Physical state detection method and device based on big data

Abstract

The invention is suitable for the technical field of data recognition, and provides a big data-based body state detection method and device, and the method comprises the steps: collecting high-frequency impedance, low-frequency impedance and a current phase difference through a foot electrode; calculating body composition according to the high-frequency impedance and the low-frequency impedance; wherein the body components comprise body fat percentage, muscle mass, total body moisture, basal metabolic rate and bone mass; calculating the body age according to the body composition; generating body change information according to the body composition and the current phase difference; wherein the body state information comprises body change information and body age. According to the technical scheme, comprehensive measurement of the high-frequency impedance, the low-frequency impedance and the current phase difference is introduced, and analysis and calculation based on big data are carried out, so that comprehensive, accurate and personalized detection of the body state is realized. And the accuracy and the reliability of a measurement result are improved.

Description

Technical Field

[0001] This invention belongs to the technical field of data recognition, and in particular relates to a method and device for detecting body status based on big data. Background Technology

[0002] In modern society, with improved living standards and increased health awareness, people are paying more and more attention to their physical condition. Body fat scales, as a convenient device for measuring body composition, are increasingly favored by consumers. Traditional body fat scales primarily use bioelectrical impedance analysis (BIA) technology to measure body composition indicators such as body fat percentage. However, with technological advancements and changing user needs, the measurement accuracy and functionality of traditional body fat scales can no longer fully meet user requirements, especially in personalized health management and long-term physical condition monitoring.

[0003] Existing body fat scales typically only provide body composition data at a single point in time, such as body fat percentage, muscle mass, and total body water. Without further analysis and correlation, this data provides only limited information, insufficient to support users in gaining a comprehensive understanding of their physical condition and managing it over the long term. Summary of the Invention

[0004] In view of this, embodiments of the present invention provide a body condition detection method and device based on big data to solve the technical problem that existing body fat scales can only provide limited information, which is insufficient to support users' comprehensive understanding and long-term management of their own body condition.

[0005] A first aspect of this invention provides a body condition detection method based on big data, the body condition detection method based on big data comprising: High-frequency impedance, low-frequency impedance, and current phase difference are acquired via foot electrodes; Body composition is calculated based on the high-frequency impedance and the low-frequency impedance; wherein, the body composition includes body fat percentage, muscle mass, total body water, basal metabolic rate, and bone mass; Calculate body age based on the stated body composition; Based on the body composition and the current phase difference, body change information is generated; wherein, the body state information includes body change information and body age.

[0006] Further, the step of calculating body composition based on the high-frequency impedance and the low-frequency impedance includes: Based on the preset model, the initial body fat percentage, initial muscle mass, initial total body water, initial basal metabolic rate, and initial bone mass corresponding to the high-frequency impedance and the low-frequency impedance are calculated. Temperature compensation is applied to the initial body fat percentage, initial muscle mass, initial total body water, initial basal metabolic rate, and initial bone mass to obtain the current body fat percentage, current muscle mass, current total body water, current basal metabolic rate, and current bone mass. The current body fat percentage, current muscle mass, current total body water, current basal metabolic rate, and current bone mass are used as the body components.

[0007] Furthermore, the step of calculating the initial body fat percentage, initial muscle mass, initial total body water, initial basal metabolic rate, and initial bone mass corresponding to the high-frequency impedance and the low-frequency impedance based on the preset model includes: Obtain the user's weight, height, age, and gender; The initial body fat percentage is obtained by substituting the high-frequency impedance, height, age, and gender into the body fat model. The body fat model is as follows: ; in, Indicates initial body fat percentage. Indicates height, Indicates high-frequency impedance. Indicates height, Indicates gender, male. =1, female =0; The initial muscle mass is obtained by substituting the low-frequency impedance, weight, height, age, and gender into the muscle mass model. The muscle mass model is as follows: ; in, Indicates initial muscle mass. Indicates low-frequency impedance. represents weight; Substituting the low-frequency impedance, the high-frequency impedance, the height, and the weight into the body water model, the initial total body water is obtained; The body water model is as follows: ; in, This represents the initial total body water content; Substitute the initial muscle mass, height, age, and gender into the metabolic rate model to obtain the initial basal metabolic rate; The metabolic rate model is as follows: ; in, This represents the initial basal metabolic rate; The initial bone mass is obtained by substituting the high-frequency impedance, the low-frequency impedance, the body weight, and the age into the bone mass model. The bone mass model is as follows: ; in, This indicates the initial bone mass.

[0008] Further, the step of performing temperature compensation on the initial body fat percentage, initial muscle mass, initial total body water, initial basal metabolic rate, and initial bone mass to obtain the current body fat percentage, current muscle mass, current total body water, current basal metabolic rate, and current bone mass includes: Body surface temperature is collected using a foot temperature sensor; Calculate the temperature correction factor corresponding to the body surface temperature; where, the temperature correction factor = Multiply the initial body fat percentage by the temperature correction factor to obtain the current body fat percentage; Multiply the initial muscle mass by the temperature correction coefficient to obtain the current muscle mass; Multiply the initial total body water content by the temperature correction coefficient to obtain the current total body water content; Multiply the initial basal metabolic rate by the temperature correction factor to obtain the current basal metabolic rate; The initial bone mass is multiplied by the temperature correction factor to obtain the current bone mass.

[0009] Furthermore, the step of calculating body age based on the body composition includes: Calculate the age coefficient corresponding to each of the body components; Obtain the preset weight corresponding to each of the body components; Based on the preset weights, the age coefficients corresponding to each body component are weighted and summed to obtain initial values; The body age is obtained by multiplying the initial value by a regulation factor; wherein, the regulation factor , represents the initial value.

