Motion armband monitoring system and method

Abstract

The invention discloses a motion armband monitoring system and method, and the method comprises the following steps: setting an initial respiratory rate detection period based on the normal motion intensity and physiological needs of a user, building a standardized baseline, and guaranteeing conventional monitoring on the premise of not interfering the normal activities of the user; during respiratory rate detection, blood oxygen levels are monitored in real time by a sensor. According to the method, a personalized initial respiratory frequency detection period is set, monitoring is carried out according to the exercise intensity and physiological needs of a user, it is ensured that exercise is not disturbed, after the blood oxygen change trend is quantized, the future trend is predicted through a support vector machine regression model, the detection period is dynamically adjusted through a clustering analysis algorithm, and the accuracy of the detection period is improved. The method can flexibly adapt to physiological changes of a user, can accurately recognize the blood oxygen decreasing trend in real time, improves the system reaction sensitivity by intelligently adjusting the detection period, ensures that the user is timely intervened in the case of a low oxygen risk, and effectively prevents health crises such as acute hypoxia.

Description

Technical Field

[0001] The present invention relates to the technical field of sports armband monitoring, and in particular to a sports armband monitoring system and method. Background Art

[0002] The sports armband monitoring system is a wearable device that is usually worn on the upper arm and is used to monitor the user's physiological data, exercise status and health indicators in real time. Such systems usually integrate sensors such as heart rate sensors, accelerometers, gyroscopes, etc., which can collect key data during exercise, such as heart rate, cadence, stride, exercise intensity, calorie consumption, etc. These data are transmitted to smartphones or other terminal devices via wireless connections (such as Bluetooth) for analysis and storage. By analyzing the user's exercise data, the sports armband monitoring system can provide users with personalized health advice, real-time feedback on exercise effects, help optimize exercise plans and improve exercise effects. In addition, some high-end systems also have functions such as real-time positioning, fatigue monitoring and environmental adaptability, which enhances its diversity and richness of application scenarios.

[0003] The role of the sports armband monitoring system in monitoring blood oxygen saturation (SpO2) is to track and evaluate the oxygen content in the user's blood during exercise in real time, helping to determine whether the body can maintain an adequate oxygen supply during exercise. Blood oxygen saturation is an important indicator of the health of the heart, lungs, and blood vessels. Especially in high-intensity exercise or high-altitude environments, monitoring blood oxygen levels can effectively prevent hypoxia and overexertion and ensure that the body's oxygen needs are met. If the blood oxygen saturation is too low, it may mean that the oxygen supply is insufficient, which may lead to symptoms such as decreased athletic performance, shortness of breath, increased fatigue, or dizziness. Therefore, the sports armband system can monitor blood oxygen saturation in real time, provide early warning information, and help users adjust exercise intensity or take rest measures, thereby improving exercise safety and effectiveness and avoiding the risks of overtraining.

[0004] The prior art has the following deficiencies: When monitoring blood oxygen saturation, the sports armband monitoring system is usually unable to intelligently adjust the respiratory rate detection cycle according to the trend of blood oxygen changes. When the blood oxygen saturation drops, the respiratory rate changes rapidly as the body's immediate response to the low oxygen state. If the respiratory rate detection cycle is too long, the system may not be able to capture these changes in time, resulting in the failure to identify the risk of low oxygen in time. In this case, the system cannot adjust the exercise intensity or recommend rest in time, and the user may continue to exercise in a state of continuous low oxygen, further exacerbating the lack of oxygen supply, and may eventually cause acute hypoxia, seriously endangering health or even life.

[0005] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0006] The purpose of the present invention is to provide a sports armband monitoring system and method, which can monitor the user's exercise intensity and physiological needs by setting a personalized initial respiratory rate detection cycle, ensure that the exercise is not interfered with, obtain blood oxygen saturation data synchronously in real time, and eliminate interference through data preprocessing technology to ensure the accuracy of the data. After quantifying the trend of blood oxygen changes, the support vector machine regression model is used to predict future trends, and the detection cycle is dynamically adjusted through a cluster analysis algorithm to flexibly adapt to the user's physiological changes. This method can not only accurately identify the trend of blood oxygen decline in real time, but also improve the system response sensitivity by intelligently adjusting the detection cycle, ensuring that users receive timely intervention when they are at risk of hypoxia, and effectively preventing health crises such as acute hypoxia, so as to solve the problems in the above-mentioned background technology.

[0007] In order to achieve the above object, the present invention provides the following technical solution: a sports armband monitoring method, comprising the following steps: Set an initial respiratory rate detection cycle based on the user's normal exercise intensity and physiological needs to establish a standardized baseline to ensure regular monitoring without interfering with the user's normal activities; During the respiratory rate detection, the sensor monitors the blood oxygen level in real time and simultaneously obtains the user's blood oxygen saturation data so as to capture subtle changes in blood oxygen saturation; Pre-process the acquired blood oxygen saturation data to eliminate measurement errors and interference from the external environment to ensure data accuracy; After the data preprocessing is completed, the features reflecting the decrease in blood oxygen saturation are extracted, and the extracted blood oxygen saturation change features are input into the monitoring window for real-time analysis to quantify the decreasing trend of blood oxygen saturation; After quantitatively analyzing the trend of blood oxygen saturation decrease, the quantified features are input into the pre-trained support vector machine regression model, and the future trend of blood oxygen saturation decrease is predicted by the support vector machine regression model; According to the downward trend of blood oxygen saturation predicted by the support vector machine regression model, the cluster analysis algorithm is used to intelligently adjust the actual respiratory rate detection period to flexibly adapt to different physiological changes, thereby efficiently capturing the dynamic changes of respiratory rate.

