Real-time health state monitoring system based on biosensor

Abstract

The invention discloses a real-time health state monitoring system based on a biosensor, and relates to the technical field of health monitoring, the real-time health state monitoring system comprises a sensor module, a motion association module, a project state evaluation module, a risk early warning module, a training adjustment module and an optimization feedback module, the sensor module comprises a physiological sensing unit and a posture sensing unit, the physiological sensing unit and the posture sensing unit collect data through flexible sensors installed on the surface of the body of an athlete, physiological data and posture data are obtained, the relation between posture changes and the physiological data is analyzed through an exercise association module, and meanwhile, a project state evaluation module is combined to evaluate the state of the athlete. Based on the individual fitness and historical data of athletes, personalized health monitoring is realized, health risks are quantified through the risk early warning module, the training intensity is dynamically adjusted, the sports injury risk is reduced through the training adjustment module, and finally the adjusted training performance is evaluated through the optimization feedback module, so that the training is optimized and improved.

Description

Technical Field

[0001] This invention relates to the field of health monitoring technology, specifically a real-time health status monitoring system based on biosensors. Background Technology

[0002] With the increasing professionalization of competitive sports, professional athletes have an increasingly urgent need for data-driven, intelligent, and personalized approaches to training, preparation, and post-competition recovery. Athletes in different sports face varying physical loads and performance management challenges. Meanwhile, the widespread adoption of IoT technology enables real-time collection of multi-dimensional data, providing technical support for data integration and analysis, and driving the development of health monitoring systems towards cross-sport adaptability and dynamic early warning. Against this backdrop, the development of health monitoring systems for the full-cycle performance management of professional athletes has become an essential industry requirement.

[0003] Currently, most systems collect physiological and postural data separately, and usually only trigger alarms when the indicators exceed preset thresholds. At this time, the athlete may already be in a state close to injury. The system lacks the ability to predict future risks in advance and cannot dynamically adjust training strategies according to the athlete's actual health status. This increases safety risks and reduces training quality. In addition, abnormal physiological load during training is often closely related to postural movements, but traditional monitoring cannot quantify the causal relationship between the two, making it difficult to locate the root cause of the problem, resulting in low monitoring quality of the system. Summary of the Invention

[0004] The purpose of this invention is to provide a real-time health status monitoring system based on biosensors, which solves the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a real-time health status monitoring system based on biosensors, comprising a sensor module, a motion correlation module, a project status assessment module, a risk warning module, a training adjustment module, and an optimization feedback module;

[0006] The sensor module includes a physiological sensing unit and a body posture sensing unit. Both the physiological sensing unit and the body posture sensing unit collect data through flexible sensors installed on the surface of the athlete's body to obtain physiological data and body posture data.

[0007] The motion correlation module is used to analyze the changes in physiological data and body posture data over time, assess the correlation between physiological data and body posture data, and obtain a correlation coefficient that reflects the strength of the correlation between the two types of data.

[0008] The project status assessment module combines the athlete's individual adaptation, the weight of the sports project indicators, and the correlation coefficient to analyze the training status of each sports project and obtain a comprehensive training status value.

[0009] The risk warning module analyzes the future health risk based on the change of the comprehensive value of the training status over time, obtains the health risk value, and determines whether the training intensity needs to be adjusted based on the health risk value.

[0010] The training adjustment module adjusts the current training intensity based on the health risk value and the comprehensive training status value. The optimization feedback module analyzes the changes in the comprehensive training status value after the training intensity adjustment and adjusts the data in the project status assessment module accordingly.

[0011] Optionally, the motion association module first calculates the change in each physiological data point, sets a time interval, compares the change in each physiological data point with the time interval, and obtains the instantaneous rate of change of the physiological data.

[0012] Then calculate the change in body posture data, and add the physiological response lag time according to the different characteristics of physiological data to ensure the accuracy of the correlation between physiological data and body posture data. Compare the change in body posture data with the time interval to obtain the instantaneous change rate of body posture data.

