A personalized activity monitoring and full-process management system for children's health care

Abstract

The present invention discloses a personalized activity monitoring and full-process management system for child health care, which relates to the field of Internet technology. When the system is in operation, it can reflect the child's activity status information in real time by obtaining the child's activity status assessment index AS, solving the problem of delayed and highly subjective evaluation based on questionnaires and interviews. The child's activity status assessment index AS is compared with the health assessment status threshold T through the detection and triggering module to generate a grade abnormality standard AB, providing parents with timely and personalized adjustment guidance and avoiding adverse consequences caused by problems not being detected in time. In addition, the improvement of the child's activity status is evaluated within a fixed period and a new activity status assessment index ASN is generated, and the intervention suggestion generation plan is continuously optimized. It can not only achieve accurate evaluation of multi-dimensional children's activities and psychological states, but also generate intervention suggestions in real time and continuously optimize them.

Description

Technical Field

[0001] The present invention relates to the field of Internet technology, and in particular to a personalized activity monitoring and full-process management system for children's health care. Background Art

[0002] Child health is a critical and highly regarded branch of modern medicine and public health, encompassing comprehensive monitoring and intervention for children's physical health, psychological development, and behavioral patterns. In recent years, with increasing societal attention to children's mental health, child activity monitoring has evolved into a multidimensional assessment system encompassing physical activity, emotional behavior, and psychological state. Management platforms that integrate wearable devices, biosensors, and intelligent algorithms are becoming increasingly mainstream, capable of recording children's activity data in real time.

[0003] Although some technologies and systems for monitoring children's activities and mental states have been put into use, most of them have the problems of single functions and insufficient data integration. For example, many devices can only record single indicators such as step count or heart rate, and lack a multi-dimensional assessment of children's mental state. In addition, traditional mental state assessments often rely on questionnaires or interviews, which take a long time, rely on the initiative of parents, and are easily affected by subjective factors, making real-time monitoring impossible. For children, this method often cannot capture the dynamic characteristics of psychological changes in a timely manner, thus missing the best time to intervene. Especially in complex emotional scenarios, such as emotional fluctuations caused by learning pressure, family conflicts or social problems, it is often impossible to accurately predict the child's mental state through a single activity data or emotional marker, and thus it is impossible to provide effective intervention suggestions. Summary of the Invention

[0004] In view of the shortcomings of the existing technology, the present invention provides a personalized activity monitoring and full-process management system for children's health care, which solves the problems mentioned in the background technology.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: a personalized activity monitoring and full-process management system for child health care, including a data monitoring module, a data processing and extraction module, an activity evaluation module, a detection and triggering module, a suggestion generation module and an iterative optimization module;

[0006] The data monitoring module collects real-time data of children through wearable devices and generates a children's activity feature vector set FV;

[0007] The data processing and extraction module performs preprocessing and feature extraction on the children's activity feature vector set FV to generate a standard feature vector set SFV;

[0008] The activity assessment module evaluates the child's activity status information based on the standard feature vector set SFV, including activity intensity, frequency fluctuation, and activity duration, and establishes an activity status assessment algorithm formula for evaluation to obtain the child's activity status assessment index AS;

[0009] The detection and triggering module compares the preset health assessment state threshold T with the child activity state assessment index AS to obtain the child assessment state result, and generates the grade abnormality standard AB according to the child assessment state result;

[0010] The suggestion generation module generates a child intervention suggestion generation plan based on the abnormality level criteria AB, the activity status information, and the inactivity status information, and provides prompts and notifications based on the content of the child intervention suggestion generation plan;

[0011] The iterative optimization module performs fixed-period iterative evaluation of the child's activity status according to the child intervention suggestion generation plan, generates a new activity status evaluation index ASN, and compares it with the child activity status evaluation index AS to determine the effectiveness of the child intervention suggestion generation plan.

[0012] Preferably, the data monitoring module includes a data acquisition unit;

[0013] The data acquisition unit collects real-time data on the child through a wearable device, including using a three-axis accelerometer to record the acceleration change value per unit time to obtain the activity intensity AIt at time t; using a microphone to collect the child's voice signal SF; using a camera to obtain a sequence of child facial video frames to obtain the smile frequency EF within a fixed period; using a heart rate sensor and an accelerometer to collect the child's sleep information to obtain the resting heart rate HR and the number of turning over M, and generating a child activity feature vector set FV = {AIt, SF, EF, HR, M}.

