Respiratory rehabilitation training guiding system based on virtual reality

Abstract

The invention relates to the field of respiratory rehabilitation training systems, in particular to a respiratory rehabilitation training guiding system based on virtual reality. The intelligent breathing rehabilitation system integrates virtual reality, breathing monitoring, health assessment and personalized training; the virtual reality display terminal displays a training scene and a real-time evaluation result, so that the interactive experience is enhanced; the respiration monitoring module accurately collects data, and the health evaluation module analyzes the data in real time and feeds back the data to the display terminal; the respiratory rehabilitation training platform dynamically adjusts training content and intensity according to a user instruction and evaluation feedback; the rehabilitation monitoring module tracks a breathing curve in real time, and training accuracy is ensured. And the sound output module provides prompts to guide correct breathing. The system realizes intelligent, personalized and interesting training, improves the effect and efficiency, enhances the enthusiasm and compliance of patients, and brings an innovative breakthrough to the field of respiratory rehabilitation.

Description

Technical Field

[0001] The invention relates to the field of respiratory rehabilitation training systems, in particular to a respiratory rehabilitation training guidance system based on virtual reality. Background Art

[0002] With the increasing incidence of chronic respiratory diseases, the importance of respiratory rehabilitation training in disease management has become increasingly prominent. Traditional respiratory rehabilitation training methods mainly rely on face-to-face guidance from respiratory therapists, and patients use simple auxiliary equipment to perform basic training such as pursed-lip breathing and diaphragmatic breathing. Although this method can improve the patient's respiratory function to a certain extent, it has many limitations.

[0003] First, traditional methods lack real-time monitoring and feedback mechanisms. It is difficult for therapists to accurately grasp the patient's real-time physiological state and adjust the training intensity and content in time. This not only affects the training effect, but also may bring potential risks to patients due to improper training intensity. Secondly, traditional training methods are monotonous and boring, and it is difficult to maintain patients' long-term interest and compliance. After the initial enthusiasm, many patients often give up gradually because of the boring training process, which greatly reduces the rehabilitation effect.

[0004] In recent years, the application of virtual reality (VR) technology in the field of medical rehabilitation has attracted widespread attention. Some researchers have tried to introduce VR technology into respiratory rehabilitation training and developed a simple VR respiratory training system. This type of system provides basic visual guidance through a virtual environment, which improves the fun of training to a certain extent. However, these preliminary attempts still have many shortcomings. Most systems only stay at the level of providing visual feedback and lack in-depth analysis and utilization of patients' physiological data. They cannot dynamically adjust the training content according to the patient's real-time status, nor can they provide personalized training plans. In addition, these systems usually only focus on respiratory training itself, ignoring the actual needs of patients in daily life, and it is difficult to effectively transform the training effect into an improvement in quality of life.

[0005] Given the limitations of existing technologies, there is an urgent need for a respiratory rehabilitation training system that can integrate virtual reality technology, real-time physiological monitoring, intelligent assessment, and personalized training. Such a system should be able to monitor the patient's physiological state in real time, dynamically adjust the training content, and provide an immersive training experience, while taking into account the patient's long-term rehabilitation needs and quality of life improvement. Summary of the invention

[0006] The virtual reality-based respiratory rehabilitation training guidance system of the present invention is designed specifically for the above technical problems. By integrating multiple functional modules such as virtual reality display technology, real-time respiratory monitoring, intelligent health assessment, and adaptive training platform, the system aims to provide a comprehensive, intelligent, and personalized respiratory rehabilitation training solution.

[0007] The present invention proposes a virtual reality-based respiratory rehabilitation training guidance system, including:

[0008] A virtual reality display terminal, configured to:

[0009] Display virtual reality scene images to guide users in respiratory rehabilitation training;

[0010] Show real-time assessment results of rehabilitation training effects;

[0011] A respiratory monitoring module, communicatively connected to the virtual reality display terminal, configured to:

[0012] Collect respiratory monitoring data of users during rehabilitation training;

[0013] Send the respiratory monitoring data to the health assessment module;

[0014] A health assessment module, communicatively connected to the respiratory monitoring module and the virtual reality display terminal, configured to:

[0015] Receive the respiratory monitoring data sent by the respiratory monitoring module;

[0016] Based on the respiratory monitoring data, conduct real-time assessment of the rehabilitation training effects;

[0017] Send assessment feedback information to the virtual reality display terminal for display;

[0018] A respiratory rehabilitation training platform, communicatively connected to the virtual reality display terminal and the health assessment module, configured to:

[0019] Receive input instructions from users;

[0020] Generate corresponding virtual reality scene images according to the input instructions;

[0021] Based on the assessment feedback information of the health assessment module, adjust the rehabilitation training content and training intensity;

[0022] A rehabilitation monitoring module, communicatively connected to the respiratory rehabilitation training platform, configured to:

[0023] Monitor users' rehabilitation training according to the instructions of the respiratory rehabilitation training platform;

[0024] Obtain users' training respiratory curves in real time;

[0025] A sound output module, communicatively connected to the virtual reality display terminal and the rehabilitation monitoring module, for:

[0026] Providing voice prompts to the user according to the requirements of respiratory rehabilitation training.

[0027] Preferably, the respiratory monitoring module includes:

[0028] A pulse sensor for obtaining the user's pulse data in real time;

[0029] An oxygen saturation sensor for obtaining the user's oxygen saturation data in real time;

[0030] A data processing unit, communicatively connected to the pulse sensor and the oxygen saturation sensor, for:

[0031] Receiving the data collected by the pulse sensor and the oxygen saturation sensor;

[0032] Generating respiratory monitoring data based on the pulse data and the oxygen saturation data.

