Gamification guide system for respiratory rehabilitation training

Abstract

The invention relates to the technical field of respiratory training, in particular to a gamification guide system for respiratory rehabilitation training, which comprises a data acquisition module, a data analysis module, a training adaptation module, a training guide module and a training adjustment module, the breathing state parameter and the thoracic cavity fluctuation reference value are determined, the data analysis module calculates the breathing abnormity characterization value by determining the breathing difference value and the facial feature fluctuation value, divides the breathing rehabilitation training abnormity tendency and calls the corresponding module, and the training adaptation module adjusts the number of breathing force bearing points. The training guide module determines a stress reference value, a training operation characterization value is calculated by combining the stress reference value with the thoracic cavity fluctuation reference value, and the training adjustment module adjusts the number of times of training based on the training operation characterization value. According to the breathing rehabilitation training method and device, the breathing pre-training is carried out, the appropriate training parameters are determined in a targeted mode, the training content is determined according to the real-time training condition, and the breathing rehabilitation training efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of breathing training, and particularly to a gamified guidance system for breathing rehabilitation training. Background Art

[0002] Breathing rehabilitation training refers to a training method that comprehensively uses various exercise methods to help people with reduced breathing function recover their physical functions. In particular, with the continuous progress of information technologies such as the Internet, big data, artificial intelligence, and virtual reality, it provides technical support for the research and development of breathing rehabilitation training guidance systems. Through the Internet of Things technology, remote communication and data transmission between patients and medical staff can be realized, enabling patients to receive professional rehabilitation guidance at home. At the same time, by using big data and artificial intelligence algorithms, the breathing data of patients can be analyzed and processed to formulate personalized training plans for patients and monitor the training effects in real time, reducing the patients' running back and forth to the hospital and the occupation of medical resources, and also reducing the medical costs of patients.

[0003] Chinese Patent Publication No.: CN114470671A, discloses a slow breathing training guidance system, including a main control module, a physiological parameter monitoring module, a breathing rhythm auxiliary guidance module, and a visualization module; the main control module sends a monitoring control signal to the physiological parameter monitoring module; the main control module processes the received physiological parameters and sends a breathing adjustment signal to the breathing rhythm auxiliary guidance module; after receiving the control signal, the physiological parameter monitoring module monitors the physiological parameters of the user and sends the physiological parameters to the main control module; the breathing rhythm auxiliary guidance module guides the user to perform inhalation or exhalation movements according to the breathing adjustment signal and adjusts the speed of the user's breathing rhythm; the present invention can effectively guide the user to perform slow breathing, thereby effectively adjusting the user's breathing rhythm.

[0004] Chinese Patent Publication No.: CN115006801A, discloses a radiotherapy patient breathing movement management autonomous training system and its control method. The invention can automatically analyze the breathing rules of the exerciser and give a personalized training standard curve suitable for the exerciser according to the breathing characteristics of different exercisers, and also avoid the limitations of artificial inhalation volume setting; the exerciser can independently perform breathing training through the active guidance mode or the following mode in the training system at home, and can complete the free switching training of free breathing and breath holding. Especially, before precise radiotherapy of chest and abdominal tumors, breathing and breath holding training are carried out to improve the compliance of patients during radiotherapy; the breathing training data is functionally integrated into a mobile phone client or a VR glasses for display, and the exerciser observes the breathing curve trend in the mobile phone or VR glasses and the coincidence effect with the training standard curve to complete the evaluation of the exerciser's breathing training and breathing effect, and guide the exerciser to independently manage breathing movements.