[0013] Finally, the instantaneous rate of change of physiological data and the instantaneous rate of change of body posture data are compared with the standard instantaneous rate of change set by the system. Then, based on the correlation strength between physiological data and body posture data and individual differences among athletes, correlation weights are set, and a time decay term is introduced to obtain the correlation coefficient C between the i-th physiological index and the j-th body posture index at time t. ij (t).

[0014] Optionally, the project status assessment module first filters the correlation coefficients required for training projects, and then assesses the training status. The assessment process is as follows:

[0015]

[0016] In the above formula, S(t) is the comprehensive score of the athlete's training status at time t, which reflects the athlete's training status in their own sport at time t.

[0017] β(t) is the individual fitness influence coefficient at time t, which reflects the individual athlete's ability to adapt to training intensity and the stability of historical state.

[0018] C ij (t) is the correlation coefficient between the i-th physiological index and the j-th physical state index at time t;

[0019] W pro (j) is the influence coefficient of the customized indicators for the project, with a value range of 0 to 1;

[0020] N represents the number of core indicators, which refers to the number of physiological and postural correlation indicators in the training program;

[0021] The project status assessment module selects corresponding physiological and physical data based on different sports and incorporates individual fitness data to achieve personalized health status monitoring for different athletes.

[0022] Optionally, the project status assessment module first filters the correlation coefficients required for training projects, and then assesses the training status. The assessment process is as follows:

[0023]

[0024] In the above formula, S(t) is the comprehensive score of the athlete's training status at time t, which reflects the athlete's training status in their own sport at time t.

[0025] β(t) is the individual fitness influence coefficient at time t, which reflects the individual athlete's ability to adapt to training intensity and the stability of historical state.

[0026] C ij (t) is the correlation coefficient between the i-th physiological index and the j-th physical state index at time t;

[0027] W pro (j) is the influence coefficient of the customized indicators for the project, with a value range of 0 to 1;

[0028] N represents the number of core indicators, which refers to the number of physiological and postural correlation indicators in the training program;

[0029] The project status assessment module selects corresponding physiological and physical data based on different sports and incorporates individual fitness data to achieve personalized health status monitoring for different athletes.

[0030] Optionally, the training adjustment module is based on the health risk value P at time t + Δt. risk The process involves analyzing (t + Δt) and the difference between the current training state and the optimal training state to generate a quantified training intensity adjustment range. The process is as follows:

[0031] First, calculate the health risk value P at time t + Δt. risk The difference between (t + Δt) and the system-set threshold;

[0032] Then calculate the difference between the training state comprehensive score S(t) and the system's set optimal state score;

[0033] Based on the health risk value P at time t + Δt riskThe training intensity is adjusted by the difference between (t+Δt) and the system-set threshold, and the difference between the comprehensive training state score S(t) and the system-set optimal state score. The adjustment magnitude increases with the increase of the difference, and finally the training adjustment coefficient K at time t is obtained. adj (t).

[0034] Optionally, after adjusting the training intensity, the training adjustment module analyzes the adjusted training state comprehensive score through the project state evaluation module, then calculates the difference between the adjusted training state comprehensive score and the training state comprehensive score S(t) to obtain the state change score ΔS, and compares it with the training adjustment coefficient K at time t. adj The individual fitness influence coefficient β(t) is analyzed in combination with the system's optimal state score to quantify the adjustment range of the individual fitness influence coefficient β(t) at time t, and to obtain the individual fitness influence coefficient at time t+1.

[0035] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0036] I. This invention analyzes the changes in physiological data during postural changes through a motion correlation module, and considers the lag time of physiological responses to avoid erroneous correlations caused by blindly using data from the same moment. At the same time, it introduces a time decay term to filter out outdated or irrelevant postural changes, making the results more consistent with the real physiological mechanism, quantifying the impact of postural changes on physiological responses, locating the root cause of the problem, and providing the system with a real-time and accurate basis for health status assessment.

