Multi-parameter feedback regulation method for postoperative pulmonary function rehabilitation training

Abstract

The present application belongs to the technical field of intelligent rehabilitation equipment control, and relates to a multi-parameter feedback regulation method for postoperative lung function rehabilitation training, which comprises: acquiring an electrocardiosignal, a respiratory signal and a limb movement signal of a patient; identifying a heart rate fluctuation pattern of the electrocardiosignal to generate an abnormal fluctuation period, analyzing a time difference between an inhalation phase and an exhalation phase of the respiratory signal to generate a starting point of respiratory rhythm disorder; judging a cardiorespiratory risk level; automatically adjusting a movement intensity of a rehabilitation training device based on the cardiorespiratory risk level, and dynamically selecting a regulation mode according to a ratio change between a chest fluctuation amplitude and an abdominal fluctuation amplitude; gradually increasing the movement intensity when a heart rate recovery rate is lower than a preset reference and a respiratory rhythm stabilization time exceeds a critical value; calculating a movement intensity correction coefficient and triggering a movement posture correction instruction; and generating a cardiorespiratory function adaptability rating report. The present application solves the problem of lack of comprehensive quantitative evaluation of rehabilitation efficiency in the prior art.

Description

Technical Field

[0001] The present invention belongs to the technical field of intelligent rehabilitation equipment control and relates to a multi-parameter feedback control method for postoperative pulmonary function rehabilitation training. Background Art

[0002] In the field of rehabilitation medicine, traditional training models often face critical technical bottlenecks in real-time monitoring of patients' cardiopulmonary function and dynamic adaptation to exercise load. Existing technologies rely on single physiological parameter monitoring methods, such as independent analysis of electrocardiogram (ECG) signals or respiratory rate, but lack the ability to correlate abnormalities in cardiopulmonary coordination, resulting in an inability to promptly identify the risk of exercise-induced cardiopulmonary decompensation. Furthermore, intensity control in exercise equipment often relies on preset fixed thresholds or the therapist's empirical judgment, making it difficult to respond promptly to changes in the patient's individual physiological state.

[0003] Current solutions primarily rely on a combination of discrete monitoring devices and manual intervention. For example, medical staff manually adjust training parameters after an ECG monitor warns of an arrhythmia. While these methods can provide basic safety protection, they suffer from low data integration and significant response delays. Traditional respiratory monitoring technologies, often based on chest strap pressure sensors, struggle to distinguish between chest and abdominal breathing patterns. Movement posture assessment typically relies on visual observation or simple accelerometers, which cannot accurately quantify the correlation between three-dimensional motion trajectory and cardiopulmonary load.

[0004] Based on the above problems, the disadvantages of traditional methods are concentrated in the fact that existing technologies lack comprehensive quantitative evaluation of rehabilitation effectiveness, and feedback on training effects is mostly limited to single indicators such as heart rate recovery, which restricts the optimization process of personalized rehabilitation plans. Summary of the Invention

[0005] In order to solve the above problems, the present invention provides a multi-parameter feedback control method for postoperative pulmonary function rehabilitation training.

[0006] The multi-parameter feedback control method for postoperative pulmonary function rehabilitation training includes the following steps:

[0007] S1. Acquire the patient's real-time electrocardiogram (ECG) signal, respiratory signal, and limb motion signal, wherein the respiratory signal includes a chest-to-abdomen fluctuation amplitude ratio parameter, and the limb motion signal includes a three-dimensional acceleration trajectory parameter;

[0008] S2, identifying the heart rate fluctuation pattern of the ECG signal to generate an abnormal fluctuation period, and analyzing the time difference between the inhalation phase and the exhalation phase of the respiratory signal to generate the starting point of the respiratory rhythm disorder;

[0009] S3. Determine the cardiopulmonary risk level based on the overlap between the abnormal fluctuation period and the starting point of the respiratory rhythm disorder;

[0010] S4. Automatically adjust the exercise intensity of the rehabilitation training equipment based on the cardiopulmonary risk level, and dynamically select the control mode based on the ratio of chest and abdominal rise and fall;

[0011] S5. Monitor the heart rate recovery rate and respiratory rhythm stabilization time at the adjusted exercise intensity. When the heart rate recovery rate is lower than the preset benchmark and the respiratory rhythm stabilization time exceeds the critical value, gradually increase the exercise intensity;

[0012] S6. Calculate the exercise intensity correction coefficient and trigger the exercise posture correction instruction by combining the three-dimensional acceleration trajectory parameters of the limb movement signal with the real-time heart rate and respiratory rate correlation curve parameters;

[0013] S7. After completing each stage of training, a cardiopulmonary fitness rating report is generated based on the normalized summation of heart rate fluctuation recovery time, abdominal breathing maintenance time, and acceleration trajectory smoothness.

[0014] A further solution of the present invention is to obtain a patient's real-time electrocardiogram signal, respiratory signal, and limb movement signal, comprising the following steps:

[0015] A flexible ECG patch attached to the patient's chest continuously records the heart's ECG signals;

[0016] A breathing belt is placed between the patient's sternum and navel to detect the rise and fall of the chest and abdomen during breathing;

[0017] The motion sensing device deployed at the end of the limb obtains the fusion data of the three-axis accelerometer and gyroscope, and calculates the three-dimensional acceleration trajectory parameters.

[0018] A further embodiment of the present invention, wherein the respiratory signal includes a chest and abdomen fluctuation amplitude ratio parameter, comprises the following steps:

[0019] The peak-to-peak value of the piezoelectric sensor output voltage in the sternum motion area is recorded as A, and the peak-to-peak value of the piezoelectric sensor output voltage in the rectus abdominis motion area is recorded as B. The respiratory signal contains the ratio parameter of the chest rise and fall amplitude A to the abdomen rise and fall amplitude B;

[0020] The breathing ratio threshold is determined based on the breathing pattern ratio threshold of historical healthy people at rest;

[0021] If the breathing ratio reaches the threshold, it is determined to be an abdominal breathing dominant state;

[0022] If the breathing ratio reaches the threshold, it is determined to be a state of chest breathing dominance.

[0023] A further solution of the present invention is to identify the heart rate fluctuation pattern of the electrocardiogram signal to generate an abnormal fluctuation period, including the following steps:

[0024] A flexible ECG patch attached to the patient's chest continuously records the heart's ECG signals;

[0025] The peak point of the QRS complex is detected by the adaptive threshold method. The time interval between the peak points of two adjacent QRS complexes is defined as the RR interval. When the coefficient of variation RR-CV of the RR interval exceeds the first threshold, the period is determined to be an abnormal fluctuation period.

