Cardiac patient personalized rehabilitation plan generation and optimization method

Abstract

The invention discloses a cardiac patient personalized rehabilitation plan generation and optimization method, and relates to the technical field of medical treatment, and the method comprises the steps: S1, carrying out the real-time collection and analysis of the physiological parameters and behavior patterns of a patient based on multi-source data, S2, monitoring the psychological state of the patient through psychological scale evaluation and an emotion calculation model, s3, dynamically adjusting the personalized rehabilitation plan according to the psychological state to reduce sympathetic nerve hyperexcitation; S4, optimizing the rehabilitation plan based on patient feedback and clinical data; according to the personalized rehabilitation plan generation and optimization method for the heart disease patient, a comprehensive rehabilitation management scheme is provided for the heart disease patient, potential inducements of cardiovascular events are effectively reduced, the heart burden is relieved, and an important guarantee is provided for long-term rehabilitation of the patient; and the psychological state of the patient is dynamically monitored and actively intervened to regulate and control so as to solve the problem of hyperexcitation of sympathetic nerves caused by psychological stress.

Description

Technical Field

[0001] The present invention relates to the field of medical technology, and in particular to a method for generating and optimizing a personalized rehabilitation plan for a heart disease patient. Background Art

[0002] In modern medicine, heart disease has become one of the diseases with the highest mortality and disability rates worldwide. The treatment of heart disease requires not only solving physiological problems through drugs, surgery and other means, but also focusing on the management of the patient's psychological state. Studies have shown that the mental health of heart disease patients is closely related to their rehabilitation effect. Due to surgical trauma, the decline in quality of life caused by the disease, and concerns about health, patients usually experience varying degrees of anxiety, depression and psychological stress.

[0003] Psychological stress may cause the patient's sympathetic nervous system to be continuously overexcited, which will further increase the burden on the heart and increase the risk of cardiovascular events. In existing rehabilitation technologies, although some programs try to combine psychological intervention methods, such as providing psychological counseling or relaxation training, these methods are mostly passive responses, and it is difficult to capture the dynamic changes in the patient's psychological state in a timely manner, resulting in insufficient intervention timeliness. In addition, existing psychological monitoring methods usually rely on regular questionnaires and manual evaluations, which are highly subjective and lack continuity and accuracy. There is still a lack of effective technical means for dynamic monitoring and intervention of the psychological state of heart disease patients. Existing technologies have obvious deficiencies in the following aspects: insufficient dynamic monitoring of psychological state, most psychological assessment methods cannot achieve continuous and real-time monitoring, and it is difficult to capture the rapid changes in the patient's psychological state during the rehabilitation process; intervention measures are not timely, and current psychological intervention measures are mostly based on standardized processes, making it difficult to design adaptive regulation plans for individualized psychological stressors; the degree of technical integration is low, and the psychological monitoring system and the rehabilitation plan optimization system usually operate separately, lacking coordinated optimization, resulting in difficulty in effectively combining psychological intervention and physiological treatment, and inability to achieve comprehensive rehabilitation management. Summary of the invention

[0004] The purpose of the present invention is to provide a method for generating and optimizing a personalized rehabilitation plan for heart disease patients, dynamically monitor the patient's psychological state and actively intervene to regulate it so as to solve the problem of sympathetic nerve overexcitation caused by psychological pressure.

[0005] To achieve the above object, the present invention provides the following technical solution: a method for generating and optimizing a personalized rehabilitation plan for a heart disease patient, the method comprising:

[0006] S1. Real-time collection and analysis of patients’ physiological parameters and behavior patterns based on multi-source data;

[0007] S2. Monitor the patient's mental state through psychological scale evaluation and emotional computing model, including collecting the patient's psychological evaluation data and calculating the rate of change of his or her mental state. The specific formula is: ;

[0008] Among them, r represents the rate of change of psychological state, B represents the initial fluctuation rate of the patient's psychological state, C represents emotional stability, K represents the psychological adjustment constant, D represents the psychological stress level value, and e represents the natural logarithm base;

[0009] Set the threshold value T1 of the rate of change of psychological state. If the calculated r>T1, conduct relaxation training for the patient.

[0010] S3. Dynamically adjust the personalized rehabilitation plan according to the psychological state to reduce sympathetic nerve overexcitation, including calculating the patient's psychological recovery speed. The specific formula is: ;

[0011] Among them, Δb represents the speed of rehabilitation progress, c represents the efficiency of the patient's psychological state improvement, q1 represents the patient's initial psychological burden, q2 represents the patient's current psychological burden, and ln represents the logarithmic function;

[0012] Set the rehabilitation progress speed threshold T2. If Δb<T2, increase the value of the efficiency c of psychological intervention in improving the patient's psychological state;

[0013] S4. Optimize rehabilitation programs based on patient feedback and clinical data.