[0037] Second, this invention uses a project status assessment module to select corresponding physiological and physical data based on different training projects, introduce individual fitness, and monitor and quantitatively display the personalized health status of different athletes based on their historical training data. Then, through a risk warning module, it quantitatively analyzes the future health risks based on the changes in the comprehensive value of training status over time, obtains a health risk value, and achieves the effect of predicting future risks in advance. The training intensity is dynamically adjusted according to the level of the health risk value to avoid injury to athletes, reduce safety hazards, and avoid affecting the training progress due to excessive conservatism.

[0038] Third, this invention dynamically adjusts the training intensity based on the level of health risk, analyzes the adaptability of athletes based on their actual performance after the adjustment, and evaluates the effect of the adjustment to facilitate subsequent personalized training. In turn, the system monitors real-time health status to optimize and improve the training, thereby reducing safety hazards.

[0039] Optionally, a visualization module is also included, which is used to display and visualize the athlete's training data in real time. The visualization module includes a remote connection unit, through which coaches and athletes connect to the system via terminal devices and the remote connection unit to view monitoring data and receive alarms.

[0040] Optionally, the sensor module includes a preprocessing unit, which is used to remove noise from the flexible sensor array, fill in missing values, and align physiological data and body posture data along the time axis. Attached Figure Description

[0041] Figure 1 This is a system flowchart of the present invention;

[0042] Figure 2 The feedback flowchart for this invention has been optimized. Detailed Implementation

[0043] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0044] Example: Please refer to Figure 1 and Figure 2 This implementation provides a real-time health status monitoring system based on biosensors, including a sensor module, a motion correlation module, a project status assessment module, a risk warning module, a training adjustment module, and an optimization feedback module;

[0045] The visualization module is used to display athletes' training data in real time and visualize it. The visualization module includes a remote connection unit. Coaches and athletes connect to the system through terminal devices and remote connection units. Terminal devices include smart devices such as mobile phones and computers to view monitoring data and receive alarms.

[0046] The sensor module includes a physiological sensing unit and a body posture sensing unit. Both the physiological sensing unit and the body posture sensing unit collect data through flexible sensors installed on the athlete's body surface to obtain physiological data and body posture data.

[0047] The sensor module includes a preprocessing unit, which is used to remove noise from the flexible sensor array, fill in missing values, and align physiological data and body posture data along the time axis.

[0048] Specifically, flexible sensors can be attached to the athlete's body surface to collect data in real time. For example, heart rate can be collected by attaching flexible ECG electrodes to the left side of the athlete's chest, electromyography signals can be collected by a flexible dry electrode array, integrated into a stretchable elastic bandage, and fixed to the center of the target muscle belly with Velcro, and joint angles can be collected by integrating flexible strain sensors into elastic bandages or sports protective gear, which can be attached to the skin surface of the joint and deform synchronously with the joint movement.

[0049] The motion correlation module is used to analyze the changes in physiological and postural data over time, assess the correlation between physiological and postural data, and obtain the correlation coefficient, which reflects the strength of the correlation between the two types of data.

[0050] Specifically, the motion correlation module is used to quantify the impact of postural changes on physiological responses, and the process is as follows:

[0051]

[0052] In the above formula, C ij (t) is the correlation coefficient between the i-th physiological indicator and the j-th postural indicator at time t. The larger the value, the more significant the causal effect of the change in the postural indicator on the physiological indicator, and the greater the exercise load and health risk.

[0053] Postural indicators include joint angles, movement trajectory, muscle force distribution, and landing impact force;

[0054] Physiological indicators include heart rate, electromyography signal, lactate concentration, and blood oxygen saturation;

[0055] LP i,th The standard rate of change of the i-th physiological indicator is determined by the sport and the athlete's historical data.