[0026] The first threshold is the RR interval variation coefficient threshold, which is set based on the clinical standard of heart rate variability and the calibration of individual patient differences;

[0027] The ratio of the standard deviation of the RR interval series to the mean of the RR interval gives the dimensionless coefficient of variation RR-CV of the RR interval.

[0028] A further solution of the present invention is to analyze the time difference between the inspiratory phase and the expiratory phase of the respiratory signal to generate the starting point of the respiratory rhythm disorder, comprising the following steps:

[0029] The duration from the rising edge to the falling edge of the output voltage of the piezoelectric sensor in the sternal motion area is the duration of the inspiratory phase, and the duration from the falling edge to the next rising edge of the output voltage of the piezoelectric sensor in the sternal motion area is the duration of the expiratory phase;

[0030] Calculate the absolute value of the difference between the duration of the inspiratory phase and the duration of the expiratory phase in each respiratory cycle;

[0031] If the absolute value of the difference between three consecutive respiratory cycles exceeds a second threshold, marking the starting point of the respiratory cycle as the starting point of respiratory rhythm disorder;

[0032] The second threshold is the respiratory rhythm symmetry deviation threshold. The setting of the second threshold is based on the physiological standard of respiratory rhythm symmetry and disease adaptation.

[0033] A further embodiment of the present invention determines the cardiopulmonary risk level, comprising the following steps:

[0034] For each abnormal fluctuation period, the preset time window is extended forward and backward based on the start time and end time; for each starting point of respiratory rhythm disorder, a coverage interval with the same time window length as the center is generated;

[0035] If the coverage interval of the starting point of a respiratory rhythm disorder intersects with the extended time window of any abnormal fluctuation period, the two are considered synchronous events, triggering an increase in the cardiopulmonary risk level by one level;

[0036] If only a single type of abnormality is present or there is no temporal overlap between the two types of abnormalities, the baseline cardiopulmonary risk level should be maintained.

[0037] A further solution of the present invention is to gradually increase the exercise intensity when the heart rate recovery rate is lower than a preset benchmark and the respiratory rhythm stabilization time exceeds a critical value, comprising the following steps:

[0038] Automatically adjust the exercise intensity of rehabilitation training equipment based on cardiopulmonary risk level. This adjustment includes reducing the resistance of the exercise equipment or slowing down the exercise frequency. Dynamically select the control mode based on the ratio of chest and abdominal rise and fall.

[0039] If the breathing ratio threshold is reached and it is determined that the patient is in an abdominal breathing dominant state, the breathing guidance instruction will be triggered. The breathing guidance instruction includes a pre-recorded abdominal breathing guidance voice, which guides the patient to increase the depth of abdominal breathing through audio prompts; if the breathing ratio threshold is reached and it is determined that the patient is in a chest breathing dominant state, the current exercise parameters will be maintained.

[0040] A further solution of the present invention calculates the exercise intensity correction coefficient and triggers the exercise posture correction instruction, including the following steps:

[0041] Extract data from the three-axis accelerometer and gyroscope of the motion sensing device and calculate the angular change rate of the limb motion vector;

[0042] If the product of the angular change rates of the sagittal, coronal, and horizontal planes at a certain moment exceeds the preset angle change rate threshold, it is determined to be an acceleration direction sudden change event;

[0043] When a sudden change in acceleration occurs, if the heart rate to respiratory rate ratio exceeds the baseline range and persists for longer than the set duration, it is determined that the exercise posture does not match the cardiorespiratory load. A correction factor for the exercise intensity adjustment range is generated, triggering a posture correction instruction.

[0044] A further solution of the present invention, which calculates the exercise intensity correction coefficient and triggers the exercise posture correction instruction, further includes the following steps:

[0045]

[0046] in, is the correction factor; The adjustment coefficient determined in step S4; It is the ratio of real-time heart rate to respiratory rate; It is the center value of the baseline interval of healthy people in the resting state recorded in history; is the duration of the sudden change in acceleration direction; For reference time, it is set according to industry standards.

[0047] A further embodiment of the present invention generates a cardiopulmonary fitness rating report, comprising the following steps:

[0048] The length of the time period during which the heart rate recovery rate reaches the standard is extracted as the heart rate fluctuation recovery time;

[0049] The continuous maintenance time of the abdominal breathing dominant state is counted in the recorded breathing pattern ratio data;

[0050] In the three-dimensional acceleration trajectory, the inverse of the standard deviation of the acceleration changes in the sagittal, coronal, and horizontal planes was calculated as the trajectory smoothness index;

[0051] The three indicators are normalized to values ​​between 0 and 1 and then summed to obtain the fitness rating result.

[0052] In summary, the present invention has the following beneficial technical effects:

[0053] 1. Through real-time synchronous analysis of the spatiotemporal correlation between abnormal ECG signal fluctuations and respiratory rhythm disorders, a dynamic cardiopulmonary risk warning level is established. This system can automatically trigger graded exercise intensity control when patients experience abnormal cardiopulmonary function coupling. This effectively avoids the risk of overload caused by delayed physiological status monitoring in traditional rehabilitation training, and reduces the incidence of cardiovascular accidents.

[0054] 2. Based on a collaborative judgment mechanism combining chest and abdominal breathing pattern recognition with three-dimensional motion trajectory analysis, combined with the patient's real-time cardiopulmonary function and posture stability data, a self-learning intensity control model is established. This overcomes the limitations of traditional fixed training models and enables exercise parameter adjustments to meet medical safety standards while adapting to individual rehabilitation progress.

[0055] 3. By integrating heterogeneous data such as heart rate recovery dynamics, breathing pattern maintenance ability, and motion trajectory smoothness, a quantitative fitness rating is established, expanding traditional single heart rate monitoring to a cardiopulmonary and exercise synergy performance assessment, providing clinicians with structured feedback including risk warning, training effect, functional compensation and other factors. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. The drawings are used to provide a further understanding of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0057] Figure 1 A schematic diagram of the flow chart in the embodiment of the present application is disclosed.

[0058] Figure 2 The present invention discloses a schematic structural diagram in an embodiment of the present application. DETAILED DESCRIPTION

[0059] In order to make the purposes, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are some but not all of the embodiments of the present application. All other embodiments obtained by a person of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0060] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Figure 1-Figure 2 The preferred detailed description of the present application is made.