[0008] Preferably, the specific steps of setting the initial breathing rate detection cycle based on the user's normal exercise intensity and physiological needs are as follows: First, collect the user's physiological data and normal exercise intensity; Next, based on the collected data, an initial detection cycle suitable for the user is determined by analyzing the changing trend of the user's breathing rate under different exercise intensities; Finally, make personalized adjustments based on the initial detection cycle.

[0009] Preferably, the specific steps of monitoring the blood oxygen level in real time through the sensor and synchronously obtaining the user's blood oxygen saturation data are as follows: First, the sensor is accurately installed on the user's skin surface to ensure that the sensor meets the requirements for contact with the user's blood vessels; Then, the oxygen content in the blood in the blood vessels is measured through sensors, and the blood oxygen saturation is calculated in real time.

[0010] Preferably, features reflecting a decrease in blood oxygen saturation are extracted, wherein the extracted features include a deviation between a current blood oxygen saturation and an expected value and a blood oxygen saturation decay rate. After acquisition, the deviation between the current blood oxygen saturation and the expected value and the blood oxygen saturation decay rate are input into a monitoring window for real-time analysis, and a blood oxygen expected deviation reference value and a blood oxygen attenuation reference value are generated respectively. The decreasing trend of blood oxygen saturation is quantified by the blood oxygen expected deviation reference value and the blood oxygen attenuation reference value.

[0011] Preferably, the specific steps of inputting the deviation between the current blood oxygen saturation and the expected value into the monitoring window for real-time analysis to generate the blood oxygen expected deviation reference value are as follows: The absolute deviation value is calculated based on the deviation between the current blood oxygen saturation and the expected blood oxygen saturation. The calculation expression is: ,in: is the absolute deviation between the blood oxygen saturation at time t and the expected blood oxygen saturation, is the blood oxygen saturation monitored by the user at time t, The expected value of blood oxygen saturation set at time t; In order to quantify the downward trend of blood oxygen saturation, the absolute deviation value at each moment in the monitoring window is weighted and summed to generate the expected deviation reference value of blood oxygen. The weighted summation process considers the impact of historical deviation on the current deviation, and dynamically adjusts the weight of historical data by introducing an exponential weighted attenuation coefficient. The calculation expression is: ,in: is the blood oxygen expected deviation reference value, which indicates the quantitative blood oxygen decline trend within the monitoring window. It is the expected deviation reference value of blood oxygen at the previous moment, reflecting the impact of historical blood oxygen changes. is the size of the monitoring window, indicating the time range of interest, For monitoring windowi The absolute deviation between the blood oxygen saturation at the moment and the expected value, is an exponentially weighted decay factor, where is the attenuation coefficient, which controls the influence of the deviation on the current blood oxygen expected deviation reference value over time.

[0012] Preferably, in the monitoring window, the specific steps of analyzing the attenuation rate of blood oxygen saturation to generate a blood oxygen attenuation reference value are as follows: The blood oxygen saturation data is monitored in real time in the monitoring window, and its rate of change in each time step is calculated. The blood oxygen saturation decay rate is defined as the instantaneous rate of decrease of blood oxygen saturation. The calculation expression is: ,in, represents the blood oxygen saturation decay rate at time t, and are time t and Blood oxygen saturation data at all times, is the time interval, which captures the changing trend of the attenuation rate by calculating the logarithmic change of blood oxygen saturation, reflecting the rapid decrease of blood oxygen saturation; In order to more accurately quantify the downward trend of blood oxygen saturation, a weighted exponential attenuation model is introduced to generate a blood oxygen attenuation reference value based on the instantaneous change rate and the attenuation weighting factor at the attenuation moment. The expression for generating the blood oxygen attenuation reference value is: ,in, is the blood oxygen attenuation reference value, In the monitoring window The instantaneous rate of change within It means from the current time t to the past The offset of the time step, is the decay parameter, which is used to control the weight of past decay information. n is the length of the historical monitoring window considered.

[0013] Preferably, after quantitatively analyzing the downward trend of blood oxygen saturation, the quantified blood oxygen expected deviation reference value and blood oxygen attenuation reference value are input into a pre-trained support vector machine regression model, and a downward trend index is output based on the model to predict the future trend of the downward trend of blood oxygen saturation.

[0014] Preferably, according to the downward trend of blood oxygen saturation predicted by the support vector machine regression model, a cluster analysis algorithm is used to intelligently adjust the actual respiratory rate detection period, and the specific steps are as follows: After obtaining the decline trend index, the cluster analysis algorithm is used to classify the trend of blood oxygen saturation decline. Clustering divides the data into several categories according to the rate of blood oxygen saturation decline. Each category represents a different blood oxygen change state. The specific classification formula is as follows: , , is the downward trend index at time t, K for Total number of; The first downward trend index threshold and the second downward trend index are set to divide the downward trend index into three categories, where First Downward Trend Index Greater than the second downward trend index, the downward trend index The specific division steps are: If the downward trend index is greater than the first downward trend index , the trend of decreased blood oxygen saturation is divided into ; If the downward trend index is greater than the second downward trend index and is less than the first downward trend index , the trend of decreased blood oxygen saturation is divided into ; If the downward trend index is less than the second downward trend index , the trend of decreased blood oxygen saturation is divided into ; After obtaining the blood oxygen change state, the actual respiratory rate detection cycle is intelligently adjusted according to different blood oxygen attenuation trends. The adjustment process is as follows: If the blood oxygen level drops rapidly, the respiratory rate detection cycle is shortened to monitor the user's respiratory rate changes more frequently. The adjustment formula is: ,in, is the initial respiratory rate detection cycle, is the adjustment factor, is the minimum breathing rate detection cycle, is the actual respiratory rate detection period after adjustment; If the blood oxygen level decreases slowly, the detection period is slightly extended, and the formula for adjusting the actual respiratory rate detection period is: ,in, is the elongation factor; If the blood oxygen is in a stable state, the initial respiratory rate detection cycle is maintained, and the formula is: .

