Training management system for lung function rehabilitation

Abstract

The invention relates to the technical field of lung function rehabilitation, and discloses a training management system for lung function rehabilitation, which comprises a basic monitoring module, an accurate evaluation module, a training monitoring module and a feedback management module, and is characterized in that the basic monitoring system is used for constructing a patient lung portrait data set and a patient sign data set; the precise evaluation module is used for calculating a patient training adaptation coefficient and generating a training suggestion after matching with a built-in training intensity rule based on a patient training adaptation coefficient calculation result, and the training monitoring module is used for integrating patient training parameters to construct a patient training data set and generating a training adjustment suggestion according to the patient training data set; and the feedback management module is used for performing feedback display on the training adjustment suggestions and the monitoring process. The system provides a consideration basis based on the self-condition of the patient for lung function rehabilitation of the patient, realizes gradient design of training intensity rules, and provides accurate suggestions based on the self-adaptation condition of the patient for training adjustment.

Description

Technical Field

[0001] This invention relates to the field of pulmonary function rehabilitation technology, specifically to a training and management system for pulmonary function rehabilitation. Background Technology

[0002] Pulmonary rehabilitation is a comprehensive medical intervention aimed at restoring and improving lung function after respiratory diseases or injuries. Based on scientific assessment, it combines personalized exercise training (such as breathing exercises and aerobic endurance training), breathing technique guidance (diaphragmatic breathing, pursed-lip breathing, etc.), physical therapy methods (vibration expectoration, postural drainage), and health education to help patients strengthen their diaphragm, improve ventilation efficiency, promote secretion clearance, and gradually rebuild normal gas exchange capacity. This process not only focuses on improving physiological indicators but also emphasizes the restoration of psychological adaptation and social function. It is applicable to various clinical scenarios such as chronic obstructive pulmonary disease, sequelae of pneumonia, and postoperative cardiopulmonary complications, aiming to improve patients' quality of life, reduce the risk of acute exacerbations, and ultimately maximize and maintain long-term stability of spontaneous breathing capacity.

[0003] Current pulmonary function rehabilitation training programs are mostly based on standardized procedures. Although they emphasize individualization, in practice, it is often difficult to develop the optimal program based on each patient's specific situation with complete precision. At the same time, it is difficult to accurately reflect the patient's physical condition in real time during the training process and adjust the training intensity according to the patient's actual physical condition, thus affecting the rehabilitation efficiency. Summary of the Invention

[0004] (a) Technical problems to be solved To address the shortcomings of existing technologies, this invention provides a training and management system for pulmonary function rehabilitation. This system first constructs a patient lung profiling dataset and a patient vital signs dataset, providing a basis for assessing the patient's individual condition for pulmonary function rehabilitation. It then monitors the training parameters of trained patients, providing a reference basis for evaluating training effectiveness and directly reflecting whether the training volume has met the target. By calculating the patient training fit coefficient, it achieves gradient-based design of training intensity rules, ensuring that training remains within the optimal physiological response range, improving rehabilitation gains for patients at different baseline levels and shortening the clinical improvement cycle. Finally, the patient training effectiveness index more comprehensively and objectively reflects the patient's training completion effect, more accurately measures the actual impact of training on the patient, and provides accurate suggestions for training adjustment based on the patient's own adaptation status.

[0005] (II) Technical Solution To achieve the above objectives, the present invention provides the following technical solution: a training management system for pulmonary function rehabilitation, comprising establishing a basic monitoring module, establishing a precision assessment module that is connected to the basic monitoring module via a network, establishing a training monitoring module that shares data with the precision assessment module, and establishing a feedback management module applied to the training monitoring module; The basic monitoring system is used to work with the hospital data management platform to obtain multiple lung profile data and multidimensional vital sign data of patients to construct patient lung profile datasets and patient vital sign datasets. The precise assessment module is used to link the patient's lung image dataset and the patient's vital signs dataset to calculate the patient training fit coefficient, and generate training suggestions based on the calculated patient training fit coefficient and the built-in training intensity rules. The training monitoring module is used to monitor the training parameters of patients undergoing training, integrate the patient training parameters to construct a patient training dataset, and generate training adjustment suggestions based on the patient training dataset. The feedback management module is used to display feedback on training adjustment suggestions and the monitoring process.

[0006] Preferably, the lung profile dataset includes multiple lung profile data specifically: forced expiratory volume in the first second as a percentage of the predicted value, vital capacity, carbon monoxide diffusion as a percentage of the predicted value, and maximum inspiratory pressure.

