A method for monitoring moderate activity intensity in preschool children

Abstract

The present application provides a method for monitoring moderate activity intensity of preschool children, comprising: obtaining personalized indicators of the preschool children to be monitored and acceleration signals in the state to be monitored. The personalized indicators and acceleration signals are input into a moderate activity intensity assessment model, and moderate-to-strong activity indication information is output based on the moderate activity intensity threshold. The moderate activity intensity threshold range for preschool children is 3.2-5.3 Mets. The moderate activity intensity assessment model is constructed based on the fusion of personalized indicators of multiple preschool children, acceleration signals in different states, and motion energy consumption label values corresponding to synchronously recorded acceleration signals. Subsequently, when the moderate activity intensity assessment model is used to monitor the motion energy consumption of preschool children, the moderate-to-strong activity indication information of the preschool children can be obtained, so as to obtain indication information of moderate activity intensity for daily assessment activities of preschool children and improve the accuracy of motion energy consumption monitoring.

Description

Technical Field

[0001] The present application relates to the medical field, specifically to the field of measuring physiological parameters of children, and more specifically to a method and related device for monitoring the moderate activity intensity of preschool children. Background Art

[0002] Physical activity (PA) refers to any bodily movement resulting from skeletal muscle contraction that increases energy expenditure and is an important dimension in supporting early childhood growth and development. Physical activity is primarily categorized as light physical activity (LPA), moderate physical activity (MPA), and vigorous physical activity (VPA). Growing evidence suggests that lower levels of sedentary behavior (SB) and appropriate physical activity intensity can effectively improve children's physical health. Because the preschool period (which can be defined as children aged 3 to 6 years) is a critical period for developing good physical activity habits, physical activity habits during this stage can influence physical activity behaviors in later developmental stages. To promote early childhood growth and development, it is necessary to monitor the intensity of physical activity in preschoolers.

[0003] Typically, the intensity of physical activity can be determined by monitoring the energy expenditure of preschoolers. Energy expenditure can be expressed in terms of absolute energy (EE) or relative energy expenditure (METs). Thus, the intensity level of physical activity can be determined based on the EE or MET values. Currently, based on the MET values, the energy expenditure range for moderate-intensity activity is generally considered to be 3-6 METs. However, data collection and testing within this range is only applicable to adolescents and adults. Due to growth and developmental factors, the relationship between acceleration signals recorded simultaneously during exercise in preschoolers and METs differs significantly from that in children, adolescents, or adults. Furthermore, no specific energy expenditure thresholds for moderate-intensity activity are available for preschoolers aged 3, 4, 5, and 6 years. Therefore, the accuracy of existing energy expenditure monitoring models for preschoolers is low.

[0004] Currently, methods for monitoring energy expenditure in exercise are primarily based on accelerometer activity counts (AC). AC, which is obtained by processing acceleration signals through specific filters, loses a significant amount of motion-related information, thus affecting the accuracy of these methods. Accelerometer sensor devices can also record the tester's raw acceleration signal data. Existing evidence suggests that compared to models based on AC, models based on raw acceleration signal characteristics have significantly improved estimation accuracy. However, these models were all constructed using children from abroad, and their reproducibility and accuracy are still unsatisfactory.

[0005] In addition, in the research on the method for estimating the exercise energy consumption of preschool children based on the original acceleration signal characteristics, personalized indicators such as height, weight, gender, and age are not integrated or only partially integrated, which also means that the accuracy of the model constructed in estimating the exercise energy consumption value of preschool children still has room for improvement.

[0006] Based on the practices of medical institutions and parents, time is generally used to assess whether physical activity is meeting recommended levels. Previous methods typically output energy expenditure values, but lack models for estimating moderate activity intensity for preschoolers. A model that outputs the duration of moderate-intensity physical activity for preschoolers would be more suitable for assessing whether preschoolers meet recommended levels of moderate-intensity activity. Summary of the Invention

[0007] In view of this, the present application is dedicated to providing a method and related device for monitoring moderate activity intensity in preschool children. This method can be used to monitor preschool children's exercise energy consumption and determine whether their daily physical activity intensity and corresponding activity duration meet standards. This monitoring method helps improve the accuracy of preschool children's exercise energy consumption monitoring.

[0008] In a first aspect, the present application provides a method for monitoring moderate activity intensity in preschool children, the method comprising:

[0009] Obtaining personalized indicators of the preschool child to be monitored and acceleration signals in the state to be monitored;

[0010] The personalized index and the acceleration signal are input into a moderate activity intensity assessment model, and the output result is output by a determination system as moderate-to-strong activity indication information based on a moderate activity intensity threshold, wherein the moderate activity intensity threshold range for preschool children is 3.2-5.3 Mets;

[0011] Among them, the moderate activity intensity assessment model is constructed based on the fusion of personalized indicators of multiple preschool children, acceleration signals under different states, and motion energy consumption label values corresponding to the acceleration signals.

[0012] In one possible implementation, the moderate activity intensity threshold for preschool children aged 3-6 years old ranges from 3.2-5.3 Mets.

[0013] In one possible implementation, the inputting the personalized index and the acceleration signal into a moderate activity intensity assessment model, and the outputting of moderate-to-strong activity indication information by a determination system based on a moderate activity intensity threshold, includes:

[0014] Dividing the acceleration signal according to preset time windows to obtain first acceleration signals corresponding to a plurality of preset time windows respectively;

[0015] Determining an input feature corresponding to each preset time window based on the first acceleration signal of each preset time window and the personalized indicator;

[0016] The input feature is input into the moderate activity intensity assessment model, and the moderate-to-strong activity indication information is output based on the moderate activity intensity threshold.

[0017] In a possible implementation, the moderate to strong activity indication information includes a daily average moderate to strong activity index.

[0018] In one possible implementation, inputting the input features into the moderate activity intensity assessment model, and outputting the moderate-to-strong activity indication information by the determination system based on the moderate activity intensity threshold, includes:

[0019] Inputting the input features corresponding to each preset time window into the moderate activity intensity assessment model to obtain the exercise energy consumption data corresponding to each preset time window;

[0020] Comparing the exercise energy consumption data with the moderate activity intensity threshold, and determining a target preset time window corresponding to the exercise energy consumption data that meets the moderate activity intensity threshold;

[0021] The target preset time window is counted to determine the average daily moderate to vigorous activity index of the preschool child.

[0022] The so-called daily moderate-intensity activity index for preschool children represents the statistical information of daily moderate-intensity exercise. This statistical information may include the total duration of daily moderate-intensity activity, and may also include the integral of the curve of metabolic equivalent METs value changing over time in the corresponding time period under certain set conditions (i.e., the sum of the areas under the METs value curve in a certain time period) and other information.

[0023] In one possible implementation, the first acceleration signal includes an x-axis acceleration signal, a y-axis acceleration signal, and a z-axis acceleration signal, and determining the input feature corresponding to each preset time window based on the first acceleration signal in each preset time window and the personalized indicator includes:

[0024] Determining a one-dimensional vector sum signal of the x-axis acceleration signal, the y-axis acceleration signal, and the z-axis acceleration signal;

[0025] respectively determining statistical characteristic indicators of the x-axis acceleration signal, the y-axis acceleration signal, the z-axis acceleration signal, and the one-dimensional vector sum signal;

[0026] Based on the statistical feature index corresponding to each preset time window and the personalized index, the input feature corresponding to each preset time window is determined.

[0027] In a possible implementation, determining the input feature based on the statistical feature indicator and the personalized indicator includes:

[0028] Using a feature selection algorithm, selecting a preset number of indicators from the statistical feature indicators and the personalized indicators;

[0029] The input features are determined based on the preset number of indicators.

[0030] In a possible implementation, the acceleration signal is measured by an accelerometer worn by the preschool child to be monitored, where the accelerometer is worn on the right waist.

[0031] In a possible implementation, the accelerometer is worn for at least one day.

[0032] In one possible implementation, the moderate activity intensity threshold range for 3-year-old preschool children is 3.2-4.4 Mets.

[0033] In one possible implementation, the moderate activity intensity threshold range for 4-year-old preschool children is 3.4-4.6 Mets.

[0034] In one possible implementation, the moderate activity intensity threshold for 5-year-old preschoolers ranges from 3.7 to 4.8 Mets.

[0035] In one possible implementation, the moderate activity intensity threshold for 6-year-old preschoolers ranges from 3.8 to 5.1 Mets.

[0036] In one possible implementation, the age of a preschool child is calculated based on the age corresponding to the actual date of birth.

[0037] In one possible implementation, obtaining the personalized index of the preschool child to be monitored and the acceleration signal in the state to be monitored includes:

[0038] Acquiring original personalized indicators of the preschool child to be monitored and original acceleration signals in the state to be monitored;

[0039] Preprocessing the original personalized index and the original acceleration signal to obtain the personalized index and the acceleration signal;

[0040] The pretreatment process includes one or more of the following:

[0041] impute missing values; or,

[0042] One-hot encoding of categorical data; or,

[0043] Normalize continuous data; or,

[0044] Normalize the continuous data and convert the normalized data into data with an approximate normal distribution.

[0045] In a possible implementation, the personalized indicator includes at least one of gender, age, height, weight, and body mass index.