[0056] LP i (t) represents the instantaneous rate of change of the i-th physiological indicator at time t, used to reflect the speed of change in physiological state, where ;

[0057] ΔP i (t) represents the change in the i-th physiological indicator at time t, and Δt is the time interval;

[0058] LB j,th Let be the standard rate of change of the j-th individual's physical characteristics;

[0059] LB j (t-L ij ) is t-L ijThe instantaneous rate of change of the postural index at time j reflects the natural time difference of the physiological response after the postural change. This avoids erroneous correlations caused by blindly using data from the same instant, and is more consistent with the real physiological mechanism, such as the movement occurring first and then the body reacting. The process is as follows:

[0060]

[0061] ΔB j (t-L ij ) is t-L ij The change in the postural index at time j, such as the knee angle changing from 120 degrees to 150 degrees;

[0062] L ij This refers to the physiological lag time; for example, after an athlete jumps, it takes 0.5 seconds for the heart rate to begin to rise.

[0063] W ij The correlation weight between the i-th physiological indicator and the j-th postural indicator ranges from 0 to 1 and is determined by system presets. Different postures are adjusted based on the actual sport and individual athlete differences. For example, if the j-th postural indicator is the knee joint angle and the i-th physiological indicator is the heart rate, the two postural indicators are significantly correlated because the knee joint repeatedly flexes and extends during running, directly stimulating the cardiovascular system. Therefore, the correlation weight W between the i-th physiological indicator and the j-th postural indicator is calculated. ij The initial value is 0.8, and then it is adjusted to individual differences among athletes. If the athlete has a previous knee injury, the injured area is more sensitive to impact and the physiological reaction is stronger. In this case, the value can be increased to 0.9.

[0064] |Δt-L ij | represents the absolute difference between the time interval and the physiological response lag time;

[0065] T0 is the time decay constant, used to control the decay rate, and its value is 1;

[0066] This is a time decay term used to filter out outdated or irrelevant postural changes. It is applied when the time interval Δt is close to the physiological response lag time L. ij When this value is closer to 1, it indicates a stronger influence of body posture changes on physiological responses; conversely, when the time interval Δt deviates from the physiological response lag time L... ij When this value is closer to 0, its impact on subsequent analysis is negligible, thus filtering outdated body posture data and data with very little correlation, thereby improving the accuracy of the results.

[0067] The motion correlation module quantifies the impact of postural changes on physiological responses, integrating previously independent postural and physiological data into a unified analytical dimension. This enables the identification of root causes of problems and provides the system with real-time and accurate health status assessments. Based on system testing, the correlation coefficient C between the i-th physiological indicator and the j-th postural indicator at time t is set. ij The first correlation threshold for (t) is 1.2, and the second correlation threshold is 0.6;

[0068] When C ij When (t)≥1.2, it means that the correlation between the i-th physiological index and the j-th physical index at time t is high, and the influence of the current physical change on the physiological response exceeds the normal range. For example, when an athlete jumps and lands, the knee joint angle deviates from the standard value, and the heart rate rises sharply after 0.5 seconds. At this time, the system will issue an alarm, indicating that the exercise load is excessive or the risk of injury is high, and it is necessary to standardize the movement or pay attention to rest.

[0069] When 0.5 < C ij When (t) < 1.2, it also indicates that the correlation between the i-th physiological index and the j-th physical index is strong at time t, and the influence of physical changes on physiological response is consistent with the athlete's historical training pattern.

[0070] When C ij When (t)≤0.5, it means that the correlation between the i-th physiological index and the j-th body posture index at time t is weak, there is no significant causal relationship between body posture changes and physiological responses, or the current body posture changes have no effective impact on the physiological state.

[0071] In practical applications, corresponding thresholds can be set according to the main related items of each training item to avoid too much data that is irrelevant to the item and reduce the efficiency of system operation. For example, in basketball-related training items, jumping, changing direction and knee joints are high-load parts, and heart rate variability is sensitive to abnormal body posture. Corresponding thresholds can be set based on the correlation between high-load parts and heart rate variability. When the system alarms, it can accurately understand what kind of training caused it and improve the quality of health status monitoring.

[0072] The project status assessment module analyzes the training status of each sport by combining the individual athlete's adaptation, the weight of the sport's indicators, and the correlation coefficient.