[0033] Preferably, the health assessment module includes:

[0034] A data comparison unit for:

[0035] Receiving the respiratory monitoring data;

[0036] Comparing the respiratory monitoring data with preset normal data;

[0037] An evaluation result generation unit, communicatively connected to the data comparison unit, for:

[0038] Generating a real-time evaluation result of the rehabilitation training effect based on the data comparison result;

[0039] Dividing the user's rehabilitation training status into training up to standard, insufficient training, and over-fast training;

[0040] A feedback information generation unit, communicatively connected to the evaluation result generation unit, for:

[0041] Generating evaluation feedback information according to the real-time evaluation result;

[0042] Sending the evaluation feedback information to the virtual reality display terminal and the respiratory rehabilitation training platform.

[0043] Preferably, the evaluation result generation unit is further configured to:

[0044] Determine that the training meets the standard when the following conditions are met:

[0045] The actual breathing curve of the user conforms to the standard breathing curve;

[0046] The frequency of the actual breathing curve reaches 90% of the frequency of the standard breathing curve;

[0047] The duration of the above state reaches 60%;

[0048] It is determined that the training is insufficient when one of the following conditions is met:

[0049] The frequency of the actual breathing curve is lower than 80% of the frequency of the standard breathing curve;

[0050] The frequency of the actual breathing curve is higher than the standard breathing frequency but lower than 120% of the standard breathing frequency; It is determined that the training is too fast when the following conditions are met:

[0051] The actual breathing curve is higher than 120% of the frequency of the standard breathing curve;

[0052] The time higher than 120% of the standard breathing frequency exceeds 10%.

[0053] Preferably, the breathing rehabilitation training platform includes:

[0054] A virtual scene generation module, used for:

[0055] Generating corresponding virtual reality scene images according to the input instructions of the user;

[0056] Dynamically calling different virtual scenes according to the changes in the rehabilitation training content;

[0057] A training content adjustment module, communicatively connected to the health assessment module, used for:

[0058] Receiving the evaluation feedback information from the health assessment module;

[0059] Adjusting the rehabilitation training content and training intensity based on the evaluation feedback information;

[0060] A training plan generation module, communicatively connected to the training content adjustment module, used for:

[0061] Generating a personalized breathing rehabilitation training plan according to the adjusted training content and intensity. Preferably, the training content adjustment module is also used for:

[0062] When the user is in a state of insufficient training:

[0063] Increasing the frequency of the training breathing curve;

[0064] Reducing the amplitude of the training breathing curve;

[0065] Increasing the number of vibrations of the training breathing curve;

[0066] When the user is in a state of over - rapid training:

[0067] Reduce the frequency of the training breathing curve;

[0068] Increase the amplitude of the training breathing curve;

[0069] Reduce the number of vibrations of the training breathing curve.

[0070] Preferably, the rehabilitation monitoring module includes:

[0071] A breathing curve acquisition unit for:

[0072] Obtaining the user's training breathing curve in real - time;

[0073] A data analysis unit, communicatively connected to the breathing curve acquisition unit, for:

[0074] Receiving the training breathing curve;

[0075] Comparing the training breathing curve with a preset standard breathing curve;

[0076] When it is detected that the difference between the user's actual breathing curve and the standard breathing curve continuously exceeds a set threshold, generating an alarm signal;

[0077] An alarm trigger unit, communicatively connected to the data analysis unit, for:

[0078] Receiving the alarm signal;

[0079] Triggering the breathing rehabilitation training platform to stop the training progress or issue an alarm.

[0080] Preferably, it further includes a big data analysis module, communicatively connected to the rehabilitation monitoring module, for:

[0081] Data collection, for:

[0082] Collecting multiple training data of the user within a set time range;

[0083] Statistical analysis, for:

[0084] Analyzing the multiple training data to obtain the user's normal breathing parameters;

[0085] Generating a curve of the user's training stability degree;

[0086] Personalized parameter generation, for:

[0087] Generating the user's personalized training parameters based on the normal breathing parameters and the training stability degree curve;

[0088] Send the personalized training parameters to the respiratory rehabilitation training platform for optimizing the training plan.

[0089] Preferably, it further includes a virtual ward environment simulation module, which is communicatively connected to the virtual reality display terminal and is used for:

[0090] Scene construction, which is used for:

[0091] Construct a virtual ward environment, including elements such as ward layout and medical equipment;

[0092] Training guidance, which is used for:

[0093] Display the breathing training curve in the virtual ward environment;

[0094] Guide the user to inhale and exhale through virtual objects;

[0095] Difficulty adjustment, which is used for:

[0096] Set different virtual activities in the virtual ward environment according to the user's training status;

[0097] Adjust the difficulty of the virtual activities to increase the challenge of the training.

[0098] Preferably, it further includes a data management module, which is communicatively connected to the respiratory rehabilitation training platform and is used for:

[0099] Data recording, which is used for:

[0100] Record the user's training data, including training duration, breathing curve, evaluation results, etc.;

[0101] Data analysis, which is used for:

[0102] Conduct statistical analysis on the recorded training data;

[0103] Generate a rehabilitation trend report for the user;

[0104] Data export, which is used for:

[0105] Export the analysis results and the rehabilitation trend report for medical staff to evaluate and use;

[0106] Privacy protection, which is used for:

[0107] Encrypt and store the user's training data;

[0108] Implement permission management for data access.

[0109] The beneficial effects of the present invention are reflected at multiple levels. From a macroscopic perspective, the system realizes the deep integration of technology and medicine, bringing a revolutionary change to the field of respiratory rehabilitation. It not only improves the efficiency and effectiveness of training, but also greatly enhances the training experience of patients, providing new possibilities for the long-term management of chronic respiratory diseases.