[0046] In a second aspect, the present application provides a device for monitoring moderate activity intensity of preschool children, the device comprising:

[0047] An acquisition unit, used to acquire personalized indicators of the preschool child to be monitored and an acceleration signal in the state to be monitored;

[0048] A monitoring and determination unit is configured to input the personalized indicators and the acceleration signal into a moderate activity intensity assessment model; the determination system therein outputs moderate-to-strong activity indication information based on a moderate activity intensity threshold, wherein the moderate activity intensity threshold for preschool children aged 3-6 years old ranges from 3.2 to 5.3 meters; wherein the moderate activity intensity assessment model is constructed based on the fusion of personalized indicators of multiple preschool children, acceleration signals in different states, and motion energy consumption label values corresponding to the acceleration signals.

[0049] In a third aspect, the present application provides an electronic device, the device comprising: a memory and a processor;

[0050] The memory is used to store relevant program codes;

[0051] The processor is used to call the program code to execute the method for monitoring moderate activity intensity of preschool children as described in any one of the implementations of the first aspect above.

[0052] In a fourth aspect, the present application provides a computer-readable storage medium for storing a computer program for executing the method for monitoring moderate activity intensity for preschool children as described in any one of the implementations of the first aspect above.

[0053] In a fifth aspect, the present application provides a computer program product, which includes a computer program / instructions, and when the computer program / instructions are executed by a processor, it implements the method for monitoring the moderate activity intensity of preschool children as described in any one of the implementation methods of the first aspect above.

[0054] The invention of the present invention is:

[0055] 1. Provides a moderate activity intensity threshold range suitable for the characteristics of Chinese preschool children;

[0056] 2. Provides precise moderate activity intensity thresholds suitable for children aged 3, 4, 5, and 6 years old;

[0057] 3. Integrates personalized indicators that are specific to the development of Chinese preschool children;

[0058] 4. The concept of daily average moderate-intensity activity index was proposed for the first time. The model and judgment system can directly output the cumulative duration of moderate-intensity activities, as well as the integral of the curve of metabolic equivalents (METs) values changing over time in the corresponding time period under certain set conditions (that is, the sum of the areas under the METs value curve in a certain time period) and other information.

[0059] In the above-mentioned implementation method of the present application, the personalized indicators of the preschool children to be monitored and the acceleration signals in the monitored state are obtained. First of all, in combination with the developmental characteristics of preschool children, this research team integrated the personalized physical development indicators of Chinese children, height, weight, gender, age, BIM, and these seemingly conventional indicators actually have a great impact on the accuracy of the preschool children's exercise evaluation model. During the research process, five personalized indicators reflecting the characteristics of preschool children were fused with the original acceleration data and the corresponding motion energy consumption label values to construct a prediction model, and the personalized indicators and acceleration signals were input into the moderate activity intensity evaluation model, and the medium-strong activity indication information was output based on the moderate activity intensity threshold. Among the personalized indicators, the height indicator has the greatest impact on the model prediction accuracy. These characteristics can only be discovered by sorting the feature labels by importance when training the model, which directly affects the accuracy of the prediction model.

[0060] Secondly, the moderate activity intensity threshold range for preschool children aged 3-4 is 3.2-5.3 Mets. Among them, the moderate activity intensity assessment model is constructed based on the fusion of personalized indicators of multiple preschool children, acceleration signals in different states, and motion energy consumption label values corresponding to the acceleration signals. In other words, a moderate activity intensity assessment model can be obtained by collecting acceleration signals, personalized indicators, and their motion energy consumption-related data for training of preschool children. Subsequently, when the moderate activity intensity assessment model is used to monitor the motion energy consumption of preschool children, in order to obtain indication information of the moderate activity intensity for daily assessment activities of preschool children and improve the accuracy of motion energy consumption monitoring.

[0061] Compared to existing technologies, this research provides a more accurate threshold range for moderate activity intensity, and for the first time proposes precise threshold ranges for moderate activity intensity for children aged 3, 4, 5, and 6 years old. Since the current globally accepted ranges are statistically analyzed for international adolescents, our threshold ranges are derived from data collected for Chinese preschool children, and the resulting threshold ranges are consistent with the developmental characteristics of Chinese preschool children. Therefore, the corresponding moderate activity intensity threshold ranges are more precise, improving the accuracy of the relevant assessment results for moderate-intensity activity in preschool children.

[0062] The concept of daily average moderate-intensity activity index was proposed for the first time. According to the constructed model, at least the cumulative duration of daily average moderate-intensity activity can be directly obtained. By counting the time preschool children meet the requirements of moderate-intensity activity when wearing corresponding accelerometer sensors, and then comparing this time with the data table of the moderate-intensity activity time required for physical development of Chinese preschool children of the corresponding age group, it can be known whether the preschool child has met the moderate-intensity activity time required for their physical development. This information can then be used to assist in the diagnosis of the preschool child in fields such as sports medicine, so as to avoid the impact of insufficient moderate-intensity activity time on preschool children. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments provided in the present application. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0064] Figure 1 This is a flowchart of a method for monitoring moderate activity intensity for preschool children provided in an embodiment of the present application.

[0065] Figure 2 A schematic diagram of the test results of an exercise energy consumption monitoring model provided in an embodiment of the present application.

[0066] Figure 3 A schematic diagram of a moderate activity intensity monitoring system for preschool children provided in an embodiment of the present application.

[0067] Figure 4 This is a schematic diagram of a moderate activity intensity monitoring device for preschool children provided in an embodiment of the present application.

[0068] Figure 5 A schematic diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0069] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. The described embodiments are only exemplary implementation methods of the present application and are not all implementation methods. Those skilled in the art can combine the embodiments of the present application to obtain other embodiments without creative work, and these embodiments are also within the scope of protection of the present application.

[0070] Because the preschool period (ages 3 to 6) is a critical period for developing good physical activity habits, and physical activity habits during this period may influence physical activity behaviors in later developmental stages, it is necessary to monitor the intensity of preschool children's physical activity to promote their early growth and development.

[0071] Typically, the intensity of physical activity can be determined by monitoring the energy expenditure of preschoolers. Currently, the main method for monitoring individuals using accelerometers is to estimate their energy expenditure. However, this method is primarily applicable to adolescents and adults. The physical activity habits of preschoolers differ significantly from those of adolescents (typically aged 11 to 17) and adults. Therefore, the accuracy of energy consumption monitoring for preschoolers using existing energy consumption monitoring models is low.

[0072] In addition, current methods for monitoring energy consumption during exercise are all based on accelerometer activity counts (AC). AC is the data obtained by processing the acceleration signal through a specific filter, which will lose a lot of exercise-related information, thus affecting the accuracy of the exercise energy consumption monitoring method.

[0073] There are currently some studies that have constructed models for estimating exercise energy expenditure in preschool children based on raw acceleration signal characteristics. However, these models were all constructed using children from abroad, and their reproducibility and accuracy are still not ideal.

[0074] Based on this, an embodiment of the present application provides a method for monitoring the moderate activity intensity of preschool children, so as to improve the accuracy of monitoring the energy consumption of preschool children. And taking Chinese preschool children as the research subjects, the monitoring results are more in line with the growth and development characteristics of Chinese preschool children. In the specific implementation, the personalized indicators of the preschool children to be monitored and the acceleration signals in the monitored state are obtained. The personalized indicators and the acceleration signals are input into the moderate activity intensity evaluation model, and the output result is output by the judgment system based on the moderate activity intensity threshold value to output medium-strong activity indication information. Among them, the moderate activity intensity threshold range of preschool children is 3.2-5.3Mets. Among them, the moderate activity intensity evaluation model is obtained based on the personalized indicators of multiple preschool children, the acceleration signals in different states, and the energy consumption label values corresponding to the acceleration signals. That is, a moderate activity intensity assessment model can be obtained by collecting acceleration signals, personalized indicators and exercise energy consumption related data of preschool children. When the moderate activity intensity assessment model is subsequently used to monitor the exercise energy consumption of preschool children, indicative information of moderate activity intensity for daily assessment activities of preschool children can be obtained, and the accuracy of exercise energy consumption monitoring can be improved.

[0075] In order to facilitate understanding of the technical solutions provided by the embodiments of the present application, a detailed introduction will be given below in conjunction with the drawings in the embodiments.

[0076] See also Figure 1 , which is a flow chart of a method for monitoring moderate activity intensity of preschool children provided in an embodiment of the present application.

[0077] Optionally, the method may be executed by a processing device, which may be an electronic device or other device, and is not limited in this embodiment of the present application.

[0078] The method may include the following steps:

[0079] S101: Obtaining personalized indicators of the preschool child to be monitored and acceleration signals in the state to be monitored.

[0080] In order to monitor the motion energy consumption state of preschool children, it is first necessary to obtain the personalized indicators of the preschool children to be monitored and the acceleration signal under the state to be monitored. Among them, the preschool children to be detected represent children who need to be monitored for motion energy consumption. In the embodiment of the present application, motion energy consumption monitoring is mainly performed on children between the ages of 4 and 6. The personalized indicators of the preschool children to be monitored may include: gender, age, height, weight, and body mass index, etc. The acceleration signal can be used to characterize the motion state of the preschool children to be monitored. The acceleration signals corresponding to the preschool children to be monitored under different states to be monitored are different, which leads to different motion energy consumption. For example, the state to be monitored of the preschool children to be monitored may include various motion states such as sitting still, walking slowly, walking fast, running, and jumping.