[0014] Preferably, S1 includes using a wearable device to collect the patient's behavior data, determine the patient's behavior intensity and behavior duration, and calculate the rate of change of the patient's behavior. The specific formula is:

[0015] g = E / H;

[0016] Among them, g represents the rate of change of the patient's behavior, E represents the intensity of the patient's behavior, and H represents the duration of the behavior;

[0017] Adjust the rehabilitation program and optimize exercise intensity based on the rate of change in the patient's behavior.

[0018] Preferably, S4 includes collecting the patient's rehabilitation effect score and subjective satisfaction score, determining the weight coefficient according to the patient's rehabilitation plan goal, and calculating the patient's overall rehabilitation satisfaction. The specific formula is: U = a × +b× ;

[0019] Among them, U represents the patient's overall rehabilitation satisfaction, Represents the patient's rehabilitation effect score, represents the patient's subjective satisfaction score on rehabilitation experience, a and b represent weight coefficients;

[0020] The patient's overall rehabilitation satisfaction threshold T3 is set. If T3>U, the patient's rehabilitation effect score or the patient's subjective satisfaction score on the rehabilitation experience is improved to improve the patient's overall rehabilitation satisfaction.

[0021] Preferably, S1 also includes collecting biological signals from multiple sensors, preprocessing the collected data, including filtering noise and synchronizing data acquisition time, and using machine learning algorithms to identify biomarkers related to sympathetic nerve excitation. If HRV<Z and ΔQ>p×Q, where HRV represents heart rate variability, Z represents the empirical reference heart rate variability value, ΔQ represents the increment of skin electrode activity, p represents the impact proportional constant, and Q represents the standard baseline of skin electrode activity.

[0022] Preferably, the multi-source data in S1 includes heart rate signals, electrodermal activity signals, breathing signals and behavior pattern data.

[0023] Preferably, the psychological scales in S2 include the GAD-7 Self-Rating Anxiety Scale and the PHQ-9 Self-Rating Depression Scale.

[0024] Preferably, the optimization of the rehabilitation plan in S4 includes combining breathing training, psychological intervention and exercise intensity adjustment.

[0025] Preferably, the wearable device in S1 includes a heart rate monitor, a pedometer and a skin electrode activity sensor.

[0026] Preferably, optimizing the exercise intensity in S1 includes adjusting the patient's walking speed, exercise time and rest time.

[0027] Preferably, improving the patient's subjective satisfaction score with the rehabilitation experience in S4 includes providing personalized psychological support, optimizing the rehabilitation environment and increasing patient participation.

[0028] It can be seen from the above technical solution that the present invention has the following beneficial effects:

[0029] The method for generating and optimizing personalized rehabilitation plans for heart disease patients collects and analyzes the patient's physiological parameters and behavioral patterns in real time based on multi-source data, monitors the patient's psychological state through psychological scale evaluation and emotional computing models, dynamically adjusts the personalized rehabilitation plan according to the psychological state to reduce sympathetic nerve overexcitation, optimizes the rehabilitation plan based on patient feedback and clinical data, realizes real-time and continuous monitoring of the psychological state, can capture the rapid changes in the patient's psychological state, improves the accuracy and objectivity of monitoring, and effectively overcomes the problem of insufficient timeliness of intervention in the existing technology. The rehabilitation plan can design differentiated intervention measures according to the individual characteristics of the patient, accurately regulate the patient's psychological stress source, and significantly improve the rehabilitation effect, making up for the deficiency of system separation operation in the existing technology, promoting the deep integration of psychological intervention and physiological treatment, providing a comprehensive rehabilitation management plan for heart disease patients, effectively reducing the potential causes of cardiovascular events, reducing the burden on the heart, and providing important guarantees for the long-term rehabilitation of patients. The patient's psychological state is dynamically monitored and actively intervened to regulate to solve the problem of sympathetic nerve overexcitation caused by psychological pressure. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 The figure is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0031] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0032] like Figure 1 As shown, the present invention provides a technical solution: a method for generating and optimizing a personalized rehabilitation plan for a heart disease patient, the method comprising:

[0033] S1. Real-time collection and analysis of patients’ physiological parameters and behavior patterns based on multi-source data;

[0034] S2. Monitor the patient's mental state through psychological scale evaluation and emotional computing model, including collecting the patient's psychological evaluation data and calculating the rate of change of his or her mental state. The specific formula is: ;

[0035] Among them, r represents the rate of change of psychological state, B represents the initial fluctuation rate of the patient's psychological state, C represents emotional stability, K represents the psychological adjustment constant, D represents the psychological stress level value, and e represents the natural logarithm base;

[0036] Set the threshold value T1 of the rate of change of psychological state. If the calculated r>T1, conduct relaxation training for the patient.