[0061] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Figure 1 The present application proposes a multi-parameter feedback regulation method for postoperative lung function rehabilitation training, comprising the following steps:

[0062] S1, acquiring real-time electrocardiosignal, respiration signal and limb movement signal of a patient, wherein the respiration signal comprises a chest and abdomen fluctuation amplitude ratio parameter, and the limb movement signal comprises a three-dimensional acceleration trajectory parameter;

[0063] S2, identifying a heart rate fluctuation pattern of the electrocardiosignal to generate an abnormal fluctuation period, and analyzing a time difference between an inspiration phase and an expiration phase of the respiration signal to generate a starting point of respiratory rhythm disorder;

[0064] S3, judging a cardiopulmonary risk level according to a coincidence of the abnormal fluctuation period and the starting point of the respiratory rhythm disorder;

[0065] S4, automatically adjusting a movement intensity of a rehabilitation training device based on the cardiopulmonary risk level, and dynamically selecting a regulation mode according to a proportion change between a chest fluctuation amplitude and an abdomen fluctuation amplitude;

[0066] S5, monitoring a heart rate recovery rate and a respiratory rhythm stabilization time under the adjusted movement intensity, and gradually increasing the movement intensity when the heart rate recovery rate is lower than a preset reference and the respiratory rhythm stabilization time exceeds a critical value;

[0067] S6, combining the three-dimensional acceleration trajectory parameter of the limb movement signal with a real-time heart rate and respiration rate correlation curve parameter to calculate a movement intensity correction coefficient and trigger a movement posture correction instruction;

[0068] S7, after completing training in each stage, generating a cardiopulmonary function adaptability rating report by calculating a heart rate fluctuation recovery time, an abdomen breathing maintenance time length and an acceleration trajectory smoothness normalization sum.

[0069] In one of the embodiments of the present application, step S1 comprises the following steps:

[0070] The real-time electrocardio signal, respiratory signal and limb movement signal of the patient are acquired, the respiratory signal includes a ratio parameter of chest fluctuation amplitude and abdominal fluctuation amplitude, and the limb movement signal includes an acceleration change trajectory in three-dimensional space.

[0071] Specifically, based on existing clinical anatomy and biomechanics principles, a flexible electrocardio patch attached to the chest of the patient is used to continuously record the electrocardio signal of the heart, the flexible electrocardio patch includes three electrode contacts, the three electrode contacts are respectively placed at the third to fifth intercostal spaces of the right sternal border (for monitoring the electrical activity of the right ventricle), the fourth to sixth intercostal spaces of the midline of the left clavicle (for monitoring the electrical activity of the right ventricle), and the right groin area (the electrode contact at this position serves as a reference electrode for eliminating common-mode interference), so as to acquire a complete electrocardio signal.

[0072] A flexible respiratory belt is fixed around the patient between the xiphoid process of the sternum and the umbilicus, the respiratory belt is internally provided with two groups of piezoelectric sensors, which correspond to the chest movement area and the rectus abdominis movement area respectively, and the chest fluctuation amplitude and the abdominal fluctuation amplitude in the respiratory process are detected in real time. The flexible respiratory belt is fixed around the patient between the xiphoid process of the sternum and the umbilicus, the chest and abdominal breathing amplitudes are quantified by using the chest movement in the xiphoid process area and the rectus abdominis contraction in the umbilicus area, and the breathing mode is accurately distinguished.

[0073] A motion sensing device is fixedly installed at the wrists and ankles of the patient, the motion sensing device is internally provided with a three-axis accelerometer and a gyroscope, and continuously records the acceleration changes of the limbs in the front-back, left-right and up-down directions. The motion sensing device is arranged at the wrists and ankles of the patient, the wrists and ankles belong to unobstructed parts and are suitable for long-term wearing, the three-dimensional acceleration and angular velocity of the limb ends of the wrists and ankles of the patient are fused, the stability of the joint activity trajectory and posture of the limbs in the rehabilitation training is captured, the postoperative wound area is avoided, and the non-invasive monitoring demand is met.

[0074] The fusion of three-dimensional acceleration and angular velocity satisfies the following formula:

[0075]

[0076] Among them, is a motion vector, that is, a three-dimensional acceleration trajectory parameter; and are instantaneous accelerations (unit: m / s²) of the x-axis and the y-axis measured by the three-axis accelerometer, respectively; is the gravitational acceleration (unit: m / s²), which is used to compensate for the gravity component in the inclined state of the patient; is the pitch angle of the rotation around the y-axis measured by the gyroscope; is the roll angle of the rotation around the x-axis measured by the gyroscope;

[0077] The accelerometer range normalization coefficient (unit: m / s2) is determined according to the specifications of the sensor; is an integral variable, representing the process of time accumulation.

[0078] The ratio parameter of the chest fluctuation amplitude and the abdominal fluctuation amplitude is obtained by the following method:

[0079] The peak-to-peak value of the piezoelectric sensor output voltage in the sternum movement area is denoted as A, and the peak-to-peak value of the piezoelectric sensor output voltage in the rectus abdominis movement area is denoted as B. The breathing signal contains the ratio parameter of the chest fluctuation amplitude A and the abdominal fluctuation amplitude B. According to the breathing pattern ratio threshold of the historical healthy population in a resting state, the breathing ratio threshold is determined; if the breathing ratio threshold, it is determined that the abdominal breathing dominant state is dominant; if the breathing ratio threshold, it is determined that the chest breathing dominant state is dominant.

[0080] In one embodiment of the present application, step S2 comprises the following steps:

[0081] Based on the electrocardiogram signal and the breathing signal obtained in step S1, the heart rate fluctuation pattern in the electrocardiogram signal is identified, and the abnormal fluctuation period is marked. The time difference between the inspiration phase and the expiration phase in the breathing signal is analyzed, and the starting point of the breathing rhythm disorder is marked.

[0082] Specifically, the continuous electrocardiogram signal waveform recorded by the flexible electrocardiogram patch is detected by the adaptive threshold method to detect the peak point of the QRS complex. The time interval between the peak points of two adjacent QRS complexes is defined as the RR interval. When the coefficient of variation RR-CV of the RR interval exceeds the first threshold value, it is determined that the period is an abnormal fluctuation period.