[0081] Optionally, the preschool child to be monitored can be made to wear an accelerometer, and the acceleration signal of the preschool child in different motion states can be measured by the accelerometer. In order to objectively and accurately monitor the preschool child's exercise energy consumption, the preschool child to be monitored can be made to wear an accelerometer on the right waist to measure the acceleration signal. The wearing period of the accelerometer is at least 1 day. For example, the preschool child can be made to wear an accelerometer for 7 consecutive days, and the personalized indicators and acceleration signals of the preschool child to be monitored within 7 days can be counted to achieve the monitoring of the preschool child's exercise energy consumption.

[0082] Since the relevant data obtained for preschool children may contain erroneous records or missing data, in order to accurately process the data, the collected data can be preprocessed first. In one possible implementation, the original personalized indicators of the preschool children to be monitored and the original acceleration signals under the monitored state can be obtained. The original personalized indicators and the original acceleration signals are then preprocessed to obtain personalized indicators and acceleration signals; wherein the preprocessing process includes one or more of the following: interpolation of missing values; or, unique hot encoding of categorical data; or, normalization of continuous data; or, normalization of continuous data and conversion of the normalized data into data with an approximate normal distribution.

[0083] In practice, missing values in the data can be interpolated. Specifically, when missing values are present in the personalized metrics or acceleration signals of preschool children, these missing values can be interpolated to avoid impacting subsequent data processing. For example, missing values in categorical data (e.g., male or female) are interpolated with "NaN" values, while missing values in numerical data (e.g., age) are interpolated with the mean.

[0084] Since most models cannot directly process categorical data, one-hot encoding is required to convert categorical data into numerical data. One-hot encoding, also known as single-bit encoding, involves using an N-bit state register to encode N states. Each state has a separate register bit, taking a value of 0 or 1, and only one bit is active at any given time. For example, to encode the categorical data "boy" and "girl," since there are only two types in this category, N=2, and "01" and "10" can be used to represent "boy" and "girl."

[0085] For continuous data, normalization can be performed to simplify data processing. Alternatively, the normalized continuous data can be converted into data with a near-normal distribution to improve data processing efficiency.

[0086] S102: The personalized index and the acceleration signal are input into a moderate activity intensity assessment model, and the output result is output by the determination system as moderate-strong activity indication information based on a moderate activity intensity threshold.

[0087] Among them, the moderate activity intensity assessment model is trained based on the personalized indicators of multiple preschool children, acceleration signals under different states, and the motion energy consumption label values corresponding to the acceleration signals. The specific training process can be found in the subsequent embodiments and will not be introduced here.

[0088] That is, by collecting acceleration signals, personalized indicators and exercise energy consumption related data of preschool children in different states, a moderate activity intensity assessment model can be trained. When the moderate activity intensity assessment model is subsequently used to monitor the exercise energy consumption of the monitored preschool children, the accuracy of exercise energy consumption monitoring can be improved, and the moderate-to-strong activity indication information of preschool children can be obtained more accurately.

[0089] The medium-to-strong activity indication information may be used to indicate that the preschool child is in a state of medium activity intensity. For example, the medium-to-strong activity indication information may include a daily average medium-to-strong activity index, that is, the length of time the preschool child is in medium activity intensity.

[0090] In one possible implementation, the collected acceleration signals of a preschool child may correspond to motion states in multiple time periods, each of which may have different durations. To unify the data format and facilitate processing, the acceleration signals can be divided according to preset time windows to obtain first acceleration signals corresponding to each of the multiple preset time windows. That is, each preset time window corresponds to the divided first acceleration signal. The multiple preset time windows may not overlap.

[0091] For each preset time window, an input feature corresponding to the preset time window can be determined based on the first acceleration signal and the personalized indicator in the preset time window. The input feature corresponding to each preset time window is input into the moderate activity intensity assessment model, and moderate-to-strong activity indication information is output based on the moderate activity intensity threshold.

[0092] Generally speaking, knowing whether preschool children are in a state of moderate activity intensity is more conducive to understanding their physical health status. Therefore, the time preschool children are in moderate-intensity physical activity can be counted to better monitor their exercise energy consumption. The intensity level of physical activity is divided based on metabolic equivalents (METs).

[0093] In specific implementations, the input features corresponding to each preset time window are fed into the moderate activity intensity assessment model to obtain the exercise energy consumption data corresponding to each preset time window. The determination system compares this exercise energy consumption data with the moderate activity intensity threshold and determines the target preset time window corresponding to the exercise energy consumption data that meets the moderate activity intensity threshold. The total duration of each target preset time window is calculated to determine the daily average moderate-to-vigorous activity index.

[0094] Among them, the exercise energy consumption data includes metabolic equivalents, that is, the metabolic equivalents corresponding to each preset time window are compared with the moderate activity intensity threshold to determine the target preset time window corresponding to the metabolic equivalent that meets the moderate activity intensity threshold.

[0095] Optionally, the moderate activity intensity threshold may include a first moderate intensity threshold and a second moderate intensity threshold, with the first moderate intensity threshold serving as a lower limit of the moderate activity intensity threshold and the second moderate intensity threshold serving as an upper limit of the moderate activity intensity threshold. The metabolic equivalent is compared with the first moderate intensity threshold and the second moderate intensity threshold. When the metabolic equivalent is greater than or equal to the first moderate intensity threshold and less than or equal to the second moderate intensity threshold, it indicates that the metabolic equivalent meets the moderate activity intensity threshold, and the preset time window corresponding to the metabolic equivalent is determined as the target preset time window.

[0096] In actual application scenarios, the moderate activity intensity thresholds for preschool children of different age groups are also different. In an embodiment of the present application, the moderate activity intensity thresholds corresponding to preschool children of different age groups can be determined in advance through experimental statistics. For example, the moderate activity intensity threshold for a 4-year-old preschool child is [3.4, 4.6] Mets; the moderate activity intensity threshold for a 5-year-old preschool child is [3.7, 4.8] Mets; and the moderate activity intensity threshold for a 6-year-old preschool child is [3.8, 5.1] Mets. Among them, the age of the preschool child is calculated according to the age corresponding to the actual date of birth.

[0097] Among them, according to the numerical pattern corresponding to the moderate activity intensity threshold of preschool children aged 4-6, the moderate activity intensity threshold of 3-year-old preschool children is estimated to be [3.2, 4.4] Mets;

[0098] It should be noted that the range of the moderate activity intensity threshold provided in the embodiment of the present application is determined through experimental statistics and is only an exemplary description, and is not limited to the above-mentioned implementation method. The embodiment of the present application does not impose any formal limitation on the specific method of determining the moderate activity intensity threshold.

[0099] Based on the above embodiment, it can be seen that for each preset time window, the input feature corresponding to the preset time window can be determined based on the first acceleration signal and the personalized index of the preset time window. This step will be described in detail below.

[0100] In one possible implementation, the acceleration signal includes an x-axis acceleration signal, a y-axis acceleration signal, and a z-axis acceleration signal, and a one-dimensional vector sum signal can be determined based on the x-axis acceleration signal, the y-axis acceleration signal, and the z-axis acceleration signal. That is, the square sum of the x-axis acceleration signal, the y-axis acceleration signal, and the z-axis acceleration signal is calculated, and then the arithmetic square root of the square sum is calculated as the one-dimensional vector sum signal.

[0101] After the acceleration signal is divided according to preset time windows, the first acceleration signal within each preset time window includes acceleration signals collected at multiple time points. Therefore, based on the multiple x-axis acceleration signals, multiple y-axis acceleration signals, multiple z-axis acceleration signals, and multiple one-dimensional vector sum signals within the preset time window, the statistical feature indicators of the x-axis acceleration signal, the statistical feature indicators of the y-axis acceleration signal, the statistical feature indicators of the z-axis acceleration signal, and the statistical feature indicators of the one-dimensional vector sum signal can be determined respectively. Then, based on the statistical feature indicators and personalized indicators corresponding to each preset time window, the input features corresponding to each preset time window are determined.

[0102] Among them, the statistical characteristic indicators can represent mathematical statistical characteristics, for example, they can include maximum value, minimum value, mean value, standard deviation, variance, percentile, coefficient of variation, etc. In one possible implementation, the statistical characteristic indicators of the x-axis acceleration signal include at least the maximum value of multiple x-axis acceleration signals. The statistical characteristic indicators of the y-axis acceleration signal include at least the peak-to-peak value of multiple y-axis acceleration signals, wherein the peak-to-peak value represents the difference between the maximum value and the minimum value. The statistical characteristic indicators of the z-axis acceleration signal include at least the minimum value of multiple z-axis acceleration signals. In order to find the key features representing different motion directions. And the statistical characteristic indicators of the one-dimensional vector sum signal include at least the mean value and variance of multiple one-dimensional vector sum signals.

[0103] It should be noted that the specific types and numbers of statistical characteristic indicators are not limited in the embodiments of the present application and can be set according to actual needs. In one possible application scenario, for the x-axis acceleration signal, y-axis acceleration signal, z-axis acceleration signal, one-dimensional vector and signal, a total of 91 statistical characteristic indicators including maximum value, minimum value, mean value, standard deviation, variance, percentile, coefficient of variation, etc. can be determined. Then, combined with five personalized indicators of gender, age, height, weight, and body mass index, the statistical characteristic indicators and personalized indicators are integrated to obtain 96 indicators to determine the input features.

[0104] When input features include a large number of indicators, data processing becomes more complex and may affect model accuracy. Therefore, in one possible implementation, a feature selection algorithm can be used to select a preset number of indicators from the statistical and personalized indicators determined above. Input features are then determined based on this number of selected indicators. In other words, a subset of the statistical and personalized indicators can be selected as input features, ensuring feature diversity while also appropriately reducing the amount of data (the number of indicators), lowering data processing complexity, and increasing model processing speed.