[0037] S3. Dynamically adjust the personalized rehabilitation plan according to the psychological state to reduce sympathetic nerve overexcitation, including calculating the patient's psychological recovery speed. The specific formula is: ;

[0038] Among them, Δb represents the speed of rehabilitation progress, c represents the efficiency of the patient's psychological state improvement, q1 represents the patient's initial psychological burden, q2 represents the patient's current psychological burden, and ln represents the logarithmic function;

[0039] Set the rehabilitation progress speed threshold T2. If Δb<T2, increase the value of the efficiency c of psychological intervention in improving the patient's psychological state;

[0040] S4. Optimize rehabilitation programs based on patient feedback and clinical data.

[0041] This method dynamically monitors and analyzes the psychological state of heart disease patients and their changing trends by integrating multi-source data collection, psychological assessment and emotional computing models. By collecting the patient's physiological parameters (such as heart rate, blood pressure, etc.) and behavioral patterns in real time and combining them with psychological scale assessment, core indicators such as the initial fluctuation rate of the patient's psychological state, emotional stability and psychological stress level are obtained. Formula for the rate of change of psychological state The natural exponential function is used to model the changing trend of the patient's psychological state, where B reflects the initial volatility, C measures the stability of emotions, and K and D are related to the psychological adjustment constant and stress level, respectively, to ensure accurate adaptation to the individual differences of different patients. A rate threshold T1 is set. When the rate of change of the psychological state r exceeds the threshold, targeted relaxation training intervention is used to help patients reduce psychological stress. At the same time, the rehabilitation progress formula By quantifying the patient's psychological burden (the change from the initial burden q1 to the current burden q2), the improvement efficiency is calculated, and the logarithmic function is used to highlight the proportion of burden change and clarify the patient's rehabilitation progress. When the rehabilitation speed is lower than the set threshold T2, the rehabilitation effect is further optimized by dynamically adjusting psychological intervention measures (such as intensive psychological support or cognitive behavioral therapy). Finally, the optimization iteration is combined with patient feedback and clinical data to continuously improve the rehabilitation plan and truly realize personalized and dynamic rehabilitation management. This method significantly improves the rehabilitation efficiency and experience of heart disease patients. First, through multi-source data collection and analysis, it can accurately capture the dynamic changes of patients' psychological state, realize real-time adjustment of personalized rehabilitation plans, and avoid the limitations of traditional static rehabilitation programs. Secondly, the introduction of psychological scales and emotional computing models improves the scientificity and objectivity of psychological state assessment and reduces the errors that may be caused by relying on subjective judgment. By accurately describing the rate of change of psychological state and the speed of rehabilitation progress through quantitative formulas, patients with heavy psychological burden or poor rehabilitation effect can be quickly found, and intervention measures can be taken in time to improve the pertinence and efficiency of rehabilitation. In addition, by introducing patient feedback and clinical data optimization mechanisms, rehabilitation plans can continuously adapt to the actual needs of patients, enhance patient compliance and trust, and ultimately improve the efficiency and stability of psychological state improvement. Not only that, this method is also highly versatile and can be extended to personalized rehabilitation management of other chronic diseases (such as diabetes or hypertension), providing an innovative solution for the field of chronic disease rehabilitation.

[0042] S1 includes using wearable devices to collect patient behavior data, determine the patient's behavior intensity and behavior duration, and calculate the rate of change of the patient's behavior. The specific formula is: g = E / H;

[0043] Among them, g represents the rate of change of the patient's behavior, E represents the intensity of the patient's behavior, and H represents the duration of the behavior;

[0044] Adjust the rehabilitation program and optimize exercise intensity based on the rate of change in the patient's behavior.