[0015] A sports armband monitoring system, comprising an initial detection cycle setting module, a blood oxygen data real-time monitoring module, a data preprocessing and error elimination module, a blood oxygen change feature extraction and analysis module, a blood oxygen decline trend prediction module and a respiratory rate detection cycle adjustment module; The initial detection cycle setting module sets an initial respiratory rate detection cycle based on the user's normal exercise intensity and physiological needs, establishes a standardized baseline, and ensures routine monitoring without interfering with the user's normal activities; The blood oxygen data real-time monitoring module monitors the blood oxygen level in real time through sensors during respiratory rate detection, and simultaneously obtains the user's blood oxygen saturation data so as to capture subtle changes in blood oxygen saturation; The data preprocessing and error elimination module preprocesses the acquired blood oxygen saturation data to eliminate measurement errors and interference from the external environment to ensure data accuracy; The blood oxygen change feature extraction and analysis module extracts the features reflecting the decrease of blood oxygen saturation after data preprocessing is completed, inputs the extracted blood oxygen saturation change features into the monitoring window for real-time analysis, and quantifies the decreasing trend of blood oxygen saturation; The blood oxygen decline trend prediction module quantitatively analyzes the decline trend of blood oxygen saturation, inputs the quantified features into the pre-trained support vector machine regression model, and predicts the future trend of blood oxygen saturation decline through the support vector machine regression model; The respiratory rate detection cycle adjustment module uses a cluster analysis algorithm to intelligently adjust the actual respiratory rate detection cycle according to the downward trend of blood oxygen saturation predicted by the support vector machine regression model, flexibly adapting to different physiological changes, and thus efficiently capturing the dynamic changes of respiratory rate.

[0016] In the above technical solution, the technical effects and advantages provided by the present invention are: The present invention first sets the initial respiratory rate detection cycle according to the user's normal exercise intensity and physiological needs, establishes a personalized monitoring baseline, and ensures effective monitoring without interfering with the user's normal exercise. Secondly, during the monitoring process, the system obtains blood oxygen saturation data in real time and synchronously, and combines data preprocessing technology to eliminate external interference and measurement errors, thereby ensuring high accuracy and reliability of the data. Based on the quantitative analysis of the blood oxygen change trend, the pre-trained support vector machine regression model is used to predict the future blood oxygen change trend, and the respiratory rate detection cycle is further dynamically adjusted through the cluster analysis algorithm, so that the system can flexibly adapt to the user's physiological changes and capture the dynamic changes of the respiratory rate in time. Not only can it identify the downward trend of blood oxygen saturation in real time and accurately, but it also improves the response sensitivity of the system by intelligently adjusting the detection cycle, ensuring that users can get timely intervention suggestions when hypoxia risks occur, thereby effectively preventing health crises such as acute hypoxia, and ensuring that users always exercise in a safe physiological state. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0018] Figure 1 The present invention is a method flow chart of a sports armband monitoring method.

[0019] Figure 2 The module schematic diagram of a sports armband monitoring system of the present invention. DETAILED DESCRIPTION

[0020] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these example embodiments are provided so that the description of the present disclosure will be more comprehensive and complete, and the concept of the example embodiments will be fully conveyed to those skilled in the art.

[0021] The present invention provides Figure 1 A sports armband monitoring method shown includes the following steps: Set an initial respiratory rate detection cycle based on the user's normal exercise intensity and physiological needs to establish a standardized baseline to ensure regular monitoring without interfering with the user's normal activities; The specific steps for setting the initial breathing rate detection cycle based on the user's normal exercise intensity and physiological needs are as follows: First, collect the user's basic physiological data (such as age, gender, weight, health status, etc.) and normal exercise intensity (such as daily activity level, exercise type and intensity). Then, combine these data and analyze the trend of the user's breathing rate changes under different exercise intensities to determine an initial detection cycle suitable for the user. This cycle should be able to effectively reflect the changes in the user's breathing rate during routine activities, while avoiding excessive measurements that interfere with the user's daily exercise. Finally, according to the initial detection cycle, make personalized adjustments. If the user's exercise intensity or physiological state changes (such as increased exercise intensity or changes in physical condition), fine-tuning can be performed through subsequent feedback mechanisms to ensure the continued accuracy and adaptability of monitoring. The core of this process is to ensure that the initial detection cycle can provide sufficient monitoring data without affecting the user's normal activities.

[0022] During the respiratory rate detection, the sensor monitors the blood oxygen level in real time and simultaneously obtains the user's blood oxygen saturation data so as to capture subtle changes in blood oxygen saturation; These data will be used for analysis and further processing so that intelligent responses can be made according to changes in blood oxygen during the detection process. Real-time acquisition of blood oxygen saturation data is the core of the system's ability to evaluate the user's physiological state. Blood oxygen saturation directly reflects the oxygen supply status in the user's body. Especially in strenuous exercise or high altitude environments, changes in blood oxygen saturation have a profound impact on health. Through real-time data collection, the system can capture rapidly changing blood oxygen values ​​and promptly determine whether it is necessary to adjust the detection frequency or intervene in exercise intensity.

[0023] The specific steps for monitoring blood oxygen levels in real time through sensors and synchronously obtaining the user's blood oxygen saturation data are as follows: First, select a suitable sensor (such as a pulse oximeter sensor or an optical sensor) and accurately install it on the user's skin surface (such as fingers, earlobes, or wrists) to ensure that the sensor is in good contact with the user's blood vessels. Then, the sensor begins to measure the oxygen content in the blood passing through the blood vessels through optical principles (such as the difference in reflection and absorption of infrared light and visible light), and calculates the blood oxygen saturation (SpO2) in real time. During the entire monitoring process, the sensor will continuously collect data and transmit these real-time blood oxygen saturation data to the monitoring system through wireless or wired communication. The system will continuously monitor and analyze this data, capture subtle changes in blood oxygen saturation, and use it for subsequent health assessments and risk predictions. This process ensures the real-time and accuracy of blood oxygen saturation data, providing a key basis for user health monitoring.