[0105] In one possible implementation, the feature selection algorithm can be a minimum redundancy maximum correlation method. The main idea of this method is to select features from the dataset that are highly correlated with the target variable but have low redundancy with each other. In other words, from statistical feature indicators and personalized indicators, indicators that are highly correlated with exercise energy consumption data and have low redundancy with each other are selected.

[0106] In one possible implementation, the moderate activity intensity assessment model may include an exercise energy consumption monitoring model and a moderate activity intensity monitoring and determination unit. The personalized indicators and acceleration signals of the preschool child to be monitored are input into the exercise energy consumption monitoring model, and the exercise energy consumption data of the preschool child to be monitored can be output. Furthermore, the exercise energy consumption monitoring model is constructed based on the fusion of personalized indicators of multiple preschool children, acceleration signals in different states, and exercise energy consumption label values corresponding to the acceleration signals. That is, the exercise energy consumption monitoring model is trained using personalized indicators of multiple preschool children, acceleration signals in different states, and exercise energy consumption label values corresponding to the acceleration signals.

[0107] The moderate activity intensity monitoring and determination unit is further configured to compare the exercise energy consumption data with a moderate activity intensity threshold and output moderate-strong activity indication information.

[0108] In specific implementation, the acceleration signal is divided according to the preset time window to obtain the first acceleration signals corresponding to the multiple preset time windows. Based on the first acceleration signal and personalized indicators of each preset time window, the input features corresponding to each preset time window are determined. The input features corresponding to each preset time window are input into the motion energy consumption monitoring model, and the corresponding motion energy consumption data is output. The moderate activity intensity monitoring and determination unit obtains the motion energy consumption data of each preset time window, and compares the motion energy consumption data with the moderate activity intensity threshold to determine the target preset time window corresponding to the motion energy consumption data that meets the moderate activity intensity threshold. The target preset time window is counted to determine the average daily moderate-strong activity index.

[0109] In order to facilitate understanding of the implementation principle of the sports energy consumption monitoring model, the training process of the sports energy consumption monitoring model is first introduced below.

[0110] To train an energy consumption monitoring model, we first need to obtain sample data for training. Since the energy consumption monitoring model is designed to accurately monitor the energy consumption of preschool children, we need to collect data related to their energy consumption. The process of training the energy consumption monitoring model mainly includes the following steps: A1-A4:

[0111] A1: Obtain personalized indicators, triaxial acceleration signals, and motion energy consumption label values corresponding to the triaxial acceleration signals for preschool children.

[0112] Preschool children mainly include children between the ages of 3 and 6. The specific age range for data collection can be determined based on actual needs. The collected samples can include as many age groups as possible, thereby increasing sample diversity and making the trained exercise energy consumption monitoring model more accurate.

[0113] Personalized indicators for preschool children include gender, age, height, weight, and body mass index. Triaxial acceleration signals, including x-axis, y-axis, and z-axis acceleration signals, can be used to characterize a preschooler's movement state. For example, preschoolers can wear an accelerometer to measure the triaxial acceleration signals under different movement states. Furthermore, they can wear a gas metabolism analyzer. While the accelerometer measures the triaxial acceleration signals, the gas metabolism analyzer also measures exercise energy consumption under different movement states. This serves as an exercise energy consumption label value, or sample ground truth value, so that each triaxial acceleration signal corresponds to an exercise energy consumption label value. The gas metabolism analyzer uses breath analysis to measure oxygen uptake and carbon dioxide output, and calculates the exercise energy consumption label value. The personalized indicators and triaxial acceleration signals can be used as inputs for model training, with the exercise energy consumption label value serving as the sample ground truth value. The training goal is to ensure that the exercise energy consumption data output by the exercise energy consumption monitoring model more closely matches the exercise energy consumption label value.

[0114] Optionally, the exercise energy consumption tag value may include energy expenditure (EE) or metabolic equivalents. When the acquired exercise energy consumption tag value includes energy expenditure, the trained exercise energy consumption monitoring model can output the energy expenditure of the monitored preschool child. When the acquired exercise energy consumption tag value includes metabolic equivalents, the trained exercise energy consumption monitoring model can output the metabolic equivalents of the monitored preschool child. In other words, different samples (exercise energy consumption tag values) can be used to train different exercise energy consumption monitoring models to monitor different exercise energy consumption data.

[0115] A2: Determine sample characteristics based on personalized indicators and triaxial acceleration signals of preschool children.

[0116] Because the data collected about preschool children may contain errors or omissions, in order to simplify the model's data processing, one possible implementation method is to preprocess the collected data to obtain preprocessed personalized indicators and preprocessed triaxial acceleration signals. Sample features are then determined based on these preprocessed personalized indicators and preprocessed triaxial acceleration signals.

[0117] In practice, missing values in the data can be interpolated. Specifically, when missing values are present in the personalized metrics or triaxial acceleration signals of preschool children, these missing values can be interpolated to avoid impacting subsequent data processing. For example, missing values in categorical data (e.g., male or female) are interpolated with "NaN" values, while missing values in numerical data (e.g., age) are interpolated with the mean.

[0118] Since most models cannot directly process categorical data, one-hot encoding is required to convert categorical data into numerical data. One-hot encoding, also known as single-bit encoding, involves using an N-bit state register to encode N states. Each state has a separate register bit, taking a value of 0 or 1, and only one bit is active at any given time. For example, to encode the categorical data "boy" and "girl," since there are only two types in this category, N=2, and "01" and "10" can be used to represent "boy" and "girl."

[0119] For continuous data, normalization can be performed to simplify data processing. Alternatively, the normalized continuous data can be converted into data with a near-normal distribution to improve data processing efficiency.

[0120] In a possible implementation, the personalized index and the three-axis acceleration signal can be fused as sample features.

[0121] In one possible implementation, the three-axis acceleration signal includes an x-axis acceleration signal, a y-axis acceleration signal, and a z-axis acceleration signal. To increase the diversity and richness of samples and enhance the ability of the training model, a one-dimensional vector sum signal can be determined based on the x-axis acceleration signal, the y-axis acceleration signal, and the z-axis acceleration signal. That is, the square sum of the x-axis acceleration signal, the y-axis acceleration signal, and the z-axis acceleration signal is calculated, and then the arithmetic square root of the square sum is calculated as the one-dimensional vector sum signal.

[0122] According to the above embodiment, the collected three-axis acceleration signals of preschool children may correspond to the motion state of multiple time periods, and the duration of each time period may be different. In order to unify the format of sample features and facilitate the processing of sample features, the acquired three-axis acceleration signals can be divided according to preset time windows to obtain first acceleration signals corresponding to multiple preset time windows. That is, each preset time window corresponds to the first acceleration signal obtained by division. Among them, the first acceleration signal in each preset time window includes multiple x-axis acceleration signals, multiple y-axis acceleration signals and multiple z-axis acceleration signals. For each preset time window, the first sample feature corresponding to the preset time window can be determined based on the first acceleration signal of the preset time window and the personalized index.

[0123] Specifically, for any preset time window, based on multiple x-axis acceleration signals, multiple y-axis acceleration signals, multiple z-axis acceleration signals, and corresponding multiple one-dimensional vector sum signals within the preset time window, the statistical characteristic indicators of the x-axis acceleration signal, the statistical characteristic indicators of the y-axis acceleration signal, the statistical characteristic indicators of the z-axis acceleration signal, and the statistical characteristic indicators of the one-dimensional vector sum signal are calculated respectively. Among them, the statistical characteristic indicators can represent mathematical statistical characteristics, for example, they can include maximum value, minimum value, mean, standard deviation, variance, percentile, coefficient of variation, etc. Then, based on the statistical characteristic indicators and personalized indicators corresponding to the preset time window, the first sample feature corresponding to the preset time window is determined. For example, the various statistical characteristic indicators and personalized indicators can be fused to determine the first sample feature.

[0124] In one possible implementation, the statistical characteristic indicators of the x-axis acceleration signal include at least the maximum values of multiple x-axis acceleration signals. The statistical characteristic indicators of the y-axis acceleration signal include at least the peak-to-peak values of multiple y-axis acceleration signals, where the peak-to-peak value represents the difference between the maximum value and the minimum value. The statistical characteristic indicators of the z-axis acceleration signal include at least the minimum values of multiple z-axis acceleration signals. The statistical characteristic indicators of the one-dimensional vector sum signal include at least the mean and variance of the multiple one-dimensional vector sum signals.

[0125] It should be noted that the specific types and numbers of statistical characteristic indicators are not limited in the embodiments of the present application and can be set according to actual needs. In one possible application scenario, for the x-axis acceleration signal, y-axis acceleration signal, z-axis acceleration signal, one-dimensional vector and signal, a total of 91 statistical characteristic indicators including maximum value, minimum value, mean value, standard deviation, variance, percentile, coefficient of variation, etc. can be determined. Then, combined with five personalized indicators of gender, age, height, weight, and body mass index, the statistical characteristic indicators and personalized indicators are integrated to obtain 96 indicators to determine the first sample feature.