[0007] Preferably, the patient vital signs dataset includes the following multidimensional vital signs data: heart rate, arterial oxygen saturation, respiratory rate, tidal volume, subjective fatigue score, and Borg dyspnea score.

[0008] Preferably, the patient training fit coefficient The calculation formula is: ; ; ; ; In the formula, Represents the patient's training fit coefficient. The baseline lung function coefficient represents the patient's overall lung function. Represents the patient's real-time status coefficient; This represents the percentage of the predicted volume of air exhaled in the first second. This represents the percentage of the expected forced expiratory volume in the first second. The weight representing the percentage of the predicted volume of forced exhalation in the first second. Represents lung capacity, Represents the expected lung capacity. The weight representing vital capacity, This represents the percentage of carbon monoxide dispersion relative to the projected value. This represents the percentage of the expected carbon monoxide dispersion. The weight representing the percentage of carbon monoxide dispersion relative to the predicted value. Represents the maximum inhalation pressure. This represents the expected maximum inhalation pressure. The weight representing the maximum inhalation pressure; Preferably, the training intensity rule is formulated based on a high threshold and a low threshold of the patient training fit coefficient.

[0009] Preferably, when the patient's training fit coefficient When the patient's training fit coefficient reaches a high threshold, advanced training rules are applied to the patient. When the patient's training fit coefficient Patient training fit coefficient high threshold, and If the patient's training fit coefficient is at a low threshold, apply intermediate training rules to the patient. When the patient's training fit coefficient If the patient's training fit coefficient is at a low threshold, a low-level training rule is applied to the patient.

[0010] Preferably, the patient training dataset includes training duration and action standardization.

[0011] Preferably, the training adjustment suggestion is generated by jointly calculating the patient training effect index based on the patient training dataset and the continuously monitored patient training fit coefficient calculation results. The calculation formula is: ; In the formula, Represents the patient's training effectiveness index. This represents the patient-training fit coefficient before the patient begins training. The patient-training fit coefficient represents the patient's fitness level after training. Represents training duration. This represents the expected training duration. Represents the degree of standard of movement. The representative factor takes into account the changes in the patient's lungs and physical signs during the training process, and the patient's training effectiveness index is the result of this assessment.

[0012] Preferably, when the patient's training effect index The patient's training efficiency index threshold represents a good training effect, and provides training adjustment suggestions to increase training intensity. When the patient's training effect index Patient training efficiency index threshold, and The patient training standard index threshold represents the achievement of training objectives, and provides training adjustment suggestions to maintain the current training intensity. When the patient's training effect index The patient's training baseline threshold indicates that the training load is too high, and the output provides training adjustment suggestions to reduce the current training intensity.

[0013] Preferably, the feedback management module displays training adjustment suggestions via a screen and voice mode, while simultaneously providing feedback to the doctor management platform.

[0014] Compared with the prior art, the present invention provides a training and management system for pulmonary function rehabilitation, which has the following beneficial effects: 1. This invention acquires multiple lung profile data and multidimensional vital sign data of patients through a basic monitoring system to construct a patient lung profile dataset and a patient vital sign dataset. This provides a basis for patients' lung function rehabilitation based on their own conditions, and constructs a precise dynamic assessment system for lung function. It can not only quantitatively assess the patient's lung function status, but also reflect exercise tolerance and physiological load status in real time. This provides a reference basis for improving the patient's exercise endurance and assessing the patient's training adaptability. By monitoring the patient's training parameters and integrating the patient's training parameters to construct a patient training dataset, it provides a reference basis for evaluating the patient's training effect, intuitively reflects whether the training volume is up to standard, avoids insufficient stimulation of target muscles and reduced training effect due to insufficient training time, and can also prevent excessive fatigue or sports injury caused by excessive training time. It can also promptly detect non-standard movements and prevent training from deviating from the target.

[0015] 2. This invention combines the patient's baseline lung function coefficient with the patient's real-time status coefficient to comprehensively calculate the patient's training fit coefficient. It takes into account the patient's own lung function and physical condition adaptation, and then formulates different levels of training intensity rules based on the training fit coefficient. By designing training intensity rules in a gradient manner, the training is always in the optimal physiological response range, which significantly improves the rehabilitation gains of patients at different baseline levels and shortens the clinical improvement cycle.