[0005] However, the following problems still exist in the prior art:

[0006] In actual situations, before performing respiratory rehabilitation training, although a pre-training assessment is carried out, due to the lack of professionals and the existence of most remote rehabilitation guidance, it may cause errors in the assessment and thus lead to deviations in parameter setting. At this time, inappropriate parameter setting may result in low efficiency of respiratory rehabilitation training or even ineffective training. Summary of the Invention

[0007] For this reason, the present invention provides a gamification guidance system for respiratory rehabilitation training to solve the problem that in actual situations, before performing respiratory rehabilitation training, although a pre-training assessment is carried out, due to the lack of professionals and the existence of most remote rehabilitation guidance, it may cause errors in the assessment and thus lead to deviations in parameter setting. At this time, inappropriate parameter setting may result in low efficiency of respiratory rehabilitation training or even ineffective training.

[0008] To achieve the above object, the present invention provides a gamification guidance system for respiratory rehabilitation training, which includes:

[0009] A data acquisition module for acquiring data of pre-respiratory training, including respiratory state parameters and thoracic lifting parameters of the corresponding client, where the respiratory state parameters include the number of respiratory force points, respiratory form, and facial movements;

[0010] A data analysis module connected to the data acquisition module for obtaining facial feature data points based on the facial movements, determining the respiratory difference and the facial feature fluctuation value, calculating the respiratory abnormality characterization value, dividing the abnormal tendency of respiratory rehabilitation training, and determining the module to be called;

[0011] A training adaptation module connected to the data analysis module, in response to the division result of the abnormal tendency of respiratory rehabilitation training, adjusting the number of respiratory force points based on the respiratory abnormality characterization value, continuously calculating the respiratory abnormality characterization value to make the respiratory abnormality characterization value meet the preset regulations, and calling the training guidance module;

[0012] A training guidance module connected to the data acquisition module, the data analysis module, and the training adaptation module for determining the force reference value based on the number of respiratory force points, determining the thoracic fluctuation reference value based on the thoracic lifting parameters, calculating the training operation characterization value, and determining the training operation state;

[0013] A training adjustment module connected to the training guidance module, in response to the determination result of the training operation state, for adjusting the number of training times based on the training operation characterization value;

[0014] Wherein, the preset regulation is that the respiratory abnormality characterization value is less than or equal to the preset respiratory abnormality characterization value.

[0015] Further, the data analysis module determines a respiration difference value and a facial feature fluctuation value, including:

[0016] Recording a plurality of inhalation moments of the user terminal to determine a plurality of inhalation time differences;

[0017] Determining the average value of the inhalation time differences as the respiration difference value;

[0018] Obtaining facial feature points of the user terminal to determine the path lengths corresponding to the facial feature points;

[0019] Recording the change values of the path lengths;

[0020] Determining the average value of the change values as the facial feature fluctuation value;

[0021] Wherein, the facial feature points include two eye feature points, a nose feature point, a mouth feature point and two eyebrow feature points.

[0022] Further, the data analysis module calculates a respiration abnormality characterization value, including:

[0023] Determining the ratio of the respiration difference value to a reference respiration difference value as a respiration difference influence factor;

[0024] Determining the ratio of the facial feature fluctuation value to a reference facial feature fluctuation value as a facial feature influence factor;

[0025] Determining the weighted sum value of the respiration difference influence factor and the facial feature influence factor as the respiration abnormality characterization value.

[0026] Further, the data analysis module divides the respiration rehabilitation training abnormality tendency and determines the module to be called. Among them,

[0027] If the respiration abnormality characterization value is greater than a preset respiration abnormality characterization value, dividing the respiration rehabilitation training abnormality tendency as an abnormal tendency and calling a training adaptation module;

[0028] If the respiration abnormality characterization value is less than or equal to the preset respiration abnormality characterization value, dividing the respiration rehabilitation training abnormality tendency as a normal tendency and calling a training guidance module.

[0029] Further, the training adaptation module adjusts the number of respiration force application points based on the respiration abnormality characterization value, including:

[0030] Calculating the difference between the respiration abnormality characterization value and the preset respiration abnormality characterization value;

[0031] Increasing the number of respiration force application points based on the difference;

[0032] Among them, the difference is positively correlated with the number of the breathing force application points.

[0033] Further, the training guidance module determines a force reference value based on the number of the breathing force application points, including:

[0034] Determine the number of the force application points and record the force values corresponding to each force application point;

[0035] Determine the average value of each force value as the force reference value.