[0010] Furthermore, the step of calculating the age coefficient corresponding to each body component includes: Obtain baseline values ​​for body fat percentage, muscle mass, minimum healthy bone mass, and metabolic rate; The age coefficient corresponding to body fat percentage = 1 + 0.02 × (current body fat percentage - baseline body fat percentage). The age coefficient corresponding to muscle mass = 0.8 + 0.2 × (current muscle mass / baseline muscle mass). The age coefficient corresponding to the total body water content = current total body water content / (0.6 × body weight); The age coefficient corresponding to basal metabolic rate = current basal metabolic rate / (metabolic baseline value × 1.1); The age coefficient corresponding to bone mass = 0.7 + 0.006 × (current bone mass - minimum healthy bone mass).

[0011] Furthermore, the step of generating body change information based on the body composition and the current phase difference includes: Obtain historical phase difference, historical muscle mass, historical body fat percentage, and historical total body water. Calculate the current phase difference and subtract it from the historical phase difference to obtain the first difference value; The second difference is obtained by subtracting the current muscle mass from the historical muscle mass; the third difference is obtained by subtracting the current body fat percentage from the historical body fat percentage. If the first difference is greater than 0, and the second difference is greater than 0, and the second difference is not greater than 0, then the body change information is determined to be normal muscle growth. If the first difference is not greater than 0, and the second difference is not greater than 0, and the second difference is greater than 0, then the body change information is determined to be an abnormal increase in body fat. If the first difference is not greater than 0, and the second difference is not greater than 0, then the body change information is determined to be in the stage of abnormal muscle consumption. The fourth difference is obtained by subtracting the current total body water from the historical total body water. If the first difference is not greater than 0 and the fourth difference is greater than 0, then the body change information is determined to be body edema; If the first difference is not greater than 0 and the fourth difference is not greater than 0, then the body change information is determined to be body dehydration.

[0012] A second aspect of the present invention provides a body condition detection device based on big data, comprising: The acquisition unit is used to acquire high-frequency impedance, low-frequency impedance, and current phase difference via foot electrodes; The first calculation unit is used to calculate body composition based on the high-frequency impedance and the low-frequency impedance; wherein the body composition includes body fat percentage, muscle mass, total body water, basal metabolic rate, and bone mass; The second calculation unit is used to calculate the body age based on the body composition; A generation unit is used to generate body change information based on the body composition and the current phase difference; wherein the body state information includes body change information and body age.

[0013] A third aspect of the present invention provides a terminal device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps in the big data-based body state detection method described in the first aspect.

[0014] A fourth aspect of the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps in the big data-based body state detection method described in the first aspect.

[0015] The beneficial effects of this invention compared to existing technologies are as follows: by simultaneously acquiring high-frequency impedance and low-frequency impedance data, it overcomes the limitations of traditional body fat scales that rely solely on low-frequency impedance measurement. High-frequency impedance helps improve the accuracy of body water and muscle mass measurements, while low-frequency impedance is more suitable for measuring body fat percentage. The combination of the two makes the calculation of body composition more accurate. By introducing the current phase difference, it can more accurately reflect the electrophysiological characteristics inside the body, helping to improve the accuracy of measurement results. This method can not only provide basic body composition data (such as body fat percentage, muscle mass, total body water, basal metabolic rate, and bone mass), but also calculate body age from this data, thus providing a more intuitive and comprehensive health indicator. In summary, this technical solution, by introducing the comprehensive measurement of high-frequency impedance, low-frequency impedance, and current phase difference, as well as analysis and calculation based on big data, achieves comprehensive, accurate, and personalized detection of body status. This not only improves the accuracy and reliability of measurement results, but also provides users with richer and more intuitive health information, promotes the realization of personalized health management, and significantly improves user experience and health management effectiveness. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 A schematic flowchart of a body state detection method based on big data provided by the present invention is shown; Figure 2 This diagram illustrates a body condition detection device based on big data according to an embodiment of the present invention. Figure 3 A schematic diagram of a terminal device provided in an embodiment of the present invention is shown. Detailed Implementation

[0018] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of the invention. However, those skilled in the art will understand that the invention can be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods are omitted so as not to obscure the description of the invention with unnecessary detail.

[0019] This invention provides a method and device for detecting body condition based on big data, in order to solve the technical problem that existing body fat scales can only provide limited information, which is insufficient to support users' comprehensive understanding and long-term management of their own body condition.

[0020] First, this invention provides a method for detecting body condition based on big data. Please see below. Figure 1 , Figure 1 A schematic flowchart of a body state detection method based on big data provided by the present invention is shown. Figure 1 As shown, this big data-based body condition detection method may include the following steps: Step 101: Acquire high-frequency impedance, low-frequency impedance, and current phase difference via foot electrodes; Bioelectrical impedance analysis (BIA) is performed using foot electrodes. By placing electrodes on the soles of the feet, the body's electrical impedance can be measured.

[0021] Different frequencies of current will produce different impedance values ​​in body tissues. High-frequency currents can penetrate cell membranes to measure the impedance of intracellular fluid, while low-frequency currents mainly remain in extracellular fluid to measure its impedance.

[0022] Phase difference measurements can provide more information about body tissues. Phase difference refers to the difference in phase angle between voltage and current when an electric current passes through the body.

