Intelligent training methods and devices driven by ischemic preconditioning mechanisms

Abstract

This invention discloses an intelligent training method and device driven by ischemic preconditioning mechanisms, specifically relating to the field of mechanism-driven technology. It acquires real-time data such as blood oxygen saturation, heart rate, blood pressure, and blood flow from the trainee through a physiological data acquisition device. Combined with preset training goals, it calculates personalized parameters such as ischemic intensity, time, frequency, and interval duration. A controllable ischemic device is used to precisely treat designated areas with ischemia, dynamically adjusting the ischemic state while continuously monitoring and optimizing the training process. The system generates a training effect evaluation report and stores data for subsequent optimization. Through closed-loop control, the system achieves personalized training and safety assurance, effectively solving the problems of uncertainty in training effects and potential risks caused by individual differences. It is suitable for diverse populations, including ordinary users and patients with chronic diseases.

Description

Technical Field

[0001] This invention relates to the field of mechanism-driven technology, specifically to an intelligent training method and training device driven by ischemic preconditioning mechanisms. Background Technology

[0002] With the development of medical technology and sports rehabilitation science, ischemic preconditioning (IPC), a technique that enhances the body's resistance to ischemia by intermittently and temporarily blocking local blood flow, has gradually attracted widespread attention. Studies have shown that IPC can activate a series of protective mechanisms in the body, thereby improving tissue tolerance to ischemia-reperfusion injury. It has not only demonstrated significant effects in the prevention and treatment of clinical cardiovascular diseases, but also shown potential in sports rehabilitation and physical training to promote muscle endurance and improve athletic performance. However, traditional IPC training methods rely on human intervention or fixed equipment, lacking intelligent and personalized adjustment methods, making it difficult to meet the precise needs of different individuals for training load and effects. Intelligent training methods based on the ischemic preconditioning mechanism promise to achieve more efficient and safer training programs through real-time data acquisition and intelligent algorithm adjustments.

[0003] The existing technology has the following shortcomings:

[0004] Due to individual differences in hemodynamics, physiological tolerance, and metabolic levels, different users have vastly different needs regarding ischemic intensity, duration, and frequency. Traditional equipment struggles to monitor and provide real-time feedback on these key physiological indicators. This lack of control can lead to uncertainty in training outcomes and even pose potential risks to specific populations (such as patients with chronic diseases or those with circulatory abnormalities). Therefore, developing an intelligent system capable of dynamically adjusting ischemic conditions and monitoring training status in real time has become a crucial technical challenge that urgently needs to be addressed in this field. Summary of the Invention

[0005] The purpose of this invention is to provide an intelligent training method and training device driven by ischemic preconditioning mechanism to address the shortcomings of the prior art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: an intelligent training method driven by ischemic preconditioning mechanism, comprising the following steps:

[0007] S1: Acquire real-time physiological data of the trainee through at least one physiological data acquisition device, wherein the real-time physiological data includes blood oxygen saturation, heart rate, blood pressure and blood flow;

[0008] S2: Based on the trainee's real-time physiological data and preset training goals, calculate the training program parameters for ischemic preconditioning, including ischemic intensity, time, frequency and interval duration;

[0009] S3: Using a controllable ischemia device, ischemia is applied to a designated area of ​​the trainee, and the ischemia state is dynamically adjusted according to changes in the training program parameters;

[0010] S4: Monitor and analyze the real-time physiological data of the adjusted trainees, continuously optimize the ischemic preconditioning program, generate a training effect evaluation report for the trainees based on the collected real-time physiological data, and store the relevant data in the database for subsequent optimization and query.

[0011] Preferably, in S3, during the ischemia treatment process, the real-time changes in blood flow are monitored by a Doppler ultrasound sensor through a physiological data acquisition device and a controllable ischemia device. The deviation of the ratio of blood flow changes before and after occlusion is calculated, and a blood flow change rate deviation index is generated. The method for obtaining the blood flow change rate deviation index is as follows:

[0012] Before ischemia treatment, baseline blood flow at the target site was acquired using a Doppler ultrasound sensor and labeled as BF. 基线 Monitor blood flow during the occlusion period and label it as BF. 当前 In ischemia management, the real-time blood flow change rate R is defined. BF The ratio of the change in blood flow during the occlusion period to the change in baseline blood flow: Define the target blood flow rate range R BF,目标 Calculate the real-time deviation value ΔR BF The deviation value is the deviation between the real-time blood flow rate of change and the target range, expressed as: ΔR BF =R BF -R BF,目标 Where: if ΔR BF A negative value indicates that the blood flow has not reached the target rate of change, and the occlusion pressure needs to be increased; if ΔR BF A positive value indicates that the rate of change in blood flow exceeds the target value, and the occlusion pressure needs to be reduced.