[0036] Further, the training guidance module determines a chest cavity fluctuation reference value based on the chest cavity lifting parameter, including:

[0037] Construct a time domain curve of the chest cavity fluctuation height;

[0038] Determine several peak points of the curve to calculate the variance;

[0039] Determine the variance as the chest cavity continuous fluctuation reference value;

[0040] The peak points include the highest lifting point and the lowest descending point of the chest cavity in the breathing state.

[0041] Further, the training guidance module calculates a training operation characterization value, including:

[0042] Determine the ratio of the force reference value to the reference force reference value as the force influence factor;

[0043] Determine the ratio of the chest cavity fluctuation reference value to the reference chest cavity fluctuation reference value as the chest cavity fluctuation influence factor;

[0044] Determine the weighted sum value of the force influence factor and the chest cavity fluctuation influence factor as the training operation characterization value.

[0045] Further, the training guidance module divides the training operation state, where:

[0046] If the training operation characterization value is greater than the preset training operation characterization value, divide the training operation state into the continue training state;

[0047] If the training operation characterization value is less than or equal to the preset training operation characterization value, divide the training operation state into the training completed state.

[0048] Further, the training adjustment module adjusts the number of training times based on the training operation characterization value, including:

[0049] If the training operation state is the continue training state, increase the number of training times and calculate the new training operation characterization value;

[0050] Determine the difference between the new training operation characterization value and the training operation characterization value;

[0051] When the difference meets the verification condition, stop increasing the training times;

[0052] Wherein, the verification condition is that the difference is less than or equal to the reference difference.

[0053] Compared with the prior art, the present invention is provided with a data acquisition module, a data analysis module, a training adaptation module, a training guidance module and a training adjustment module. The data acquisition module acquires pre-training data of breathing, determines respiratory state parameters and a reference value of chest cavity undulation. The data analysis module calculates a respiratory abnormality characterization value by determining a respiratory difference and a facial feature fluctuation value, divides the abnormal tendency of respiratory rehabilitation training, and calls the corresponding module. The training adaptation module adjusts the number of respiratory force application points. The training guidance module determines a reference force value, calculates a training operation characterization value in combination with the reference value of chest cavity undulation. The training adjustment module adjusts the training times based on the training operation characterization value. By performing pre-training of breathing, the present invention specifically determines appropriate training parameters, determines the training content according to the real-time training situation, and improves the efficiency of respiratory rehabilitation training.

[0054] In particular, by performing pre-training of breathing, determining the respiratory difference and the facial feature fluctuation value, and calculating the respiratory abnormality characterization value to divide the abnormal tendency of respiratory rehabilitation training, a data basis is provided for determining subsequent operations. In actual situations, when setting parameters for rehabilitation training, it is mostly determined by evaluation. However, if the evaluation is incorrect, the parameter setting will be inappropriate, which will affect the effect and efficiency of respiratory rehabilitation training, and even lead to ineffective training. Based on this, the present invention calculates the respiratory abnormality characterization value through pre-training of breathing, analyzes the abnormal tendency of respiratory rehabilitation training, determines the module to be called subsequently, and adjusts the number of respiratory force application points in respiratory rehabilitation training, making the parameter setting of respiratory rehabilitation training reasonable and improving the efficiency of respiratory rehabilitation training.

[0055] In particular, by determining the reference force value and the reference value of chest cavity undulation to calculate the training operation characterization value, a data basis is provided for subsequently adjusting the training times. In actual situations, the training times are mostly fixed values or periodically changing values. However, during the training process, the set training times may not match the training intensity. At this time, if the original training times are still maintained, the role of respiratory rehabilitation training cannot be improved, and even because the training intensity is insufficient, it will cause a counterproductive effect on training. Based on this, the present invention obtains the number of respiratory force application points and the chest cavity lifting parameters during the training process, calculates the corresponding reference force value and the reference value of chest cavity undulation, and then obtains the training operation characterization value, and adjusts the training times of respiratory rehabilitation training based on the actual situation, improving the efficiency of respiratory rehabilitation training.