[0083] The coefficient of variation RR-CV of the RR interval is calculated by the following method: the ratio of the standard deviation of the RR interval sequence to the mean value of the RR interval, to obtain the dimensionless coefficient of variation RR-CV of the RR interval. For the breathing signal collected in the breathing band, the duration from the rising edge to the falling edge of the output voltage of the sternum movement area piezoelectric sensor is the inspiration phase length, and the duration from the falling edge to the next rising edge of the output voltage of the sternum movement area piezoelectric sensor is the expiration phase length. The absolute value of the difference between the inspiration phase length and the expiration phase length in each breathing cycle is calculated to quantify the degree of symmetry deviation of the breathing rhythm. If the absolute values of the differences of three consecutive breathing cycles exceed the second threshold value, the starting point of the breathing cycle is marked as the starting point of the breathing rhythm disorder.

[0084] The peak point of the QRS complex refers to the highest amplitude point of the QRS complex in the ECG signal waveform, representing the peak moment of ventricular depolarization. The RR interval refers to the time interval between two adjacent QRS complex peak points, reflecting the length of the heartbeat cycle. The starting point of respiratory rhythm disorder refers to the starting moment of the first respiratory cycle that meets the symmetry deviation standard for three consecutive respiratory cycles. The adaptive threshold method refers to an algorithm that dynamically adjusts the QRS wave detection threshold, updating the threshold in real time according to the signal noise level.

[0085] The first threshold is the RR interval coefficient of variation threshold. When the coefficient of variation RR-CV of the RR interval exceeds the first threshold, the patient's heart is overloaded or there is a potential risk of arrhythmia. The setting of the first threshold is based on the clinical standards of heart rate variability and calibration of individual patient differences.

[0086] The second threshold is the respiratory rhythm symmetry deviation threshold. Symmetry deviation reflects abnormal breathing pattern (such as wheezing, respiratory muscle fatigue); the setting of the second threshold is based on the physiological standards of respiratory rhythm symmetry and disease adaptation.

[0087] The mean RR interval is the arithmetic mean of all RR intervals (intervals between adjacent heartbeats) within a period of time, and satisfies the following formula:

[0088]

[0089] The standard deviation of the RR interval series refers to the degree of dispersion of the RR interval series, quantifying the fluctuation range of the heartbeat interval and satisfying the following formula:

[0090]

[0091] in, is the mean RR interval, which represents the average heartbeat interval time; For the The RR interval represents the time interval between the peak points of adjacent QRS complexes; is the total number of heartbeats; is the standard deviation of the RR interval, which indicates the fluctuation amplitude of the heartbeat interval.

[0092] For example, when the patient performs upper limb stretching training, the QRS wave group intervals recorded by the flexible ECG patch are 0.85 seconds, 1.12 seconds, 0.78 seconds, and 1.05 seconds in 20 consecutive seconds. The mean RR interval is calculated as The standard deviation of the RR interval is 0.95 seconds. The time interval is 0.15 seconds, the coefficient of variation is 0.15 to 0.95, which is about 0.158. The first threshold is set to 0.12, and it is determined that there is abnormal heart rate fluctuation in this period.

[0093] The piezoelectric sensor in the sternum area measured that the inspiratory phase of the respiratory cycle lasted 1.2 seconds and the expiratory phase lasted 2.8 seconds. The absolute value of the difference was 1.2 minus 2.8, which was 1.6 seconds. The second threshold was set to the absolute value of the difference accounting for 20% of the inspiratory phase duration. The absolute value of the difference accounted for 133% of the inspiratory phase duration. The absolute value of the difference between the two subsequent consecutive respiratory cycles accounted for more than 20% of the inspiratory phase duration. The starting moment of the first respiratory cycle that exceeded the standard was marked as the starting point of the respiratory rhythm disorder and was associated with the three-dimensional limb motion trajectory data at the corresponding moment.

[0094] In one embodiment of the present invention, step S3 includes the following steps:

[0095] Based on the abnormal fluctuation period marked in step S2 and the starting point of the respiratory rhythm disorder, the cardiopulmonary risk level is determined through spatiotemporal correlation analysis. When the two occur in the same time window, the cardiopulmonary risk level is increased.

[0096] Specifically, the abnormal fluctuation period output by step S2 is defined as a continuous time period in which the coefficient of variation of the RR interval in the electrocardiogram signal continues to exceed the standard. The starting point of the respiratory rhythm disorder output by step S2 is defined as the first time point in which the absolute value of the difference between the inspiratory phase and the expiratory phase in the respiratory signal continues to exceed the standard. Each abnormal fluctuation period, with its start time and end time as the benchmark, extends the preset time window forward and backward; for each starting point of respiratory rhythm disorder, a coverage interval with the point as the center and the same time window length is generated. If the coverage interval of the starting point of a respiratory rhythm disorder intersects with the extended time window of any abnormal fluctuation period, the two are determined to be synchronous events, triggering an increase in the cardiopulmonary risk level by one level. If there is only a single type of abnormality or there is no time overlap between the two types of abnormalities, the basic cardiopulmonary risk level is maintained.

[0097] The pre-set time window is expanded forward and backward, using the start and end times of the abnormal fluctuation period as a benchmark. A fixed duration is set based on the average duration of the rehabilitation training movement cycle, and this fixed duration is expanded forward and backward to form a larger time interval. For example, upper limb training movements are expanded forward and backward by 1.5 seconds each, for a total window length of 3 seconds; lower limb training movements are expanded forward and backward by 2.5 seconds each, for a total window length of 5 seconds. The coverage interval is centered at the starting point of the respiratory rhythm disorder, generating a time interval of the same length as the expanded window during the abnormal fluctuation period.

[0098] Synchronous event, the coverage interval of the starting point of respiratory rhythm disorder overlaps with the extended time window of any abnormal fluctuation period; the conditions for determining synchronous events are: the starting time of the coverage interval of the starting point of respiratory rhythm disorder The end time of the extended time window during the abnormal fluctuation period, and the end time of the interval covered by the starting point of the respiratory rhythm disorder The start time of the extended time window during the abnormal fluctuation period.

[0099] A heart-lung risk level, a risk level divided by the number of synchronous events, is used to quantify the risk of heart-lung load in patient training. The heart-lung risk level is divided into three levels: the basic level is level 1, and the level is increased by 1 for each synchronous event, and the highest level is not more than 3.