[0013] Define the Blood Flow Rate of Change Deviation Index (BFVPI) to comprehensively quantify the deviation of blood flow rate of change: N represents the number of sampling times, i.e., the total number of blood flow data points recorded during the occlusion period. It is the square of the deviation value of the blood flow rate change at time i.

[0014] Preferably, in S3, the RR interval is collected by an electrocardiogram sensor, the autonomic nervous system response state of the trainee is calculated, and the heart rate variability index is generated after analyzing the autonomic nervous system response state. The method for obtaining the heart rate variability index is as follows:

[0015] ECG data is recorded in real time using an ECG sensor, and the time interval between two adjacent R waves is extracted and denoted as the RR interval.i The expression is: RR i =t R(i+1) -t R(i) ;t R(i) For the occurrence time of the i-th R wave, the standard deviation SDNN of the RR interval is calculated. SDNN is the standard deviation of all RR intervals, reflecting the overall heart rate variability. The root mean square (RMSSD) of the difference between adjacent RR intervals is calculated, reflecting the state of parasympathetic nerve activity. The RR intervals are converted into a spectrum through fast Fourier transform, and the LF / HF ratio is extracted. The heart rate variability index HRVI is obtained by weighted averaging the standard deviation SDNN of the RR intervals, the root mean square (RMSSD) of the difference between adjacent RR intervals, and the LF / HF ratio, which is used to quantify the autonomic nervous system response state.

[0016] Preferably, in S3, the blood flow rate change deviation index and the heart rate variability index are normalized, and the comprehensive score of ischemic state is calculated using the normalized blood flow rate change deviation index and heart rate variability index.

[0017] Preferably, the obtained ischemic state comprehensive score is compared with a preset ischemic state comprehensive score reference threshold. If the ischemic state comprehensive score is greater than or equal to the preset ischemic state comprehensive score reference threshold, it indicates that the blocking effect is insufficient, and the pressure needs to be gradually increased until the blood flow change rate deviation index and heart rate variability index reach the target values, and the ischemic duration is shortened. If the ischemic state comprehensive score is less than the preset ischemic state comprehensive score reference threshold, it indicates that the load is high, and the pressure needs to be reduced to avoid tissue damage or discomfort, and the ischemic time is gradually prolonged.

[0018] Preferably, in S4, the real-time collected physiological data are the blood flow rate of change deviation index and the heart rate variability index; in order to achieve the training objective, the actual physiological parameters are compared with the preset target values, and the deviation values ​​of each physiological data are calculated.

[0019] Using deviation values ​​and control algorithms, the ischemic preconditioning scheme is dynamically optimized. The optimization formula is as follows: P 新 (t) represents the real-time adjusted blocking pressure, P 当前 (t) represents the current pressure value, K P w is the pressure adjustment coefficient used to control the magnitude of pressure changes. i The weight of the i-th parameter reflects the importance of different physiological parameters to the training effect, ΔP. i (t) represents the real-time deviation value of the i-th parameter;

[0020] The formula for optimizing ischemia duration is: T 新T is the adjusted duration of ischemia. 当前 K represents the current duration of ischemia. T This is a time adjustment factor used to control the magnitude of time changes;

[0021] Interval time optimization formula: Rest 新 =Rest 当前 +K R ·max(ΔP i (t))(;Rest 新 Adjusted rest time, Rest 当前 K represents the current interval time. R This is the interval adjustment factor, used to extend the interval by a certain amount, max(ΔP) i (t))( is the maximum value among all physiological parameter deviations;

[0022] Based on real-time monitored physiological parameters, the objective function for optimization is to minimize the Comprehensive Deviation Score (CPS). CPS(t) is the optimized comprehensive deviation score, representing the overall deviation between the current training parameters and the target value. M is the number of physiological parameters monitored, and P... 目标,i The target value of the i-th parameter.

[0023] Preferably, if CPS(t) ≤ T threshold, it indicates that the training effect is good and no adjustment is needed;

[0024] If CPS(t) > T threshold, key training parameters need to be dynamically adjusted: when pressure is insufficient, increase pressure; when load is high, decrease pressure and extend interval time.