[0056] Specifically, during the process of increasing the training times, real-time monitoring of the client is maintained, and the operation of increasing the times is stopped in a timely manner. In the actual training process, if the training times are blindly increased without monitoring, it will lead to overloading training, which will not only not improve the training efficiency, but also reduce the training effect, and even affect the training effect and the rehabilitation process. Based on this, the present invention continuously monitors the client for increasing the training times, synchronously calculates the new training operation representation value, compares it with the training operation representation value, and if it is found that the training of the client has reached the standard, stops increasing the training times, ensuring the safety of training and improving the efficiency of respiratory rehabilitation training. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 It is a schematic structural diagram of a gamification guidance system for respiratory rehabilitation training according to an embodiment of the invention;

[0058] Figure 2 It is a logic block diagram for dividing the abnormal tendency of respiratory rehabilitation training and determining the module to be called according to an embodiment of the invention;

[0059] Figure 3 It is a logic block diagram for dividing the training operation state according to an embodiment of the invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0060] In order to make the objectives and advantages of the present invention clearer and more understandable, the present invention will be further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0061] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are only used to explain the technical principles of the present invention and do not limit the protection scope of the present invention.

[0062] It should be noted that in the description of the present invention, unless otherwise clearly specified and limited, the term "connection" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be directly connected or indirectly connected through an intermediate medium, and it can be the communication inside two components. For those skilled in the art, the specific meaning of the above terms in the present invention can be understood according to specific circumstances.

[0063] Please refer to Figure 1 , Figure 1 It is a schematic structural diagram of a gamification guidance system for respiratory rehabilitation training according to an embodiment of the invention. A gamification guidance system for respiratory rehabilitation training of the present invention includes:

[0064] The data acquisition module is used to acquire the data for respiratory pre-training, including the respiratory state parameters and the thoracic cavity lifting parameters of the corresponding client. The respiratory state parameters include the number of respiratory force points, the respiratory form, and the facial movements. It is understandable that the acquisition of the data has obtained the authorization of the client.

[0065] The data analysis module is connected to the data acquisition module and is used to obtain the facial feature data points based on the facial movements, determine the respiratory difference and the facial feature fluctuation value, calculate the respiratory abnormality characterization value, divide the abnormal tendency of respiratory rehabilitation training, and determine the module to be called.

[0066] The training adaptation module is connected to the data analysis module. In response to the division result of the abnormal tendency of respiratory rehabilitation training, it adjusts the number of respiratory force points based on the respiratory abnormality characterization value, continuously calculates the respiratory abnormality characterization value to make the respiratory abnormality characterization value meet the preset regulations, and calls the training guidance module.

[0067] The training guidance module is connected to the data acquisition module, the data analysis module, and the training adaptation module. It determines the force reference value based on the number of respiratory force points, determines the thoracic cavity fluctuation reference value based on the thoracic cavity lifting parameters, calculates the training operation characterization value, and is used to determine the training operation state.

[0068] The training adjustment module is connected to the training guidance module. In response to the determination result of the training operation state, it is used to adjust the number of training times based on the training operation characterization value.

[0069] Wherein, the preset regulation is that the respiratory abnormality characterization value is less than or equal to the preset respiratory abnormality characterization value.

[0070] Specifically, in this embodiment, an AR training device is configured for the client to improve the training professionalism of the home client and improve the training efficiency.

[0071] Specifically, there is no limitation on the way to obtain the facial movements. For example, it can be by obtaining the real-time monitoring video during the training, dividing the video into several video frames to analyze the facial movements, which will not be elaborated here.

[0072] Specifically, there is no limitation on the way to obtain the number of force points. For example, in this embodiment, during the respiratory pre-training, because the client is configured with a VR training device, the device will apply pressure to the face to promote the completion of the respiratory training, and transmit the number of pressure application points to the system to determine the number of force points. Of course, those skilled in the art can also determine the number of force points according to other methods, which will not be elaborated here.