[0045] This method collects the patient's behavioral data in real time through wearable devices (such as smart bracelets, sports trackers, etc.), including behavioral intensity and duration. The behavioral intensity E can be quantified by parameters such as activity acceleration, calorie consumption or exercise heart rate, while the behavioral duration H is represented by the patient's continuous investment time in a certain behavioral activity. The behavioral change rate formula g = E / H describes the dynamic change trend of the patient's behavior as the ratio of behavioral intensity to duration, helping to quantify the patient's daily activity level. Combined with the patient's behavioral change rate g and its change trend, when g is higher than the preset threshold, the patient's exercise intensity can be reduced by adjusting the rehabilitation plan to avoid excessive fatigue; when g is lower than the threshold, the exercise intensity is appropriately increased or more active forms of activity are introduced to ensure that the patient reaches the minimum amount of activity required for rehabilitation. Through dynamic adjustment, it is ensured that the rehabilitation plan is both in line with the patient's physical condition and can effectively improve the rehabilitation effect. This method collects patient behavioral data in real time, accurately quantifies the behavioral change rate, and can quickly respond to changes in the patient's behavioral pattern and adjust the rehabilitation plan. Compared with traditional static rehabilitation plans, this method has dynamic regulation capabilities, which can avoid low rehabilitation efficiency or excessive body load problems caused by inappropriate exercise intensity. The introduction of the behavioral change rate provides a scientific evaluation indicator, which helps to more accurately quantify the behavioral change trend of patients and improve the adaptability and scientificity of rehabilitation plans. In addition, collecting data through wearable devices reduces the reliance on manual observation and recording, and improves the convenience and accuracy of data collection. Dynamic adjustment of exercise intensity can more effectively meet the personalized needs of different patients, promote the recovery of patients' physical functions, enhance their confidence and compliance in rehabilitation, and thus significantly improve the overall effect of the rehabilitation plan.

[0046] S4 includes collecting the patient's rehabilitation effect score and subjective satisfaction score, determining the weight coefficient according to the patient's rehabilitation plan goals, and calculating the patient's overall rehabilitation satisfaction. The specific formula is:

[0047] U=a× +b× ;

[0048] Among them, U represents the patient's overall rehabilitation satisfaction, Represents the patient's rehabilitation effect score, represents the patient's subjective satisfaction score on rehabilitation experience, a and b represent weight coefficients;

[0049] The patient's overall rehabilitation satisfaction threshold T3 is set. If T3>U, the patient's rehabilitation effect score or the patient's subjective satisfaction score on the rehabilitation experience is improved to improve the patient's overall rehabilitation satisfaction.

[0050] This method evaluates the patient's overall acceptance of the rehabilitation plan and the optimization effect by comprehensively analyzing the patient's rehabilitation effect score and subjective satisfaction score. The rehabilitation effect score X quantifies the patient's physical recovery, including the degree of improvement in physiological parameters (such as heart rate and blood pressure recovery) and the improvement in psychological state (such as decreased anxiety level). The subjective satisfaction score Y is collected through patient self-assessment questionnaires or interviews, reflecting the patient's subjective feelings about the rehabilitation process and service experience. The calculation formula for overall rehabilitation satisfaction U is U = a× +b× The rehabilitation effect score and the subjective satisfaction score are weighted and combined, where the weight coefficients a and b are set according to the rehabilitation goals, and the index c is used to amplify or reduce the impact weight of the rehabilitation effect score. The satisfaction threshold T3 is set. When the patient's overall rehabilitation satisfaction U is lower than the threshold, the patient's acceptance of the rehabilitation plan can be enhanced by improving the rehabilitation effect score (such as adjusting the rehabilitation plan to optimize the effect) or increasing the subjective satisfaction score (such as improving the patient experience and providing psychological support). This method combines objective data with subjective feelings and provides a scientific basis for the dynamic optimization of the rehabilitation plan. This method significantly improves the scientificity and personalization of the patient's rehabilitation plan. By integrating the patient's rehabilitation effect score and subjective satisfaction score, it can not only fully reflect the patient's recovery, but also effectively capture the patient's subjective experience of the rehabilitation process, thereby achieving two-way optimization of the rehabilitation plan. Using the weight coefficient and index adjustment, the formula can flexibly adapt to the needs and rehabilitation goals of different patients, ensuring the accuracy and personalization of the evaluation results. The setting of the satisfaction threshold provides a clear direction for the optimization of the rehabilitation plan, helping to quickly discover the deficiencies in rehabilitation and make timely improvements. In addition, by improving patients' subjective satisfaction (such as optimizing service processes and increasing patients' psychological support), patients' sense of participation and compliance can be effectively enhanced, thereby further improving rehabilitation efficiency and satisfaction. This method is also widely applicable and can be extended to other chronic disease rehabilitation fields, providing scientific support for personalized rehabilitation management.

[0051] S1 also includes collecting biological signals from multiple sensors, preprocessing the collected data, including filtering noise and synchronizing data collection time, and using machine learning algorithms to identify biomarkers related to sympathetic nerve excitation. If HRV < Z and ΔQ > p × Q, where HRV represents heart rate variability, Z represents the empirical reference heart rate variability value, ΔQ represents the increment of skin electrode activity, p represents the impact proportional constant, and Q represents the standard baseline of skin electrode activity.