[0025] This invention also provides an intelligent training device driven by ischemic preconditioning mechanism, including a data acquisition module, a training parameter calculation module, an ischemia control module, and an optimization module:

[0026] Data acquisition module: Acquires real-time physiological data of trainees through at least one physiological data acquisition device, including blood oxygen saturation, heart rate, blood pressure and blood flow;

[0027] Training parameter calculation module: Based on the trainee's real-time physiological data and preset training goals, calculate the training parameters of the ischemic preconditioning program, including ischemic intensity, time, frequency and interval duration;

[0028] Ischemia control module: Through a controllable ischemia device, ischemia treatment is applied to designated areas of the trainee, and the ischemia state is dynamically adjusted according to changes in training program parameters;

[0029] Optimization module: Monitors and analyzes the real-time physiological data of the trainees after adjustment, continuously optimizes the ischemic preconditioning program, generates a training effect evaluation report for the trainees based on the collected real-time physiological data, and stores the relevant data in the database for subsequent optimization and query.

[0030] The technical effects and advantages provided by the present invention in the above technical solution are as follows:

[0031] 1. This invention collects key physiological indicators such as blood oxygen saturation, heart rate, blood pressure, and blood flow in real time, and calculates personalized training parameters (including ischemia intensity, time, frequency, and interval duration) based on preset training goals. It then dynamically adjusts the ischemic state using a controllable ischemia device. Through comprehensive analysis and normalization of the blood flow rate change deviation index and heart rate variability index, a comprehensive ischemia state score is generated, ensuring the accuracy and safety of ischemia treatment. Simultaneously, the pressure, time, and interval duration are dynamically adjusted according to an optimization algorithm, achieving fully closed-loop intelligent control of the training process.

[0032] 2. This invention generates a quantitative training effect evaluation report through real-time monitoring and optimization, intuitively reflecting the trainee's physiological response and adaptability, and stores the data in a database to support personalized training optimization and long-term effect tracking. This technology not only improves the scientific rigor and efficiency of ischemic preconditioning training but also significantly reduces potential risks caused by individual differences, making it suitable for ordinary users, patients with chronic diseases, and other special populations. Through precise physiological data feedback and dynamic adjustment, this invention significantly enhances the safety, effectiveness, and adaptability of ischemic training, providing a significant technological breakthrough in the field of ischemic preconditioning training. Attached Figure Description

[0033] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0034] Figure 1 This is a flowchart of the method of the present invention.

[0035] Figure 2 This is a system module diagram of the present invention. Detailed Implementation

[0036] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0037] Example 1, please refer to Figure 1 As shown in this embodiment, the intelligent training method driven by the ischemic preconditioning mechanism includes the following steps:

[0038] S1: Acquire real-time physiological data of the trainee through at least one physiological data acquisition device, wherein the real-time physiological data includes blood oxygen saturation, heart rate, blood pressure and blood flow;

[0039] S2: Based on the trainee's real-time physiological data and preset training goals, calculate the training program parameters for ischemic preconditioning, including ischemic intensity, time, frequency and interval duration;

[0040] S3: Using a controllable ischemia device, ischemia is applied to a designated area of ​​the trainee, and the ischemia state is dynamically adjusted according to changes in the training program parameters;

[0041] S4: Monitor and analyze the real-time physiological data of the adjusted trainees, continuously optimize the ischemic preconditioning program, generate a training effect evaluation report for the trainees based on the collected real-time physiological data, and store the relevant data in the database for subsequent optimization and query.

[0042] In S1, the physiological data acquisition device typically consists of the following modules:

[0043] Sensor module: The core component used to monitor specific physiological indicators, including photoelectric sensors (such as photoplethysmography sensors for measuring blood oxygen saturation), pressure sensors (for measuring blood pressure), electrocardiogram sensors (for heart rate monitoring), and flow sensors (for detecting blood flow).

[0044] Signal processing module: Converts analog signals acquired by sensors into digital signals and preprocesses the data, such as noise filtering and signal enhancement.

[0045] Data transmission module: transmits the collected data to the intelligent control module via wireless communication (such as Bluetooth, Wi-Fi) or wired connection.

[0046] Power module: Provides the necessary power to the device, usually using a rechargeable battery.