[0014] Preferably, the data processing and extraction module includes a preprocessing unit and a feature extraction unit;

[0015] The preprocessing unit preprocesses the children's activity feature vector set FV, including data cleaning preprocessing, data denoising preprocessing and data normalization preprocessing, to obtain a preprocessed feature vector set FVp;

[0016] Among them, data cleaning preprocessing includes using linear interpolation method to process missing values ​​of feature vector set FV, using box plot method to process outliers of feature vector set FV, and then checking and processing outliers and missing values ​​in feature vector set FV;

[0017] Data denoising preprocessing includes using a sliding average filter method to perform time series data smoothing on the feature vector set FV after data cleaning preprocessing, and using a low-pass filter to filter high-frequency noise on the feature vector set FV after data cleaning preprocessing, thereby checking and eliminating noise caused by equipment and environmental interference in the feature vector set FV after data cleaning preprocessing;

[0018] The data normalization preprocessing includes using the minimum-maximum normalization method to normalize the feature vector set FV after data denoising preprocessing, and then mapping the feature vector set FV after data denoising preprocessing to a data range of 0 to 1 to eliminate dimensional differences. The data normalization preprocessing feature vector set FV = {AItn, SFn, EFn, HRn, Mn}.

[0019] Preferably, the feature extraction unit performs feature extraction on the preprocessed feature vector set FVp to obtain activity intensity feature information, sound feature information, expression feature information and sleep feature information;

[0020] Among them, the activity intensity feature information includes the average activity intensity AIm and the activity fluctuation amplitude AIv; the sound feature information includes the sound signal energy average SFe and the sound signal peak frequency SFf; the expression feature information includes the smiling behavior proportion EFs; the sleep feature information includes the sleep score SDs, and the standard feature vector set SFV = {AIm, AIv, SFe, SFf, EFs, SDs} is generated.

[0021] Preferably, the activity evaluation module includes a state evaluation unit and an evaluation index unit;

[0022] The state evaluation unit evaluates the activity state information and inactivity state information of the child based on the standard feature vector set SFV, including activity intensity, frequency fluctuation and activity continuity, and obtains activity intensity SAI, frequency fluctuation SFV and activity continuity SAC marking information;

[0023] The activity intensity is fitted by the average activity intensity AIm, the average sound signal energy SFe and the smiling behavior ratio EFs, and marked as activity intensity SAI, which reflects the activity intensity of children;

[0024] The frequency fluctuation is fitted by the average activity intensity AIm, the peak frequency SFf of the sound signal and the proportion of smiling behavior EFs, and is marked as frequency fluctuation SFV, which reflects the fluctuation of children's activity frequency;

[0025] The activity duration is fitted by the sleep score SDs and the activity fluctuation amplitude AIv, and is marked as activity duration SAC.

[0026] Preferably, the evaluation index unit evaluates the activity intensity SAI, frequency fluctuation SFV, and activity duration SAC based on the acquired activity intensity SAI, frequency fluctuation SFV, and activity duration SAC marking information by establishing an activity state evaluation algorithm formula to reflect the comprehensive performance of activity intensity, frequency fluctuation, and duration in children's activities, and obtain the child activity state evaluation index AS;

[0027] The children's activity status assessment index AS is Obtain the calculation formula, where e represents the exponential function.

[0028] Preferably, the detection and triggering module includes an anomaly detection unit and an intervention suggestion generation unit;

[0029] The abnormality detection unit compares the preset health assessment state threshold T with the child activity state assessment index AS to obtain the child assessment state result;

[0030] The child assessment status results are obtained by comparing:

[0031] When the child's activity status assessment index AS is less than the health assessment status threshold T, the child's assessment status result is obtained as a qualified state, marked as Status = 0, indicating that the child's activity status is normal and no special intervention is required. The child should continue to maintain his activity habits and routine.