[0073] It is understandable that configuring the VR device can enable the client to better perform respiratory training and improve the followability and professionalism of the respiratory training.

[0074] Specifically, the data analysis module determines the respiration difference value and the facial feature fluctuation value, including,

[0075] Recording several inhalation moments of the user terminal to determine several inhalation time differences;

[0076] Determining the average value of the inhalation time differences as the respiration difference value;

[0077] Obtaining the facial feature points of the user terminal to determine the path length corresponding to the facial feature points;

[0078] Recording the change value of the path length;

[0079] Determining the average value of the change value as the facial feature fluctuation value;

[0080] Among them, the facial feature points include two eye feature points, a nose feature point, a mouth feature point, and two eyebrow feature points.

[0081] Specifically, there is no limitation on the method for determining the path length corresponding to the facial feature points. In this implementation, the facial feature points are first connected, the sub-path lengths between each facial feature point are recorded, and the sum of the several sub-path lengths is determined as the path length corresponding to the facial feature points. Of course, those skilled in the art can also adopt other methods to determine the path length, as long as it is reasonable, which will not be elaborated here.

[0082] It can be understood that the change value of the path length is the difference between two adjacent path lengths.

[0083] Specifically, the data analysis module calculates the respiration abnormality characterization value, including,

[0084] Determining the ratio of the respiration difference value to the reference respiration difference value as the respiration difference influence factor;

[0085] Determining the ratio of the facial feature fluctuation value to the reference facial feature fluctuation value as the facial feature influence factor;

[0086] Determining the weighted sum value of the respiration difference influence factor and the facial feature influence factor as the respiration abnormality characterization value.

[0087] Specifically, the reference respiration difference value is calculated based on historical data. Several normal respiration rehabilitation training data are obtained in advance to determine several respiration difference values, and the average value of the several respiration difference values is determined as the reference respiration difference value.

[0088] Specifically, the reference facial feature fluctuation value is calculated based on historical data. Several normal respiration rehabilitation training data are obtained in advance to determine several facial feature fluctuation values, and the average value of the several facial feature fluctuation values is determined as the reference facial feature fluctuation value.

[0089] Specifically, the sum of the weight coefficients of the respiratory difference influence factor and the facial feature influence factor is 1. The weight coefficient of the respiratory difference influence factor is 0.54, and the weight coefficient of the facial feature influence factor is 0.46.

[0090] Specifically, through respiratory pre-training, the respiratory difference and the facial feature fluctuation value 0 are determined, and the respiratory abnormality characterization value is calculated to divide the abnormal tendency of respiratory rehabilitation training, providing a data basis for determining subsequent operations. In actual situations, when setting parameters for rehabilitation training, it is mostly determined through evaluation. However, if the evaluation is incorrect, it will lead to inappropriate parameter settings, thereby affecting the effect and efficiency of respiratory rehabilitation training, and even rendering the training ineffective. Based on this, the present invention calculates the respiratory abnormality characterization value through respiratory pre-training, analyzes the abnormal tendency of respiratory rehabilitation training, determines the modules to be called subsequently, and adjusts the number of respiratory force application points in respiratory rehabilitation training, making the parameter settings of respiratory rehabilitation training reasonable and improving the efficiency of respiratory rehabilitation training.

[0091] Please refer to Figure 2 , Figure 2 For the logical block diagram of dividing the abnormal tendency of respiratory rehabilitation training in the embodiment of the invention and determining the modules to be called. Specifically, the data analysis module divides the abnormal tendency of respiratory rehabilitation training and determines the modules to be called. Among them,

[0092] If the respiratory abnormality characterization value is greater than the preset respiratory abnormality characterization value, the abnormal tendency of respiratory rehabilitation training is divided into an abnormal tendency, and the training adaptation module is called;

[0093] If the respiratory abnormality characterization value is less than or equal to the preset respiratory abnormality characterization value, the abnormal tendency of respiratory rehabilitation training is divided into a normal tendency, and the training guidance module is called.