[0047] Blood oxygen saturation: Based on the principle of photoplethysmography (PPG), it uses light-emitting diodes (LEDs) and photosensitive detectors to analyze the proportion of oxyhemoglobin in the blood by illuminating the skin with light of different wavelengths and detecting the transmittance. The sensor is usually placed on the finger, earlobe, or wrist.

[0048] Heart rate: Also relies on a PPG sensor or an electrocardiogram (ECG) sensor. The PPG sensor calculates heart rate by detecting fluctuations in blood flow caused by the heartbeat, while the ECG sensor extracts heart rate information by analyzing the R-wave intervals of the electrocardiogram signal.

[0049] Blood pressure: Non-invasive blood pressure measurement methods (such as the cuff oscillation method) are used. A pressure sensor measures the degree of arterial blood flow obstruction during cuff inflation and deflation, and an algorithm is used to calculate systolic and diastolic blood pressure. Some devices can also indirectly estimate blood pressure using pulse wave transit time (PWTT).

[0050] Blood flow: The velocity and volume of blood flow are measured using an ultrasonic Doppler sensor or laser Doppler technology. This technique utilizes the Doppler effect to calculate changes in blood flow by analyzing the frequency shift of sound or light waves reflected by red blood cells.

[0051] The collected physiological data is sent to the central data processor in real time via the data transmission module for analysis, including: determining the physiological effects of blood flow occlusion (such as the decrease in blood oxygen); assessing the physiological tolerance of trainees (such as the range of changes in heart rate and blood pressure); and dynamically adjusting the parameters of ischemic preconditioning (such as prolonging or shortening ischemic time).

[0052] S2: Based on the trainee's real-time physiological data and preset training goals, calculate the training program parameters for ischemic preconditioning, including ischemic intensity, time, frequency and interval duration;

[0053] The following real-time physiological parameters were acquired using a physiological data acquisition device:

[0054] Blood oxygen saturation (SpO2): Reflects changes in the oxygen content in the blood and is used to assess the effectiveness of ischemic conditions.

[0055] Heart rate (HR): Monitors the workload on the heart and helps prevent cardiovascular risks caused by overtraining.

[0056] Blood pressure (BP): Dynamically assess the impact of blood flow occlusion or restoration on the circulatory system.

[0057] Blood flow (BF): directly reflects the degree of blood flow obstruction in the target limb or area.

[0058] Training goals are typically determined by user needs or medical advice, including: improving tissue tolerance to hypoxia; enhancing cardiovascular function and muscle endurance; adapting to specific health conditions (such as low-intensity training for patients with chronic diseases); and ensuring training safety (avoiding extreme physiological loads).

[0059] The intensity of ischemia determines the degree of blood flow occlusion, which can be calculated by measuring blood pressure and blood flow at the target site: Initial ischemic pressure (P0): usually 1.2 to 1.5 times the individual's systolic blood pressure (SBP) to ensure sufficient occlusion effect.

[0060] Dynamic pressure adjustment: The pressure of the blocking device is adjusted based on real-time monitoring of blood flow and oxygen saturation. For example, if blood flow does not decrease significantly, the pressure is gradually increased; if oxygen saturation drops rapidly to a dangerous threshold (e.g., below 90%), the pressure is appropriately reduced.

[0061] Ischemia time is the duration of a single blood flow interruption, usually determined based on the theory of ischemic preconditioning and individual tolerance: the initial time is set to 3-5 minutes; combined with real-time physiological responses: if the change in heart rate or blood pressure exceeds the set threshold (e.g., heart rate increases by more than 15%), the ischemia time is shortened; if blood oxygen saturation drops too quickly, the time is shortened to protect tissue safety.

[0062] Ischemia frequency refers to the number of repetitions of ischemia-reperfusion cycles during training, which is usually set according to the individual's training goals and health condition: generally 3-5 cycles; for those with poor tolerance or those who are training for the first time, the frequency is reduced (e.g., 1-2 times); the interval and frequency of the next cycle are determined based on the heart rate recovery speed and blood pressure recovery.

[0063] The rest interval is the recovery phase in the ischemia-reperfusion cycle, ensuring adequate tissue reperfusion: initially set to 1.5 times the ischemia time (e.g., 7.5 minutes of rest if ischemia is 5 minutes); if blood oxygen saturation or heart rate does not return to baseline levels during the rest interval, extend the rest interval; if physiological indicators recover quickly, the rest interval can be appropriately shortened to improve training efficiency.