[0024] Pre-process the acquired blood oxygen saturation data to eliminate measurement errors and interference from the external environment to ensure data accuracy; The preprocessing steps include denoising, smoothing data, removing outliers, and standardization. The purpose is to eliminate measurement errors or interference from the external environment and ensure the accuracy of the data. The preprocessed data will be more suitable for feature extraction and subsequent analysis. Data preprocessing is a key step to ensure the accuracy of system analysis. Data that has not been preprocessed may contain noise or errors, affecting the accuracy of subsequent feature extraction. By cleaning and standardizing blood oxygen data, stable and reliable data can be obtained, ensuring that the analysis results and predictions are more credible.

[0025] After the data preprocessing is completed, the features reflecting the decrease in blood oxygen saturation are extracted, and the extracted blood oxygen saturation change features are input into the monitoring window for real-time analysis to quantify the decreasing trend of blood oxygen saturation; Features reflecting the decrease in blood oxygen saturation are extracted, wherein the extracted features include the deviation between the current blood oxygen saturation and the expected value and the blood oxygen saturation decay rate. After acquisition, the deviation between the current blood oxygen saturation and the expected value and the blood oxygen saturation decay rate are input into the monitoring window for real-time analysis, and a blood oxygen expected deviation reference value and a blood oxygen attenuation reference value are generated respectively. The decreasing trend of blood oxygen saturation is quantified by the blood oxygen expected deviation reference value and the blood oxygen attenuation reference value.

[0026] When the deviation between the current blood oxygen saturation and the expected value is large, it usually indicates that the user's blood oxygen saturation is tending to decrease. The expected value is usually set based on the user's health status, exercise intensity and physiological needs, representing a reasonable range of blood oxygen saturation. When the actual blood oxygen saturation deviates from this expected value by a large amount, it means that the oxygen supply in the blood has failed to reach the expected level, which may be caused by insufficient oxygen intake, decreased breathing efficiency or other health problems. A large deviation is often an indicator of decreased blood oxygen saturation, especially when the user is exercising vigorously or under high-intensity load. This deviation is more significant. By real-time monitoring of the deviation between blood oxygen saturation and the expected value, the trend of decreased blood oxygen can be identified early, and the exercise intensity can be adjusted in time or rest advice can be provided.

[0027] The specific steps of inputting the deviation between the current blood oxygen saturation and the expected value into the monitoring window for real-time analysis to generate the blood oxygen expected deviation reference value are as follows: The absolute deviation value is calculated based on the deviation between the current blood oxygen saturation and the expected blood oxygen saturation. The calculation expression is: ,in: is the absolute deviation between the blood oxygen saturation at time t and the expected blood oxygen saturation, is the blood oxygen saturation monitored by the user at time t, The expected blood oxygen saturation value set at time t based on the user's normal exercise intensity, health status and other factors; The absolute deviation value reflects the degree of deviation of the current blood oxygen saturation from the normal or expected value and is a key indicator for measuring the trend of hypoxia. The larger the absolute deviation value, the lower the blood oxygen saturation and the greater the risk.

[0028] In order to quantify the downward trend of blood oxygen saturation, the absolute deviation value at each moment in the monitoring window will be weighted and summed to generate the expected deviation reference value of blood oxygen. The weighted summation process will consider the impact of historical deviation on the current deviation, and dynamically adjust the weight of historical data by introducing an exponential weighted attenuation coefficient. The calculation expression is: ,in: is the blood oxygen expected deviation reference value, which indicates the quantitative blood oxygen decline trend within the monitoring window. It is the expected deviation reference value of blood oxygen at the last moment (i.e. the expected deviation reference value of blood oxygen quantified in the last monitoring window), reflecting the impact of historical blood oxygen changes. is the size of the monitoring window, which indicates the time range that the system pays attention to. For monitoring window i The absolute deviation between the blood oxygen saturation at the moment and the expected value, is an exponentially weighted decay factor, where is the attenuation coefficient, which controls the influence of the deviation on the current blood oxygen expected deviation reference value over time; By weighted summation and attenuation adjustment of the deviation at each moment in the monitoring window, a dynamically updated blood oxygen expected deviation reference value is generated, thereby achieving a quantitative assessment of the blood oxygen decline trend. Through exponential weighting, the system can give a higher weight to the recent blood oxygen changes to ensure a timely response to the decline in blood oxygen.

[0029] It can be seen from the blood oxygen expected deviation reference value that, under the monitoring window, the larger the performance value of the blood oxygen expected deviation reference value generated after analyzing the deviation between the current blood oxygen saturation and the expected value, the greater the deviation between the current blood oxygen saturation and the expected value, reflecting the more significant trend of blood oxygen saturation decline. This is because the blood oxygen expected deviation reference value quantifies the blood oxygen deviation at each moment in the monitoring window and dynamically updates it through weighting and attenuation. When the difference between the blood oxygen saturation and the expected value is large, it means that the user's blood oxygen level has dropped significantly relative to the expected state, and the deviation value is large, indicating that the risk of hypoxia is increasing. When the deviation is small, it means that the blood oxygen saturation is close to the expected value, the downward trend is not obvious, and the risk of hypoxia is small. Therefore, the blood oxygen expected deviation reference value can effectively reflect the changing trend of blood oxygen saturation, thereby providing a timely decision-making basis for the monitoring system.

