Posture capture module: used to capture the patient's head position information in real time, including three-dimensional spatial motion trajectory, motion speed, motion acceleration, etc.

[0094] (3) Feedback control module: used to adjust the visual and auditory feedback in the virtual environment and guide patients to perform correct posture control training.

[0095] (4) Training intensity adjustment module: adjust the training difficulty according to the patient's training situation to prevent the patient from giving up training due to frustration.

[0096] When the completion rate of APA and CPA tasks is less than 60%, the training system will automatically adjust the training difficulty according to the patient's performance. If the patient fails to complete the scheduled actions within the specified time, the system will slow down the game, simplify the complexity of the task (such as reducing the number of thrown objects or reducing the difficulty of the task), and help the patient improve the task completion rate by adding voice prompts or visual prompts. This dynamic adjustment mechanism ensures that patients gradually master posture adjustment skills during the rehabilitation process without losing confidence because the task is too difficult.

[0097] If the patient fails to meet the set standards when performing APA or CPA tasks, the system can reduce the patient's exercise burden by reducing the training intensity to avoid aggravating pain or fatigue due to excessive load.

[0098] Reduce the range of motion: For example, in the APA task, if the patient makes many errors or fails to complete the movement during training, the system can reduce the angle range of head rotation (such as from 30 degrees to 15 degrees) to reduce the patient's exercise intensity.

[0099] Reduce the number of repetitions or time: In the CPA task, if the patient fails to successfully make compensatory posture adjustments, the system can reduce the number of repetitions of the action or the duration of each action to ensure that the patient can complete the training with a lighter burden.

[0100] Reduce movement speed: For patients with poor control ability, the system can reduce the speed of movement and the precision required for the movement, thereby reducing the burden on the patient.

[0101] Reduce the frequency of training: For patients with slow progress, the system can appropriately reduce the frequency of training (such as from 5 times a day to 3 times a day) to give patients more recovery time and avoid fatigue caused by overtraining.

[0102] Extended recovery intervals: If a patient performs poorly in a training cycle, the system can extend the rest period between training sessions (e.g., increase the rest time between each training session) so that the patient can recover and improve performance in the next training session.

[0103] Gradually increase the frequency: When the patient makes good progress at a certain stage, the system will gradually increase the frequency of training according to his performance to improve the recovery process.

[0104] (5) Data analysis and processing module: Analyze the data during the patient's training process, evaluate the training effect, and provide a basis for doctors to adjust the training plan.

[0105] (6) Human-computer interaction module: enables patients to interact with the virtual environment, displaying training progress, training scores, and feedback information. Therapists can monitor the patient's real-time training screen and training status through the monitor and provide corresponding guidance and adjustments.

[0106] In another embodiment, a system usage process is involved, including:

[0107] Step 1: The patient wears the VR device and adjusts the position of the data acquisition components (camera, sensor);

[0108] Step 2: Determine whether the patient is using the system for the first time; if yes, proceed to step 3; if not, proceed to step 4;

[0109] Step 3: Register an account, log in to the system to enter the evaluation process, and obtain the corresponding training plan after the evaluation is completed;

[0110] Step 4: Train according to the training plan;

[0111] Step 5: You can choose to update the training plan, collect historical training data for evaluation and obtain the adjusted training plan.

[0112] The above-mentioned embodiments only express the specific implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the protection scope of the present application. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the technical solution concept of the present application, and these all belong to the protection scope of the present application.

Claims

1. A VR chronic neck pain rehabilitation training system based on CPPC technology, characterized in that: Including data acquisition components, data processing modules, planning modules, VR equipment, control center, database, and medical terminal; The data processing module is used to analyze and process the action posture data to obtain analysis results; The control center is used to push the analysis results to the medical and nursing end, and then receive guidance and adjustment opinions from the medical and nursing end; The plan making module is used to generate a training plan according to the guidance opinions and adjust the training plan according to the adjustment opinions; The VR device is used to build a virtual scene according to the training plan and guide the patient to execute the training plan in the virtual scene; The database is used to store data related to rehabilitation training.

2. According to claim 1, a VR chronic neck pain rehabilitation training system based on CPPC technology is characterized in that: The VR device includes a scene construction module and a training guidance module. The scene construction module is used to generate a specified training scene according to a training plan, and the training guidance module is used to generate suggestive text or images in the training scene according to the training plan, and give corresponding feedback after the patient completes the operation.