[0110] Looking deeper into the specific implementation of the system, the advantages of the present invention become even more obvious. First of all, the combination of the virtual reality display terminal and the respiratory monitoring module enables the system to collect the physiological data of patients in real time while providing an immersive visual experience. This real-time monitoring not only ensures the safety of training, but also lays a data foundation for subsequent intelligent evaluation and adjustment.

[0111] The introduction of the health assessment module is another highlight of the present invention. By analyzing and evaluating the collected respiratory monitoring data in real time, the system can accurately grasp the training status of patients. This immediate evaluation mechanism solves the problem that the training plan cannot be adjusted in time in traditional methods, greatly improving the pertinence and effectiveness of training.

[0112] The respiratory rehabilitation training platform is the core of the present invention. It dynamically generates and adjusts virtual training scenarios by receiving user instructions and evaluation feedback. This adaptive mechanism can not only optimize the training content according to the real-time state of patients, but also formulate personalized training plans based on long-term data analysis. This solves the problem that the training plans in the existing technology are all the same and lack personalization.

[0113] The design of the rehabilitation monitoring module and the sound output module further enhances the comprehensiveness of the system. Real-time training monitoring ensures the safety of the training process, while sound prompts provide additional sensory guidance for patients, making the training more intuitive and acceptable.

[0114] From a microscopic perspective, the collaborative work among various modules produces significant synergistic effects. For example, the cooperation between the respiratory monitoring module and the health assessment module not only provides real-time training guidance, but also accumulates long-term data to provide a comprehensive evaluation of the patient's rehabilitation process. The combination of the virtual reality display terminal and the respiratory rehabilitation training platform creates a real and diverse training environment, effectively solving the problem of the monotony of traditional training methods.

[0115] Generally speaking, through the organic combination of various modules, the present invention realizes the intelligentization, personalization and interestingness of respiratory rehabilitation training. It not only improves the effect and efficiency of training, but also significantly enhances the training enthusiasm and compliance of patients. This all-round improvement will ultimately translate into a significant improvement in the patient's respiratory function and an overall improvement in the quality of life, opening up a new way for the long-term management of chronic respiratory diseases. BRIEF DESCRIPTION OF THE DRAWINGS

[0116] Figure 1 It is the overall logic block diagram of the system of the present invention;

[0117] Figure 2 It is the logic block diagram of the breathing monitoring module of the present invention;

[0118] Figure 3 It is the logic block diagram of the health assessment module of the present invention;

[0119] Figure 4 It is the logic block diagram of the breathing rehabilitation training platform of the present invention;

[0120] Figure 5 It is the logic block diagram of the rehabilitation monitoring module of the present invention;

[0121] Figure 6 It is the logic block diagram of the big data analysis module of the present invention;

[0122] Figure 7 It is the logic block diagram of the virtual ward environment simulation module of the present invention;

[0123] Figure 8 It is the logic block diagram of the data management module of the present invention. Detailed implementation manners

[0124] Please refer to the appendix Figure 1-8 The present invention provides a breathing rehabilitation training guidance system based on virtual reality. This system provides an immersive training experience for breathing rehabilitation patients through virtual reality technology, and at the same time realizes real-time monitoring and personalized guidance. The following will detail the specific implementation manners of the present invention.

[0125] The breathing rehabilitation training guidance system based on virtual reality of the present invention includes a virtual reality display terminal 1, a breathing monitoring module 2, a health assessment module 3, a breathing rehabilitation training platform 4, a rehabilitation monitoring module 5, and a sound output module 6. These modules cooperate with each other through communication connections to jointly form a complete breathing rehabilitation training ecosystem.

[0126] The virtual reality display terminal 1 is the core output device of the system, and is used to display the virtual reality scene images for guiding breathing rehabilitation training to the user. Preferably, the virtual reality display terminal 1 can be a head-mounted display (HMD) to provide the best immersion. The virtual reality display terminal 1 is also used to display the real-time evaluation results of the rehabilitation training effect, enabling the user to intuitively understand their training progress.

[0127] The respiratory monitoring module 2 is communicatively connected to the virtual reality display terminal 1 and is responsible for collecting the respiratory monitoring data of the user during the rehabilitation training. In one embodiment of the present invention, the respiratory monitoring module 2 includes a pulse sensor 21 and a blood oxygen sensor 22. The pulse sensor 21 is used to obtain the user's pulse data in real time, while the blood oxygen sensor 22 is used to obtain the user's blood oxygen data in real time. These two types of data together constitute comprehensive respiratory monitoring data, providing a basis for subsequent health assessment.

[0128] It should be noted that the pulse sensor 21 and the blood oxygen sensor 22 can adopt the non-invasive photoplethysmography (PPG) technology to collect data through sensors worn on the user's fingers or earlobes and other parts. This method can not only ensure the accuracy of the data but also cause no discomfort to the user, which is beneficial to improving the user's training compliance.

[0129] The health assessment module 3 is communicatively connected to the respiratory monitoring module 2 and the virtual reality display terminal 1 and is one of the core processing units of the system. The health assessment module 3 first receives the respiratory monitoring data sent by the respiratory monitoring module 2, and then based on these data, it conducts a real-time assessment of the rehabilitation training effect. The assessment results are sent to the virtual reality display terminal 1 in the form of assessment feedback information for display, enabling the user to timely understand their training status.