[0073] Furthermore, the correlation coefficient C between the i-th physiological index and the j-th physical index at time t is obtained. ij After (t), the training status of each sport is analyzed by combining the individual athlete's adaptation and the weight of the sport's indicators through the project status assessment module. The analysis process is as follows:

[0074]

[0075] In the above formula, S(t) is the comprehensive score of the athlete's training status at time t, which can reflect the athlete's training status in their own sport at time t. The closer the score is to 1, the better the health status during training; the further the score is from 1, the worse the health status.

[0076] β(t) is the individual fitness influence coefficient at time t, reflecting the individual athlete's ability to adapt to training intensity and the stability of historical state. It is obtained by analyzing the athlete's historical training data, such as the amplitude of state fluctuation, recovery speed and injury history, etc. The value ranges from 0.7 to 1.5, with an initial value of 1. The higher the value of this item is for athletes with strong adaptability and stable state, the lower the value of this item is for novices or athletes with large state fluctuations.

[0077] C ij (t) is the correlation coefficient between the i-th physiological indicator and the j-th body posture indicator at time t. The larger the value, the more significant the causal effect of the change in the body posture indicator on the physiological indicator.

[0078] W pro (j) is the project-customized indicator influence coefficient, with a value range of 0 to 1. It reflects the importance of each physiological and physical correlation indicator in different projects. It is set based on the project technical specifications. For example, track and field projects focus on running frequency, while basketball focuses on knee joint angle. This allows the project status assessment module to pay more attention to the key needs of each project, reduce the interference of irrelevant indicators, and improve the accuracy of project status assessment.

[0079] If the i-th physiological indicator is heart rate and the j-th physical indicator is stride frequency, these two correlated indicators are very important for track and field training. The correlation coefficient C between the i-th physiological indicator and the j-th physical indicator at time t is... ij (t) is a key indicator in track and field events, and the influence coefficient W of the customized indicator for the event is... pro (j) Set the maximum value;

[0080] N represents the number of core indicators, which refers to the number of key physiological and postural indicators customized for the project, covering the core movements and risk points of the project. For example, in basketball, indicators such as knee joint angle and heart rate, quadriceps force distribution and stride frequency changes, ankle landing impact force and respiratory rate, and waist twisting amplitude and heart rate variability are selected. By selecting key physiological and postural indicators according to specific projects, interference caused by unnecessary indicators is reduced, ensuring the accuracy of evaluation results while improving the efficiency of system operation. In practical applications, basketball projects can also be divided, such as basketball projects including shuttle run, jumping, and shooting.

[0081] The project status assessment module selects corresponding physiological and physical indicators based on different sports, and introduces individual fitness. Based on the athlete's historical training data, it directly adjusts the athlete's comprehensive training status score S(t) at time t, avoiding the bias of standardized assessment and adapting the results to the differences in individual abilities, thereby realizing personalized health status monitoring for different athletes.

[0082] The risk warning module analyzes the future health risk based on the changes in the comprehensive value of the training status over time, obtains a health risk value, and determines whether the training intensity needs to be adjusted based on the health risk value.

[0083] Furthermore, after obtaining the comprehensive training state score S(t), the risk warning module analyzes the future health risk based on the change of the comprehensive training state score S(t) over time, as follows:

[0084]

[0085] In the above formula, P risk (t+Δt) is the health risk value at time t+Δt. The larger the value, the higher the probability of the athlete suffering a training injury.

[0086] S(t) is the comprehensive score of the athlete's training status at time t, which reflects the athlete's training status in their own sport at time t. The closer the score is to 1, the better the health status during training. The further the score is from 1, the worse the health status.

[0087] dS(t) is the differential of the athlete's overall training state score at time t, representing the change in the athlete's state score at time t;

[0088] dt is the differential at time t. This represents the absolute value of the rate of change of the training state score over time, reflecting the speed at which the training state deteriorates or improves. A larger value indicates a faster rate of deterioration or improvement in the training state. A score greater than 0 indicates an increase in the athlete's training performance score, suggesting an improvement in the athlete's condition. A score less than 0 indicates a decline in the athlete's training performance, suggesting that the athlete's condition is deteriorating.