[0052] This method uses multiple sensors (such as electrocardiogram sensors, skin electrodermal sensors, etc.) to collect patients' biological signals in real time, including parameters such as heart rate, skin electrodermal activity, and blood oxygen level. First, the collected data is preprocessed, and signal processing techniques (such as bandpass filtering and time domain synchronization) are used to filter environmental noise, and the timestamp of data acquisition is calibrated to ensure the synchronization of the data. Then, machine learning algorithms (such as support vector machines, neural networks, or random forests) are used to analyze the preprocessed data to extract biomarkers related to sympathetic nerve excitement. Key biomarkers include heart rate variability (HRV) and the increment of skin electrodermal activity (ΔQ). When HRV is lower than the empirical reference value Z and ΔQ is greater than the ratio p of the standard baseline Q, it can be judged that the patient is in a state of sympathetic nerve hyperexcitation. By identifying this state, the rehabilitation plan can be dynamically adjusted, such as adding relaxation training, breathing regulation, or other interventions to reduce sympathetic nerve excitement, to help patients restore psychological balance and reduce physical stress. This method achieves accurate monitoring of the patient's sympathetic nerve excitement state through the combination of multi-sensor biological signal acquisition and machine learning algorithms. Compared with the traditional single signal evaluation method, this method can extract complex biomarkers from multidimensional data, improving the scientificity and accuracy of the evaluation. Through the comprehensive analysis of HRV and skin electrodermal activity, the patient's sympathetic nerve overexcitation state can be quickly identified, providing a reliable basis for the dynamic adjustment of the rehabilitation plan. The noise filtering and time synchronization preprocessing steps effectively ensure the data quality, thereby reducing the errors that may be introduced during the data acquisition process. In addition, the machine learning algorithm is used to dynamically adapt the data characteristics of different patients to achieve personalized evaluation and optimization, and improve the accuracy and flexibility of the rehabilitation plan. Overall, this method not only enhances the patient's rehabilitation effect, but also improves the patient's compliance and satisfaction with the rehabilitation plan.

[0053] The multi-source data in S1 include heart rate signals, skin electrodermal activity signals, breathing signals and behavioral pattern data. This method collects multi-source data and comprehensively analyzes heart rate signals, skin electrodermal activity signals, breathing signals and behavioral pattern data to provide comprehensive physiological and behavioral characteristics information support for personalized rehabilitation plans for heart disease patients. Heart rate signals are used to evaluate the patient's cardiac activity and quantify the patient's sympathetic and parasympathetic regulation ability through parameters such as heart rate variability (HRV); skin electrodermal activity signals reflect the changes in the patient's skin conductivity and are used as an indirect indicator of sympathetic nerve activity to evaluate psychological and emotional stress levels; breathing signals analyze the patient's breathing regulation ability and physical fatigue by monitoring breathing rate and breathing depth; behavioral pattern data are collected based on wearable devices or other sensors, including activity volume, exercise frequency and daily behavior patterns, to provide behavioral support for the dynamic adjustment of rehabilitation plans. Through the synchronous collection and cross-analysis of multi-source data, the patient's physiological state and behavioral characteristics can be fully understood, providing a reliable basis for the precise design of rehabilitation plans. This method significantly improves the scientificity and comprehensiveness of rehabilitation plan design by introducing multi-source data collection and analysis. Compared with the traditional method that relies only on a single data source, this solution integrates four types of data: heart rate, skin electricity, breathing, and behavioral patterns, and comprehensively evaluates the patient's physiological condition and behavioral characteristics from multiple dimensions, thereby improving the accuracy of the evaluation. The combination of multi-source data can cross-validate the associations between different signals, help identify potential health problems, and provide diverse information support for the optimization of rehabilitation plans. In addition, by monitoring behavioral pattern data, it is possible to promptly identify patients' behaviors that do not follow rehabilitation plans in their daily lives, and then make targeted suggestions to improve patients' rehabilitation compliance. This method provides strong data support and technical guarantees for the personalized rehabilitation management of heart disease patients, and at the same time has wide applicability and can be extended to other chronic disease management areas.