[0100] For example, when the patient is training with weight-bearing deep squatting, the abnormal fluctuation period is detected from the 10th second to the 25th second, the coefficient of variation of RR interval RR-CV is calculated to be 0.18, which exceeds the first threshold value 0.12, and the extended time window is from the 7th second to the 28th second (extended by 3 seconds forward and backward). The starting point of the respiratory rhythm disorder is marked at the 15th second, and the coverage interval is from the 12th second to the 18th second (extended by 3 seconds forward and backward, and the window length is 6 seconds). There is an overlap between the 12th second to the 18th second and the 7th second to the 28th second, and it is determined as a synchronous event, and the heart-lung risk level is increased from the basic level 1 to level 2. Subsequently, the synchronous event occurs again at the 40th second, and the heart-lung risk level is further increased to level 3, triggering a real-time alarm to prompt intervention.

[0101] In one embodiment of the present application, step S4 comprises the following steps:

[0102] The motion intensity of the rehabilitation training equipment is automatically adjusted according to the heart-lung risk level, and the adjustment includes reducing the resistance of the motion machine or slowing down the motion frequency, and the control mode is dynamically selected according to the ratio change of the chest fluctuation amplitude and the abdominal fluctuation amplitude.

[0103] In step S1, if the ratio of the chest fluctuation amplitude to the abdominal fluctuation amplitude is less than the breathing ratio threshold value, it is determined that the abdominal breathing is dominant, and the breathing guidance instruction is triggered; if the ratio of the chest fluctuation amplitude to the abdominal fluctuation amplitude is greater than the breathing ratio threshold value, it is determined that the chest breathing is dominant, and the current motion parameter is maintained. In step S1, if the ratio of the chest fluctuation amplitude to the abdominal fluctuation amplitude is less than the breathing ratio threshold value, it is determined that the abdominal breathing is dominant, and the breathing guidance instruction is triggered; if the ratio of the chest fluctuation amplitude to the abdominal fluctuation amplitude is greater than the breathing ratio threshold value, it is determined that the chest breathing is dominant, and the current motion parameter is maintained.

[0104] Specifically, when the heart-lung risk level reaches level 2 or level 3, a strength adjustment instruction is sent to the rehabilitation training equipment:

[0105] For resistance adjustment type equipment, the resistance value driven by the motor is reduced by a preset proportion gradient, satisfying the following formula:

[0106]

[0107] wherein, is the resistance adjustment amount, the resistance value that the rehabilitation training equipment needs to reduce. is the basic resistance value, indicating the current output resistance of the rehabilitation training equipment. is the heart-lung risk level, which is divided in combination with expert experience and clinical trials, and the value of level 1 is 1, the value of level 2 is 2, and the value of level 3 is 3. ​is an adjustment coefficient, which is dynamically set according to the evaluation result of the heart-lung function of the patient, and the value range is usually 0.1-0.3.

[0108] For the frequency adjustment type instrument, by slowing down the periodic action frequency of the motion platform, the following formula is satisfied:

[0109]

[0110] wherein, is the adjusted output frequency of the rehabilitation training equipment; is the current output frequency of the rehabilitation training equipment; is the heart-lung risk level, which is divided in combination with expert experience and clinical trials, and the value of the first level is 1, the value of the second level is 2, and the value of the third level is 3. is an adjustment coefficient, which is dynamically set according to the evaluation result of the heart-lung function of the patient, and the value range is usually 0.1-0.3.

[0111] The peak-to-peak value A of the output voltage of the sternum motion area piezoelectric sensor and the peak-to-peak value B of the output voltage of the rectus abdominis motion area piezoelectric sensor in the acquisition step S1 are obtained, the ratio of the current chest fluctuation amplitude to the abdominal fluctuation amplitude is calculated, and if When the respiration ratio threshold is reached, it is determined that the abdominal respiration dominant state is triggered, and the respiration guidance instruction is triggered. The respiration guidance instruction includes a pre-recorded abdominal respiration guidance voice, and the audio prompt is used to guide the patient to increase the abdominal respiration depth; if When the respiration ratio threshold is reached, it is determined that the chest respiration dominant state is maintained, and the current motion parameter is maintained.

[0112] For example, when the patient trains the rowing machine and the heart-lung risk level is determined to be the second level, the initial resistance value of the rehabilitation training equipment is 50N, the adjustment coefficient is set to 0.2, the resistance adjustment amount is calculated , and the resistance value of the rehabilitation training equipment is reduced from 50N to 40N. The peak-to-peak value of the voltage measured by the sternum motion area piezoelectric sensor is A=2.8mV, the peak-to-peak value of the voltage measured by the rectus abdominis motion area piezoelectric sensor is B=3.5mV, and if the set respiration ratio threshold is 0.9, =0.8 When the respiration ratio threshold 0.9 is reached, it is determined that the abdominal respiration dominant state is triggered, and the respiration guidance instruction is triggered. The voice prompt of “please slowly contract the abdomen and take a deep breath” is played.

[0113] If the heart-lung risk level is upgraded to the third level, the resistance adjustment amount is calculated , the resistance value of the rehabilitation training equipment is reduced from 50N to 30N, and the change of the respiration ratio is continuously monitored until the respiration ratio returns to above the respiration ratio threshold.

[0114] In one of the embodiments of the application, step S5 includes the following steps:

[0115] The heart rate recovery rate in the electrocardio signal and the rhythm stabilization time in the respiratory signal are continuously monitored under the adjusted exercise intensity, and when the heart rate recovery rate is lower than a preset reference and the respiratory rhythm stabilization time exceeds a critical value, the exercise intensity is gradually increased.

[0116] Specifically, for the RR interval sequence recorded by the flexible electrocardio patch, the difference between two adjacent RR intervals is calculated, and in the first minute after the exercise intensity is adjusted, if the difference between the adjacent RR intervals continuously increases and the cumulative value reaches a preset baseline, it is determined that the heart rate recovery rate meets the expectation, and if the difference between the adjacent RR intervals continuously increases and the cumulative value does not reach the preset baseline, it is determined that the heart rate recovery rate is insufficient. For the respiratory signal collected by the elastic respiratory belt, the cumulative time of continuous normal respiratory cycles since the starting point of the last respiratory rhythm disorder is counted, and if the cumulative time exceeds the critical value and the absolute values of the length difference between the inspiration phase and the expiration phase during the cumulative time do not exceed a second threshold, it is determined that the respiratory rhythm reaches a stable state. When the heart rate recovery rate is insufficient and the respiratory rhythm stabilization time exceeds the limit, the resistance value or the exercise frequency of the rehabilitation training equipment is gradually increased by a preset gradient until the initial exercise intensity is restored or the heart-lung risk level is triggered again.