[0126] While including more indicators in the sample features can increase sample diversity, it also increases the complexity of data processing and the difficulty of training the exercise energy consumption monitoring model, which may lead to overfitting in training and affect the accuracy of the exercise energy consumption monitoring model. Based on this, in one possible implementation, a feature selection algorithm can be used to select a preset number of indicators from the statistical feature indicators and personalized indicators determined above, and then the first sample feature is determined based on the selected preset number of indicators. In other words, some indicators from the statistical feature indicators and personalized indicators can be selected as the first sample feature, which can not only ensure sample diversity, but also appropriately reduce the amount of sample data (the number of indicators), reduce the complexity of data processing, and increase the speed of model training.

[0127] For example, the feature selection algorithm can be the minimum redundancy maximum correlation method, which primarily selects features from the dataset that are highly correlated with the target variable but have low redundancy with each other. Specifically, from statistical feature indicators and personalized indicators, indicators that are highly correlated with exercise energy consumption are selected, while also having low redundancy with each other.

[0128] Optionally, the preset number of selected indicators can be determined through multiple experiments. That is, multiple different numbers can be determined in advance, and multiple indicators can be selected for each number to determine it as a sample feature. After the sample feature training is used to obtain the sports energy consumption monitoring model, it is determined that the accuracy of the sports energy consumption monitoring model obtained by training with which number of sample features is higher can be determined as the preset number. Subsequently, when the sports energy consumption monitoring model is applied to determine the sports energy consumption data of preschool children, a preset number of indicators can be selected to determine the sample features, so that the sports energy consumption data predicted by the sports energy consumption monitoring model is more accurate.

[0129] Based on this, after determining the first sample feature corresponding to each preset time window, since the multiple three-axis acceleration signals within the preset time window correspond to multiple motion energy consumption label values, the sample true value corresponding to the first sample feature can be determined to be the average of the multiple motion energy consumption label values within the preset time window. In other words, for each preset time window, multiple motion energy consumption label values within the preset time window are obtained, and the average motion energy consumption label value of the multiple motion energy consumption label values is calculated as the sample true value corresponding to the first sample feature. Thus, the motion energy consumption monitoring model is trained using the first sample feature as input and the average motion energy consumption label value corresponding to the first sample feature as the sample true value.

[0130] A3: Input the sample features and the corresponding exercise energy consumption label values into the machine learning algorithm to obtain the predicted exercise energy consumption value.

[0131] After determining the sample features for training, the sample features can be used as independent variables and the exercise energy consumption label values corresponding to the sample features can be input into the machine learning algorithm as target variables, and the machine learning algorithm can output the exercise energy consumption prediction value.

[0132] In a possible implementation, the machine learning algorithm may include an extreme random tree regression algorithm, a random forest regression algorithm, a light gradient boosting machine (LGBM) algorithm, a CatBoost algorithm, or a linear regression algorithm.

[0133] A4: Train the machine learning algorithm based on the predicted exercise energy consumption value and the exercise energy consumption label value until the training cutoff condition is met to obtain a trained exercise energy consumption monitoring model.

[0134] Among them, the training goal is to make the exercise energy consumption prediction value output by the exercise energy consumption monitoring model closer to the exercise energy consumption label value.

[0135] Once the predicted energy consumption value is obtained, since the three-axis acceleration signal in the sample features corresponds to the energy consumption label value (i.e., the true value of the sample), and the predicted energy consumption value represents the predicted value of the machine learning algorithm, the machine learning algorithm can be trained based on the predicted energy consumption value and the energy consumption label value. The training is iterative until the training cutoff condition is met. The goal of the training is to reduce the error between the predicted energy consumption value and the energy consumption label value.

[0136] In a specific implementation, an estimation error can be determined based on the predicted value of exercise energy consumption and the label value of exercise energy consumption, and the estimation error is used to represent the error between the predicted value of exercise energy consumption and the label value of exercise energy consumption. When the estimation error is larger, it indicates that the error between the predicted value of exercise energy consumption and the label value of exercise energy consumption is also larger, and the accuracy of the machine learning algorithm is lower. When the estimation error is greater than or equal to the preset value, it indicates that the error between the predicted value of exercise energy consumption and the label value of exercise energy consumption is large and does not meet the requirements. It is necessary to readjust the parameters of the machine learning algorithm and re-execute steps A3 and A4, that is, re-execute the training process of inputting the sample features into the machine learning algorithm after adjusting the parameters and the subsequent determination of the estimation error until the training cutoff condition is met.

[0137] In one possible implementation, the training cutoff condition may be that the estimation error is less than a preset value, or the number of iterative training reaches a preset number (the number of times the parameters of the machine learning algorithm are adjusted reaches a preset number). At this time, the training process can be stopped to obtain a trained exercise energy consumption monitoring model.

[0138] In one possible implementation, the k-fold cross-validation method can be used to train an exercise energy consumption monitoring model. The main principle of the k-fold cross-validation method is to randomly divide the sample data into k parts, of which k-1 parts are used as training sets, and the remaining 1 part is used as a test set. During training and testing, the training set and the test set corresponding to the training set are selected in turn. For example, the ten-fold cross-validation method is to divide the sample data into 10 sub-samples, a single sub-sample is used as the test set for verifying the test model, and the remaining 9 sub-samples are used for training. The cross-validation is repeated 10 times, and each sub-sample is verified once as a test set. In other words, the sample features can be divided into a training set and a test set, and the training set is used to train the exercise energy consumption monitoring model, and the test set is used to test the exercise energy consumption monitoring model. The results obtained from the 10 tests are then averaged as the final test result.

[0139] When testing the exercise energy consumption monitoring model, the test set is input into the exercise energy consumption monitoring model to obtain the exercise energy consumption test value. Based on the exercise energy consumption test value and the exercise energy consumption label value corresponding to the test set, the test error is determined. Optionally, the root mean square error (RMSE) of the exercise energy consumption test value and the exercise energy consumption label value can be calculated as the test error. For example, when using the ten-fold cross-validation method for training, after the exercise energy consumption test value is output in each test process, the difference between the exercise energy consumption test value and the exercise energy consumption label value can be calculated. Then the mean of the sum of squares of the ten differences is calculated, and the arithmetic square root of the mean of the sum of squares is calculated as the test error. The ten-fold cross-validation method can obtain 10 test errors, and the average of the 10 test errors is calculated as the final test result of the exercise energy consumption monitoring model.

[0140] Based on the embodiment of determining the sample features introduced in step A2 above, it can be seen that the acquired three-axis acceleration signal can be divided according to the preset time window, and the corresponding first sample feature can be determined for each preset time window. And the average value of the multiple motion energy consumption label values within the preset time window (the average motion energy consumption label value) is calculated as the sample true value corresponding to the first sample feature. Then, the first sample feature corresponding to each preset time window is used as the independent variable, and the average motion energy consumption label value corresponding to the first sample feature is input into the machine learning algorithm as the target variable to obtain the first motion energy consumption prediction value corresponding to each preset time window. Then, the machine learning algorithm is trained based on the first motion energy consumption prediction value and the average motion energy consumption label value corresponding to each preset time window, thereby obtaining a trained motion energy consumption monitoring model.

[0141] The length of the preset time windows may affect the accuracy of the training of the exercise energy consumption monitoring model. Therefore, multiple different time windows can be pre-determined. The exercise energy consumption monitoring model is trained once for each time window, and the accuracy of the exercise energy consumption monitoring model is tested. The time window corresponding to the exercise energy consumption monitoring model with the highest accuracy is determined as the preset time window. When the exercise energy consumption monitoring model is subsequently applied to monitor the exercise of preschool children, the acceleration signal can be divided according to the preset time windows.

[0142] In specific implementation, multiple different time windows are first determined. The different lengths of the multiple time windows can be divided based on experience and actual needs. For example, the length of the time window can be set to 15 seconds, 30 seconds, 60 seconds, etc. For the first time window, the acquired three-axis acceleration signal is divided according to the first time window, thereby obtaining the second acceleration signals corresponding to the multiple first time windows. The first time window represents any time window among the multiple different time windows. Based on the second acceleration signal and the personalized indicator, the second sample feature corresponding to the first time window is determined. The second sample features corresponding to the multiple first time windows and the average motion energy consumption label value corresponding to the second sample feature of each first time window are fused to determine the training set and the test set. For example, the second sample features of all first time windows and the average motion energy consumption label value corresponding to the second sample feature of each first time window can be fused to obtain a data set, and then the data set can be divided into a training set and a test set according to a preset ratio. For example, the fused data set can be divided into a training set and a test set according to an 8:2 ratio. The training set includes multiple second sample features and the average motion energy consumption label value corresponding to each second sample feature, and the test set also includes multiple second sample features and the average motion energy consumption label value corresponding to each second sample feature.

[0143] The training set is then input into the machine learning algorithm to obtain the predicted energy consumption value output by the machine learning algorithm. The machine learning algorithm is trained based on the predicted energy consumption value and the average energy consumption label value corresponding to the first time window until the training cutoff condition is met, thereby obtaining a trained energy consumption monitoring model. The process of determining the average energy consumption label value corresponding to the first time window can be found in the above embodiment and will not be further described here.

[0144] After obtaining the trained motion energy consumption monitoring model, the accuracy of the motion energy consumption monitoring model can be verified using a test set. Specifically, based on the second sample features corresponding to multiple first time windows, a test set is determined. For example, a portion of the second sample features from multiple first time windows is divided as a test set. The test set is input into the motion energy consumption monitoring model to obtain the motion energy consumption test value. Based on the motion energy consumption test value and the motion energy consumption label value corresponding to the test set, the test error is determined. The above process is performed for multiple different time windows, and the test error corresponding to each time window can be determined. Based on the test errors corresponding to multiple different time windows, the minimum test error is determined, so that the time window corresponding to the minimum test error can be determined as the preset time window. When the motion energy consumption monitoring model is subsequently used to monitor the motion energy consumption of preschool children, the acceleration signal can be divided according to the preset time window.