[0130] In a preferred embodiment of the present invention, the health assessment module 3 adopts an innovative assessment algorithm. This algorithm takes into account multiple factors, including but not limited to the user's respiratory rate, blood oxygen saturation, pulse variability, etc. The assessment results are calculated through the following formula:

[0131]

[0132] where E is the assessment score, R, S, and P are the user's current respiratory rate, blood oxygen saturation, and pulse variability respectively, R std , S std , P std are the corresponding standard values, and w1, w2, and w3 are the weight coefficients. The selection of the weight coefficients can be adjusted according to the specific conditions of different users. For example, for patients with chronic obstructive pulmonary disease (COPD), more attention may be paid to blood oxygen saturation, so w2 can be set larger.

[0133] The respiratory rehabilitation training platform 4 is another core component of the system. It is communicatively connected to the virtual reality display terminal 1 and the health assessment module 3. This platform is responsible for receiving the user's input instructions and generating corresponding virtual reality scene images according to these instructions. More importantly, the respiratory rehabilitation training platform 4 can dynamically adjust the rehabilitation training content and training intensity based on the assessment feedback information of the health assessment module 3.

[0134] For example, when the evaluation result shows that the user is in a state of insufficient training, the respiratory rehabilitation training platform 4 may increase the training difficulty, such as extending the inhalation time or increasing the breathing frequency. Conversely, if the user is in a state of overtraining, the platform may reduce the difficulty and give the user more rest time. This adaptive adjustment mechanism ensures the personalization and effectiveness of the training.

[0135] The rehabilitation monitoring module 5 is communicatively connected to the respiratory rehabilitation training platform 4. Its main function is to monitor the user's rehabilitation training according to the instructions of the respiratory rehabilitation training platform 4. Specifically, the rehabilitation monitoring module 5 will obtain the user's training respiratory curve in real time. This curve records the user's breathing pattern during the training process and is an important basis for evaluating the training effect.

[0136] It should be noted that the rehabilitation monitoring module 5 adopts advanced respiratory curve analysis technology. This technology decomposes the respiratory curve into components of different frequencies through Fourier transform, so as to accurately identify the regularity and abnormalities of breathing. The specific analysis formula is as follows:

[0137]

[0138] Among them, F(ω) is the spectrum of the respiratory curve, f(t) is the original respiratory curve, t is time, and ω is the angular frequency. By analyzing the characteristics of F(ω), the system can identify whether the user's breathing pattern meets the training requirements.

[0139] Finally, the sound output module 6 is communicatively connected to the virtual reality display terminal 1 and the rehabilitation monitoring module 5, and is used to provide sound prompts to the user according to the requirements of respiratory rehabilitation training. These sound prompts may include guidance on breathing rhythm, encouraging words, warning messages, etc., aiming to enhance the user's training experience and effect.

[0140] Generally speaking, the virtual reality-based respiratory rehabilitation training guidance system of the present invention provides a comprehensive, intelligent and personalized respiratory rehabilitation training solution through the close cooperation of each module. The system can not only monitor the user's physiological state in real time, but also dynamically adjust the training content according to the evaluation results. At the same time, with the help of virtual reality technology, the training interest and attraction are improved. This innovative training method is expected to significantly improve the effect of respiratory rehabilitation and bring benefits to the majority of patients with respiratory diseases.

[0141] In a preferred embodiment of the present invention, the health assessment module 3 can not only perform real-time evaluation, but also classify the user's rehabilitation training status in more detail. Specifically, the health assessment module 3 includes an evaluation result generation unit 31, which can classify the user's rehabilitation training status into three types: training up to standard, insufficient training, and overtraining.

[0142] The evaluation result generation unit 31 adopts a set of complex judgment criteria to ensure the accuracy and reliability of the evaluation results. For the training compliance status, the system requires that the user's actual breathing curve highly coincides with the standard breathing curve. Specifically, when the user's actual breathing curve matches the standard breathing curve, and the frequency of the actual breathing curve reaches more than 90% of the frequency of the standard breathing curve, and this state lasts for more than 60% of the entire training time, the system will determine that the user has reached the training compliance status.

[0143] The threshold of 90% is selected for the frequency matching degree because this level can not only ensure the training effect but also not bring excessive pressure to the user. The 60% duration requirement takes into account the possible short-term fluctuations during the user's training process and also provides a certain adaptation time for the user.

[0144] For the insufficient training status, the system of the present invention adopts a more flexible judgment criterion. When the frequency of the user's actual breathing curve is lower than 80% of the frequency of the standard breathing curve, the system will determine it as insufficient training. In addition, if the frequency of the actual breathing curve is higher than the standard breathing frequency but lower than 120% of the standard breathing frequency, the system will also determine it as insufficient training. This design takes into account the differences in the physical conditions and training abilities of different users and avoids the frustration that may be brought by overly strict criteria.

[0145] It should be noted that the present invention also particularly focuses on the easily overlooked state of over-training. When the frequency of the user's actual breathing curve is higher than 120% of the frequency of the standard breathing curve and this situation lasts for more than 10% of the entire training time, the system will determine that the user is in the over-training state. The setting of this judgment criterion aims to prevent users from over-training and avoid possible physical injuries.

[0146] In another embodiment of the present invention, the respiratory rehabilitation training platform 4 includes a virtual scene generation module 41, a training content adjustment module 42, and a training plan generation module 43. The collaborative work of these modules enables the system of the present invention to provide a highly personalized and dynamically adjustable training experience.

[0147] The virtual scene generation module 41 is responsible for generating corresponding virtual reality scene images according to the user's input instructions. Preferably, this module can also dynamically call different virtual scenes according to the changes in the rehabilitation training content. For example, when performing deep breathing training, the system may generate a serene forest scene; while when performing rapid breathing training, it may switch to a vibrant beach scene. This dynamic scene switching can not only increase the fun of training but also help users better adjust their breathing rhythm through visual cues.