[0117] The cumulative value of the difference of the heart rate recovery rate is defined as the sum of the absolute values of the difference between adjacent RR intervals in each minute after the exercise intensity is adjusted. The preset baseline is calculated by the average heart rate variability in the resting state of the patient. The critical value of the respiratory rhythm stabilization time is set according to the age and the basic lung function of the patient, for example, 90 seconds for young and healthy people and 120 seconds for old patients.

[0118] For example, when the patient performs rowing training with a resistance of 30N after step S4 is adjusted, it is monitored that the cumulative value of the difference between RR intervals is 0.6 seconds in the first minute after the adjustment, the cumulative value of the difference is 0.6 seconds which is lower than the preset baseline of 0.8 seconds, and it is determined that the heart rate recovery rate is insufficient. The respiratory rhythm has been stable for 130 seconds since the starting point of the last disorder, which exceeds the critical value of 120 seconds for old patients. The regulation mode is triggered, and the resistance is increased by 3N each time according to the calculation of the adjustment coefficient 0.2 multiplied by the safety gain factor 0.5 (the safety gain factor is a constant less than 1, used to control the gentleness of the intensity recovery, and is set according to the age and the basic lung function of the patient), and the resistance is increased every 2 minutes, and if the heart-lung risk level does not increase, the gradient is continued to be increased until it is restored to the initial 50N.

[0119] In one embodiment of the present application, step S6 includes the following steps:

[0120] According to the three-dimensional acceleration trajectory change of the limb movement signal, combined with the correlation curve of the real-time heart rate and the respiratory rate, the movement intensity adjustment amplitude is corrected, wherein when the acceleration direction mutation is accompanied by the heart rate and the respiratory rate ratio deviating from the normal interval, the movement posture correction instruction is triggered.

[0121] Specifically, the data of the three-axis accelerometer and the gyroscope of the motion sensing device are extracted, the direction angle change rate of the limb movement vector is calculated, and if the product of the direction angle change rates of the sagittal plane, the coronal plane and the horizontal plane at a certain moment exceeds the threshold of the preset angle change rate, it is determined that the acceleration direction mutation event occurs. The real-time heart rate is calculated based on the reciprocal of the mean RR interval of step S2, and the real-time respiratory rate is calculated based on the number of respiratory cycles of step S2. The ratio of the heart rate to the respiratory rate is compared with the reference interval of the historical healthy population in a resting state. When the acceleration direction mutation event occurs, if the ratio of the heart rate to the respiratory rate at this time exceeds the reference interval and lasts for more than a set mutation time, it is determined that the movement posture and the cardiopulmonary load are mismatched, a correction coefficient of the movement intensity adjustment amplitude is generated, and a posture correction instruction is triggered. The correction coefficient is calculated by the following method:

[0122]

[0123] wherein, is the correction coefficient; is the adjustment coefficient determined in step S4; is the ratio of the real-time heart rate to the respiratory rate; is the center value of the reference interval of the historical record of the healthy population in a resting state; is the duration of the acceleration direction mutation; is the reference time, which is set according to the industry standard.

[0124] wherein, the product of the direction angle change rates is defined as the absolute value of the product of the sagittal plane pitch angle change rate , the coronal plane roll angle change rate , and the horizontal plane yaw angle change rate . The threshold of the preset angle change rate is set according to the standard joint range of motion of rehabilitation training.

[0125] The reference interval refers to the heart rate and respiratory rate data of healthy subjects under the same movement mode, and the mean value plus or minus twice the standard deviation is determined. The posture correction instruction includes voice prompts such as “please adjust the trunk upright angle”, “please tighten the core and keep the back straight” and other patient posture correction prompts.

[0126] For example, when the patient performs the sit-up rowing training, the motion sensing device detects the sagittal plane pitch angle change rate , the coronal plane roll angle change rate , and the horizontal plane yaw angle change rate The product of the angular change rate is 15 × 8 × 20 = 2400, exceeding the preset angle change rate threshold of 2000, and is determined to be a sudden change in acceleration direction. The real-time heart rate is 110 beats / minute, the respiratory rate is 24 breaths / minute, and the ratio is 4.58, exceeding the baseline range of 3.2-4.5 for healthy people. The center value of the baseline range is 3.85. The duration of the sudden change in acceleration direction is 5 seconds, and the reference time is set to 1 second. Substituting into the correction coefficient formula:

[0127]

[0128] From the above calculation, it can be seen that if the correction coefficient is 0.4, the resistance reduction is corrected to 50N×0.4=20N, and the voice prompt "Please tighten your core and keep your back straight" is triggered at the same time.

[0129] In one embodiment of the present invention, step S7 includes the following steps:

[0130] After completing each stage of training, the multi-dimensional monitoring data from S1 to S6 is integrated to generate a feedback report containing a cardiopulmonary function fitness rating. The fitness rating is determined by the normalized sum of the heart rate fluctuation recovery time, the duration of abdominal breathing maintenance, and the smoothness of the acceleration trajectory.

[0131] Specifically, step S5 extracts the length of the time period in which the heart rate recovery rate meets the standard as the heart rate fluctuation recovery time, the continuous maintenance time of the abdominal breathing advantage state is counted in the breathing pattern ratio data recorded in step S4, and the inverse of the standard deviation of the acceleration changes in the sagittal, coronal, and horizontal planes is calculated in the three-dimensional acceleration trajectory of step S6 as the trajectory smoothness index. The three indicators are normalized to values ​​between 0 and 1 and then summed to obtain the fitness rating result. The fitness rating is divided into five levels, with level 1 being the lowest fitness and level 5 being the best fitness. The grading interval of the fitness rating is determined by cluster analysis of historical data of healthy people. For example, the grading interval set for level 1 is [0-1.2), the grading interval set for level 2 is [1.2-1.8), the grading interval set for level 3 is [1.8-2.4), the grading interval set for level 4 is [2.4-2.7), and the grading interval set for level 5 is [2.7-3.0).

[0132] The duration of continuous maintenance of the abdominal breathing advantage state is defined as the cumulative time during which the peak-to-peak ratio of the output voltage of the piezoelectric sensor in the sternum area and the rectus abdominis area is continuously less than the breathing ratio threshold in step S4.

[0133] The trajectory smoothness index is calculated using the following formula:

[0134]

[0135] in, represents the trajectory smoothness index; It represents the standard deviation of the acceleration changes in the sagittal, coronal, and transverse planes recorded by the triaxial accelerometer. 、 、 is the sensitivity coefficient of the corresponding direction, which is obtained through the instrument calibration experiment. For example, in stepping training, the sagittal plane is set to 0.6, the coronal plane is set to 0.2, and the horizontal plane is set to 0.2.