[0054] The psychological scales in S2 include the GAD-7 anxiety self-rating scale and the PHQ-9 depression self-rating scale. In the process of evaluating the patient's psychological state, this method provides scientific and standardized psychological tools to quantify the patient's anxiety and depression levels by introducing the GAD-7 anxiety self-rating scale and the PHQ-9 depression self-rating scale. The GAD-7 anxiety self-rating scale is used to assess the patient's anxiety symptoms in the past two weeks. Its score ranges from 0 to 21 points. The higher the score, the more severe the patient's anxiety. The PHQ-9 depression self-rating scale is used to quantify the severity of the patient's depression symptoms. The score ranges from 0 to 27 points. The total score of the question items reflects the patient's depression state. Through the comprehensive analysis of these two scales, we can fully understand the patient's health status at the psychological level, and combine other physiological parameters (such as heart rate variability, skin electrodermal activity, etc.) to determine whether the patient's psychological state is at a risk level. Once the scale score exceeds the preset threshold, the rehabilitation plan can be adjusted in a targeted manner, such as adding psychological intervention measures, cognitive behavioral therapy, or relaxation training to help patients relieve anxiety and depression symptoms and promote overall rehabilitation. This method significantly improves the scientificity and accuracy of the patient's psychological state assessment by introducing two internationally recognized psychological scales, GAD-7 and PHQ-9. Compared with the traditional assessment method that relies on the doctor's subjective judgment, the scale provides patients with a structured and autonomous assessment method, reducing the possibility of human errors and enhancing the patient's sense of participation. Quantitative analysis of anxiety and depression levels can provide a key basis for the design of personalized rehabilitation plans, especially when adjusting psychological intervention measures for patients. It plays an important guiding role. In addition, anxiety and depression are common psychological problems in the rehabilitation process of heart disease patients. Through the application of standardized scales, these problems can be identified earlier and targeted interventions can be implemented, thereby reducing the negative impact of psychological problems on patients' physical rehabilitation and improving the overall rehabilitation effect.

[0055] The 4-step optimized rehabilitation plan includes a combination of breathing training, psychological intervention and exercise intensity adjustment. This method combines breathing training, psychological intervention and exercise intensity adjustment to form a comprehensive and dynamically optimized rehabilitation plan to meet the multiple physiological, psychological and behavioral needs of patients. Breathing training is to enhance lung function, improve oxygen supply, help reduce sympathetic nerve excitement and promote psychological relaxation by guiding patients to perform deep breathing, abdominal breathing or rhythmic breathing. Psychological intervention relieves patients' anxiety and depression through cognitive behavioral therapy (CBT), relaxation training or mindfulness meditation, while improving patients' compliance and confidence in the rehabilitation plan. Exercise intensity adjustment is personalized according to the patient's physical fitness level and real-time physiological data (such as heart rate, respiratory rate), ensuring that patients can not only perform moderate exercise to promote physical recovery, but also avoid excessive physical load caused by excessive exercise intensity. Through the combined optimization of multiple intervention measures, the rehabilitation plan can dynamically adapt to the patient's real-time status and achieve coordinated improvement of physiology, psychology and behavior at each stage of rehabilitation. This method significantly improves the comprehensiveness and accuracy of personalized rehabilitation plans. By organically combining breathing training, psychological intervention and exercise intensity adjustment, the patient's rehabilitation effect can be comprehensively promoted from three aspects: enhancing physiological function, improving psychological state and recovering exercise ability. Breathing training, as a simple and effective means, can significantly improve the patient's blood oxygen level and psychological stress; psychological intervention provides patients with tools for emotional regulation and psychological support, which helps to alleviate common anxiety and depression during the rehabilitation process; exercise intensity adjustment ensures that the patient's exercise meets the actual needs of physical rehabilitation, thereby optimizing the exercise effect and reducing the burden on the heart. The combination of the three not only improves the overall effect of the rehabilitation plan, but also enhances the patient's experience and compliance, providing patients with a scientific, comprehensive and flexible rehabilitation plan. At the same time, this method can dynamically adapt to the patient's rehabilitation process, timely optimize intervention measures, and further improve the patient's rehabilitation efficiency and quality of life.

[0056] The wearable devices in S1 include heart rate monitors, pedometers and skin electrode activity sensors. This method realizes real-time monitoring of patients' physiological parameters and behavior patterns through the combined use of multiple wearable devices. The heart rate monitor is used to continuously record the patient's heart rate and heart rate variability (HRV) to reflect the patient's sympathetic and parasympathetic nerve activity levels, and provide basic data related to cardiac function for the adjustment of rehabilitation plans. The pedometer is mainly used to collect the patient's steps, exercise frequency and activity intensity, help quantify the patient's daily activity level and evaluate whether his behavior pattern meets the rehabilitation goals. The skin electrode activity sensor monitors the patient's psychological stress level and sympathetic nerve excitement in real time by detecting changes in skin conductivity. Through the data collected by these three types of wearable devices, the system can comprehensively analyze the patient's physiological state and behavioral dynamics, and accurately adjust the intervention measures in combination with the goals of the rehabilitation plan, such as optimizing exercise intensity, adding relaxation training or psychological intervention, so as to achieve personalized and dynamic optimization of the rehabilitation plan. This method significantly improves the scientificity and real-time nature of rehabilitation plan design and optimization by integrating heart rate monitors, pedometers and skin electrode activity sensors. The heart rate monitor can accurately reflect the patient's cardiac load status and provide data support for the adjustment of exercise intensity during the rehabilitation process; the pedometer provides a reliable basis for the quantification of the patient's daily activities and helps to assess whether the patient's activity level has reached the rehabilitation goal; the electrodermal activity sensor quantifies the level of sympathetic nerve activity and provides key parameters for psychological stress assessment and intervention. The three types of equipment work together to achieve full coverage of the patient's physiological and behavioral data, providing reliable support for the design and dynamic optimization of rehabilitation plans. In addition, these devices are portable, easy to use, and can continuously collect data, reducing interference with patients' daily lives, while improving the convenience of rehabilitation monitoring and patient compliance.