[0148] The training content adjustment module 42 maintains a close communication connection with the health assessment module 3. It receives the assessment feedback information from the health assessment module 3 and adjusts the rehabilitation training content and training intensity in real time based on this information. This adjustment is a major feature of the system of the present invention, reflecting the intelligence and adaptability of the system.

[0149] Specifically, when the user is in a state of insufficient training, the training content adjustment module 42 will take a series of measures to increase the training intensity. This may include increasing the frequency of the training respiration curve, for example, increasing the respiration rate from 12 times per minute to 15 times; reducing the amplitude of the training respiration curve, such as reducing the inhalation volume from 2000 ml to 1800 ml; or increasing the number of vibrations of the training respiration curve, such as increasing the number of small and rapid breaths in one respiration cycle.

[0150] On the contrary, when the user is in a state of over-training, the training content adjustment module 42 will appropriately reduce the training intensity. This may be manifested as reducing the frequency of the training respiration curve, such as reducing the respiration rate from 20 times per minute to 16 times; increasing the amplitude of the training respiration curve, such as increasing the inhalation volume from 1500 ml to 1800 ml; or reducing the number of vibrations of the training respiration curve.

[0151] The training plan generation module 43 generates a personalized respiratory rehabilitation training plan based on the output of the training content adjustment module 42. This plan not only includes the specific content and intensity of each training, but also arranges a reasonable training frequency and rest time. Preferably, the training plan generation module 43 also takes into account the user's daily routine and personal preferences to ensure the feasibility and sustainability of the training plan.

[0152] In another embodiment of the present invention, the rehabilitation monitoring module 5 includes a respiration curve acquisition unit 51, a data analysis unit 52, and an alarm trigger unit 53. The design of these units aims to achieve a full range of monitoring of the user's training process to ensure the safety and effectiveness of the training.

[0153] The respiration curve acquisition unit 51 is responsible for obtaining the user's training respiration curve in real time. This process is usually completed with the help of a high-precision respiration flow sensor. Preferably, the sensor adopted in the present invention can capture the change of the user's respiration flow at a sampling frequency of 100 Hz, and this high temporal resolution ensures the accuracy of the respiration curve.

[0154] The data analysis unit 52 receives the training respiration curve from the respiration curve acquisition unit 51 and compares it with a preset standard respiration curve. This comparison process employs advanced signal processing techniques, including but not limited to Fourier transform, wavelet analysis, etc. Through these techniques, the system can accurately quantify the difference between the user's actual respiration curve and the standard respiration curve. When processing the respiration curve, to remove interference noise and accurately quantify the difference between the actual respiration curve and the standard respiration curve, short-time Fourier transform (STFT) combined with a Hann window can be used for signal analysis. Assuming a sampling rate of 100 Hz, the window size is selected as 1 second (i.e., 100 sample points), and the step size is set to half of the window size (50 sample points) to capture the changes in the respiration signal while maintaining sufficient frequency resolution. In specific operations, the Hann window is used to reduce spectral leakage, making power line interference such as 50 Hz more obvious for subsequent filtering processing.

[0155] In particular, when it is detected that the difference between the user's actual respiration curve and the standard respiration curve continuously exceeds the set threshold, the data analysis unit 52 generates an alarm signal. The setting of this threshold is determined based on a large amount of clinical data and expert opinions. For example, for patients with mild respiratory disorders, the threshold may be set at a 20% deviation; for severe patients, considering their possible respiratory instability, the threshold may be relaxed to 30%.

[0156] The alarm trigger unit 53 receives the alarm signal from the data analysis unit 52 and triggers corresponding safety measures accordingly. This may include instructing the respiratory rehabilitation training platform 4 to pause the training progress or emitting an alarm sound through the sound output module 6. Preferably, the alarm trigger unit 53 can also take different measures according to the severity of the alarm. For example, for a minor deviation, the system may only display a gentle reminder on the virtual reality display terminal 1; for a severe deviation, the system may immediately interrupt the training and notify the medical staff.

[0157] Through this multi-level monitoring and early warning mechanism, the system of the present invention can maximize the guarantee of the user's training safety and also provide the possibility of timely intervention for medical staff. This not only improves the effectiveness of the training but also greatly enhances the confidence of users and medical staff in the system.

[0158] In a preferred embodiment of the present invention, the system further includes a big data analysis module 8, which is communicatively connected to the rehabilitation monitoring module 5 and aims to provide a more accurate personalized training plan through large-scale data analysis. The big data analysis module 8 mainly includes a data collection unit 81, a statistical analysis unit 82, and a personalized parameter generation unit 83.

[0159] The data collection unit 81 is responsible for collecting multiple pieces of training data of the user within a set time range. This time range is usually set to 4 - 6 weeks because this period is sufficient to reflect the user's training effect and physiological change trend. The collected data includes not only the breathing curves of each training session but also multi-dimensional information such as the user's heart rate changes, blood oxygen saturation, and training duration. Preferably, the data collection unit 81 also records the user's subjective feelings and daily living habits for more comprehensive analysis.

[0160] The statistical analysis unit 82 conducts in-depth analysis on the multiple pieces of training data collected to obtain the user's normal breathing parameters. This process involves complex statistical methods and machine learning algorithms. For example, the system may use principal component analysis (PCA) to reduce the data dimension and then apply the K-means clustering algorithm to identify the user's typical breathing patterns. The specific mathematical model can be expressed as follows:

[0161] PCA: X = W∑V T ,

[0162]

[0163] L - where X represents the original data matrix, W and V are the left and right singular vectors respectively, and ∑ is the singular value matrix. In the K-means formula, S represents the clustering result, k is the number of clusters, and μ i is the center of the i-th cluster.