[0023] Phase angle (PA) is an important indicator used to assess cellular health and body composition. It is calculated using the two components of impedance, resistance and reactance. Phase angle = , where represents reactance, Representing resistance. Reactance and resistance are derived from bioelectrical impedance analysis. Resistance (R): represents the water-free parts of the body, such as fat and bone. Reactance (Xc): represents the water-containing parts of the body, such as intracellular and extracellular fluid.

[0024] Step 102: Calculate body composition based on the high-frequency impedance and the low-frequency impedance; wherein the body composition includes body fat percentage, muscle mass, total body water, basal metabolic rate, and bone mass; Using high-frequency and low-frequency impedance values, and through a specific algorithm model, the different components of the body are calculated. These components include, but are not limited to: Body fat percentage: The percentage of fat in the body.

[0025] Muscle mass: The mass of muscles in the body.

[0026] Total body water: The total amount of water in the body, including intracellular fluid and extracellular fluid.

[0027] Basal metabolic rate: Basal metabolic rate refers to the minimum energy expenditure required to maintain basic life activities at rest.

[0028] Bone mass: The quality of bone tissue.

[0029] Specifically, step 102 includes steps 1021 to 1023: Step 1021: Calculate the initial body fat percentage, initial muscle mass, initial total body water, initial basal metabolic rate, and initial bone mass corresponding to the high-frequency impedance and the low-frequency impedance based on the preset model; The preset model is a mathematical model built upon historical data and research, used to convert measured high-frequency and low-frequency impedance values ​​into initial estimates of body composition. The preset model takes into account human physiological characteristics and the physical principles of impedance measurement.

[0030] Initial body fat percentage, initial muscle mass, initial total body water, initial basal metabolic rate, and initial bone mass are rough estimates calculated directly from impedance data using a pre-set model, representing preliminary body composition analysis results.

[0031] Specifically, step 1021 includes steps A1 to A6: Step A1: Obtain the user's weight, height, age, and gender; Step A2: Substitute the high-frequency impedance, height, age, and gender into the body fat model to obtain the initial body fat percentage; The body fat model is as follows: ; in, Indicates initial body fat percentage. Indicates height, Indicates high-frequency impedance. Indicates height, Indicates gender, male. =1, female =0; Reflects the cross-sectional area of ​​the current path, This is the baseline offset. This indicates that as we age, we lose muscle mass, and our body fat percentage naturally increases. The value indicates gender; women have a thicker subcutaneous fat layer (the average difference in body fat between men and women is 5-8%). 495 represents the scaling factor (an empirical coefficient that converts impedance ratios to percentages), and 0.153 represents the impedance-to-body fat conversion rate.

[0032] Step A3: Substitute the low-frequency impedance, weight, height, age, and gender into the muscle mass model to obtain the initial muscle mass; The muscle mass model is as follows: ; in, Indicates initial muscle mass. Indicates low-frequency impedance. represents weight; ≈ A surrogate indicator of extracellular fluid volume (ECV), muscle tissue water content is stable at 72-74%, with a coefficient of 0.73 converting total water into muscle mass. This is a compensation item.

[0033] Step A4: Substitute the low-frequency impedance, the high-frequency impedance, the height, and the weight into the body water model to obtain the initial total body water. The body water model is as follows: ; in, This represents the initial total body water content; At an impedance of 20 kHz, the current only penetrates the extracellular fluid (ECW), with a contribution factor of 0.396.

[0034] At an impedance of 100 kHz, the current penetrates the intracellular fluid (ICW) + ECW, with a contribution factor of 0.143.

[0035] It indicates the body's basic water content.

[0036] Step A5: Substitute the initial muscle mass, height, age, and gender into the metabolic rate model to obtain the initial basal metabolic rate; The metabolic rate model is as follows: ; in, This represents the initial basal metabolic rate; 13.7 is the muscle metabolic rate (based on mitochondrial respiratory chain oxygen consumption). This is a height compensation item. This is an age-compensated item. This is the gender compensation term, and 655 is an empirical constant.

[0037] Step A6: Substitute the high-frequency impedance, the low-frequency impedance, the body weight, and the age into the bone mass model to obtain the initial bone mass; The bone mass model is as follows: ; in, This indicates the initial bone mass.

[0038] This represents the fitting coefficient between bone mass and impedance. This is a comprehensive compensation item based on weight and age.

[0039] In the embodiments corresponding to steps A1 to A6, initial values ​​of body composition are calculated using the user's basic information and impedance measurements through a series of preset mathematical models. These initial values ​​will provide a basis for further assessment of body condition.

[0040] Step 1022: Perform temperature compensation on the initial body fat percentage, initial muscle mass, initial total body water, initial basal metabolic rate, and initial bone mass to obtain the current body fat percentage, current muscle mass, current total body water, current basal metabolic rate, and current bone mass; Impedance measurement results are affected by ambient temperature and body surface temperature. Therefore, temperature compensation is an important step to improve measurement accuracy by correcting for the influence of temperature on impedance and body composition calculations. Temperature compensation can be based on real-time temperature data measured by the sensor or a preset temperature compensation curve.

[0041] Temperature-compensated body composition values ​​more accurately reflect the user's actual physical condition in the current environment.

[0042] Specifically, step 1022 includes steps B1 to B7: Step B1: Collect body surface temperature using a foot temperature sensor; measure the user's body surface temperature using the foot temperature sensor. Body surface temperature may affect the accuracy of bioelectrical impedance analysis, therefore calibration using body surface temperature is necessary.

[0043] Step B2: Calculate the temperature correction factor corresponding to the body surface temperature; wherein, the temperature correction factor = ; Based on the collected body surface temperature, a temperature correction factor is calculated. This temperature correction factor is used to compensate for the temperature differences in the initially calculated body components. This indicates that for every 1°C decrease in skin temperature, the impedance increases by 2%.