[0145] Among them, the method for determining the test error can be to calculate the root mean square error between the motion energy consumption test value and the motion energy consumption label value corresponding to the test set, or to calculate the mean absolute error between the motion energy consumption test value and the motion energy consumption label value corresponding to the test set, etc. The embodiment of the present application does not limit this.

[0146] After multiple training and testing processes, when the preset time window is determined to be 15 seconds in the embodiment of the present application, the accuracy of the exercise energy consumption monitoring model obtained through training is relatively high.

[0147] According to the above embodiment, the three-axis acceleration signal can include an x-axis acceleration signal, a y-axis acceleration signal, a z-axis acceleration signal, and their vector sum signal, and the statistical characteristic index of the x-axis acceleration signal, the statistical characteristic index of the y-axis acceleration signal, the statistical characteristic index of the z-axis acceleration signal, and the statistical characteristic index of the vector sum signal can be determined respectively. When determining the sample characteristics through various statistical characteristic indicators and personalized indicators, since the sample characteristics include a large number of indicators, the complexity of data processing will increase, affecting the accuracy of the training of the sports energy consumption monitoring model. Therefore, some indicators can be selected from various statistical characteristic indicators and personalized indicators to train the sports energy consumption monitoring model. The accuracy of the trained sports energy consumption monitoring model will also be different depending on the number of selected indicators. Based on this, a variety of different numbers can be pre-determined to select indicators, and sample characteristics can be determined for each number of indicators to train the sports energy consumption monitoring model, and the accuracy of the sports energy consumption monitoring model can be tested, so that the number of indicators corresponding to the sports energy consumption monitoring model with the highest accuracy is selected as the preset number.

[0148] In specific implementation, a feature selection algorithm is used to select a variety of indicators of different numbers from statistical feature indicators and personalized indicators, and determine the sample features corresponding to the multiple different numbers of indicators. For the third sample feature corresponding to the indicator of the first number, a training set and a test set are determined. Among them, the first number represents any number among a variety of different numbers. That is, for any first number, a feature selection algorithm can be used to select the indicator of the first number from statistical feature indicators and personalized indicators, and the selected indicators are fused to determine the third sample feature. The third sample feature and the exercise energy consumption label value corresponding to the third sample feature are fused, and then the fused data set is divided into a training set and a test set. The training set is used to train the machine learning algorithm to obtain an exercise energy consumption monitoring model. Among them, the specific training process is not further described.

[0149] The test set is then input into the exercise energy consumption monitoring model to obtain the exercise energy consumption test value. The test error is determined based on the exercise energy consumption test value and the exercise energy consumption label value corresponding to the test set. The minimum test error is determined based on the test errors corresponding to a plurality of different numbers of indicators. The number corresponding to the minimum test error is determined to be a preset number. When subsequently applying the exercise energy consumption monitoring model to monitor exercise energy consumption, a preset number of indicators can be selected from the statistical feature indicators and personalized indicators as the features input into the exercise energy consumption monitoring model.

[0150] The motion energy consumption label value in the embodiment of the present application includes energy consumption or metabolic equivalent, and two motion energy consumption monitoring models can be trained for energy consumption and metabolic equivalent. In order to obtain a motion energy consumption monitoring model for monitoring energy consumption, after obtaining the personalized indicators, three-axis acceleration signals and energy consumption label values of preschool children, it is possible to determine a sample feature composed of a plurality of indicators to train and obtain the motion energy consumption monitoring model. After completing the training and testing process using a plurality of indicators, the embodiment of the present application determines that when the sample feature includes 80 indicators, the accuracy of the motion energy consumption monitoring model obtained in monitoring energy consumption is higher.

[0151] Similarly, in order to obtain an exercise energy consumption monitoring model for monitoring metabolic equivalents, after obtaining personalized indicators, three-axis acceleration signals, and metabolic equivalent label values for preschool children, a sample feature consisting of multiple numbers of indicators can be determined to train the exercise energy consumption monitoring model. After completing the training and testing process using multiple numbers of indicators, the embodiment of the present application determined that when the sample feature includes 96 indicators, the obtained exercise energy consumption monitoring model has a higher accuracy in monitoring metabolic equivalents.

[0152] Table 1

[0153]

[0154] For details, see Table 1 above, which shows an example of determining sample features. The total number of samples represents the number of sample features. Whether feature selection was performed indicates whether only some of the statistical feature indicators and personalized indicators were selected to form the sample features.

[0155] According to the above embodiment, in order to enable the trained exercise energy consumption monitoring model to more accurately monitor the exercise energy consumption of preschool children, multiple machine learning algorithms can be used to train the exercise energy consumption monitoring model. For example, multiple machine learning algorithms commonly used in data modeling of this problem in this field may include an extreme random tree regression algorithm, a random forest regression algorithm, LGBM, a CatBoost algorithm, or a linear regression algorithm. By first training these multiple exercise energy consumption monitoring models and testing their accuracy, the most accurate exercise energy consumption monitoring model among these multiple exercise energy consumption monitoring models is selected.

[0156] During specific implementation, the sample features can be divided into a training set and a test set. For each exercise energy consumption monitoring model, the training set is used to train the exercise energy consumption monitoring model to obtain a trained exercise energy consumption monitoring model. The specific training process can be found in the above embodiment and will not be repeated here. After obtaining the trained exercise energy consumption monitoring model, the test set is input into the exercise energy consumption monitoring model corresponding to the exercise energy consumption monitoring model to obtain the exercise energy consumption test value. Based on the exercise energy consumption test value and the exercise energy consumption label value corresponding to the test set, the test error is determined. Since each corresponding machine learning model obtained by training the machine learning algorithm corresponds to a test error, as the accuracy test result of the exercise energy consumption monitoring model, the minimum test error can be determined based on the test errors corresponding to multiple exercise energy consumption monitoring models, and the exercise energy consumption monitoring model corresponding to the minimum test error is determined as the preferred exercise energy consumption monitoring model. That is, the exercise energy consumption monitoring model corresponding to the minimum test error is used as the exercise energy consumption monitoring model for subsequent monitoring of preschool children.

[0157] Among them, the method for determining the test error can be to calculate the root mean square error between the exercise energy consumption test value and the exercise energy consumption label value, or to calculate the mean absolute error between the exercise energy consumption test value and the exercise energy consumption label value, etc., and the embodiments of the present application do not limit this.

[0158] In the examples of this application, five trained exercise energy consumption monitoring models were tested. It was found that among the five exercise energy consumption monitoring models, the exercise energy consumption monitoring model trained with the extreme randomized tree regression algorithm had the smallest test error and the highest accuracy. Therefore, when subsequently monitoring exercise energy consumption in preschool children, the exercise energy consumption monitoring model trained with the extreme randomized tree regression algorithm can be selected for monitoring.

[0159] In the embodiment of the present application, the Mets of the preschool children to be monitored estimated by the trained exercise energy consumption monitoring model can also be compared with the Mets of the preschool children to be monitored obtained in advance to test the accuracy of the exercise energy consumption monitoring model. Figure 2The figure shows a schematic diagram of the test results of an exercise energy consumption monitoring model provided by an embodiment of the present application. The embodiment of the present application can obtain five exercise energy consumption monitoring models based on five algorithm training, wherein et represents the extreme random tree regression model, catboost represents the CatBoost regression model, lightgbm represents the lightweight gradient boosting machine model, rf represents the random forest regression model, and lr represents the linear regression model. The Mets of the preschool children to be monitored estimated by the five exercise energy consumption monitoring models are then compared with the Mets of the preschool children to be monitored obtained in advance. The five comparative test results are shown as (a), (b), (c), (d), and (e), respectively. According to Figure 2 It can be seen that the Mets estimated by the extreme random tree regression model is closest to the measured Mets.

[0160] After describing the process of training the above-mentioned exercise energy consumption monitoring model, the moderate activity intensity assessment model can be used to monitor the exercise energy consumption of preschool children, wherein the moderate activity intensity assessment model includes the exercise energy consumption monitoring model. According to the above embodiment, it can be seen that the exercise energy consumption monitoring model can be trained using the extreme randomized tree regression algorithm.

[0161] In one possible implementation, the moderate activity intensity assessment model may include an exercise energy consumption monitoring model and a moderate activity intensity monitoring and determination unit.

[0162] In one possible implementation, after obtaining the personalized index and acceleration signal of the preschool child to be monitored, the acceleration signal can be divided according to preset time windows to obtain first acceleration signals corresponding to multiple preset time windows. For each preset time window, the input features corresponding to the preset time window are determined based on the first acceleration signal and personalized index of the preset time window. The input features corresponding to each preset time window are then input into the exercise energy consumption monitoring model to obtain exercise energy consumption data corresponding to each preset time window.

[0163] It should be noted that the method for determining the preset time window can refer to the preset time window determined in the above training process. For example, the length of the preset time window can be set to 15 seconds, and the specific process will not be described here.