[0089] γ is the fatigue accumulation coefficient, ranging from 0.5 to 1.5. It reflects the rate at which an athlete's fatigue accumulates over time. The higher the value, the faster the fatigue accumulates. It is derived from the athlete's historical training data, such as fatigue recovery time.

[0090] T riskThe project safety threshold is set based on historical injury data and sports medicine standards, and represents a lower limit for training status scores for different training programs. When the training status score falls below the project injury threshold T, the lower limit is determined. risk At that time, health risks increase;

[0091] The deviation term from the safety threshold refers to the degree of deviation of the current state score from the customized injury threshold of the project. If the athlete's overall training state score S(t) at time t is lower than the project's safety threshold T, then... risk The larger this value is, the closer the current training state is to the injury threshold and the greater the risk of injury. Conversely, the higher the athlete's overall training state score S(t) at time t, the smaller this value is and the lower the risk of injury.

[0092] The risk warning module predicts training risks in the future based on the dynamic rate of change of state scores, and combines this with the project safety threshold T. risk This makes risk prediction more closely aligned with the characteristics of sports injuries. For example, in basketball, the focus is on the knee joint, which can improve the accuracy of prediction results so that timely adjustments can be made to avoid athlete injuries. When the health risk value is too high, an alarm should be issued to notify the athlete to rest or reduce training supervision. Alternatively, when the athlete is in good physical condition, the training intensity can be increased. In specific applications, a health risk threshold of 0.8 can be set for health risk one and 0.3 for health risk two.

[0093] When P risk When (t+Δt)≥0.8, it indicates an abnormal condition, the athlete's training condition is deteriorating too rapidly, the risk of injury is greatly increased, the system will issue an alarm, and the coach or athlete needs to be notified to reduce the training intensity.

[0094] When 0.3 < P risk When (t+Δt)<0.8, it indicates that the athlete's training status is normal and there is no need to adjust the training intensity.

[0095] When P risk When (t+Δt)≤0.3, it indicates that the athlete's body is in a safe redundancy state, and the training intensity should be increased.

[0096] The training adjustment module adjusts the current training intensity based on a combined health risk value and training status value. The process is as follows:

[0097]

[0098] In the above formula, K adj (t) is the training adjustment coefficient at time t. The larger the value, the greater the adjustment of training intensity. The adjustment of training intensity can include training time, and specifically can include running distance, the intensity of stretching equipment, etc.

[0099] Prisk (t+Δt) represents the health risk value at time t+Δt. The greater the health risk, the stronger the adjustment.

[0100] P mid The intermediate threshold for health risk is set at 0.5, [P] risk (t+Δt)-P mid Health risk compensation item, when P risk When (t+Δt)≥0.8, this term is positive, indicating that the training adjustment coefficient K at time t needs to be reduced. adj (t) is a positive number less than 1, and the higher the health risk value, the greater the training adjustment coefficient K at time t. adj The smaller (t) is, the more dynamic the adjustment effect is achieved. When making adjustments, the original training intensity is multiplied by the training adjustment coefficient K at time t. adj (t) is sufficient; for example, if the original training time is 100 seconds, the training adjustment coefficient K is determined at time t. adj If (t) is set to 0.8, then the training time after intensity adjustment is 100 × 0.8 = 80 seconds, thus achieving the effect of reducing training intensity;

[0101] Similarly, when it is necessary to increase the training intensity, since P... risk (t+Δt)≤0.3, therefore [P risk (t+Δt)-P mid The result of the health risk compensation item will be negative, and the training adjustment coefficient K at time t will be negative. adj The result of (t) will be a positive number greater than 1. For example, if the original training time is 100 seconds, and the training adjustment coefficient K is at time t... adj If (t) is set to 1.2, then the training time after intensity adjustment is 100 × 1.2 = 120 seconds, thus achieving the effect of increasing training intensity;

[0102] S(t) is the athlete's overall training status score at time t;

[0103] S opt To determine the ideal training state score, a baseline value for the ideal state is set based on the characteristics of the sport. The closer the athlete's overall training state score at time t is to the ideal training state score S, the better. opt The smaller the adjustment range, the larger the adjustment range;

[0104] The training adjustment module can dynamically adjust the intensity of subsequent training based on the actual training status. When the training status is good, the training intensity is increased to improve the training effect, and when the training status is poor, the training intensity is reduced to avoid injury to athletes and reduce safety risks. At the same time, it avoids affecting the training progress due to overly conservative training. After the training adjustment module adjusts the intensity, it continues to collect data through the sensor module for subsequent analysis.