[0057] Optimizing exercise intensity in S1 includes adjusting the patient's walking speed, exercise time and rest time. This method ensures that the patient obtains appropriate exercise load during the rehabilitation process by optimizing the management of the patient's exercise intensity, including dynamic adjustment of walking speed, exercise time and rest time. The walking speed is adjusted based on the patient's real-time heart rate, respiratory rate and fatigue score data to dynamically optimize the walking rhythm, which not only avoids excessive exercise intensity leading to excessive heart load, but also ensures that the patient's exercise intensity reaches the minimum effective value for rehabilitation. The adjustment of exercise time is based on the patient's daily activity data and heart function recovery. For example, the patient's endurance and cardiopulmonary function are enhanced by gradually extending the exercise time, while the exercise time is appropriately reduced in the case of excessive fatigue. The optimization of rest time is to judge the patient's recovery ability by monitoring indicators such as the patient's heart rate recovery rate (HRR), and reasonably set the ratio of exercise to rest, so that the patient has sufficient time to recover after each exercise, while maintaining an appropriate exercise frequency to promote the gradual improvement of physical function. This comprehensive adjustment method ensures that the patient's exercise intensity is appropriate and avoids the adverse effects of insufficient or excessive exercise on rehabilitation. This method significantly improves the scientificity and adaptability of the rehabilitation plan by dynamically optimizing the management of the patient's walking speed, exercise time, and rest time. The adjustment of walking speed ensures the personalized adaptation of the patient's exercise rhythm to avoid affecting the rehabilitation effect due to too fast or too slow speed; the flexible setting of exercise time can not only improve the patient's exercise endurance, but also achieve gradual improvement according to the patient's physical condition; the scientific management of rest time effectively reduces the patient's rehabilitation risk caused by fatigue and ensures that the heart function can be fully restored after each exercise. Overall, this method of optimizing exercise intensity closely combines the patient's physiological data with the rehabilitation goal, avoids the limitations of single, fixed-intensity exercise on the patient's rehabilitation effect, and improves the safety, efficiency and long-term effect of rehabilitation. In addition, this optimization method has strong flexibility and universality, and can be adjusted in real time according to the condition and rehabilitation stage of different patients to ensure the dynamic optimization and personalized implementation of the rehabilitation plan.

[0058] Improving patients' subjective satisfaction scores with rehabilitation experience in S4 includes providing personalized psychological support, optimizing the rehabilitation environment, and increasing patient participation. This method improves patients' subjective satisfaction with the rehabilitation program through multi-dimensional intervention measures. First, provide personalized psychological support, including regular psychological counseling, emotional regulation guidance, and stress management skills training to help patients relieve anxiety and depression during rehabilitation and enhance their confidence in the rehabilitation program. Second, optimize the rehabilitation environment, by improving the comfort and convenience of rehabilitation facilities (such as setting up quiet and warm rehabilitation rooms or introducing natural landscape elements), and creating a positive social atmosphere (such as organizing support groups or rehabilitation activities among patients), so that patients feel more comfortable and relaxed during rehabilitation. In addition, by increasing patient participation and adopting patient-led rehabilitation program design, patients are encouraged to set rehabilitation goals independently, choose rehabilitation activities suitable for themselves, and participate in the feedback and optimization process of the rehabilitation plan, so that they can gain more control and sense of achievement. These three measures complement each other and jointly improve patient satisfaction and ensure that the rehabilitation plan achieves good results both physiologically and psychologically. This method significantly enhances patients' satisfaction and compliance with the rehabilitation experience through the combination of psychological support, environmental optimization, and participation enhancement. The introduction of personalized psychological support effectively alleviates patients' negative emotions during the rehabilitation process, helps them maintain a positive attitude and improves rehabilitation effects; the optimization of the rehabilitation environment provides patients with a more comfortable and pleasant rehabilitation venue, reduces discomfort during the rehabilitation process, and further improves the quality of the rehabilitation experience; the increase in patient participation allows patients to play a more active role in the rehabilitation plan, which not only enhances their recognition of the rehabilitation plan, but also improves patients' control and sense of responsibility for their own rehabilitation process. In summary, this multidimensional intervention can maximize patient satisfaction, improve rehabilitation effects, and establish a sustainable rehabilitation management model, thereby significantly improving patients' quality of life.