[0164] In addition, the statistical analysis unit 82 is also responsible for generating the user's training stability degree curve. This curve reflects the performance stability of the user at different training stages and is of great significance for evaluating the training effect and adjusting the training plan. The generation of the curve adopts a combination of the moving average method and the exponential smoothing method, which can not only reflect the overall trend but also capture short-term fluctuations.

[0165] The personalized parameter generation unit 83 generates the user's personalized training parameters based on the results of the statistical analysis. These parameters include but are not limited to the recommended training frequency, the optimal duration of each training session, and the reasonable range of breathing frequency, etc. When generating personalized training parameters for the user, in the face of parameters with different ranges and magnitudes, these data can be standardized by normalization. A common practice is to use min-max normalization to linearly map all data to between 0 and 1, and the formula is This is suitable for the case where the data boundaries are known. If the data distribution is unknown or there are outliers, Z-score normalization is a good choice, which transforms the data into a form with a mean of 0 and a standard deviation of 1, and the calculation formula is Doing so can not only eliminate the impact of different scales, but also improve the performance of the algorithm, making the personalized training parameters generated based on the user's physiological characteristics, training goals and historical performance, such as recommended training frequency, optimal duration of each training, etc., more accurate and effective. Through appropriate normalization methods, it can be ensured that these diverse parameters can be better utilized by the optimization algorithm, thereby providing more personalized fitness recommendations. In particular, the present invention adopts an innovative parameter optimization algorithm that comprehensively considers the user's physiological characteristics, training goals and historical performance. The core idea of ​​the algorithm can be expressed by the following formula:

[0166]

[0167] Among them, P opt is the optimal personalized parameter set, P is all possible parameter combinations, H represents the user's historical data, G is the training target, and F is a comprehensive scoring function.

[0168] These generated personalized training parameters are then sent to the respiratory rehabilitation training platform 4 for optimizing the training plan. In this way, the system of the present invention can continuously adjust and optimize the training program to ensure that it always meets the actual needs and physical condition of the user.

[0169] In another embodiment of the present invention, the system further includes a virtual ward environment simulation module 9. The module is connected to the virtual reality display terminal 1 in communication with the virtual reality display terminal 1, and is intended to provide a user with a realistic and functional virtual training environment. The virtual ward environment simulation module 9 mainly includes a scene construction unit 91, a training guidance unit 92 and a difficulty adjustment unit 93.

[0170] The scene construction unit 91 is responsible for constructing a virtual ward environment, including elements such as ward layout and medical equipment.

[0171] The training guidance unit 92 displays the breathing training curve in the virtual ward environment and guides the user to inhale and exhale through virtual objects. The virtual object here may be a friendly virtual nurse, or an intuitive visual indicator, such as a balloon that rises and falls with the breathing rhythm. In particular, the present invention adopts an innovative breathing visualization technology to convert the user's breathing pattern into a dynamic element in the virtual environment. For example, the user's inhalation may make the plants in the virtual room grow, while exhalation may make the room brighter. This design not only increases the fun of training, but also helps users perceive their breathing patterns more intuitively.

[0172] The difficulty adjustment unit 93 sets different virtual activities in the virtual ward environment according to the user's training status and adjusts the difficulty of these activities to increase the challenge of training. For example, for novice users, the system may set simple breathing coordination tasks, such as breathing following the rise and fall of a virtual balloon; while for advanced users, the system may add more complex tasks, such as completing a virtual jigsaw puzzle while performing breathing training.

[0173] Finally, the system of the present invention further includes a data management module 10, which is communicatively connected to the respiratory rehabilitation training platform 4 and is responsible for functions such as data recording, analysis, export, and privacy protection of the system. The data management module 10 includes a data recording unit 101, a data analysis unit 102, a data export unit 103, and a privacy protection unit 104.

[0174] The data recording unit 101 is responsible for recording the user's training data, including training duration, breathing curve, evaluation results, etc.

[0175] The data analysis unit 102 performs statistical analysis on the recorded training data to generate a rehabilitation trend report for the user. This process involves various advanced statistical methods and machine learning techniques, such as time series analysis, regression analysis, etc.

[0176] The data export unit 103 is responsible for exporting the analysis results and the rehabilitation trend report for medical staff to evaluate. The exported data adopts a standardized format, such as JSON or XML, to ensure compatibility with various medical information systems. In addition, the present invention also provides a WebAPI interface, allowing authorized medical institutions to directly obtain the required data from the system, thereby realizing seamless integration and real-time sharing of data.

[0177] The privacy protection unit 104 encrypts and stores the user's training data and implements permission management for data access. The present invention adopts the industry-leading AES-256 encryption algorithm to ensure the security of data during storage and transmission. At the same time, the system also implements a role-based access control (RBAC) mechanism to finely manage the access permissions of different users to the data.

[0178] Through these carefully designed modules and units, the virtual reality-based respiratory rehabilitation training guidance system of the present invention not only provides users with an efficient, safe, and personalized training experience, but also provides valuable data support and analysis tools for medical institutions, and is expected to bring a revolutionary change in the field of rehabilitation treatment of respiratory diseases.

[0179] To verify the superiority of the virtual reality-based breathing rehabilitation training guidance system of the present invention, we designed a set of comparative experiments. 60 patients with chronic obstructive pulmonary disease (COPD) aged 55 - 70 years and with a disease course of 3 - 8 years were selected as the research subjects. The patients were randomly divided into three groups of 20 people each, and were respectively given breathing rehabilitation training for 8 weeks using the system of the present invention (Example 1), the traditional breathing training method (Comparative Example 1), and the simple VR breathing training system (Comparative Example 2).