[0105] The feedback module is optimized by analyzing the changes in the comprehensive value of training status after the training intensity is adjusted, and by adjusting the data in the project status assessment module.

[0106] Specifically, the coefficient K is adjusted based on the training time t. adj (t) After adjusting the training intensity, the project state evaluation module is used again to analyze and obtain the adjusted training state comprehensive value, and the difference between the adjusted and the original training state comprehensive value is calculated to obtain the state change score ΔS. To avoid the system overreacting to small changes, the optimization feedback module is only activated when the state change score ΔS > the change threshold Y1. The change threshold Y1 can be obtained based on the actual state changes after multiple training adjustments, as follows:

[0107]

[0108] In the above formula, β(t+1) is the influence coefficient of individual fitness at time t+1;

[0109] β(t) is the influence coefficient of individual fitness at time t, where β is the influence coefficient of individual fitness.

[0110] ΔS is the state change score, which is obtained by calculating the difference between the adjusted training state composite value and the original training state composite value. If the state change score ΔS > 0, it means that the training adjustment module is effective; if ΔS < 0, it means that the adjustment is excessive or ineffective.

[0111] K adj (t) is the training adjustment coefficient at time t;

[0112] The adjustment range used to quantify the influence coefficient of individual fitness is as follows: the larger the state change score ΔS, the better the training adjustment effect. The larger the value of this item, the more significant the improvement in the athlete's adaptability, and the more significantly the individual fitness influence coefficient β needs to be increased. When the state change score ΔS is smaller, or even negative, it indicates that the adaptability has not improved or has decreased, and the individual fitness influence coefficient β needs to be conservatively updated or reduced.

[0113] The range of the individual fitness influence coefficient β is set to 0.7 to 1.5. When the value exceeds or falls below the set range, it is directly determined to be the maximum or minimum value of the range. This avoids overly aggressive adjustments and avoids drastic fluctuations in the evaluation results caused by an excessively large coefficient range, which is in line with the dynamic adaptation law of human physiology.

[0114] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A real-time health status monitoring system based on biosensors, characterized in that, It includes a sensor module, a motion correlation module, a project status assessment module, a risk warning module, a training adjustment module, and an optimization feedback module; The sensor module includes a physiological sensing unit and a body posture sensing unit. Both the physiological sensing unit and the body posture sensing unit collect data through flexible sensors installed on the surface of the athlete's body to obtain physiological data and body posture data. The motion correlation module is used to analyze the changes in physiological data and body posture data over time, assess the correlation between physiological data and body posture data, and obtain a correlation coefficient that reflects the strength of the correlation between the two types of data. The project status assessment module combines the athlete's individual adaptation, the weight of the sports project indicators, and the correlation coefficient to analyze the training status of each sports project and obtain a comprehensive training status value. The risk warning module analyzes the future health risk based on the change of the comprehensive value of the training status over time, obtains the health risk value, and determines whether the training intensity needs to be adjusted based on the health risk value. The training adjustment module adjusts the current training intensity based on the health risk value and the comprehensive training status value. The optimization feedback module analyzes the changes in the comprehensive training status value after the training intensity adjustment and adjusts the data in the project status assessment module accordingly.