3. According to claim 1, a VR chronic neck pain rehabilitation training system based on CPPC technology is characterized in that: The data acquisition component includes an image acquisition unit and a sensor unit. The image acquisition unit is used to acquire the patient's motion images according to the instructions issued by the control center, and the sensor unit is used to obtain the patient's head and neck motion data in real time.

4. The VR chronic neck pain rehabilitation training system based on CPPC technology according to claim 1 is characterized in that: The control center sends an evaluation instruction to the VR device, and the VR device executes the evaluation instruction and broadcasts a voice prompt; the evaluation instruction includes a static evaluation instruction and a dynamic evaluation instruction, and the voice prompts corresponding to the static evaluation instruction include "evaluation starts", "sit upright", "keep still for n seconds", and "evaluation ends"; the voice prompts corresponding to the dynamic evaluation instruction include "evaluation starts", "please make specified actions", "keep still for n minutes", and "evaluation ends". When the VR device broadcasts "keep still for n seconds", the action posture data collected by the data acquisition component is used as static evaluation data, and when the VR device broadcasts "keep still for n minutes", the action posture data collected by the data acquisition component is used as dynamic evaluation data.

5. The VR chronic neck pain rehabilitation training system based on CPPC technology according to claim 4 is characterized in that: The specified movements include sitting upright, turning the head to the left, turning the head to the right, raising the head and tucking the chin.

6. The VR chronic neck pain rehabilitation training system based on CPPC technology according to claim 4 is characterized in that: The data processing module processes the static assessment data to obtain static assessment results, and processes the dynamic assessment data to obtain dynamic assessment results. The static assessment results and dynamic assessment results are then input into the analysis model to obtain the patient's comprehensive neck assessment results. Finally, the static assessment results, dynamic assessment results and comprehensive neck assessment results are sent to the medical end.

7. The VR chronic neck pain rehabilitation training system based on CPPC technology according to claim 6 is characterized in that: The data processing module analyzes the static assessment data, including: the static assessment data is the movement and posture data of the patient when he feels his body sitting upright. The control center retrieves the normal movement and posture data for comparison. At the same time, the patient's head position inclination angle is analyzed based on the three-dimensional coordinates to analyze the patient's static head and neck posture perception and control ability as the static assessment result.

8. The VR chronic neck pain rehabilitation training system based on CPPC technology according to claim 6 is characterized in that: The data processing module analyzes the dynamic assessment data: the dynamic assessment data is divided into multiple data segments according to different designated actions, and then each sub-data segment is divided into multiple time series one by one. The data corresponding to each time series in a single sub-data segment is compared and analyzed to obtain the changes of the patient's single designated action over time, and the changes over time corresponding to multiple designated actions are integrated and analyzed to assess whether the patient has cervical movement disorders or posture control disorders, and obtain dynamic assessment results.

9. The VR chronic neck pain rehabilitation training system based on CPPC technology according to claim 1 is characterized in that: A large language model is deployed in the plan making module, and the large language model is used to understand and analyze the guidance opinions and combine with the database to clarify the training direction and generate a complete action instruction sequence, and then convert the complete action instruction sequence into a number of VR device game instructions and voice prompt groups as a training plan; The large language model is used to understand and analyze the adjustment opinions, adjust the complete action instruction sequence in combination with the action instructions that need to be adjusted clearly in the database, and then convert the adjusted complete action instruction sequence into a new training plan.

10. The VR chronic neck pain rehabilitation training system based on CPPC technology according to claim 1 is characterized in that: The training program includes: Posture control training: After the patient wears the VR device, he controls the head position and aligns the crosshairs with the crosshairs. The system will also evaluate the patient's swing amplitude. Neck muscle activation: The muscles at the back of the neck are contracted to retract the head so as to select the control crosshair. If the head is extended forward, the crosshair cannot be selected. After the crosshair is selected, the training begins. APA training: Based on the APA principle, the patient's observation controls the head to make timely movements without prompting the patient; CPA training: Based on the CPA principle, when prompted in advance, the patient anticipates the direction of the movement and performs the action to improve CPA control ability.

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