[0180] Example 1: Training was carried out using the virtual reality-based breathing rehabilitation training guidance system of the present invention. The patients wore VR headsets, and the system provided an immersive training experience through the virtual ward environment simulation module. During the training process, the system real-time monitored the patients' breathing data and carried out real-time evaluation through the health assessment module, dynamically adjusting the training difficulty.

[0181] Comparative Example 1: The traditional breathing training method was adopted, and the patients were guided by a respiratory therapist to carry out pursed-lip breathing and diaphragmatic breathing training. During the training process, the therapist evaluated the training effect of the patients through observation and simple breathing monitoring equipment.

[0182] Comparative Example 2: A simple VR breathing training system was adopted, which provided a basic virtual environment and breathing guidance, but lacked real-time monitoring and personalized adjustment functions.

[0183] During the experiment, we mainly focused on the following indicators:

[0184] 1. Degree of improvement in lung function: Evaluated by measuring the forced expiratory volume in one second (FEV1).

[0185] 2. Degree of dyspnea: Evaluated using the modified Medical Research Council dyspnea scale (mMRC).

[0186] 3. Exercise endurance: Measured by the 6-minute walk test (6MWT).

[0187] 4. Quality of life: Evaluated using the St. George's Respiratory Questionnaire (SGRQ).

[0188] 5. Training compliance: Recorded the percentage of patients who completed the scheduled number of training sessions.

[0189] After 8 weeks of training, we obtained the following test results:

[0190] Index Example 1 Comparative Example 1 Comparative Example 2 FEV1 improvement rate 15.2％ 8.7％ 10.5％ Decrease in mMRC score 1.8 points 0.9 points 1.2 points Increase in 6MWT distance 48 meters 25 meters 32 meters Improvement in SGRQ score 12.5 points 6.8 points 8.7 points Training compliance 92％ 75％ 83％

[0191] It can be seen from the test results that the system of the present invention (Example 1) is significantly superior to the traditional method (Comparative Example 1) and the simple VR system (Comparative Example 2) in all indicators.

[0192] In terms of lung function, the improvement rate of FEV1 in Example 1 reached 15.2%, which was much higher than 8.7% in Comparative Example 1 and 10.5% in Comparative Example 2. This indicates that the system of the present invention can more effectively improve the lung function of patients. This may benefit from the real-time monitoring and personalized adjustment functions of the system, enabling each training to be carried out within the patient's zone of proximal development, which is both challenging and does not cause excessive fatigue.

[0193] In terms of the improvement of the degree of dyspnea, the mMRC score in Example 1 decreased by 1.8 points, while in Comparative Examples 1 and 2, it only decreased by 0.9 points and 1.2 points respectively. This shows that the system of the present invention can better help patients relieve the symptoms of dyspnea. The immersion of the virtual reality environment may psychologically reduce the patient's anxiety, thus objectively improving the feeling of dyspnea.

[0194] The improvement of exercise endurance also shows the advantages of the system of the present invention. The distance in the 6MWT in Example 1 increased by 48 meters, while in Comparative Examples 1 and 2, it only increased by 25 meters and 32 meters respectively. This may be because the system of the present invention simulates various daily activity scenarios through the virtual environment, making the training closer to real life, thereby improving the pertinence and effectiveness of the training.

[0195] The improvement of quality of life is also significant. The SGRQ score in Example 1 improved by 12.5 points, which was much higher than 6.8 points in Comparative Example 1 and 8.7 points in Comparative Example 2. This indicates that the system of the present invention not only improves the physical condition of patients, but also significantly improves their quality of life. This may be related to the immersive experience and gamification elements provided by the system, making the training process more interesting and motivating.

[0196] Finally, the data of training compliance also reflect the advantages of the system of the present invention. The training compliance in Example 1 was as high as 92%, while in Comparative Examples 1 and 2, it was only 75% and 83% respectively. This shows that patients are more willing to adhere to the training using the system of the present invention. High compliance not only ensures the training effect, but also lays a foundation for long-term rehabilitation.

[0197] In summary, the virtual reality-based respiratory rehabilitation training guidance system of the present invention shows significant advantages in improving patients' lung function, relieving dyspnea, enhancing exercise endurance, improving quality of life, and increasing training compliance. These advantages mainly stem from several innovative points of the system: the real-time monitoring and personalized adjustment functions ensure the accuracy and effectiveness of the training; the immersive experience provided by virtual reality technology enhances the fun and psychological comfort of the training; the multi-dimensional training content and difficulty adjustment mechanism meet the personalized needs of different patients.

[0198] This set of comparative experiments not only verifies the superiority of the system of the present invention, but also points out the direction for the future development of breathing rehabilitation training methods. It shows that the combination of advanced technology and medical rehabilitation can significantly improve the treatment effect and patient experience. In the future, similar intelligent and personalized rehabilitation systems are expected to play an important role in the treatment of more chronic diseases.

[0199] It should be noted that the above are only the preferred embodiments of the present invention, and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A virtual reality-based breathing rehabilitation training guidance system, characterized in that, Comprising: A virtual reality display terminal, configured to: Display virtual reality scene images for guiding the user to perform breathing rehabilitation training; Show the real-time evaluation results of the rehabilitation training effect; A breathing monitoring module, communicatively connected to the virtual reality display terminal, configured to: Collect the breathing monitoring data of the user during the rehabilitation training; Send the breathing monitoring data to the health assessment module; A health assessment module, communicatively connected to the breathing monitoring module and the virtual reality display terminal, configured to: Receive the breathing monitoring data sent by the breathing monitoring module; Based on the breathing monitoring data, perform real-time evaluation on the rehabilitation training effect; Send the evaluation feedback information to the virtual reality display terminal for display; A breathing rehabilitation training platform, communicatively connected to the virtual reality display terminal and the health assessment module, configured to: Receive the input instructions of the user; Generate corresponding virtual reality scene images according to the input instructions; Based on the evaluation feedback information of the health assessment module, adjust the rehabilitation training content and training intensity; A rehabilitation monitoring module, communicatively connected to the breathing rehabilitation training platform, configured to: According to the instructions of the breathing rehabilitation training platform, monitor the user's rehabilitation training; Obtain the training breathing curve of the user in real time; A sound output module, communicatively connected to the virtual reality display terminal and the rehabilitation monitoring module, configured to: Provide sound prompts to the user according to the requirements of the breathing rehabilitation training.