2. The real-time health status monitoring system based on biosensors according to claim 1, characterized in that: The motion correlation module first calculates the change in each physiological data point and sets a time interval. It then compares the change in each physiological data point with the time interval to obtain the instantaneous rate of change of the physiological data. Then calculate the change in body posture data, and add the physiological response lag time according to the different characteristics of physiological data to ensure the accuracy of the correlation between physiological data and body posture data. Compare the change in body posture data with the time interval to obtain the instantaneous change rate of body posture data. Finally, the instantaneous rate of change of physiological data and the instantaneous rate of change of body posture data are compared with the standard instantaneous rate of change set by the system. Then, based on the correlation strength between physiological data and body posture data and individual differences among athletes, correlation weights are set, and a time decay term is introduced to obtain the correlation coefficient C between the i-th physiological index and the j-th body posture index at time t. ij (t).

3. The real-time health status monitoring system based on biosensors according to claim 2, characterized in that: The project status assessment module first filters the correlation coefficients required for training projects, and then assesses the training status. The assessment process is as follows: ； In the above formula, S(t) is the comprehensive score of the athlete's training status at time t, which reflects the athlete's training status in their own sport at time t. β(t) is the individual fitness influence coefficient at time t, which reflects the individual athlete's ability to adapt to training intensity and the stability of historical state. C ij (t) is the correlation coefficient between the i-th physiological index and the j-th physical state index at time t; W pro (j) is the influence coefficient of the customized indicators for the project, with a value range of 0 to 1; N represents the number of core indicators, which refers to the number of physiological and postural correlation indicators in the training program; The project status assessment module selects corresponding physiological and physical data based on different sports and incorporates individual fitness data to achieve personalized health status monitoring for different athletes.

4. The real-time health status monitoring system based on biosensors according to claim 3, characterized in that: The risk warning module predicts the training risk in the future based on the dynamic rate of change of training status over time. First, it compares the athlete's comprehensive training status score S(t) at time t with the system's preset project safety threshold as a safety threshold deviation term to quantify the relative degree of risk. The time-varying rate of change of the athlete's overall training status score S(t) at time t is calculated to reflect the rate of deterioration. The deviation from the safety threshold is combined with the time-varying rate of change of the athlete's overall training status score S(t) at time t, and a fatigue accumulation coefficient based on the athlete's historical fatigue data is introduced to obtain the health risk value P at time t+Δt. risk (t+Δt) reflects the degree of training risk in the future.

5. The real-time health status monitoring system based on biosensors according to claim 4, characterized in that: The training adjustment module is based on the health risk value P at time t + Δt. risk The process involves analyzing (t + Δt) and the difference between the current training state and the optimal training state to generate a quantified training intensity adjustment range. The process is as follows: First, calculate the health risk value P at time t + Δt. risk The difference between (t + Δt) and the system-set threshold; Then calculate the difference between the training state comprehensive score S(t) and the system's set optimal state score; Based on the health risk value P at time t + Δt risk The training intensity is adjusted by the difference between (t+Δt) and the system-set threshold, and the difference between the comprehensive training state score S(t) and the system-set optimal state score. The adjustment magnitude increases with the increase of the difference, and finally the training adjustment coefficient K at time t is obtained. adj (t).

6. The real-time health status monitoring system based on biosensors according to claim 5, characterized in that: After adjusting the training intensity using the training adjustment module, the project status assessment module analyzes and obtains the adjusted training status comprehensive score. Then, the difference between the adjusted training status comprehensive score and the training status comprehensive score S(t) is calculated to obtain the state change score ΔS, which is then compared with the training adjustment coefficient K at time t. adj The individual fitness influence coefficient β(t) is analyzed in combination with the system's optimal state score to quantify the adjustment range of the individual fitness influence coefficient β(t) at time t, and to obtain the individual fitness influence coefficient at time t+1.

7. The real-time health status monitoring system based on biosensors according to claim 1, characterized in that: It also includes a visualization module, which is used to display athletes' training data in real time and visualize it. The visualization module includes a remote connection unit, through which coaches and athletes connect to the system via terminal devices and the remote connection unit to view monitoring data and receive alarms.

8. The real-time health status monitoring system based on biosensors according to claim 1, characterized in that: The sensor module includes a preprocessing unit, which is used to remove noise from the flexible sensor array, fill in missing values, and align physiological data and body posture data along the time axis.

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