[0136] Determine the upper limit of each indicator based on cluster analysis of historical healthy population data and lower limit , the three indicators are normalized separately using the following normalization formula:

[0137]

[0138] in, is the normalized value; The real-time value of each indicator; is the minimum value of this indicator in healthy people; This is the maximum value of this indicator in healthy people.

[0139] For example, after a patient completes 30 minutes of elliptical machine training, the heart rate fluctuation recovery time is 280 seconds (the target period in step S5), and the abdominal breathing maintenance time is 820 seconds (the cumulative time when A / B is less than 0.9 in step S4). The trajectory smoothness is calculated as: ,set up =0.5, =0.3, = 0.2, resulting in a trajectory smoothness index of approximately 2.7. The minimum heart rate fluctuation recovery time is 100s, the maximum heart rate fluctuation recovery time is 300s, and the normalized heart rate fluctuation recovery time value is 0.9. The minimum abdominal breathing maintenance time is 0s, the maximum abdominal breathing maintenance time is 1000s, and the normalized abdominal breathing maintenance time value is 0.82. The worst trajectory smoothness index is 1, the best trajectory smoothness index is 4, and the normalized trajectory smoothness index value is 0.57.

[0140] The fitness score, the normalized sum of heart rate fluctuation recovery time, abdominal breathing maintenance duration, and acceleration trajectory smoothness, is 0.9 + 0.82 + 0.57 = 2.29. Based on the fitness rating ranges (Level 1: [0-1.2], Level 2: [1.2-1.8], Level 3: [1.8-2.4], Level 4: [2.4-2.7], and Level 5: [2.7-3.0]), the corresponding fitness rating is 3.

[0141] See attached Figure 2 The present invention also proposes a multi-parameter feedback control system for postoperative pulmonary function rehabilitation training, including the following modules:

[0142] Signal acquisition module, used to obtain the patient's real-time ECG signal, respiratory signal, and limb movement signal;

[0143] Signal analysis module, used to identify the heart rate fluctuation pattern of the ECG signal and generate abnormal fluctuation period, used to analyze the time difference between the inspiration phase and the expiration phase of the respiratory signal and generate the starting point of the respiratory rhythm disorder;

[0144] The cardiopulmonary risk dynamic assessment module determines the cardiopulmonary risk level based on the coincidence of abnormal fluctuation periods and the starting points of respiratory rhythm disorders;

[0145] The adaptive exercise intensity control module automatically adjusts the exercise intensity of the rehabilitation training equipment based on the cardiopulmonary risk level, and dynamically selects the control mode based on the ratio of chest and abdominal rise and fall;

[0146] The cardiopulmonary function dynamic monitoring module monitors the heart rate recovery rate and respiratory rhythm stabilization time under adjusted exercise intensity. When the heart rate recovery rate is lower than the preset benchmark and the respiratory rhythm stabilization time exceeds the critical value, the exercise intensity is gradually increased;

[0147] The motion and physiological coordination correction module combines the three-dimensional acceleration trajectory parameters of the limb motion signal with the parameters of the real-time heart rate and respiratory rate correlation curve to calculate the motion intensity correction coefficient and trigger the motion posture correction instruction;

[0148] Intelligent feedback generation module, used to generate a cardiopulmonary fitness rating report.

[0149] It should be noted that the formulas described above, through the principle of dimensional consistency and mathematical standardization (e.g., normalization, dimensionless parameter conversion, or unified unit system), can translate physical quantities of different attributes into unitless standard values ​​or homogeneous, superimposable parameters. This eliminates the interference of different dimensions on operational logic, ensuring that the formulas retain the distribution characteristics of the original data while maintaining mathematical rationality and adaptability to objective laws. These are merely exemplary embodiments of the present invention and are not intended to limit the scope of the invention.

[0150] The modules can be implemented in whole or in part through software, hardware, or a combination thereof, supporting hardware embedded in or independent of a processor in a computer device, and also supporting software stored in a memory in a computer device, so that the processor can call and execute operations corresponding to the modules.

[0151] It should be noted that the human body information (including but not limited to human device information and personal information, etc.) and data (including but not limited to data used for analysis, stored data and displayed data, etc.) involved in the present invention are all information and data authorized by the human body or fully authorized by all parties. The collection, use and processing of relevant data require relevant legal standards.

[0152] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention, and should all be included in the scope of protection of the present invention.

Claims

1. A multi-parameter feedback control method for postoperative pulmonary function rehabilitation training, characterized in that: The following steps are involved: S1. Acquire the patient's real-time electrocardiogram (ECG) signal, respiratory signal, and limb motion signal, wherein the respiratory signal includes a chest-to-abdomen fluctuation amplitude ratio parameter, and the limb motion signal includes a three-dimensional acceleration trajectory parameter; S2, identifying the heart rate fluctuation pattern of the ECG signal to generate an abnormal fluctuation period, and analyzing the time difference between the inspiration phase and the expiration phase of the respiratory signal to generate the starting point of the respiratory rhythm disorder; S3. Determine the cardiopulmonary risk level based on the overlap between the abnormal fluctuation period and the starting point of the respiratory rhythm disorder; S4. Automatically adjust the exercise intensity of the rehabilitation training equipment based on the cardiopulmonary risk level, and dynamically select the control mode based on the ratio of chest and abdominal rise and fall; S5. Monitor the heart rate recovery rate and respiratory rhythm stabilization time at the adjusted exercise intensity. When the heart rate recovery rate is lower than the preset benchmark and the respiratory rhythm stabilization time exceeds the critical value, gradually increase the exercise intensity; S6. Calculate the exercise intensity correction coefficient and trigger the exercise posture correction instruction by combining the three-dimensional acceleration trajectory parameters of the limb movement signal with the real-time heart rate and respiratory rate correlation curve parameters; S7. After completing each stage of training, a cardiopulmonary fitness rating report is generated based on the normalized summation of heart rate fluctuation recovery time, abdominal breathing maintenance time, and acceleration trajectory smoothness.