[0044] Step B3: Multiply the initial body fat percentage by the temperature correction factor to obtain the current body fat percentage; Step B4: Multiply the initial muscle mass by the temperature correction factor to obtain the current muscle mass; Step B5: Multiply the initial total body water content by the temperature correction coefficient to obtain the current total body water content; Step B6: Multiply the initial basal metabolic rate by the temperature correction factor to obtain the current basal metabolic rate; Step B7: Multiply the initial bone mass by the temperature correction coefficient to obtain the current bone mass.

[0045] In the embodiments corresponding to steps B1 to B7, the user's body surface temperature is collected using a foot temperature sensor. A corresponding temperature correction coefficient is calculated using the collected body surface temperature value. The initially calculated body fat percentage, muscle mass, total body water, basal metabolic rate, and bone mass are multiplied by the temperature correction coefficient to obtain the current value after temperature compensation. Through these steps, the influence of body surface temperature can be taken into account, making the final body composition indicators more accurate and reliable. This is particularly important for body condition detection based on big data, because temperature changes may affect the measurement results of bioelectrical impedance, thereby affecting the accuracy of body composition calculations.

[0046] Step 1023: Use the current body fat percentage, the current muscle mass, the current total body water, the current basal metabolic rate, and the current bone mass as the body composition.

[0047] The current values, calculated using a pre-set model and after temperature compensation, represent the final body composition analysis results. This data will be used for subsequent body condition assessments and trend analysis.

[0048] In the embodiments corresponding to steps 1021 to 1023, initial body composition indicators are calculated using high-frequency and low-frequency impedance values ​​through a preset model. Temperature compensation is applied to the initial values ​​to correct the influence of ambient and body surface temperatures on the measurement results. The temperature-compensated current values ​​are used as the final body composition for further analysis and evaluation. This step-by-step process ensures the accuracy and reliability of the body composition calculation.

[0049] Step 103: Calculate body age based on the body composition; Based on body composition (such as body fat percentage, muscle mass, etc.), an age indicator reflecting physical condition is calculated. Body age reflects a person's chronological age and, when compared with their actual age, helps users understand their health status. Specifically, step 103 includes steps 1031 to 1034: Step 1031: Calculate the age coefficient corresponding to each of the body components; For each body component (such as body fat percentage, muscle mass, total body water, basal metabolic rate, and bone mass), a corresponding age coefficient is calculated. By calculating the age coefficient, each body component is correlated with its corresponding physiological age for further calculations.

[0050] Specifically, step 1031 includes steps C1 to C6: Step C1: Obtain baseline values ​​for body fat percentage, muscle mass, minimum healthy bone mass, and metabolic rate; Obtain baseline values ​​for body fat percentage, muscle mass, minimum healthy bone mass, and metabolism. These baseline values ​​serve as reference points for calculating the age coefficient, reflecting the standard values ​​for each body component under healthy conditions.

[0051] Step C2: Age coefficient corresponding to body fat percentage = 1 + 0.02 × (current body fat percentage - baseline body fat percentage); Multiply the difference between your current body fat percentage and the baseline body fat percentage by 0.02, then add 1. This formula reflects the impact of body fat percentage being higher or lower than the baseline on the age coefficient; the higher (or lower) the body fat percentage, the more the age coefficient will be adjusted. Adipose tissue secretes pro-inflammatory factors (such as TNF-α and IL-6), and for every 1% increase in body fat percentage, the level of systemic inflammation increases by 3-5%.

[0052] The linear coefficient of 0.02 in step C2 is based on aging research: when body fat percentage exceeds the ideal value by 5%, physiological age increases by about 1 year.

[0053] Step C3: Age coefficient corresponding to muscle mass = 0.8 + 0.2 × (current muscle mass / baseline muscle mass). Divide your current muscle mass by the baseline muscle mass value, and then add 0.8. This formula illustrates the impact of the ratio of muscle mass to the baseline value on the age coefficient; the higher the muscle mass, the higher the corresponding age coefficient.

[0054] Peak muscle mass (MM_peak) occurs between the ages of 25 and 30, and then declines by 8% every decade (Janssen study). A baseline of 0.8 ensures that the age coefficient corresponding to muscle mass in young people is approximately 1.0.

[0055] Step C4: Age coefficient corresponding to total body water = current total body water / (0.6 × body weight); Divide your current total body water content by 60% of your body weight. This formula reflects the impact of the body water-to-body-weight ratio on the age coefficient; the higher the total body water content, the higher the age coefficient. A healthy adult's water content is 60% of their body weight (ICW + ECW). 0.6 directly reflects the degree of cellular dehydration.

[0056] Step C5: Age coefficient corresponding to basal metabolic rate = current basal metabolic rate / (metabolic baseline value × 1.1); Divide your current basal metabolic rate by 1.1 times your baseline metabolic rate. This formula shows the impact of changes in basal metabolic rate relative to the baseline on the age coefficient; the higher the metabolic rate, the larger the age coefficient.

[0057] A predictive value of 1.1 times represents the upper limit of metabolic reserve in young people. A coefficient <1 indicates metabolic decline, with each 10% decrease equivalent to an increase of 5 years in physiological age.

[0058] Step C6: Age coefficient corresponding to bone mass = 0.7 + 0.006 × (current bone mass - minimum healthy bone mass).