[0030] When the blood oxygen saturation decay rate is large, it usually means that the user's blood oxygen saturation has a clear downward trend. The blood oxygen saturation decay rate refers to the speed or acceleration of the decrease in blood oxygen saturation. If the decay rate is large, it indicates that the blood oxygen reduction process is rapid and continuous. This situation usually reflects that the user may be in a state of hypoxia and the body is losing its ability to effectively absorb oxygen, which may be caused by excessive exercise, respiratory system abnormalities, environmental factors or health problems. Rapid blood oxygen saturation decay may lead to insufficient oxygen supply, which in turn triggers emergency reactions such as shortness of breath and increased heart rate. Therefore, when the blood oxygen saturation decay rate is large, it is an important indicator, indicating that timely measures need to be taken.

[0031] In the monitoring window, the specific steps for analyzing the attenuation rate of blood oxygen saturation to generate a blood oxygen attenuation reference value are as follows: The blood oxygen saturation data is monitored in real time in the monitoring window, and its rate of change in each time step is calculated. The blood oxygen saturation decay rate is defined as the instantaneous rate of decrease of blood oxygen saturation. The calculation expression is: ,in, represents the blood oxygen saturation decay rate at time t, and are time t and Blood oxygen saturation data at all times, is the time interval, which captures the changing trend of the attenuation rate by calculating the logarithmic change of blood oxygen saturation, reflecting the rapid decrease of blood oxygen saturation; The purpose of this step is to capture the rapid changes in blood oxygen saturation in real time, which serves as the basis for calculating the blood oxygen attenuation reference value.

[0032] In order to more accurately quantify the downward trend of blood oxygen saturation, a weighted exponential decay model is introduced to generate a blood oxygen attenuation reference value based on the instantaneous change rate and the attenuation weighting factor at the attenuation moment (the weighting factor is determined by the attenuation situation over the past period of time). The expression for generating the blood oxygen attenuation reference value is: ,in, is the blood oxygen attenuation reference value, In the monitoring window The instantaneous rate of change within It means from the current time t to the past The offset of the time step, is the decay parameter, which is used to control the weight of past decay information. nThe weighted attenuation model uses exponential attenuation to make the earlier blood oxygen changes contribute less to the current index. It can effectively capture the recent attenuation trend and reflect the acceleration or deceleration of the blood oxygen decline process. The role of this step is to quantify the overall trend of blood oxygen decline through a weighted model and adjust the impact of historical information according to the time decay factor.

[0033] It can be seen from the blood oxygen attenuation reference value that, under the monitoring window, the larger the performance value of the blood oxygen attenuation reference value generated by analyzing the attenuation rate of blood oxygen saturation, the more significant the trend of the user's blood oxygen saturation decreasing, and vice versa. The blood oxygen attenuation reference value is to quantify the decrease process of blood oxygen saturation by real-time monitoring of the attenuation rate of blood oxygen saturation and combining it with a weighted exponential decay model. When the value of the blood oxygen attenuation reference value is large, it means that the blood oxygen saturation has dropped rapidly and significantly within the monitoring window, which usually indicates that the user may be facing a hypoxic state and that his oxygen supply is seriously insufficient. On the contrary, if the blood oxygen attenuation reference value is small, it means that the decrease process of blood oxygen saturation is relatively gentle, the downward trend is not obvious, the user's oxygen supply is relatively sufficient, and the risk of hypoxia is low. Through this quantitative analysis, the blood oxygen attenuation reference value can help to evaluate the trend of blood oxygen changes in real time and provide a timely basis for intervention for the system.

[0034] After quantitatively analyzing the trend of blood oxygen saturation decrease, the quantified features are input into the pre-trained support vector machine regression model, and the future trend of blood oxygen saturation decrease is predicted by the support vector machine regression model; After quantitative analysis of the downward trend of blood oxygen saturation, the quantified blood oxygen expected deviation reference value and blood oxygen attenuation reference value are input into the pre-trained support vector machine regression model. Based on the downward trend index output by the model, the future trend of the downward trend of blood oxygen saturation is predicted by the downward trend index.

[0035] The pre-trained support vector machine regression model refers to a support vector machine (SVM) regression model trained based on historical data (e.g., parameters such as blood oxygen saturation, blood oxygen attenuation reference value, blood oxygen expected deviation reference value, etc.) in the construction of a sports armband monitoring system. In this model, by inputting specific physiological data features (such as blood oxygen saturation, attenuation rate, etc.) and corresponding labels (such as the trend of blood oxygen decline or specific values), the system can learn to identify the law of blood oxygen saturation changes from the data, and then use it to predict future blood oxygen change trends. SVM regression is essentially an extension of the support vector machine. Its purpose is to find an optimal hyperplane in multidimensional space to fit the nonlinear relationship between input features and output targets by minimizing the regression error and maximizing the model's interval (i.e., the distance between the hyperplane and the support vector).

[0036] Specifically, the training process of the SVM regression model usually includes several steps. First, the key features that affect the change of blood oxygen saturation are extracted from the historical data. These features may include blood oxygen attenuation rate, expected deviation index, etc., and these features are mapped to a high-dimensional space through a suitable kernel function (such as radial basis kernel function). In high-dimensional space, the SVM model can handle these nonlinear relationships and find an optimal regression function through the optimization process. This function can avoid overfitting as much as possible while maintaining a low error. After training, the SVM regression model can predict new input data, that is, according to the input blood oxygen saturation data and its attenuation trend, output a predicted decline trend index. The decline trend index can reflect the future trend of blood oxygen saturation changes, help the system identify hypoxia risks in advance, and adjust exercise intensity or rest recommendations based on the prediction results to optimize the user's health monitoring and intervention strategies.

[0037] The important advantage of this model is that it can not only process data features with nonlinearity and high dimensions, but also automatically discover the implicit rules in the data through training. In practical applications, support vector machine regression models usually require a large amount of historical data for training, and adjust model parameters through cross-validation and other techniques to ensure its accuracy and robustness in predicting changes in blood oxygen saturation. By using the SVM regression model, the sports armband monitoring system can effectively predict future trends based on the real-time detected blood oxygen attenuation and historical data, providing users with more intelligent and real-time health feedback and suggestions.