[0016] 3. This invention comprehensively considers changes in the patient's lungs and physical signs during training, combined with factors such as the patient's training fit coefficient before and after training, expected training duration, and movement standardization. It can more comprehensively and objectively reflect the patient's training completion effect, more accurately measure the actual impact of training on the patient, and provide accurate suggestions for training adjustment based on the patient's own adaptation. It can precisely judge the patient's training status, avoiding errors that may arise from subjective judgment, and provides a quantitative basis for adjusting training intensity. It can quickly provide suggestions for adjusting training intensity based on the patient's current training effect index, helping to optimize training programs in a timely manner, meet individual differences, and achieve personalized rehabilitation training. When the training effect is good, the intensity can be appropriately increased to further enhance the effect; when the training load is too large, the intensity can be reduced to avoid adverse consequences such as injury caused by overtraining. This promotes the improvement of training effect while ensuring patient safety. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the system of the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] Please see Figure 1 A training management system for pulmonary function rehabilitation includes establishing a basic monitoring module, establishing a precision assessment module that is connected to the basic monitoring module via a network, establishing a training monitoring module that shares data with the precision assessment module, and establishing a feedback management module applied to the training monitoring module. The basic monitoring system is used to link with the hospital data management platform to obtain multiple lung profile data and multidimensional vital sign data of patients to construct patient lung profile datasets and patient vital sign datasets; The patient lung profiling dataset includes multiple lung profiling data points, specifically: forced expiratory volume in one second (FEV1) as a percentage of predicted value, vital capacity, carbon monoxide diffusion as a percentage of predicted value, and maximum inspiratory pressure. The patient vital signs dataset includes the following multidimensional vital signs data: heart rate, arterial oxygen saturation, respiratory rate, tidal volume, subjective fatigue score, and Borg dyspnea score. Based on the basic monitoring system, multiple lung profile data and multidimensional vital sign data of patients are obtained to construct patient lung profile datasets and patient vital sign datasets. This provides a basis for consideration of the patient's own condition for subsequent patient training adaptation coefficient and patient lung function training matching. It constructs a precise dynamic lung function assessment system, which can not only quantitatively assess the patient's lung function status, but also reflect exercise tolerance and physiological load status in real time, providing a reference basis for improving the patient's exercise endurance and assessing the patient's training adaptation ability. The accurate assessment module is used to link the patient lung image dataset and the patient vital signs dataset to calculate the patient training fit coefficient, and then generate training suggestions based on the calculated patient training fit coefficient and the built-in training intensity rules. Patient training fit coefficient The calculation formula is: ; ; ; ; In the formula, Represents the patient's training fit coefficient. The baseline lung function coefficient represents the patient's overall lung function. The patient's real-time status coefficient is represented by the patient's baseline lung function coefficient and the patient's real-time status coefficient. The patient's training fit coefficient is calculated by combining the patient's baseline lung function coefficient and the patient's real-time status coefficient. This comprehensively considers the patient's lung function and physical condition, and achieves a comprehensive assessment of the patient's ability to receive training. This represents the percentage of the predicted volume of air exhaled in the first second. This represents the percentage of the expected forced expiratory volume in the first second. The weight representing the percentage of the predicted volume of forced exhalation in the first second. Represents lung capacity, Represents the expected lung capacity. The weight representing vital capacity, This represents the percentage of carbon monoxide dispersion relative to the projected value. This represents the percentage of the expected carbon monoxide dispersion. The weight representing the percentage of carbon monoxide dispersion relative to the predicted value. Represents the maximum inhalation pressure. This represents the expected maximum inhalation pressure. The weight representing the maximum inhalation pressure, This means that the lung function indicators from four different dimensions and units in the patient's lung profile dataset are integrated into a single, standardized dataset to represent the patient's static, inherent respiratory physiological potential, namely the patient's baseline lung function coefficient. Represents heart rate. Represents the target heart rate. Reflects the patient's cardiovascular condition. Represents blood oxygen saturation. Represents the target blood oxygen saturation. Represents the patient's oxygenation effect. This represents the patient's ventilation efficiency. Represents the Borg dyspnea score. This represents the perceived level of fatigue. Represents the patient's subjective feelings; Represents respiratory rate. Represents the target respiratory rate. Represents tidal volume. Represents the target tidal volume. This represents the ventilation efficiency of a patient who is assessed by simultaneously evaluating respiratory rate