[0164] In one possible implementation, when the exercise energy consumption data includes metabolic equivalents, the moderate activity intensity monitoring and determination unit can obtain the metabolic equivalents corresponding to each preset time window, compare the metabolic equivalents with the moderate activity intensity threshold, and determine the target preset time window corresponding to the metabolic equivalent that meets the moderate activity intensity threshold. Then, all target preset time windows that meet the conditions are counted, so that the average daily moderate-to-strong activity index of the preschool children to be monitored can be determined. In other words, the length of time that the preschool children to be monitored are at moderate activity intensity.

[0165] In one possible implementation, the acceleration signal may include an x-axis acceleration signal, a y-axis acceleration signal, and a z-axis acceleration signal. When determining the input features, a one-dimensional vector sum signal of the x-axis acceleration signal, the y-axis acceleration signal, and the z-axis acceleration signal may be first determined. Statistical feature indicators of the x-axis acceleration signal, the y-axis acceleration signal, the z-axis acceleration signal, and the one-dimensional vector sum signal may then be determined.

[0166] Optionally, after dividing the acceleration signal using preset time windows, for each preset time window, statistical characteristic indicators of the x-axis acceleration signal, the y-axis acceleration signal, the z-axis acceleration signal, and the one-dimensional vector sum signal corresponding to the preset time window can be calculated based on multiple x-axis acceleration signals, multiple y-axis acceleration signals, multiple z-axis acceleration signals, and multiple one-dimensional vector sum signals within the preset time window. Input features are then determined based on the statistical characteristic indicators and the personalized indicators. For example, the statistical characteristic indicators and the personalized indicators can be fused to determine the input features.

[0167] When the sample features include a large number of indicators, the complexity of data processing will increase. In one possible implementation method, a feature selection algorithm can be used to select a preset number of indicators from statistical feature indicators and personalized indicators, and then the input features are determined based on the selected preset number of indicators.

[0168] Currently, based on the premise that physical activity intensity levels are determined based on METs, the energy expenditure range for moderate-intensity activity is generally considered to be 3-6 METs. However, data collection and testing within this range is only applicable to adolescents and adults. Therefore, a random sample of 20 preschoolers aged 3 to 6 years was selected from a group of preschoolers participating in the training of an exercise energy expenditure monitoring model. The feasibility of the method was verified on these 20 children. When the more accurate moderate activity intensity threshold ranges for Chinese preschoolers aged 3, 4, 5, and 6 years were used to determine the daily physical activity intensity of preschoolers, rather than the currently proposed moderate activity intensity threshold ranges for Chinese children and adolescents, the accuracy of the assessment of the daily moderate activity intensity and the corresponding moderate activity duration of these preschoolers was improved. In particular, the error in calculating the cumulative duration of daily moderate activity for Chinese preschoolers using the MET threshold ranges was reduced, and the error caused by directly using the MET threshold ranges for moderate activity intensity used by Chinese children, adolescents, and adults was avoided.

[0169] Using an accelerometer-based exercise energy consumption monitoring method, participants were monitored and their METs were estimated. Then, combined with the moderate activity intensity threshold range, automated monitoring and assessment were used to determine the average daily moderate-to-vigorous activity index. Finally, based on the recommended exercise schedule for preschoolers (ages 3-6) in China, which our team spearheaded, which states that "preschoolers should accumulate at least 60 minutes of moderate-intensity or higher-intensity activity within 24 hours daily," we determined whether the moderate-intensity activity duration met the requirement.

[0170] It should be noted that the method for determining the preset number can refer to the preset number determined in the above-mentioned training process, and when the monitored exercise energy consumption data is different, the number of indicators in the input features corresponding to different exercise energy consumption monitoring models is also different. For example, when it is necessary to obtain the energy consumption of a preschool child to be monitored, 80 indicators can be selected from the statistical characteristic indicators and personalized indicators to determine the input features, and input them into the exercise energy consumption monitoring model for monitoring energy consumption, and output the energy consumption of the preschool child to be monitored. When it is necessary to obtain the metabolic equivalent of a preschool child to be monitored, 96 indicators can be selected from the statistical characteristic indicators and personalized indicators to determine the input features, and input them into the exercise energy consumption monitoring model for monitoring metabolic equivalents, and output the metabolic equivalent of the preschool child to be monitored.

[0171] Through the method provided in the embodiments of the present application, a pre-trained moderate activity intensity assessment model can be used to monitor the exercise energy consumption of preschool children, determine the moderate to strong activity indication information used for preschool children's daily assessment activities, and improve the accuracy of exercise energy consumption monitoring.

[0172] Based on the above method embodiment, the present application embodiment also provides a moderate activity intensity monitoring system for preschool children. Figure 3 , which is a schematic diagram of a moderate activity intensity monitoring system for preschool children provided in an embodiment of the present application.

[0173] The moderate activity intensity monitoring system 400 for preschool children includes: a data preprocessing module 401, a data modeling module 402, and an exercise energy consumption monitoring module 403;

[0174] The data preprocessing module 401 is used to obtain the personalized indicators of preschool children and the acceleration signals under the monitoring state, and process the personalized indicators and acceleration signals to determine the training set and the test set;

[0175] It is also used to obtain personalized indicators of the preschool children to be monitored and acceleration signals in the monitored state, and process the personalized indicators and acceleration signals to determine input features.

[0176] The data modeling module 402 is used to train multiple energy consumption monitoring models using the training set and test the trained models using the test set. The optimal model is selected based on the test results of the multiple models. A moderate activity intensity assessment model is then determined based on the energy consumption monitoring models.

[0177] The exercise energy consumption monitoring module 403 is used to monitor the exercise energy consumption of the preschool children to be monitored using a moderate activity intensity assessment model and output moderate-to-strong activity indication information.

[0178] Optionally, the exercise energy consumption monitoring module 403 can also visualize the exercise energy consumption data or moderate-to-vigorous activity indication information of the preschool child to be monitored, for example, by organizing the data into a table, a bar chart, or other format for display.

[0179] Among them, the specific implementation principles of the data preprocessing module 401, the data modeling module 402, and the sports energy consumption monitoring module 403 can be found in the above method embodiments and will not be repeated here.

[0180] Based on the above method embodiment and system embodiment, the present application embodiment also provides a device for monitoring the moderate activity intensity of preschool children. Figure 4 , which is a schematic diagram of a moderate activity intensity monitoring device for preschool children provided in an embodiment of the present application.

[0181] The apparatus 500 comprises:

[0182] An acquisition unit 501 is configured to acquire personalized indicators of the preschool child to be monitored and an acceleration signal in the state to be monitored;

[0183] a monitoring and determination unit 502, configured to input the personalized index and the acceleration signal into a moderate activity intensity assessment model, and output moderate-to-strong activity indication information based on a moderate activity intensity threshold, wherein the moderate activity intensity threshold for preschool children ranges from 3.2 to 5.3 meters;

[0184] Among them, the moderate activity intensity assessment model is constructed based on the fusion of personalized indicators of multiple preschool children, acceleration signals under different states, and motion energy consumption label values corresponding to the acceleration signals.

[0185] In one possible implementation, the moderate activity intensity threshold for preschool children aged 4-6 years old ranges from 3.4-5.1 Mets.

[0186] In one possible implementation, the monitoring and determination unit 502 is specifically used to divide the acceleration signal according to preset time windows to obtain first acceleration signals corresponding to multiple preset time windows; based on the first acceleration signal of each preset time window and the personalized index, determine the input features corresponding to each preset time window; input the input features into the moderate activity intensity assessment model, and output the moderate-to-strong activity indication information based on the moderate activity intensity threshold.

[0187] In a possible implementation, the moderate to strong activity indication information includes a daily average moderate to strong activity index.

[0188] In one possible implementation, the monitoring and determination unit 502 is specifically used to input the input features corresponding to each preset time window into the moderate activity intensity assessment model to obtain the motion energy consumption data corresponding to each preset time window; compare the motion energy consumption data with the moderate activity intensity threshold to determine the target preset time window corresponding to the motion energy consumption data that meets the moderate activity intensity threshold; and count the target preset time window to determine the average daily moderate-to-strong activity index.

[0189] In one possible implementation, the first acceleration signal includes an x-axis acceleration signal, a y-axis acceleration signal, and a z-axis acceleration signal, and the monitoring and judgment unit 502 is specifically used to determine the one-dimensional vector sum signal of the x-axis acceleration signal, the y-axis acceleration signal, and the z-axis acceleration signal; respectively determine the statistical feature indicators of the x-axis acceleration signal, the y-axis acceleration signal, the z-axis acceleration signal, and the one-dimensional vector sum signal; and determine the input feature corresponding to each preset time window based on the statistical feature indicator corresponding to each preset time window and the personalized indicator.

[0190] In a possible implementation, the monitoring and determination unit 502 is specifically configured to utilize a feature selection algorithm to select a preset number of indicators from the statistical feature indicators and the personalized indicators; and determine the input feature based on the preset number of indicators.

[0191] In a possible implementation, the acceleration signal is measured by an accelerometer worn by the preschool child to be monitored, where the accelerometer is worn on the right waist.

[0192] In a possible implementation, the accelerometer is worn for at least one day.

[0193] In one possible implementation, the moderate activity intensity threshold range for 3-year-old preschool children is 3.2-4.4 Mets.

[0194] In one possible implementation, the moderate activity intensity threshold range for 4-year-old preschool children is 3.4-4.6 Mets.

[0195] In one possible implementation, the moderate activity intensity threshold for 5-year-old preschoolers ranges from 3.7 to 4.8 Mets.