[0064] S3: Based on the trainee's real-time physiological data (such as blood oxygen saturation, heart rate, blood pressure, and blood flow) and the preset training goals, determine the initial ischemia intensity, time, frequency, and interval duration. Set the initial pressure of the controllable ischemia device (such as pressurizing the target site to 1.2 to 1.5 times the systolic blood pressure) and activate the device.

[0065] During ischemia treatment, real-time changes in blood flow are monitored using a physiological data acquisition device and a controllable ischemia device, along with a Doppler ultrasound sensor. The deviation in the ratio of blood flow changes before and after occlusion is calculated, generating a blood flow change rate deviation index. The method for obtaining the blood flow change rate deviation index is as follows:

[0066] Before ischemia treatment, baseline blood flow at the target site was acquired using a Doppler ultrasound sensor and labeled as BF. 基线 Monitor blood flow during the occlusion period and label it as BF. 当前 During ischemia treatment, blood flow values ​​are recorded in real time at a certain sampling frequency (e.g., 1 second / time).

[0067] Define the rate of change of real-time blood flow R BF The ratio of the change in blood flow during the occlusion period to the baseline blood flow: Ideal state: R BF The blockage rate should be maintained between 80% and 100%, meaning that most of the blood flow is effectively blocked. Deviation: If it is below 80%, the blockage effect is insufficient; if it is close to 100%, the blockage is complete, and safety needs to be assessed in conjunction with other parameters.

[0068] Define the target blood flow rate range R BF,目标 Typically, it is 80% to 90%, but the specific value can be set according to individual differences among trainees. The real-time deviation value ΔR is calculated. BF The deviation value is the deviation between the real-time blood flow rate of change and the target range, expressed as: ΔR BF =R BF -R BF,目标 Where: if ΔR BF A negative value indicates that the blood flow has not reached the target rate of change, and the occlusion pressure needs to be increased. If ΔR BF A positive value indicates that the rate of change in blood flow exceeds the target value, and the occlusion pressure needs to be reduced.

[0069] Define the Blood Flow Rate of Change Deviation Index (BFVPI) to comprehensively quantify the deviation of blood flow rate of change: N represents the number of sampling times, i.e., the total number of blood flow data points recorded during the occlusion period. It is the square of the deviation value of the blood flow rate change at time i.

[0070] A low BFVPI (close to 0) indicates a stable blood flow rate of change close to the target value, suggesting a good blocking effect. A high BFVPI indicates significant fluctuations in the blood flow rate of change, potentially indicating unstable blocking or excessive pressure.

[0071] The heart rate variability index (HRV) is generated by collecting the reciprocal interval (RR) data using an electrocardiogram (ECG) sensor, calculating the trainee's autonomic nervous system response status, and analyzing this response. The HRV index is obtained as follows:

[0072] Electrocardiogram (ECG) data is recorded in real time using a cardiac sensor, with a focus on detecting the R wave (the peak of ventricular depolarization) of the heartbeat signal. The time interval between two adjacent R waves is extracted and denoted as the RR interval. iThe unit is milliseconds (ms), and the expression is: RR i =t R(i+1) -t R(i) ;t R(i) The time of occurrence of the i-th R wave is expressed in milliseconds (ms).

[0073] The activity status of the sympathetic and parasympathetic nervous systems in the autonomic nervous system was assessed by statistical and frequency domain analysis of the RR intervals.

[0074] Calculate the standard deviation (SDNN) of the RR intervals. SDNN is the standard deviation of all RR intervals, reflecting overall heart rate variability: High SDNN indicates a flexible and well-regulated autonomic nervous system; Low SDNN may reflect stress or fatigue. Calculate the root mean square (RMSSD) of the differences between adjacent RR intervals, reflecting the state of parasympathetic activity: High RMSSD indicates strong parasympathetic activity; Low RMSSD may indicate increased stress or fatigue.

[0075] The RR interval is converted into a spectrum using Fast Fourier Transform (FFT), and the following key parameters are extracted:

[0076] Low-frequency power (LF, 0.04–0.15 Hz) reflects the combined effects of the sympathetic and parasympathetic nervous systems. High-frequency power (HF, 0.15–0.4 Hz) primarily reflects parasympathetic activity. The LF / HF ratio measures the balance between the sympathetic and parasympathetic nervous systems: an increased LF / HF ratio indicates sympathetic dominance, possibly related to stress or hypoxia; a decreased LF / HF ratio indicates parasympathetic dominance, usually associated with relaxation or recovery.