2. The system according to claim 1, wherein The breathing monitoring module includes: A pulse sensor, configured to obtain the pulse data of the user in real time; An oxygen saturation sensor, configured to obtain the oxygen saturation data of the user in real time; A data processing unit, communicatively connected to the pulse sensor and the oxygen saturation sensor, configured to: Receive the data collected by the pulse sensor and the oxygen saturation sensor; Based on the pulse data and oxygen saturation data, generate the breathing monitoring data.

3. The system according to claim 1, wherein The health assessment module includes: A data comparison unit, configured to: Receive the breathing monitoring data; Compare the breathing monitoring data with the preset normal data; An evaluation result generation unit, communicatively connected to the data comparison unit, configured to: Based on the data comparison result, generate the real-time evaluation result of the rehabilitation training effect; Classify the user's rehabilitation training status into training up to standard, insufficient training, and over-training; A feedback information generation unit, communicatively connected to the evaluation result generation unit, configured to: Generate evaluation feedback information according to the real-time evaluation result; Send the evaluation feedback information to the virtual reality display terminal and the breathing rehabilitation training platform.

4. The system according to claim 3, wherein, The evaluation result generation unit is further configured to: Determine that the training is up to standard when the following conditions are met: The actual breathing curve of the user matches the standard breathing curve; The frequency of the actual breathing curve reaches 90% of the frequency of the standard breathing curve; The duration of the above state reaches 60%; Determine that the training is insufficient when one of the following conditions is met: The frequency of the actual breathing curve is lower than 80% of the frequency of the standard breathing curve; The frequency of the actual breathing curve is higher than the standard breathing frequency but lower than 120% of the standard breathing frequency; Determine that the training is over-training when the following conditions are met: The actual breathing curve is higher than 120% of the frequency of the standard breathing curve; The time exceeding 120% of the standard breathing frequency is more than 10%.

5. The system according to claim 1, wherein The breathing rehabilitation training platform includes: A virtual scene generation module, configured to: Generate corresponding virtual reality scene images according to the user's input instructions; Dynamically call different virtual scenes according to the changes in the rehabilitation training content; A training content adjustment module, communicatively connected to the health assessment module, configured to: Receive the assessment feedback information from the health assessment module; Adjust the rehabilitation training content and training intensity based on the assessment feedback information; A training plan generation module, communicatively connected to the training content adjustment module, configured to: Generate a personalized breathing rehabilitation training plan according to the adjusted training content and intensity.

6. The system according to claim 5, characterized in that, The training content adjustment module is further configured to: When the user is in a state of insufficient training: Increase the frequency of the training breathing curve; Reduce the amplitude of the training breathing curve; Increase the number of vibrations of the training breathing curve; When the user is in a state of over-training: Reduce the frequency of the training breathing curve; Increase the amplitude of the training breathing curve; Reduce the number of vibrations of the training breathing curve.

7. The system according to claim 1, characterized in that, The rehabilitation monitoring module includes: A breathing curve acquisition unit, configured to: Obtain the user's training breathing curve in real time; A data analysis unit, communicatively connected to the breathing curve acquisition unit, configured to: Receive the training breathing curve; Compare the training breathing curve with a preset standard breathing curve; Generate an alarm signal when it is detected that the difference between the user's actual breathing curve and the standard breathing curve continuously exceeds a set threshold; An alarm trigger unit, communicatively connected to the data analysis unit, configured to: Receive the alarm signal; Trigger the breathing rehabilitation training platform to stop the training progress or issue an alarm.

8. The system according to claim 7, wherein It further includes a big data analysis module, communicatively connected to the rehabilitation monitoring module, configured to: Data collection, configured to: Collect multiple training data of the user within a set time range; Statistical analysis, configured to: Analyze the multiple training data to obtain the user's normal breathing parameters; Generate a training stability degree curve of the user; Personalized parameter generation, configured to: Generate the user's personalized training parameters based on the normal breathing parameters and the training stability degree curve; Send the personalized training parameters to the breathing rehabilitation training platform for optimizing the training plan.

9. The system according to claim 1, wherein It further includes a virtual ward environment simulation module, communicatively connected to the virtual reality display terminal, configured to: Scene construction, configured to: Construct a virtual ward environment, including elements such as ward layout and medical equipment; Training guidance, configured to: Display the breathing training curve in the virtual ward environment; Guide the user to inhale and exhale through virtual objects; Difficulty adjustment, configured to: Set different virtual activities in the virtual ward environment according to the user's training status; Adjust the difficulty of the virtual activities to increase the challenge of the training.

10. The system according to claim 1, wherein, It further includes a data management module, communicatively connected to the breathing rehabilitation training platform, configured to: Data recording, configured to: Record the user's training data, including training duration, breathing curve, assessment results, etc.; Data analysis, configured to: Conduct statistical analysis on the recorded training data; Generate a rehabilitation trend report of the user; Data export, for: Export the analysis results and rehabilitation trend reports for medical staff to evaluate; Privacy protection is used for: Encrypt and store the user's training data; Implement permission management for data access.

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