and depth. The training intensity rules are formulated based on the high-level threshold and low-level threshold of the patient training fit coefficient. When the patient's training fit coefficient When the patient's training fit coefficient reaches a high threshold, advanced training rules are applied to the patient. When the patient's training fit coefficient Patient training fit coefficient high threshold, and If the patient's training fit coefficient is at a low threshold, apply intermediate training rules to the patient. When the patient's training fit coefficient If the patient's training fit coefficient is at a low threshold, a low-level training rule will be applied to the patient. Advanced training rules, intermediate training rules, and low-level training rules constitute a tiered and progressive training strategy suitable for different patients. Advanced training rules can use moderate to high-intensity aerobic exercise as the core, aiming to improve cardiorespiratory endurance. Intermediate training rules focus on maintaining lung function and improving exercise tolerance, and can select low to moderate-intensity exercise. Low-level training rules are for patients with stable heart failure or chronic respiratory failure. The training aims to ensure safety and improve basic activity ability, and can use very light exercise as the core. By designing training intensity rules in a tiered manner, we can ensure that high-risk patients avoid excessive stress while providing progressive overload stimulation for those with better function. This keeps the training within the optimal physiological response range, significantly improving rehabilitation gains for patients at different baseline levels and shortening the clinical improvement cycle. The training monitoring module is used to monitor the training parameters of patients undergoing training, integrate the patient training parameters to construct a patient training dataset, and generate training adjustment suggestions based on the patient training dataset. The patient training dataset includes training duration and action standardization. A patient training dataset is constructed based on training duration and movement standardization to provide a reference basis for evaluating the training effect of patients. Monitoring the training duration can directly reflect whether the training volume is up to standard, avoiding insufficient stimulation of the target muscles and reduced training effect due to insufficient duration. It can also prevent excessive fatigue or sports injury caused by excessive duration. Monitoring the movement standardization can promptly identify non-standard movements, prevent training from deviating from the target, help trainees gradually optimize the details of the movements, and help each training session to more accurately target the target area, ultimately maximizing the training effect. Training adjustment suggestions are generated by jointly calculating the patient training effectiveness index based on the patient training dataset and the patient training fit coefficient calculated through continuous monitoring. The calculation formula is: ; In the formula, Represents the patient's training effectiveness index. This represents the patient-training fit coefficient before the patient begins training. The patient-training fit coefficient represents the patient's fitness level after training. Represents training duration. This represents the expected training duration. Represents the degree of standard of movement. The representative considers the changes in the patient's lungs and physical signs during the training process, and the patient's training effectiveness is represented by the patient training effectiveness index. The formula for calculating the patient training effectiveness index comprehensively considers changes in the patient's lungs and physical signs during training, and combines factors such as the patient's training fit coefficient before and after training, the expected training duration, and the standardization of movements. It can more comprehensively and objectively reflect the patient's training completion effect, more accurately measure the actual impact of training on the patient, and provide accurate suggestions for training adjustment based on the patient's own adaptation. When the patient's training effect index The patient's training efficiency index threshold represents a good training effect, and provides training adjustment suggestions to increase training intensity. When the patient's training effect index Patient training efficiency index threshold, and The patient training standard index threshold represents the achievement of training objectives, and provides training adjustment suggestions to maintain the current training intensity. When the patient's training effect index The patient's training baseline threshold indicates that the training load is too high, and the output provides training adjustment suggestions to reduce the current training intensity. Training adjustment suggestions, by setting clear numerical thresholds, can accurately determine the patient's training status, avoiding errors that may arise from subjective judgment. This provides a quantitative basis for adjusting training intensity and can quickly provide suggestions for adjusting training intensity based on the patient's current training effect index. This helps to optimize training programs in a timely manner, meet individual differences, and achieve personalized rehabilitation training. When the training effect is good, the intensity can be appropriately increased to further enhance the effect; when the training load is too large, the intensity can be reduced to avoid adverse consequences such as overtraining and injury. This promotes the improvement of training effect while ensuring patient safety. The feedback management module displays training adjustment suggestions via a screen and voice mode, and simultaneously provides feedback to the doctor management platform along with the training adjustment suggestions and monitoring process.