[0038] The support vector machine regression model is not specifically limited here, and can achieve the expected deviation reference value of blood oxygen and blood oxygen attenuation reference value Conduct comprehensive analysis to generate a downward trend index In order to realize the technical solution of the present invention, the present invention provides a specific implementation method; the downward trend index The resulting calculation formula is: , where , They are the blood oxygen expected deviation reference values and blood oxygen attenuation reference value The preset scaling factor of , Both are greater than 0.

[0039] It can be seen from the downward trend index that, under the monitoring window, the larger the performance value of the expected blood oxygen deviation reference value generated after analyzing the deviation between the current blood oxygen saturation and the expected value, the larger the performance value of the expected blood oxygen deviation reference value generated after analyzing the deviation between the current blood oxygen saturation and the expected value, that is, the larger the performance value of the downward trend index generated when predicting the future trend of the decrease in blood oxygen saturation through the pre-trained support vector machine regression model, the more significant the downward trend of blood oxygen saturation is, and vice versa.

[0040] According to the downward trend of blood oxygen saturation predicted by the support vector machine regression model, the cluster analysis algorithm is used to intelligently adjust the actual respiratory rate detection cycle, flexibly adapt to different physiological changes, and thus efficiently capture the dynamic changes of respiratory rate; The specific adjustment process of the actual breathing rate detection cycle is: if it is predicted that the blood oxygen saturation will drop rapidly, the actual breathing rate detection cycle will be reduced so as to monitor the user's breathing rate changes more frequently; if the blood oxygen changes slowly, the actual breathing rate detection cycle will be maintained or appropriately extended.

[0041] According to the downward trend of blood oxygen saturation predicted by the support vector machine regression model, the cluster analysis algorithm is used to intelligently adjust the actual respiratory rate detection cycle. The specific steps are as follows: After obtaining the decline trend index, cluster analysis algorithms (such as K-means, DBSCAN, etc.) are used to classify the trend of blood oxygen saturation decline. The purpose is to dynamically adjust the respiratory rate detection cycle based on the speed and pattern of blood oxygen change. Clustering divides the data into several categories according to the rate of blood oxygen saturation decline. Each category represents a different blood oxygen change state. The specific classification formula is as follows: ,in, Indicates k clusters, It is The range of the decline index of the clusters is is the downward trend index at time t, K for Total number of; Set the first downward trend index threshold and the second downward trend index The downward trend index is divided into three categories, among which the first downward trend index If the index is greater than the second downward trend index, the downward trend index data is divided into three main categories. The specific division steps are as follows: If the downward trend index is greater than the first downward trend index , the trend of decreased blood oxygen saturation is divided into ; If the downward trend index is greater than the second downward trend index and is less than the first downward trend index , the trend of decreased blood oxygen saturation is divided into ; If the downward trend index is less than the second downward trend index , the trend of decreased blood oxygen saturation is divided into .

[0042] Rapid decline: The rate of decline of blood oxygen saturation is fast, and more frequent monitoring is required; Slow decline: Blood oxygen saturation decreases slowly, and the monitoring frequency is maintained or appropriately extended; Stable state: Blood oxygen changes are small and the monitoring frequency can be reduced.

[0043] After obtaining the blood oxygen change status, the system intelligently adjusts the actual respiratory rate detection cycle according to different blood oxygen attenuation trends. The adjustment process is as follows: If the blood oxygen level drops rapidly, the respiratory rate detection cycle is shortened to monitor the user's respiratory rate changes more frequently. The adjustment formula is: ,in, is the initial respiratory rate detection cycle, is the adjustment factor, is the minimum breathing rate detection cycle, is the actual respiratory rate detection period after adjustment; This formula reduces the actual respiratory rate detection cycle To speed up the monitoring frequency and ensure that the changes in respiratory rate can be captured in time when the blood oxygen level drops rapidly; If the blood oxygen level decreases slowly, the detection period is slightly extended, and the formula for adjusting the actual respiratory rate detection period is: ,in, It is the extension factor, which is applicable to the situation where the blood oxygen changes slowly. The detection cycle can be appropriately increased to avoid unnecessary frequent detection. If the blood oxygen is in a stable state, the initial respiratory rate detection cycle is maintained, and the formula is: ; The purpose of this adjustment step is to flexibly adjust the respiratory rate detection cycle according to the downward trend of blood oxygen saturation. Based on the results of cluster analysis, the system can adaptively shorten or extend the monitoring cycle and respond to changes in blood oxygen status in real time. This helps to capture the dynamic changes of respiratory rate more efficiently, improve the monitoring accuracy of blood oxygen level changes, and ensure that the user's health risks are identified and responded to in a timely manner.

[0044] Through the above-mentioned sports armband monitoring method, it is possible to realize real-time monitoring of the trend of blood oxygen saturation decrease and intelligently adjust the respiratory rate detection cycle, thereby significantly improving the response speed and accuracy of the system, and avoiding missing the opportunity to timely identify hypoxia risks due to too long a detection cycle. First, the initial detection cycle is set based on the user's normal exercise intensity and physiological needs to provide the user with a personalized monitoring baseline to ensure effective monitoring without interfering with normal exercise. Secondly, by acquiring blood oxygen saturation data synchronously in real time and combining data preprocessing technology, external interference and measurement errors are eliminated to ensure high data accuracy. After quantifying the blood oxygen change trend, the trained support vector machine regression model is used to predict the future blood oxygen change trend, and the respiratory rate detection cycle is further dynamically adjusted through the cluster analysis algorithm, so that the system can flexibly adapt to the user's physiological changes, capture the dynamic changes of the respiratory rate, and avoid the inability to timely identify the trend of blood oxygen decrease due to too long a detection cycle. This solution can not only identify the downward trend of blood oxygen saturation in real time and accurately, but also improve the response sensitivity of the motion monitoring system by adjusting the detection cycle, ensuring that users can receive timely intervention suggestions when the risk of hypoxia occurs, thereby effectively preventing health crises such as acute hypoxia and ensuring that users exercise in a safe physiological state.