[0059] Multiply the difference between your current bone mass and the minimum healthy bone mass by 0.006, then add 0.7. This formula reflects the impact of the degree to which bone mass exceeds the minimum healthy bone mass on the age coefficient; the higher the bone mass, the higher the age coefficient.

[0060] A slope of 0.006 means that for every 0.5 kg decrease in bone mass, physiological age increases by 3 years.

[0061] In the embodiments corresponding to steps C1 to C6, different formulas are used to reflect the different effects of various body components on physiological age, thereby providing basic data for subsequent weighted summation and the calculation of the final physiological age.

[0062] Step 1032: Obtain the preset weight corresponding to each of the body components; Obtain the preset weights for each body component. These weights represent the relative importance of each body component in calculating body age. The preset weights are used to weight the age coefficients of each body component, making the calculation results more consistent with reality and the degree of influence of different body components on body age.

[0063] For example, the preset weights corresponding to each of the body components are shown in Table 1 below: system Weight Medical basis Fat metabolism system 30% Visceral fat releases inflammatory factors that accelerate vascular aging (Wilson, 2008). Musculoskeletal system 25% Sarcopenia increases all-cause mortality by three times (Rosenberg, 2011). Energy metabolism system 20% A decrease in basal metabolism directly leads to mitochondrial dysfunction (Lopez-Otin, 2013). Hydraulic transport system 15% Cellular dehydration accelerates protein misfolding (Powers, 2005) skeletal support system 10% Bone loss indirectly restricts activity and accelerates overall aging. Step 1033: Based on the preset weights, perform a weighted summation of the age coefficients corresponding to each body component to obtain an initial value; The age coefficient of each body component is multiplied by its corresponding preset weight, and then these weighted values ​​are summed to obtain an initial calculated value. By weighted summation, the age coefficients of all body components are combined to obtain an initial body age value.

[0064] Step 1034: Multiply the initial value by the adjustment factor to obtain the body age; wherein, the adjustment factor , This represents the initial value.

[0065] The initial value obtained in the previous step is multiplied by an adjustment factor. The adjustment factor is used to adjust the initial value so that the final calculated body age is more accurate and reasonable.

[0066] When the initial value is ≤1.0: the body is in a youthful state and there is no risk of accelerated aging; When the initial value is greater than 1.0: Initiate the exponential aging response; Regulatory factors are used to mimic the expression threshold effects of aging-related genes such as p16 and mTOR.

[0067] In the embodiments corresponding to steps 1031 to 1034, the age coefficient corresponding to each body component is calculated to reflect the age correlation of each body component. A preset weight is obtained for each body component, reflecting its importance in the overall calculation. The age coefficients of each body component are multiplied by their preset weights and then summed to obtain an initial value. The initial value is multiplied by an adjustment factor to obtain the final body age. Through the above steps, multiple body components can be combined to obtain a body age that reflects the actual physiological state. This method considers the relative importance of different body components and their changing trends at different ages, making the calculation results more accurate and personalized.

[0068] Step 104: Generate body change information based on the body composition and the current phase difference; wherein the body state information includes body change information and body age.

[0069] By combining body composition and current phase difference data, and through big data analysis and comparison with historical data, information on body changes is generated. This information includes, but is not limited to: Changes in body composition: such as trends in body fat percentage, increases or decreases in muscle mass, etc.

[0070] Health risk warning: For example, health risks that may arise from an imbalance in body fluids.

[0071] The resulting comprehensive information, including data on bodily changes and body age, provides users with a complete and dynamic assessment of their physical condition. This information not only helps users understand their current health status but also guides them in long-term health management and lifestyle adjustments.

[0072] Specifically, step 104 includes steps 1041 to 10410: Step 1041: Obtain historical phase difference, historical muscle mass, historical body fat percentage, and historical total body water. Acquire historical phase difference, historical muscle mass, historical body fat percentage, and historical total body water. This historical data serves as a reference point, used to compare with current data to detect changes in body composition.

[0073] Step 1042: Subtract the current phase difference from the historical phase difference to obtain the first difference value; Step 1043: Subtract the current muscle mass from the historical muscle mass to obtain the second difference; Step 1044: Subtract the current body fat percentage from the historical body fat percentage to obtain the third difference; Step 1045: If the first difference is greater than 0, and the second difference is greater than 0, and the second difference is not greater than 0, then the body change information is determined to be normal muscle growth. An increase in phase difference and muscle mass, without an increase in body fat percentage, indicates that the body is healthily building muscle.

[0074] Based on this information about changes in your body, the body fat scale will display the following message to the user: "Congratulations! Your body is undergoing positive changes. The increase in muscle mass (muscle mass↑) is accompanied by an increase in phase angle (PhA↑), indicating that not only is your muscle volume increasing, but your muscle cells are also becoming healthier and more functional. At the same time, your body fat percentage is well controlled / decreasing (body fat percentage↓ / stable), which aligns with the goal of healthy muscle gain and fat loss. Please continue with your current training (especially strength training) and nutrition strategy (ensuring sufficient protein)." Step 1046: If the first difference is not greater than 0, and the second difference is not greater than 0, and the second difference is greater than 0, then the body change information is determined to be an abnormal increase in body fat. The absence of increases in phase difference and muscle mass, coupled with an increase in body fat percentage, indicates that the increase in body fat exceeds the normal range, potentially suggesting an abnormal increase in body fat.