[0094] Specifically, the preset respiratory abnormality characterization value represents that the respiratory training set according to the existing parameters does not meet the training requirements of the user side. Therefore, the preset respiratory abnormality characterization value is selected within the interval [0.62, 0.78].

[0095] Specifically, the training adaptation module adjusts the number of respiratory force application points based on the respiratory abnormality characterization value, including,

[0096] Calculating the difference between the respiratory abnormality characterization value and the preset respiratory abnormality characterization value;

[0097] Increasing the number of respiratory force application points based on the difference;

[0098] Among them, the difference is positively correlated with the number of respiratory force application points.

[0099] Specifically, in this embodiment, the reference difference is set to 0.1 times the preset respiratory abnormality characterization value.

[0100] When the difference is less than or equal to the reference difference, keep the number of breathing force application points unchanged;

[0101] When the difference is greater than the reference difference and less than 2 times the reference difference, adjust the new number of breathing force application points to 1.5 times the number of breathing force application points;

[0102] When the difference is greater than 2 times the reference difference, adjust the new number of breathing force application points to 2 times the number of breathing force application points; of course, those skilled in the art can determine the reference difference according to the actual situation, and this will not be elaborated here.

[0103] Specifically, the training guidance module determines the force reference value based on the number of breathing force application points, including,

[0104] Determine the number of force application points and record the force values corresponding to each force application point;

[0105] Determine the average value of each force value as the force reference value.

[0106] Specifically, the method for determining the force value is not limited. In this implementation, since the user terminal is equipped with an AR training device and the AR training device is equipped with a pressure sensor, the force value can be directly obtained, and this will not be elaborated here.

[0107] Specifically, the training guidance module determines the chest cavity fluctuation reference value based on the chest cavity lifting parameter, including,

[0108] Construct a time-domain curve of the chest cavity fluctuation height;

[0109] Determine several peak points of the curve to calculate the variance;

[0110] Determine the variance as the chest cavity continuous fluctuation reference value;

[0111] The peak points include the highest lifting point and the lowest descending point of the chest cavity in the breathing state.

[0112] Specifically, the method for obtaining the chest cavity fluctuation height is not limited. In this implementation, the AR training device of the user terminal is equipped with a chest cavity setting sensor to obtain the breathing fluctuation value. Of course, those skilled in the art can also use other methods to obtain the chest cavity fluctuation height, such as image analysis, and this will not be elaborated here.

[0113] Specifically, the training guidance module calculates the training operation characterization value, including,

[0114] Determine the ratio of the force reference value to the reference force reference value as the force influence factor;

[0115] Determine the ratio of the chest cavity fluctuation reference value to the reference chest cavity fluctuation reference value as the chest cavity fluctuation influence factor;

[0116] Determine the weighted sum value of the force - affected factor and the chest - fluctuation affected factor as the training operation representation value.

[0117] Specifically, the reference value of the baseline force is pre - calculated. A number of historical force reference values during normal training are obtained in advance, and the average value of the number of historical force reference values is determined as the reference value of the baseline force.

[0118] Specifically, the reference value of the baseline chest fluctuation is pre - calculated. A number of historical chest - fluctuation reference values during normal training are obtained in advance, and the average value of the number of historical chest - fluctuation reference values is determined as the reference value of the baseline chest fluctuation.

[0119] Specifically, the sum of the weight coefficients of the force - affected factor and the chest - fluctuation affected factor is 1. The weight coefficient of the force - affected factor is 0.49, and the weight coefficient of the chest - fluctuation affected factor is 0.51.