[0045] A sports armband monitoring system, comprising an initial detection cycle setting module, a blood oxygen data real-time monitoring module, a data preprocessing and error elimination module, a blood oxygen change feature extraction and analysis module, a blood oxygen decline trend prediction module and a respiratory rate detection cycle adjustment module; The initial detection cycle setting module sets an initial respiratory rate detection cycle based on the user's normal exercise intensity and physiological needs, establishes a standardized baseline, and ensures routine monitoring without interfering with the user's normal activities; The blood oxygen data real-time monitoring module monitors the blood oxygen level in real time through sensors during respiratory rate detection, and simultaneously obtains the user's blood oxygen saturation data so as to capture subtle changes in blood oxygen saturation; The data preprocessing and error elimination module preprocesses the acquired blood oxygen saturation data to eliminate measurement errors and interference from the external environment to ensure data accuracy; The blood oxygen change feature extraction and analysis module extracts the features reflecting the decrease of blood oxygen saturation after data preprocessing is completed, inputs the extracted blood oxygen saturation change features into the monitoring window for real-time analysis, and quantifies the decreasing trend of blood oxygen saturation; The blood oxygen decline trend prediction module quantitatively analyzes the decline trend of blood oxygen saturation, inputs the quantified features into the pre-trained support vector machine regression model, and predicts the future trend of blood oxygen saturation decline through the support vector machine regression model; The respiratory rate detection cycle adjustment module uses a cluster analysis algorithm to intelligently adjust the actual respiratory rate detection cycle according to the downward trend of blood oxygen saturation predicted by the support vector machine regression model, flexibly adapting to different physiological changes, and thus efficiently capturing the dynamic changes of respiratory rate; A sports armband monitoring method provided in an embodiment of the present invention is implemented by the above-mentioned sports armband monitoring system. The specific method and process of a sports armband monitoring system are detailed in the embodiment of the above-mentioned sports armband monitoring method, which will not be repeated here.

[0046] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0047] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0048] The above description is only by way of illustration of certain exemplary embodiments of the present invention. It is undoubted that those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

Claims

1. A sports armband monitoring method, characterized in that: The following steps are involved: Set an initial respiratory rate detection cycle based on the user's normal exercise intensity and physiological needs to establish a standardized baseline to ensure regular monitoring without interfering with the user's normal activities; During the respiratory rate detection, the sensor monitors the blood oxygen level in real time and simultaneously obtains the user's blood oxygen saturation data so as to capture subtle changes in blood oxygen saturation; Pre-process the acquired blood oxygen saturation data to eliminate measurement errors and interference from the external environment to ensure data accuracy; After the data preprocessing is completed, the features reflecting the decrease in blood oxygen saturation are extracted, and the extracted blood oxygen saturation change features are input into the monitoring window for real-time analysis to quantify the decreasing trend of blood oxygen saturation; After quantitatively analyzing the trend of blood oxygen saturation decrease, the quantified features are input into the pre-trained support vector machine regression model, and the future trend of blood oxygen saturation decrease is predicted by the support vector machine regression model; According to the downward trend of blood oxygen saturation predicted by the support vector machine regression model, the cluster analysis algorithm is used to intelligently adjust the actual respiratory rate detection period to flexibly adapt to different physiological changes, thereby efficiently capturing the dynamic changes of respiratory rate.

2. A sports armband monitoring method according to claim 1, characterized in that: The specific steps for setting the initial breathing rate detection cycle based on the user's normal exercise intensity and physiological needs are as follows: First, collect the user's physiological data and normal exercise intensity; Next, based on the collected data, an initial detection cycle suitable for the user is determined by analyzing the changing trend of the user's breathing rate under different exercise intensities; Finally, make personalized adjustments based on the initial detection cycle.

3. A sports armband monitoring method according to claim 1, characterized in that: The specific steps for monitoring blood oxygen levels in real time through sensors and synchronously obtaining the user's blood oxygen saturation data are as follows: First, the sensor is accurately installed on the user's skin surface to ensure that the sensor meets the requirements for contact with the user's blood vessels; Then, the oxygen content in the blood in the blood vessels is measured through sensors, and the blood oxygen saturation is calculated in real time.

4. A sports armband monitoring method according to claim 1, characterized in that: Features reflecting the decrease in blood oxygen saturation are extracted, wherein the extracted features include the deviation between the current blood oxygen saturation and the expected value and the blood oxygen saturation decay rate. After acquisition, the deviation between the current blood oxygen saturation and the expected value and the blood oxygen saturation decay rate are input into the monitoring window for real-time analysis, and a blood oxygen expected deviation reference value and a blood oxygen attenuation reference value are generated respectively. The decreasing trend of blood oxygen saturation is quantified by the blood oxygen expected deviation reference value and the blood oxygen attenuation reference value.

5. A sports armband monitoring method according to claim 4, characterized in that: The specific steps of inputting the deviation between the current blood oxygen saturation and the expected value into the monitoring window for real-time analysis to generate the blood oxygen expected deviation reference value are as follows: The absolute deviation value is calculated based on the deviation between the current blood oxygen saturation and the expected blood oxygen saturation. The calculation expression is: ， in: is the absolute deviation between the blood oxygen saturation at time t and the expected blood oxygen saturation, is the blood oxygen saturation monitored by the user at time t, The expected value of blood oxygen saturation set at time t; In order to quantify the downward trend of blood oxygen saturation, the absolute deviation value at each moment in the monitoring window is weighted and summed to generate the expected deviation reference value of blood oxygen. The weighted summation process considers the impact of historical deviation on the current deviation, and dynamically adjusts the weight of historical data by introducing an exponential weighted attenuation coefficient. The calculation expression is: ， in: is the blood oxygen expected deviation reference value, which indicates the quantitative blood oxygen decline trend within the monitoring window. It is the expected deviation reference value of blood oxygen at the previous moment, reflecting the impact of historical blood oxygen changes. is the size of the monitoring window, indicating the time range of interest, For monitoring window i The absolute deviation between the blood oxygen saturation at the moment and the expected value, is an exponentially weighted decay factor, where is the attenuation coefficient, which controls the influence of the deviation on the current blood oxygen expected deviation reference value over time.