[0075] Based on this information about changes in your body, the body fat scale will display the following message: "We've noticed a decrease in muscle mass (muscle mass ↓), an increase in body fat percentage (body fat percentage ↑), and a decrease in phase angle (PhA ↓). This suggests that changes in body composition may be accompanied by a decline in cellular function. Possible causes include: insufficient training intensity or frequency, inadequate protein intake, insufficient rest and recovery, or underlying health / stress factors. We recommend reviewing your recent exercise and diet logs, focusing on recovery and nutrition (especially protein). If the condition persists or is accompanied by discomfort, we recommend consulting a doctor or nutritionist." Step 1047: If the first difference is not greater than 0, and the second difference is not greater than 0, then the body change information is determined to be in the stage of abnormal muscle consumption. The absence of increases in phase difference, muscle mass, and body fat percentage indicates that the body is in a phase of muscle depletion or decline.

[0076] Based on this information about changes in your body, the body fat scale will display the following message: "Your body fat percentage has decreased (body fat percentage ↓), which is usually a positive sign. However, your phase angle is also decreasing (PhA ↓), while muscle mass remains stable (muscle mass stable). This suggests that the weight loss process may be accompanied by some degree of cellular stress or mild dehydration. Please make sure to check: whether the rate of fat loss is too fast (it is recommended to lose no more than 1% of your body weight per week), whether your daily water intake is sufficient (especially before and after exercise), whether your diet is balanced (avoid very low-calorie or mono-dietary diets), and that paying attention to cellular health is crucial for maintaining long-term weight loss results." Step 1048: Subtract the current total body water from the historical total body water to obtain the fourth difference; Step 1049: If the first difference is not greater than 0 and the fourth difference is greater than 0, then the body change information is determined to be body edema; The phase difference did not increase, but the total body water increased, indicating that edema may be present.

[0077] Based on this information about changes in your body, the body fat scale will display the following message: "An increase in your extracellular water percentage (ECW↑ / ECW percentage↑) and a decrease in phase angle (PhA↓) have been detected. This is a typical sign of edema, indicating possible water retention in the body. Edema can significantly interfere with the accuracy of body fat percentage (usually leading to a lower calculated result). Common causes include: high-salt diet, premenstrual period (women), sedentary lifestyle, certain medications, or underlying kidney / heart / liver problems. It is recommended to reduce salt intake, increase physical activity to promote circulation, and monitor for swelling in the lower limbs." Step 10410: If the first difference is not greater than 0 and the fourth difference is not greater than 0, then the body change information is determined to be body dehydration.

[0078] The absence of an increase in phase difference and a decrease in total body water volume indicate possible dehydration.

[0079] Based on this information about changes in your body, the body fat scale will display the following message to the user: "Your intracellular water content has decreased (ICW↓ / ICW percentage↓), and the phase angle has also decreased (PhA↓). This suggests that your cells may be dehydrated or experiencing decreased function, affecting nutrient intake and waste removal. Please ensure you drink enough water daily (generally recommended to be at least 30ml per kg of body weight, more if you are very active), and pay attention to electrolyte balance (especially replenishing with electrolyte-containing drinks after exercise). Malnutrition (protein deficiency) can also lead to a reduction in intracellular fluid." In the embodiments corresponding to steps 1041 to 10410, historical phase difference, muscle mass, body fat percentage, and total body water are acquired as comparative references. The difference between current and historical data is calculated to reflect changes in body composition. Based on the calculated difference, it is determined whether the body is in a state of muscle gain, abnormal increase in body fat, muscle loss, edema, or dehydration. These steps, by comparing current and historical body composition data, identify trends in body changes and generate corresponding body change information, providing a basis for further body condition assessment and health management.

[0080] In the embodiments corresponding to steps 101 to 104, by simultaneously acquiring high-frequency impedance and low-frequency impedance data, the limitation of traditional body fat scales relying solely on low-frequency impedance measurement is overcome. High-frequency impedance helps improve the accuracy of measuring body water and muscle mass, while low-frequency impedance is more suitable for measuring body fat percentage. The combination of the two makes the calculation of body composition more accurate. By introducing the current phase difference, the electrophysiological characteristics inside the body can be reflected more accurately, which helps improve the accuracy of measurement results. This method can not only provide basic body composition data (such as body fat percentage, muscle mass, total body water, basal metabolic rate, and bone mass), but also calculate body age from these data, thus providing a more intuitive and comprehensive health indicator. In summary, this technical solution, by introducing the comprehensive measurement of high-frequency impedance, low-frequency impedance, and current phase difference, as well as analysis and calculation based on big data, achieves comprehensive, accurate, and personalized detection of body status. This not only improves the accuracy and reliability of measurement results, but also provides users with richer and more intuitive health information, promotes the realization of personalized health management, and significantly improves user experience and health management effectiveness.

[0081] like Figure 2 This invention provides a body condition detection device based on big data. Please refer to [link / reference]. Figure 2 , Figure 2 This diagram illustrates a body condition detection device based on big data provided by the present invention, as shown below. Figure 2The body condition detection device based on big data shown includes: Acquisition unit 21 is used to acquire high-frequency impedance, low-frequency impedance and current phase difference through foot electrodes; The first calculation unit 22 is used to calculate body composition based on the high-frequency impedance and the low-frequency impedance; wherein, the body composition includes body fat percentage, muscle mass, total body water, basal metabolic rate and bone mass; The second calculation unit 23 is used to calculate the body age based on the body composition; The generation unit 24 is used to generate body change information based on the body composition and the current phase difference; wherein the body state information includes body change information and body age.