[0077] Based on the above analysis results, the heart rate variability index (HRVI) was obtained by weighted averaging the standard deviation of the RR interval (SDNN), the root mean square of the difference between adjacent RR intervals (RMSSD), and the LF / HF ratio. This HRVI is used to quantify the autonomic nervous system response state.

[0078] The blood flow rate variation deviation index and heart rate variability index were normalized, and the comprehensive score of ischemic state was calculated using the normalized blood flow rate variation deviation index and heart rate variability index.

[0079] For example, the present invention can use the following formula to calculate the comprehensive score of ischemic state, the calculation expression is: In the formula, CPS is the comprehensive score of ischemic state, BFVPI is the blood flow rate deviation index, HRVI is the heart rate variability index, and a1 and a2 are the proportional coefficients of the blood flow rate deviation index and the heart rate variability index, respectively, with a2>a1>0.

[0080] The obtained comprehensive ischemic state score is compared with the preset comprehensive ischemic state score reference threshold. If the comprehensive ischemic state score is greater than or equal to the preset comprehensive ischemic state score reference threshold, it indicates that the blocking effect is insufficient and the pressure needs to be gradually increased until the blood flow change rate deviation index and heart rate variability index reach the target value. At the same time, the duration of ischemia is shortened, for example, from 5 minutes to 3 minutes.

[0081] If the comprehensive ischemic state score is less than the preset reference threshold for the comprehensive ischemic state score, it indicates that the load is high. In this case, it is necessary to reduce the pressure to avoid tissue damage or discomfort, and gradually prolong the ischemic time (e.g., increase by 30 seconds each time) to enhance the adaptation effect.

[0082] If the blood flow rate variation deviation index and heart rate variability index fail to return to baseline values ​​during the rest period, extend the rest time (e.g., from 6 minutes to 8 minutes). If recovery is good, gradually shorten the rest time to improve training efficiency.

[0083] S4: Monitor and analyze the real-time physiological data of the adjusted trainees, continuously optimize the ischemic preconditioning program, generate a training effect evaluation report for the trainees based on the collected real-time physiological data, and store the relevant data in the database for subsequent optimization and query.

[0084] The real-time collected physiological data include the blood flow rate of change deviation index and the heart rate variability index. To achieve the training goal, the actual physiological parameters are compared with the preset target values, and the deviation values ​​of each physiological data are calculated.

[0085] The ischemic preconditioning protocol is dynamically optimized using bias values ​​and control algorithms. Key training parameters (pressure, time, and interval duration) are adjusted, and the optimization formula is as follows: P 新 (t) represents the real-time adjusted blocking pressure (unit: mmHg), P 当前 (t) represents the current pressure value (unit: mmHg). K P This is the pressure adjustment factor, used to control the magnitude of pressure changes, expressed in mmHg / %. i The weight of the i-th parameter reflects the importance of different physiological parameters to the training effect, ΔP. i (t) represents the real-time deviation value of the i-th parameter, where the parameters are the blood flow rate deviation index and the heart rate variability index.

[0086] The formula for optimizing ischemia duration is: T 新 T is the adjusted duration of ischemia (in minutes). 当前 The current duration of ischemia (in minutes). K TThis is a time adjustment factor used to control the magnitude of time variation (unit: minutes / %).

[0087] Interval time optimization formula: Rest 新 =Rest 当前 +K R ·max(ΔP i (t)); Rest 新 Adjusted rest time (in minutes), Rest 当前 K represents the current interval time (in minutes). R This is the interval adjustment factor, used to extend the interval by a certain margin (unit: minutes / %), max(ΔP) i (t) (the maximum value among all physiological parameter deviations, used to prioritize extending the interval time to protect physiological safety).

[0088] Based on real-time monitored physiological parameters, the objective function for optimization is to minimize the Comprehensive Deviation Score (CPS). CPS(t) is the optimized overall deviation score, representing the overall deviation between the current training parameters and the target value. M is the number of physiological parameters monitored (such as blood flow, blood oxygen saturation, heart rate, etc.). P 目标,i The target value of the i-th parameter.

[0089] If CPS(t) ≤ T threshold (the set bias score reference value), it means that the training effect is good and no adjustment is needed.