[0059] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for generating and optimizing a personalized rehabilitation plan for heart disease patients, characterized in that: The method comprises: S1. Real-time collection and analysis of patients’ physiological parameters and behavior patterns based on multi-source data; S2. Monitor the patient's mental state through psychological scale evaluation and emotional computing model, including collecting the patient's psychological evaluation data and calculating the rate of change of his or her mental state. The specific formula is: ; Among them, r represents the rate of change of psychological state, B represents the initial fluctuation rate of the patient's psychological state, C represents emotional stability, K represents the psychological adjustment constant, D represents the psychological stress level value, and e represents the natural logarithm base; Set the threshold value T1 of the rate of change of psychological state. If the calculated r>T1, conduct relaxation training for the patient. S3. Dynamically adjust the personalized rehabilitation plan according to the psychological state to reduce sympathetic nerve overexcitation, including calculating the patient's psychological recovery speed. The specific formula is: ; Among them, Δb represents the speed of rehabilitation progress, c represents the efficiency of the patient's psychological state improvement, q1 represents the patient's initial psychological burden, q2 represents the patient's current psychological burden, and ln represents the logarithmic function; Set the rehabilitation progress speed threshold T2. If Δb<T2, increase the value of the efficiency c of psychological intervention in improving the patient's psychological state; S4. Optimize rehabilitation programs based on patient feedback and clinical data.

2. The method for generating and optimizing a personalized rehabilitation plan for a heart disease patient according to claim 1, characterized in that: S1 includes using a wearable device to collect the patient's behavior data, determine the patient's behavior intensity and behavior duration, and calculate the rate of change of the patient's behavior. The specific formula is: g = E / H; Among them, g represents the rate of change of the patient's behavior, E represents the intensity of the patient's behavior, and H represents the duration of the behavior; Adjust the rehabilitation program and optimize exercise intensity based on the rate of change in the patient's behavior.

3. The method for generating and optimizing a personalized rehabilitation plan for a heart disease patient according to claim 1, characterized in that: S4 includes collecting the patient's rehabilitation effect score and subjective satisfaction score, determining the weight coefficient according to the patient's rehabilitation plan goal, and calculating the patient's overall rehabilitation satisfaction. The specific formula is: U = a × +b× ; Among them, U represents the patient's overall rehabilitation satisfaction, Represents the patient's rehabilitation effect score, represents the patient's subjective satisfaction score on rehabilitation experience, a and b represent weight coefficients; The patient's overall rehabilitation satisfaction threshold T3 is set. If T3>U, the patient's rehabilitation effect score or the patient's subjective satisfaction score on the rehabilitation experience is improved to improve the patient's overall rehabilitation satisfaction.

4. The method for generating and optimizing a personalized rehabilitation plan for a heart disease patient according to claim 1, characterized in that: The S1 also includes collecting biological signals from multiple sensors, preprocessing the collected data, including filtering noise and synchronizing data collection time, and using machine learning algorithms to identify biomarkers related to sympathetic nerve excitement. If HRV<Z and ΔQ>p×Q, where HRV represents heart rate variability, Z represents the empirical reference heart rate variability value, ΔQ represents the increment of skin electrode activity, p represents the impact proportional constant, and Q represents the standard baseline of skin electrode activity.

5. The method for generating and optimizing a personalized rehabilitation plan for a heart disease patient according to claim 1, characterized in that: The multi-source data in S1 includes heart rate signals, skin electrodermal activity signals, breathing signals and behavior pattern data.

6. The method for generating and optimizing a personalized rehabilitation plan for a heart disease patient according to claim 1, characterized in that: The psychological scales in S2 include the GAD-7 anxiety self-rating scale and the PHQ-9 depression self-rating scale.

7. The method for generating and optimizing a personalized rehabilitation plan for a heart disease patient according to claim 1, characterized in that: The optimized rehabilitation program in S4 includes a combination of breathing training, psychological intervention and exercise intensity adjustment.

8. The method for generating and optimizing a personalized rehabilitation plan for a heart disease patient according to claim 2, characterized in that: The wearable device in the S1 includes a heart rate monitor, a pedometer and a skin electrode activity sensor.

9. The method for generating and optimizing a personalized rehabilitation plan for a heart disease patient according to claim 2, characterized in that: Optimizing the exercise intensity in S1 includes adjusting the patient's walking speed, exercise time and rest time.

10. The method for generating and optimizing a personalized rehabilitation plan for a heart disease patient according to claim 3, characterized in that: Improving patients' subjective satisfaction scores with their rehabilitation experience in S4 includes providing personalized psychological support, optimizing the rehabilitation environment, and increasing patients' participation.

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