[0082] This invention provides a body condition detection device based on big data. By simultaneously acquiring high-frequency impedance and low-frequency impedance data, it overcomes the limitations of traditional body fat scales that rely solely on low-frequency impedance measurement. High-frequency impedance helps improve the accuracy of measuring body water and muscle mass, while low-frequency impedance is more suitable for measuring body fat percentage. The combination of the two makes the calculation of body composition more accurate. By introducing the current phase difference, it can more accurately reflect the electrophysiological characteristics inside the body, which helps improve the accuracy of measurement results. This method can not only provide basic body composition data (such as body fat percentage, muscle mass, total body water, basal metabolic rate, and bone mass), but also calculate body age from this data, thus providing a more intuitive and comprehensive health indicator. In summary, this technical solution, by introducing the comprehensive measurement of high-frequency impedance, low-frequency impedance, and current phase difference, and by analyzing and calculating based on big data, achieves comprehensive, accurate, and personalized detection of body condition. This not only improves the accuracy and reliability of measurement results, but also provides users with richer and more intuitive health information, promotes the realization of personalized health management, and significantly improves user experience and health management effectiveness.

[0083] Figure 3 This is a schematic diagram of a terminal device provided in an embodiment of the present invention. Figure 3 As shown, a terminal device 3 in this embodiment includes: a processor 30, a memory 31, and a computer program 32 stored in the memory 31 and executable on the processor 30, such as a body condition detection program based on big data. When the processor 30 executes the computer program 32, it implements the steps in the various embodiments of the body condition detection method based on big data described above, for example... Figure 1 Steps 101 to 104 are shown. Alternatively, when the processor 30 executes the computer program 32, it implements the functions of each unit in the above-described device embodiments, for example... Figure 2 The function of the unit shown.

[0084] For example, the computer program 32 can be divided into one or more units, which are stored in the memory 31 and executed by the processor 30 to complete the present invention. The one or more units can be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program 32 in the terminal device 3. For example, the specific functions of each unit of the computer program 32 can be divided as follows: The acquisition unit is used to acquire high-frequency impedance, low-frequency impedance, and current phase difference via foot electrodes; The first calculation unit is used to calculate body composition based on the high-frequency impedance and the low-frequency impedance; wherein the body composition includes body fat percentage, muscle mass, total body water, basal metabolic rate, and bone mass; The second calculation unit is used to calculate the body age based on the body composition; A generation unit is used to generate body change information based on the body composition and the current phase difference; wherein the body state information includes body change information and body age.

[0085] The terminal device includes, but is not limited to, a processor 30 and a memory 31. Those skilled in the art will understand that... Figure 3 This is merely an example of a terminal device 3 and does not constitute a limitation on a terminal device 3. It may include more or fewer components than shown, or combine certain components, or different components. For example, the terminal device may also include input / output devices, network access devices, buses, etc.

[0086] The processor 30 can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor.

[0087] The memory 31 can be an internal storage unit of the terminal device 3, such as a hard disk or memory of the terminal device 3. The memory 31 can also be an external storage device of the terminal device 3, such as a plug-in hard disk, smart media card (SMC), secure digital card (SD), flash card, etc., equipped on the terminal device 3. Furthermore, the memory 31 can include both internal and external storage units of the terminal device 3. The memory 31 is used to store the computer program and other programs and data required by the roaming control device. The memory 31 can also be used to temporarily store data that has been output or will be output.

[0088] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0089] It should be noted that the information interaction and execution process between the above-mentioned devices / units are based on the same concept as the method embodiments of the present invention. For details on their specific functions and technical effects, please refer to the method embodiments section, which will not be repeated here.

[0090] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this invention. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0091] This invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps described in the various method embodiments above.

[0092] This invention provides a computer program product that, when run on a mobile terminal, enables the mobile terminal to implement the steps described in the above-described method embodiments.

[0093] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of the present invention can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include at least: any entity or device capable of carrying the computer program code to a photographing device / terminal device, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium. Examples include USB flash drives, portable hard drives, magnetic disks, or optical disks.

[0094] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0095] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0096] In the embodiments provided by this invention, it should be understood that the disclosed apparatus / network devices and methods can be implemented in other ways. For example, the apparatus / network device embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0097] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; they may be located in one place or distributed across multiple network units.

[0098] It should be understood that, when used in this specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0099] It should also be understood that the term “and / or” as used in this specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0100] As used in this specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if [the described condition or event] is detected" may be interpreted, depending on the context, as meaning "once determined," "in response to determination," "once [the described condition or event] is detected," or "in response to detection of [the described condition or event]."

[0101] Furthermore, in the description of this invention and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0102] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of the invention include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0103] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A method for detecting body status based on big data, characterized in that, The big data-based body condition detection method includes: High-frequency impedance, low-frequency impedance, and current phase difference are acquired via foot electrodes; Body composition is calculated based on the high-frequency impedance and the low-frequency impedance; wherein, the body composition includes body fat percentage, muscle mass, total body water, basal metabolic rate, and bone mass; Calculate body age based on the stated body composition; Based on the body composition and the current phase difference, body change information is generated; wherein, the body state information includes body change information and body age.

2. The body condition detection method based on big data as described in claim 1, characterized in that, The step of calculating body composition based on the high-frequency impedance and the low-frequency impedance includes: Based on the preset model, the initial body fat percentage, initial muscle mass, initial total body water, initial basal metabolic rate, and initial bone mass corresponding to the high-frequency impedance and the low-frequency impedance are calculated. Temperature compensation is applied to the initial body fat percentage, initial muscle mass, initial total body water, initial basal metabolic rate, and initial bone mass to obtain the current body fat percentage, current muscle mass, current total body water, current basal metabolic rate, and current bone mass. The current body fat percentage, current muscle mass, current total body water, current basal metabolic rate, and current bone mass are used as the body components.