6. A sports armband monitoring method according to claim 5, characterized in that: In the monitoring window, the specific steps for analyzing the attenuation rate of blood oxygen saturation to generate a blood oxygen attenuation reference value are as follows: The blood oxygen saturation data is monitored in real time in the monitoring window, and its rate of change in each time step is calculated. The blood oxygen saturation decay rate is defined as the instantaneous rate of decrease of blood oxygen saturation. The calculation expression is: ， in, represents the blood oxygen saturation decay rate at time t, and are time t and Blood oxygen saturation data at all times, is the time interval, which captures the changing trend of the attenuation rate by calculating the logarithmic change of blood oxygen saturation, reflecting the rapid decrease of blood oxygen saturation; In order to more accurately quantify the downward trend of blood oxygen saturation, a weighted exponential attenuation model is introduced to generate a blood oxygen attenuation reference value based on the instantaneous change rate and the attenuation weighting factor at the attenuation moment. The expression for generating the blood oxygen attenuation reference value is: ， in, is the blood oxygen attenuation reference value, In the monitoring window The instantaneous rate of change within It means from the current time t to the past The offset of the time step, is the decay parameter, which is used to control the weight of past decay information. n is the length of the historical monitoring window considered.

7. A sports armband monitoring method according to claim 6, characterized in that: After quantitative analysis of the downward trend of blood oxygen saturation, the quantified blood oxygen expected deviation reference value and blood oxygen attenuation reference value are input into the pre-trained support vector machine regression model. Based on the downward trend index output by the model, the future trend of the downward trend of blood oxygen saturation is predicted by the downward trend index.

8. A sports armband monitoring method according to claim 7, characterized in that: According to the downward trend of blood oxygen saturation predicted by the support vector machine regression model, the cluster analysis algorithm is used to intelligently adjust the actual respiratory rate detection cycle. The specific steps are as follows: After obtaining the decline trend index, the cluster analysis algorithm is used to classify the trend of blood oxygen saturation decline. Clustering divides the data into several categories according to the rate of blood oxygen saturation decline. Each category represents a different blood oxygen change state. The specific classification formula is as follows: ， , is the downward trend index at time t, K for Total number of; The first downward trend index threshold and the second downward trend index are set to divide the downward trend index into three categories, where First Downward Trend Index Greater than the second downward trend index, the downward trend index The specific division steps are: If the downward trend index is greater than the first downward trend index , the trend of decreased blood oxygen saturation is divided into ; If the downward trend index is greater than the second downward trend index and is less than the first downward trend index , the trend of decreased blood oxygen saturation is divided into ; If the downward trend index is less than the second downward trend index , the trend of decreased blood oxygen saturation is divided into ; After obtaining the blood oxygen change state, the actual respiratory rate detection cycle is intelligently adjusted according to different blood oxygen attenuation trends. The adjustment process is as follows: If the blood oxygen level drops rapidly, the respiratory rate detection cycle is shortened to monitor the user's respiratory rate changes more frequently. The adjustment formula is: ， in, is the initial respiratory rate detection cycle, is the adjustment factor, is the minimum breathing rate detection cycle, is the actual respiratory rate detection period after adjustment; If the blood oxygen level decreases slowly, the detection period is slightly extended, and the formula for adjusting the actual respiratory rate detection period is: ， in, is the elongation factor; If the blood oxygen is in a stable state, the initial respiratory rate detection cycle is maintained, and the formula is: .

9. A sports armband monitoring system, used to implement the sports armband monitoring method according to any one of claims 1 to 8, characterized in that: It includes an initial detection cycle setting module, a blood oxygen data real-time monitoring module, a data preprocessing and error elimination module, a blood oxygen change feature extraction and analysis module, a blood oxygen decline trend prediction module, and a respiratory rate detection cycle adjustment module; The initial detection cycle setting module sets an initial respiratory rate detection cycle based on the user's normal exercise intensity and physiological needs, establishes a standardized baseline, and ensures routine monitoring without interfering with the user's normal activities; The blood oxygen data real-time monitoring module monitors the blood oxygen level in real time through sensors during respiratory rate detection, and simultaneously obtains the user's blood oxygen saturation data so as to capture subtle changes in blood oxygen saturation; The data preprocessing and error elimination module preprocesses the acquired blood oxygen saturation data to eliminate measurement errors and interference from the external environment to ensure data accuracy; The blood oxygen change feature extraction and analysis module extracts the features reflecting the decrease of blood oxygen saturation after data preprocessing is completed, inputs the extracted blood oxygen saturation change features into the monitoring window for real-time analysis, and quantifies the decreasing trend of blood oxygen saturation; The blood oxygen decline trend prediction module quantitatively analyzes the decline trend of blood oxygen saturation, inputs the quantified features into the pre-trained support vector machine regression model, and predicts the future trend of blood oxygen saturation decline through the support vector machine regression model; The respiratory rate detection cycle adjustment module uses a cluster analysis algorithm to intelligently adjust the actual respiratory rate detection cycle according to the downward trend of blood oxygen saturation predicted by the support vector machine regression model, flexibly adapting to different physiological changes, and thus efficiently capturing the dynamic changes of respiratory rate.

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