[0090] If CPS(t) > the T threshold, key training parameters need to be dynamically adjusted: If the load is insufficient, increase the load, for example, by 10 mmHg. If the load is too high, decrease the load and extend the interval. By monitoring in real time and updating the objective function, CPS(t) is gradually reduced, achieving personalized and efficient training.

[0091] Based on the collected real-time physiological data, the system automatically generates a training effect evaluation report for the trainee, including dynamic change curves of key indicators (such as blood flow rate of change, rate of decrease in blood oxygen saturation, and heart rate variability index), deviation analysis, and a comprehensive ischemic status score. The evaluation report visually displays the physiological response trends during training through charts and graphs, quantifies and summarizes the training effect, and indicates optimization suggestions (such as adjusting pressure, time, or rest intervals). All collected data and evaluation results are synchronously stored in a cloud database, supporting personalized optimization of subsequent training programs, long-term trend analysis, and historical record queries, providing data support for scientific and continuously improving ischemic preconditioning programs.

[0092] In this embodiment, firstly, physiological parameters such as blood oxygen saturation, heart rate, blood pressure, and blood flow of the trainee are acquired in real time through a physiological data acquisition device. Secondly, based on the real-time data and preset training goals, key parameters of the ischemia preconditioning program are calculated, including ischemia intensity, time, frequency, and interval duration. Then, ischemia treatment is applied to designated areas using a controllable ischemia device, and the ischemia state is dynamically adjusted according to changes in training parameters. Finally, the adjusted physiological data is continuously monitored and analyzed to optimize the training program. At the same time, a training effect evaluation report is generated and the data is stored in a database to support subsequent program optimization and long-term effect tracking.

[0093] Example 2, please refer to Figure 2 As shown, the ischemia preconditioning mechanism-driven intelligent training device described in this embodiment includes a data acquisition module, a training parameter calculation module, an ischemia control module, and an optimization module:

[0094] Data acquisition module: Acquires real-time physiological data of trainees through at least one physiological data acquisition device, including blood oxygen saturation, heart rate, blood pressure and blood flow;

[0095] Training parameter calculation module: Based on the trainee's real-time physiological data and preset training goals, calculate the training parameters of the ischemic preconditioning program, including ischemic intensity, time, frequency and interval duration;

[0096] Ischemia control module: Through a controllable ischemia device, ischemia treatment is applied to designated areas of the trainee, and the ischemia state is dynamically adjusted according to changes in training program parameters;

[0097] Optimization module: Monitors and analyzes the real-time physiological data of the trainees after adjustment, continuously optimizes the ischemic preconditioning program, generates a training effect evaluation report for the trainees based on the collected real-time physiological data, and stores the relevant data in the database for subsequent optimization and query.

[0098] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0099] It should be understood that the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. A and B can be singular or plural. Additionally, the character " / " in this article generally indicates an "or" relationship between the preceding and following related objects, but it can also represent an "and / or" relationship. Please refer to the context for a more accurate understanding.