[0120] Specifically, the training operation representation value is calculated by determining the force reference value and the chest - fluctuation reference value, providing a data basis for subsequent adjustment of the training times. In actual situations, the number of training times is mostly a fixed value or a periodically changing value. However, during the training process, the set number of training times may not match the training intensity. At this time, if the original number of training times is still maintained, the effect of respiratory rehabilitation training cannot be improved, and even due to insufficient training intensity, a counter - productive training effect may occur. Based on this, the present invention obtains the number of respiratory force points and the chest - lifting and lowering parameters during the training process, calculates the corresponding force reference value and chest - fluctuation reference value, and then obtains the training operation representation value, and adjusts the number of training times of respiratory rehabilitation training based on the actual situation, improving the efficiency of respiratory rehabilitation training.

[0121] Please refer to Figure 3 , Figure 3 which is the logic block diagram for dividing the training operation state of the invention embodiment. Specifically, the training guidance module divides the training operation state, where

[0122] if the training operation representation value is greater than the preset training operation representation value, the training operation state is divided into the continue - training state;

[0123] if the training operation representation value is less than or equal to the preset training operation representation value, the training operation state is divided into the training - completed state.

[0124] Specifically, the preset training operation representation value represents the limit training value of the user side, so the preset training operation representation value is set to be selected within the interval [1.12, 1.43].

[0125] Specifically, the training adjustment module adjusts the number of training times based on the training operation representation value, including,

[0126] If the training running state is the continue training state, increase the number of training times, and calculate a new training running representation value;

[0127] Determine the difference between the new training running representation value and the training running representation value;

[0128] When the difference meets the verification condition, stop increasing the number of training times;

[0129] Wherein, the verification condition is that the difference is less than or equal to a reference difference.

[0130] Specifically, the reference difference is 0.1 times of the training running representation value.

[0131] It can be understood that when the training running representation value of the user side reaches the limit value, even if the number of training times is continuously increased, it will not produce beneficial effects on the training.

[0132] Specifically, during the process of increasing the number of training times, real-time monitoring of the user side is maintained, and the operation of increasing the number of times is stopped in time. In the actual training process, if the number of training times is blindly increased without monitoring, it will lead to overloaded training, which will not only not improve the training efficiency, but also reduce the training effect, and even affect the training effect and the rehabilitation process. Based on this, the present invention continuously monitors the user side for increasing the number of training times, synchronously calculates the new training running representation value, compares it with the training running representation value, and if it is found that the training of the user side has reached the standard, stops increasing the number of training times, ensuring the safety of the training and improving the efficiency of the respiratory rehabilitation training.

[0133] So far, the technical solution of the present invention has been described in conjunction with the preferred embodiments shown in the drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

[0134] The above are only the preferred embodiments of the present invention and are not used to limit the present invention; for those skilled in the art, the present invention can have various changes and modifications. Any modification, equivalent substitution, improvement, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A gamification guidance system for respiratory rehabilitation training, characterized in that, Including: A data acquisition module for acquiring pre - training data of breathing, including the breathing state parameters and chest lifting parameters of the corresponding client. The breathing state parameters include the number of breathing force points, breathing patterns, and facial movements; A data analysis module, which is connected to the data acquisition module, for obtaining facial feature data points based on the facial movements, determining the breathing difference and facial feature fluctuation value, calculating the breathing abnormality characterization value, dividing the abnormal tendency of breathing rehabilitation training, and determining the module to be called; A training adaptation module, which is connected to the data analysis module. In response to the division result of the abnormal tendency of breathing rehabilitation training, it adjusts the number of breathing force points based on the breathing abnormality characterization value, continuously calculates the breathing abnormality characterization value to make the breathing abnormality characterization value meet the preset regulations, and calls the training guidance module; A training guidance module, which is connected to the data acquisition module, data analysis module, and training adaptation module. It determines the force reference value based on the number of breathing force points, determines the chest fluctuation reference value based on the chest lifting parameters, calculates the training operation characterization value, and is used to determine the training operation state; A training adjustment module, which is connected to the training guidance module. In response to the determination result of the training operation state, it is used to adjust the number of training times based on the training operation characterization value; Wherein, the preset regulation is that the breathing abnormality characterization value is less than or equal to the preset breathing abnormality characterization value.