[0020] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A training and management system for pulmonary function rehabilitation, characterized in that: This includes establishing a basic monitoring module, establishing a precision evaluation module that connects to the basic monitoring module via a network, establishing a training monitoring module that shares data with the precision evaluation module, and establishing a feedback management module applied to the training monitoring module. The basic monitoring system is used to work with the hospital data management platform to obtain multiple lung profile data and multidimensional vital sign data of patients to construct patient lung profile datasets and patient vital sign datasets. The precise assessment module is used to link the patient's lung image dataset and the patient's vital signs dataset to calculate the patient training fit coefficient, and generate training suggestions based on the calculated patient training fit coefficient and the built-in training intensity rules. The training monitoring module is used to monitor the training parameters of patients undergoing training, integrate the patient training parameters to construct a patient training dataset, and generate training adjustment suggestions based on the patient training dataset. The feedback management module is used to display feedback on training adjustment suggestions and the monitoring process.

2. The training and management system for pulmonary function rehabilitation according to claim 1, characterized in that: The patient lung profiling dataset includes multiple lung profiling data, specifically: forced expiratory volume in one second as a percentage of the predicted value, vital capacity, carbon monoxide diffusion as a percentage of the predicted value, and maximum inspiratory pressure.

3. The training and management system for pulmonary function rehabilitation according to claim 1, characterized in that: The patient vital signs dataset includes the following multidimensional vital signs data: heart rate, arterial oxygen saturation, respiratory rate, tidal volume, subjective fatigue score, and Borg dyspnea score.

4. A training and management system for pulmonary function rehabilitation according to claim 2, characterized in that: The patient training fit coefficient The calculation formula is: ； ； ； ； In the formula, Represents the patient's training fit coefficient. The baseline lung function coefficient represents the patient's overall lung function. Represents the patient's real-time status coefficient; This represents the percentage of the predicted volume of air exhaled in the first second. This represents the percentage of the expected forced expiratory volume in the first second. The weight representing the percentage of the predicted volume of forced exhalation in the first second. Represents lung capacity, Represents the expected lung capacity. The weight representing vital capacity, This represents the percentage of carbon monoxide dispersion relative to the projected value. This represents the percentage of expected carbon monoxide dispersion relative to the predicted value. The weight representing the percentage of carbon monoxide dispersion relative to the predicted value. Represents the maximum inhalation pressure. This represents the expected maximum inhalation pressure. The weight representing the maximum inhalation pressure; Represents heart rate. Represents the target heart rate. Reflects the patient's cardiovascular condition. Represents blood oxygen saturation. Represents the target blood oxygen saturation. Represents the patient's oxygenation effect. This represents the patient's ventilation efficiency. Represents the Borg dyspnea score. This represents the perceived level of fatigue. Represents the patient's subjective feelings; Represents respiratory rate. Represents the target respiratory rate. Represents tidal volume. Represents the target tidal volume. This represents the ventilation efficiency of a patient who is assessed by considering both respiratory rate and depth.

5. A training and management system for pulmonary function rehabilitation according to claim 4, characterized in that: The training intensity rules are formulated based on the high threshold and low threshold of the patient training fit coefficient.

6. A training and management system for pulmonary function rehabilitation according to claim 5, characterized in that: When the patient's training fit coefficient When the patient's training fit coefficient reaches a high threshold, advanced training rules are applied to the patient. When the patient's training fit coefficient Patient training fit coefficient high threshold, and If the patient's training fit coefficient is at a low threshold, apply intermediate training rules to the patient. When the patient's training fit coefficient If the patient's training fit coefficient is at a low threshold, a low-level training rule is applied to the patient.

7. A training and management system for pulmonary function rehabilitation according to claim 1, characterized in that: The patient training dataset includes training duration and action standardization.

8. A training and management system for pulmonary function rehabilitation according to claim 4, characterized in that: The training adjustment recommendations are generated by jointly calculating the patient training effectiveness index based on the patient training dataset and the continuously monitored patient training fit coefficient. The calculation formula is: ； In the formula, Represents the patient's training effectiveness index. This represents the patient-training fit coefficient before the patient begins training. The patient-training fit coefficient represents the patient's fitness level after training. Represents training duration. This represents the expected training duration. Represents the degree of standard of movement. The representative factor takes into account the changes in the patient's lungs and physical signs during the training process, and the patient's training effectiveness index is the result of this assessment.

9. A training and management system for pulmonary function rehabilitation according to claim 8, characterized in that: When the patient's training effect index The patient's training efficiency index threshold represents a good training effect, and provides training adjustment suggestions to increase training intensity. When the patient's training effect index Patient training efficiency index threshold, and The patient training standard index threshold represents the achievement of training objectives, and provides training adjustment suggestions to maintain the current training intensity. When the patient's training effect index The patient's training baseline threshold indicates that the training load is too high, and the output provides training adjustment suggestions to reduce the current training intensity.

10. A training and management system for pulmonary function rehabilitation according to claim 8, characterized in that: The feedback management module displays training adjustment suggestions via a screen and voice mode, while simultaneously providing feedback to the doctor management platform.

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