[0100] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0101] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. Intelligent training device driven by the mechanism of ischemic preconditioning, characterized by, The application relates to a training system and method for ischemic preconditioning, comprising: a data acquisition module, a training parameter calculation module, an ischemic control module and an optimization module: the data acquisition module acquires real-time physiological data of a trainer through at least one physiological data acquisition device, wherein the real-time physiological data comprises blood oxygen saturation, heart rate, blood pressure and blood flow; the training parameter calculation module calculates training scheme parameters of ischemic preconditioning, including ischemic intensity, time, frequency and intermittent duration, based on the real-time physiological data of the trainer and a preset training target; the ischemic control module implements ischemic treatment on the specified part of the trainer through a controllable ischemic device, and dynamically adjusts the ischemic state according to the change of the training scheme parameters; during the ischemic treatment process, the real-time change of blood flow is monitored through a Doppler ultrasound sensor, the change ratio deviation of blood flow before and after blockage is calculated, a blood flow change rate deviation index is generated, and the acquisition method of the blood flow change rate deviation index is as follows: Baseline blood flow of the target site is obtained by Doppler ultrasound sensor before ischemia treatment and marked as , blood flow during occlusion is monitored and marked as , real-time blood flow change rate during ischemia treatment is defined as , the change ratio of blood flow during occlusion to baseline blood flow is defined as , target blood flow change rate range is defined as , real-time deviation value is calculated as , the deviation value is the deviation of real-time blood flow change rate from the target range, expressed as ; wherein: if is negative, it means that the blood flow does not reach the target change rate, and the occlusion pressure needs to be increased; if is positive, it means that the blood flow change rate exceeds the target value, and the occlusion pressure needs to be reduced. Defining blood flow rate variation deviation index , for comprehensive quantification of blood flow rate variation deviation condition: ; N is the sampling number, i.e. the total number of blood flow data points recorded during the blockage, is the square of the blood flow rate variation deviation value at the th time point. the R-R interval is collected through an electrocardiogram sensor, the autonomic nervous system response state of the trainer is calculated, and the autonomic nervous system response state of the trainer is analyzed to generate a heart rate variability index, and the acquisition method of the heart rate variability index is as follows: Real-time electrocardiogram data is recorded using an electrocardiogram sensor, and the time interval between two adjacent R waves is extracted, denoted as R-R interval , and the expression is: ; is the occurrence time of the R wave, the standard deviation SDNN of R-R interval is calculated, SDNN is the standard deviation of all R-R intervals, reflecting the overall heart rate variability; the root mean square RMSSD of adjacent R-R interval difference is calculated, reflecting the parasympathetic nervous activity state; the R-R interval is converted into frequency spectrum through fast Fourier transform, and LF / HF is extracted; the standard deviation SDNN of R-R interval, the root mean square RMSSD of adjacent R-R interval difference and LF / HF are weighted and averaged to obtain the heart rate variability index HRVI, which is used to quantify the autonomic nervous system response state; the optimization module monitors and analyzes the adjusted real-time physiological data of the trainer, continuously optimizes the ischemic preconditioning scheme, generates a training effect evaluation report of the trainer based on the collected real-time physiological data, and stores the related data in a database for subsequent optimization and query; the real-time collected physiological data are the blood flow change rate deviation index and the heart rate variability index; in order to achieve the training target, the actual physiological parameters are compared with the preset target value, and the deviation value of each physiological data is calculated; Utilizing the deviation value and control algorithm, dynamically optimize the ischemic preconditioning scheme, the optimization formula is: ; is the real-time adjusted occlusion pressure, is the current pressure value, is the pressure adjustment coefficient, used to control the amplitude of pressure change, is the weight of the ith parameter, reflecting the importance of different physiological parameters on the training effect, represents the real-time deviation value of the ith parameter; The ischemia duration optimization formula is: ; is the adjusted ischemia duration, is the current ischemia duration; is a time adjustment coefficient for controlling the amplitude of time variation; Intermittent time optimization formula: ; wherein i ranges from 1 to M; Adjusted intermittent time, is the current intermittent time, is the intermittent time adjustment coefficient, used to extend the magnitude of the intermittent time, is the maximum value of all physiological parameter deviation values; Based on the real-time monitored physiological parameters, the objective function to be optimized is to minimize the comprehensive deviation score value : ; For the optimized comprehensive deviation score value, it represents the overall deviation degree of the current training parameters from the target value, M is the number of monitored physiological parameters, is the target value of the i-th parameter.

2. The ischemic preconditioning mechanism driven smart trainer of claim 1, wherein: the blood flow change rate deviation index and the heart rate variability index are normalized, and the blood flow change rate deviation index and the heart rate variability index after normalization are used to calculate an ischemic state comprehensive score value.

3. The ischemic preconditioning mechanism driven smart trainer of claim 2, wherein: the obtained ischemic state comprehensive score value is compared with a pre-set ischemic state comprehensive score reference threshold value, if the ischemic state comprehensive score value is greater than or equal to the pre-set ischemic state comprehensive score reference threshold value, it is indicated that the blockage effect is insufficient, the pressure needs to be gradually increased, until the blood flow change rate deviation index and the heart rate variability index reach the target value, and the ischemic duration is shortened; if the ischemic state comprehensive score value is less than the pre-set ischemic state comprehensive score reference threshold value, it is indicated that the load is high, and the pressure needs to be reduced to avoid tissue damage or discomfort, and the ischemic time is gradually prolonged.

4. The ischemic preconditioning mechanism driven smart trainer of claim 1, wherein: if CPS(t) is less than or equal to T threshold value, it is indicated that the training effect is good, and no adjustment is needed; if CPS(t) is greater than T threshold value, the key training parameters need to be dynamically adjusted: when the pressure is insufficient, the pressure is increased; and when the load is high, the pressure is reduced and the intermittent time is prolonged.

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