[0196] In one possible implementation, the moderate activity intensity threshold for 6-year-old preschoolers ranges from 3.8 to 5.1 Mets.

[0197] In one possible implementation, the age of a preschool child is calculated based on the age corresponding to the actual date of birth.

[0198] In one possible implementation, the acquisition unit 501 is specifically used to obtain the original personalized indicators of the preschool child to be monitored and the original acceleration signal in the monitored state; preprocess the original personalized indicators and the original acceleration signal to obtain the personalized indicators and the acceleration signal; wherein the preprocessing process includes one or more of the following: interpolating missing values; or, performing one-hot encoding processing on categorical data; or, normalizing continuous data; or, normalizing continuous data and converting the normalized data into data with an approximate normal distribution.

[0199] In a possible implementation, the personalized indicator includes at least one of gender, age, height, weight, and body mass index.

[0200] Based on the above method embodiment and device embodiment, the present application also provides an electronic device, which will be described below with reference to the accompanying drawings.

[0201] See also Figure 5 , Figure 5 A schematic diagram of an electronic device provided in an embodiment of the present application.

[0202] The device 600 includes: a memory 601 and a processor 602;

[0203] The memory 601 is used to store relevant program codes;

[0204] The processor 602 is configured to call the program code to execute the method for monitoring moderate activity intensity of preschool children described in the above method embodiment.

[0205] In addition, an embodiment of the present application also provides a computer-readable storage medium, which is used to store a computer program, and the computer program is used to execute the method for monitoring moderate activity intensity of preschool children described in the above method embodiment.

[0206] An embodiment of the present application also provides a computer program product, which includes a computer program / instructions. When the computer program / instructions are executed by a processor, the method for monitoring the moderate activity intensity of preschool children described in the above method embodiment is implemented.

[0207] It should be noted that the computer-readable medium mentioned above in this application may be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or component, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0208] The computer program product may be written in any combination of one or more programming languages to implement the program code for performing the operations of the embodiments of the present application, including object-oriented programming languages such as Java, C++, and conventional procedural programming languages such as "C" or similar programming languages. The program code may be executed entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0209] It should be noted that the various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other. In particular, for system or device embodiments, since they are basically similar to method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment. The device embodiments described above are merely illustrative, wherein the units or modules described as separate components may or may not be physically separated, and the components shown as units or modules may or may not be physical modules, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the units or modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. Ordinary technicians in this field can understand and implement it without expending creative work.

[0210] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions and operations that may be implemented according to the methods, devices and equipment of various embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a module, program segment, or a part of code, and the module, program segment, or a part of code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of the boxes in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system that performs the specified function or operation, or can be implemented by a combination of dedicated hardware and computer instructions.

[0211] It should be understood that in this application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships can exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following items" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or plural.

[0212] It should also be noted that, in this application, relational terms such as first and second, etc. are merely used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article, or device comprising the element.

[0213] The steps of the methods or algorithms described in conjunction with the embodiments disclosed in this application can be implemented directly using hardware, a software module executed by a processor, or a combination of the two. The software module can be placed in random access memory (RAM), internal memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.

[0214] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application is not limited to the embodiments shown herein, but is intended to encompass the broadest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for monitoring the intensity of moderate activities of preschool children, characterized in that: The method comprises: Obtaining personalized indicators of the preschool child to be monitored and acceleration signals in the state to be monitored; The personalized index and the acceleration signal are input into a moderate activity intensity assessment model. The output result is output by the determination system as moderate-to-strong activity indication information based on a moderate activity intensity threshold, wherein the moderate activity intensity threshold for preschool children aged 3-6 years old ranges from 3.2 to 5.3 meters. The moderate activity intensity assessment model is constructed by fusing the personalized indexes of multiple preschool children, acceleration signals in different states, and motion energy consumption label values corresponding to the acceleration signals. The step of inputting the personalized index and the acceleration signal into a moderate activity intensity assessment model, and outputting moderate-to-strong activity indication information by a determination system based on a moderate activity intensity threshold, includes: Dividing the acceleration signal according to preset time windows to obtain first acceleration signals corresponding to a plurality of preset time windows respectively; Determining an input feature corresponding to each preset time window based on the first acceleration signal of each preset time window and the personalized indicator; The input feature is input into the moderate activity intensity assessment model, and according to the output result of the assessment model, the determination system outputs the moderate-strong activity indication information based on the moderate activity intensity threshold.

2. The method according to claim 1, characterized in that The moderate-to-vigorous activity indication information includes the average daily moderate-to-vigorous activity index of preschool children.

3. The method according to claim 2, characterized in that The inputting of the input features into the moderate activity intensity assessment model, and the outputting of the moderate-to-strong activity indication information by the determination system based on the moderate activity intensity threshold, includes: Inputting the input features corresponding to each preset time window into the moderate activity intensity assessment model to obtain the exercise energy consumption data corresponding to each preset time window; The determination system compares the exercise energy consumption data with the moderate activity intensity threshold, and determines a target preset time window corresponding to the exercise energy consumption data that meets the moderate activity intensity threshold; The number of the target preset time windows is counted, and the daily average moderate to strong activity index is determined in combination with the length of the time windows.

4. The method according to claim 2, characterized in that The first acceleration signal includes an x-axis acceleration signal, a y-axis acceleration signal, and a z-axis acceleration signal. Determining the input feature corresponding to each preset time window based on the first acceleration signal of each preset time window and the personalized indicator includes: Determining a one-dimensional vector sum signal of the x-axis acceleration signal, the y-axis acceleration signal, and the z-axis acceleration signal; respectively determining statistical characteristic indicators of the x-axis acceleration signal, the y-axis acceleration signal, the z-axis acceleration signal, and the one-dimensional vector sum signal; Based on the statistical feature index corresponding to each preset time window and the personalized index, the input feature corresponding to each preset time window is determined.

5. The method according to claim 4, characterized in that The determining the input feature based on the statistical feature indicator and the personalized indicator includes: Using a feature selection algorithm, selecting a preset number of indicators from the statistical feature indicators and the personalized indicators; The input features are determined based on the preset number of indicators.

6. The method according to claim 1, characterized in that The acceleration signal is measured by an accelerometer worn by the preschool child to be monitored, and the accelerometer is worn on the right waist.

7. The method according to claim 6, characterized in that The accelerometer is worn for at least one day.

8. The method according to claim 1, characterized in that The moderate activity intensity threshold for 3-year-old preschoolers ranges from 3.2 to 4.4 Mets.

9. The method according to claim 1, characterized in that The moderate activity intensity threshold for 4-year-old preschoolers ranges from 3.4 to 4.6 Mets.

10. The method according to claim 1, characterized in that The corresponding moderate activity intensity threshold for 5-year-old preschoolers ranges from 3.7 to 4.8 Mets.

11. The method according to claim 1, characterized in that The moderate activity intensity threshold for 6-year-old preschoolers ranges from 3.8 to 5.1 Mets.

12. The method according to claim 1, characterized in that The age of preschool children is calculated based on the age corresponding to their actual date of birth.

13. The method according to any one of claims 1 to 12, characterized in that The obtaining of the personalized index of the preschool child to be monitored and the acceleration signal in the state to be monitored includes: Acquiring original personalized indicators of the preschool child to be monitored and original acceleration signals in the state to be monitored; Preprocessing the original personalized index and the original acceleration signal to obtain the personalized index and the acceleration signal; The pretreatment process includes one or more of the following: impute missing values; or, One-hot encoding of categorical data; or, Normalize continuous data; or, Normalize the continuous data and convert the normalized data into data with an approximate normal distribution.

14. The method according to any one of claims 1 to 12, characterized in that The personalized indicators include: at least one of gender, age, height, weight and body mass index.

15. A device for monitoring the intensity of moderate activity for preschool children, characterized in that: The device comprises: An acquisition unit, used to acquire personalized indicators of the preschool child to be monitored and an acceleration signal in the state to be monitored; a monitoring and determination unit, configured to input the personalized index and the acceleration signal into a moderate activity intensity assessment model; wherein the determination system therein outputs moderate-to-strong activity indication information based on a moderate activity intensity threshold, wherein the moderate activity intensity threshold for preschool children aged 3-6 years ranges from 3.2 to 5.3 meters; The moderate activity intensity assessment model is constructed based on the integration of personalized indicators of multiple preschool children, acceleration signals in different states, and motion energy consumption label values corresponding to the acceleration signals. The step of inputting the personalized index and the acceleration signal into a moderate activity intensity assessment model, and outputting moderate-to-strong activity indication information by a determination system based on a moderate activity intensity threshold, includes: Dividing the acceleration signal according to preset time windows to obtain first acceleration signals corresponding to a plurality of preset time windows respectively; Determining an input feature corresponding to each preset time window based on the first acceleration signal of each preset time window and the personalized indicator; The input feature is input into the moderate activity intensity assessment model, and according to the output result of the assessment model, the determination system outputs the moderate-strong activity indication information based on the moderate activity intensity threshold.

16. An electronic device, characterized in that: The device includes: a memory and a processor; The memory is used to store relevant program codes; The processor is used to call the program code to execute the method for monitoring moderate activity intensity for preschool children according to any one of claims 1 to 14.

17. A computer-readable storage medium, characterized in that The computer-readable storage medium is used to store a computer program, and the computer program is used to execute the method for monitoring moderate activity intensity for preschool children according to any one of claims 1 to 14.

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