3. The body condition detection method based on big data as described in claim 2, characterized in that, The steps for calculating the initial body fat percentage, initial muscle mass, initial total body water, initial basal metabolic rate, and initial bone mass corresponding to the high-frequency impedance and the low-frequency impedance based on the preset model include: Obtain the user's weight, height, age, and gender; Substituting the high-frequency impedance, height, age, and gender into the body fat model, the initial body fat percentage is obtained; The body fat model is as follows: ； in, Indicates initial body fat percentage. Indicates height, Indicates high-frequency impedance. Indicates height, Indicates gender, male. =1, female =0; The initial muscle mass is obtained by substituting the low-frequency impedance, weight, height, age, and gender into the muscle mass model. The muscle mass model is as follows: ； in, This indicates the initial muscle mass. Indicates low-frequency impedance. represents weight; Substituting the low-frequency impedance, the high-frequency impedance, the height, and the weight into the body water model, the initial total body water is obtained; The body water model is as follows: ； in, This represents the initial total body water content; Substitute the initial muscle mass, height, age, and gender into the metabolic rate model to obtain the initial basal metabolic rate; The metabolic rate model is as follows: ； in, This represents the initial basal metabolic rate; The initial bone mass is obtained by substituting the high-frequency impedance, the low-frequency impedance, the body weight, and the age into the bone mass model. The bone mass model is as follows: ； in, This indicates the initial bone mass.

4. The body condition detection method based on big data as described in claim 2, characterized in that, The step of performing temperature compensation on the initial body fat percentage, initial muscle mass, initial total body water, initial basal metabolic rate, and initial bone mass to obtain the current body fat percentage, current muscle mass, current total body water, current basal metabolic rate, and current bone mass includes: Body surface temperature is collected using a foot temperature sensor; Calculate the temperature correction factor corresponding to the body surface temperature; where, the temperature correction factor = ; Multiply the initial body fat percentage by the temperature correction factor to obtain the current body fat percentage; Multiply the initial muscle mass by the temperature correction coefficient to obtain the current muscle mass; Multiply the initial total body water content by the temperature correction factor to obtain the current total body water content; Multiply the initial basal metabolic rate by the temperature correction factor to obtain the current basal metabolic rate; The initial bone mass is multiplied by the temperature correction factor to obtain the current bone mass.

5. The body condition detection method based on big data as described in claim 1, characterized in that, The step of calculating body age based on the body composition includes: Calculate the age coefficient corresponding to each of the body components; Obtain the preset weight corresponding to each of the body components; Based on the preset weights, the age coefficients corresponding to each body component are weighted and summed to obtain initial values; The body age is obtained by multiplying the initial value by a regulation factor; wherein, the regulation factor , This represents the initial value.

6. The body condition detection method based on big data as described in claim 5, characterized in that, The step of calculating the age coefficient corresponding to each body component includes: Obtain baseline values ​​for body fat percentage, muscle mass, minimum healthy bone mass, and metabolic rate; The age coefficient corresponding to body fat percentage = 1 + 0.02 × (current body fat percentage - baseline body fat percentage). The age coefficient corresponding to muscle mass = 0.8 + 0.2 × (current muscle mass / baseline muscle mass). The age coefficient corresponding to the total body water content = current total body water content / (0.6 × body weight); The age coefficient corresponding to basal metabolic rate = current basal metabolic rate / (metabolic baseline value × 1.1); The age coefficient corresponding to bone mass = 0.7 + 0.006 × (current bone mass - minimum healthy bone mass).

7. The body condition detection method based on big data as described in claim 1, characterized in that, The step of generating body change information based on the body composition and the current phase difference includes: Obtain historical phase difference, historical muscle mass, historical body fat percentage, and historical total body water. Calculate the current phase difference and subtract it from the historical phase difference to obtain the first difference value; Calculate the current muscle mass and subtract the historical muscle mass to obtain the second difference; The third difference is obtained by subtracting the current body fat percentage from the historical body fat percentage. If the first difference is greater than 0, and the second difference is greater than 0, and the second difference is not greater than 0, then the body change information is determined to be normal muscle growth. If the first difference is not greater than 0, and the second difference is not greater than 0, and the second difference is greater than 0, then the body change information is determined to be an abnormal increase in body fat. If the first difference is not greater than 0, and the second difference is not greater than 0, then the body change information is determined to be in the stage of abnormal muscle consumption. The fourth difference is obtained by subtracting the current total body water from the historical total body water. If the first difference is not greater than 0 and the fourth difference is greater than 0, then the body change information is determined to be body edema; If the first difference is not greater than 0 and the fourth difference is not greater than 0, then the body change information is determined to be body dehydration.

8. A body condition detection device based on big data, characterized in that, The big data-based body condition detection device includes: The acquisition unit is used to acquire high-frequency impedance, low-frequency impedance, and current phase difference via foot electrodes; The first calculation unit is used to calculate body composition based on the high-frequency impedance and the low-frequency impedance; wherein the body composition includes body fat percentage, muscle mass, total body water, basal metabolic rate, and bone mass; The second calculation unit is used to calculate the body age based on the body composition; A generation unit is used to generate body change information based on the body composition and the current phase difference; wherein the body state information includes body change information and body age.

9. A terminal device, characterized in that, The terminal device includes: a memory, a processor, and a big data-based body condition detection program stored in the memory and executable on the processor, the big data-based body condition detection program being configured to implement the steps of the big data-based body condition detection method as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps in the big data-based body state detection method as described in any one of claims 1 to 7.

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