2. The multi-parameter feedback control method for postoperative pulmonary function rehabilitation training according to claim 1, characterized in that: Acquiring the patient's real-time ECG signal, respiratory signal, and limb movement signal includes the following steps: A flexible ECG patch attached to the patient's chest continuously records the heart's ECG signals; A breathing belt is placed between the patient's sternum and navel to detect the rise and fall of the chest and abdomen during breathing; The motion sensing device deployed at the end of the limb obtains the fusion data of the three-axis accelerometer and gyroscope, and calculates the three-dimensional acceleration trajectory parameters.

3. The multi-parameter feedback control method for postoperative pulmonary function rehabilitation training according to claim 2, characterized in that: The respiratory signal includes chest and abdomen fluctuation amplitude ratio parameters, including the following steps: The peak-to-peak value of the piezoelectric sensor output voltage in the sternum motion area is recorded as A, and the peak-to-peak value of the piezoelectric sensor output voltage in the rectus abdominis motion area is recorded as B. The respiratory signal contains the ratio parameter of the chest rise and fall amplitude A to the abdomen rise and fall amplitude B; The breathing ratio threshold is determined based on the breathing pattern ratio threshold of historical healthy people at rest; like When the breathing ratio threshold is reached, it is determined to be a state of abdominal breathing dominance; like When the breathing ratio threshold is reached, it is determined to be a state of chest breathing dominance.

4. The multi-parameter feedback control method for postoperative pulmonary function rehabilitation training according to claim 2, characterized in that: Identifying the heart rate fluctuation pattern of the ECG signal to generate an abnormal fluctuation period includes the following steps: A flexible ECG patch attached to the patient's chest continuously records the heart's ECG signals; The peak point of the QRS complex is detected by the adaptive threshold method. The time interval between the peak points of two adjacent QRS complexes is defined as the RR interval. When the coefficient of variation RR-CV of the RR interval exceeds the first threshold, the period is determined to be an abnormal fluctuation period. The first threshold is the RR interval variation coefficient threshold, which is set based on the clinical standard of heart rate variability and the calibration of individual patient differences; The ratio of the standard deviation of the RR interval series to the mean of the RR interval gives the dimensionless coefficient of variation RR-CV of the RR interval.

5. The multi-parameter feedback control method for postoperative pulmonary function rehabilitation training according to claim 4, characterized in that: Analyzing the time difference between the inspiratory phase and the expiratory phase of the respiratory signal to generate the starting point of the respiratory rhythm disorder includes the following steps: The duration from the rising edge to the falling edge of the output voltage of the piezoelectric sensor in the sternal motion area is the duration of the inspiratory phase, and the duration from the falling edge to the next rising edge of the output voltage of the piezoelectric sensor in the sternal motion area is the duration of the expiratory phase; Calculate the absolute value of the difference between the duration of the inspiratory phase and the duration of the expiratory phase in each respiratory cycle; If the absolute value of the difference between three consecutive respiratory cycles exceeds a second threshold, marking the starting point of the respiratory cycle as the starting point of respiratory rhythm disorder; The second threshold is the respiratory rhythm symmetry deviation threshold. The setting of the second threshold is based on the physiological standard of respiratory rhythm symmetry and disease adaptation.

6. The multi-parameter feedback control method for postoperative pulmonary function rehabilitation training according to claim 4, characterized in that: Determining the cardiopulmonary risk level involves the following steps: For each abnormal fluctuation period, the preset time window is extended forward and backward based on the start time and end time; for each starting point of respiratory rhythm disorder, a coverage interval with the same time window length as the center is generated; If the coverage interval of the starting point of a respiratory rhythm disorder intersects with the extended time window of any abnormal fluctuation period, the two are considered synchronous events, triggering an increase in the cardiopulmonary risk level by one level; If only a single type of abnormality is present or there is no temporal overlap between the two types of abnormalities, the baseline cardiopulmonary risk level should be maintained.

7. The multi-parameter feedback control method for postoperative pulmonary function rehabilitation training according to claim 3, characterized in that: When the heart rate recovery rate is lower than the preset benchmark and the respiratory rhythm stability time exceeds the critical value, gradually increase the exercise intensity, including the following steps: Automatically adjust the exercise intensity of rehabilitation training equipment based on cardiopulmonary risk level. This adjustment includes reducing the resistance of the exercise equipment or slowing down the exercise frequency. Dynamically select the control mode based on the ratio of chest and abdominal rise and fall. if When the breathing ratio threshold is reached, if it is determined that the abdominal breathing is dominant, the breathing guidance instruction is triggered. The breathing guidance instruction contains a pre-recorded abdominal breathing guidance voice, which guides the patient to increase the depth of abdominal breathing through audio prompts; if When the breathing ratio threshold is reached, it is determined that the chest breathing is dominant and the current movement parameters are maintained.

8. The multi-parameter feedback control method for postoperative pulmonary function rehabilitation training according to claim 7, characterized in that: Calculating the exercise intensity correction coefficient and triggering the exercise posture correction instruction includes the following steps: Extract data from the three-axis accelerometer and gyroscope of the motion sensing device and calculate the angular change rate of the limb motion vector; If the product of the angular change rates of the sagittal, coronal, and horizontal planes at a certain moment exceeds the preset angle change rate threshold, it is determined to be an acceleration direction sudden change event; When a sudden change in acceleration occurs, if the heart rate to respiratory rate ratio exceeds the baseline range and persists for longer than the set duration, it is determined that the exercise posture does not match the cardiorespiratory load. A correction factor for the exercise intensity adjustment range is generated, triggering a posture correction instruction.

9. The multi-parameter feedback control method for postoperative pulmonary function rehabilitation training according to claim 8, characterized in that: Calculating the exercise intensity correction coefficient and triggering the exercise posture correction instruction also includes the following steps: in, is the correction factor; The adjustment coefficient determined in step S4; It is the ratio of real-time heart rate to respiratory rate; It is the center value of the baseline interval of healthy people in the resting state recorded in history; is the duration of the sudden change in acceleration direction; For reference time, it is set according to industry standards.

10. The multi-parameter feedback control method for postoperative pulmonary function rehabilitation training according to claim 9, characterized in that: Generating a report containing a cardiorespiratory fitness rating involves the following steps: The length of the time period during which the heart rate recovery rate reaches the standard is extracted as the heart rate fluctuation recovery time; The continuous maintenance time of the abdominal breathing dominant state is counted in the recorded breathing pattern ratio data; In the three-dimensional acceleration trajectory, the inverse of the standard deviation of the acceleration changes in the sagittal, coronal, and horizontal planes was calculated as the trajectory smoothness index; The three indicators are normalized to values ​​between 0 and 1 and then summed to obtain the fitness rating result.

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