2. The gamification guidance system for respiratory rehabilitation training according to claim 1, characterized in that, The data analysis module determines the breathing difference and facial feature fluctuation value, including, Recording several inhalation moments of the client to determine several inhalation time differences; Determining the average value of the inhalation time differences as the breathing difference; Obtaining the facial feature points of the client to determine the path length corresponding to the facial feature points; Recording the change value of the path length; Determining the average value of the change value as the facial feature fluctuation value; Wherein, the facial feature points include two eye feature points, a nose feature point, a mouth feature point, and two eyebrow feature points.

3. The gamification guidance system for respiratory rehabilitation training according to claim 1, characterized in that, The data analysis module calculates the breathing abnormality characterization value, including, Determining the ratio of the breathing difference to the reference breathing difference as the breathing difference influence factor; Determining the ratio of the facial feature fluctuation value to the reference facial feature fluctuation value as the facial feature influence factor; Determining the weighted sum value of the breathing difference influence factor and the facial feature influence factor as the breathing abnormality characterization value.

4. The gamification guidance system for respiratory rehabilitation training according to claim 1, wherein The data analysis module divides the abnormal tendency of breathing rehabilitation training and determines the module to be called. Among them, If the breathing abnormality characterization value is greater than the preset breathing abnormality characterization value, the abnormal tendency of breathing rehabilitation training is divided into abnormal tendency, and the training adaptation module is called; If the breathing abnormality characterization value is less than or equal to the preset breathing abnormality characterization value, the abnormal tendency of breathing rehabilitation training is divided into normal tendency, and the training guidance module is called.

5. The gamification guidance system for respiratory rehabilitation training according to claim 1, wherein The training adaptation module adjusts the number of breathing force points based on the breathing abnormality characterization value, including, Calculating the difference between the breathing abnormality characterization value and the preset breathing abnormality characterization value; Increasing the number of breathing force points based on the difference; Wherein, the difference is positively correlated with the number of breathing force points.

6. The gamification guidance system for respiratory rehabilitation training according to claim 1, wherein The training guidance module determines a force reference value based on the number of respiratory force application points, including: Determine the number of force application points and record the force values corresponding to each force application point; Determine the average value of each force value as the force reference value.

7. The gamification guidance system for respiratory rehabilitation training according to claim 1, wherein The training guidance module determines a chest cavity fluctuation reference value based on the chest cavity lifting parameter, including: Construct a time-domain curve of the chest cavity fluctuation height; Determine several peak points of the curve to calculate the variance; Determine the variance as the chest cavity continuous fluctuation reference value; The peak points include the highest lifting point and the lowest descending point of the chest cavity in the respiratory state.

8. The gamification guidance system for respiratory rehabilitation training according to claim 1, wherein The training guidance module calculates a training operation characterization value, including: Determine the ratio of the force reference value to the reference force reference value as the force influence factor; Determine the ratio of the chest cavity fluctuation reference value to the reference chest cavity fluctuation reference value as the chest cavity fluctuation influence factor; Determine the weighted sum value of the force influence factor and the chest cavity fluctuation influence factor as the training operation characterization value.

9. The gamification guidance system for respiratory rehabilitation training according to claim 1, wherein, The training guidance module divides the training operation state, where: If the training operation characterization value is greater than the preset training operation characterization value, divide the training operation state into the continue training state; If the training operation characterization value is less than or equal to the preset training operation characterization value, divide the training operation state into the training completed state.

10. The gamification guidance system for respiratory rehabilitation training according to claim 1, wherein The training adjustment module adjusts the number of training times based on the training operation characterization value, including: If the training operation state is the continue training state, increase the number of training times and calculate the new training operation characterization value; Determine the difference between the new training operation characterization value and the training operation characterization value; When the difference meets the verification condition, stop increasing the number of training times; wherein, the verification condition is that the difference is less than or equal to the reference difference.

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