[0032] When the child's activity status assessment index AS ≥ the health assessment status threshold T, the child's assessment status result is obtained as unqualified and marked as Status = 1, indicating that the child's activity status is unqualified and that the activity arrangement and content need to be adjusted. Activity habits and routines should also be adjusted based on the activity intensity SAI, frequency fluctuation SFV, and activity duration SAC marking information;

[0033] When the child's activity status assessment index AS is ≥ twice the health assessment status threshold T, the child's assessment status result is abnormal and marked as Status = 2, indicating that the child's activity is abnormal. Parents or managers are asked to adjust the activity content and the child's rest time. If the activity intensity is abnormal, an abnormality report is generated based on the activity intensity SAI, frequency fluctuation SFV, and activity duration SAC marking information.

[0034] Preferably, the intervention suggestion generating unit automatically generates a grade abnormality standard AB according to the child assessment status result, and generates a child intervention suggestion generating plan according to the grade abnormality standard AB, adjusts the frequency of notification and prompt of the child assessment status result, and adjusts the acquisition frequency of the standard feature vector set SFV and the child activity status assessment index AS;

[0035] The child intervention suggestion generation plan is generated in the following way;

[0036] When the child assessment status result Status = 0, the abnormal level standard AB = 0 is generated, and the sending frequency of notifications and prompts, as well as the acquisition frequency of the standard feature vector set SFV and the child activity status assessment index AS are not adjusted;

[0037] When the child assessment status result status is Status = 1, the abnormal level standard AB = 2 is generated, the sending frequency of notifications and reminders is adjusted to 2 times the initial frequency, and the standard feature vector set SFV and the child activity status assessment index AS are adjusted to 2 + 1 times the initial acquisition frequency;

[0038] When the child assessment status result status Status = 2, the level abnormality standard AB = 4 is generated, the sending frequency of notifications and prompts is adjusted to 4 times the initial frequency, and the standard feature vector set SFV and the child activity status assessment index AS are adjusted to 4+2 times the initial acquisition frequency.

[0039] Preferably, the iterative optimization module includes an activity status updating unit and an intervention effect evaluation unit;

[0040] The activity status update unit performs a fixed-period iterative evaluation of the child's activity status according to the child intervention suggestion generation plan, including a fixed-period iterative evaluation of the child's activity status at a frequency of 2+1 times and a fixed-period iterative evaluation of the child's activity status at a frequency of 4+2 times, generates a new activity status evaluation index ASN, and reflects the real-time activity status after the child intervention suggestion generation plan is executed.

[0041] Preferably, the intervention effect evaluation unit compares the activity state evaluation index ASN with the child activity state evaluation index AS to obtain the difference △AS, and judges the effectiveness of the child intervention suggestion generation plan based on the difference △AS;

[0042] The effectiveness of the child intervention recommendation generation plan is judged by the following methods:

[0043] When the difference △AS ≥ 0, the child intervention suggestion generation plan is invalid, triggering the iterative optimization mechanism to adjust the content of the child intervention suggestion generation plan;

[0044] When the difference △AS is less than 0, the generation plan for obtaining child intervention suggestions is effective and the iterative optimization mechanism is not triggered.

[0045] The present invention provides a personalized activity monitoring and full-process management system for children's health care, which has the following beneficial effects:

[0046] (1) When the system is running, by obtaining the child activity status assessment index AS, it can reflect the child's activity status information in real time, solving the problem of delayed and highly subjective assessment methods such as questionnaires and interviews. The child activity status assessment index AS is compared with the health assessment status threshold T through the detection and trigger module to generate the grade abnormality standard AB, providing parents with timely and personalized adjustment guidance, avoiding the adverse consequences caused by the problem not being detected in time. In addition, the improvement of the child's activity status is evaluated within a fixed period and a new activity status assessment index ASN is generated, and the intervention suggestion generation plan is continuously optimized to ensure the effectiveness of the intervention measures. It can not only achieve accurate assessment of children's activities and psychological status in multiple dimensions, but also generate intervention suggestions in real time and continuously optimize them. These functions together make up for the shortcomings of the existing system in terms of real-time, comprehensiveness and personalization.

[0047] (2) By combining multi-dimensional information such as the average activity intensity AIm, the average sound signal energy SFe, and the proportion of smiling behaviors EFs, the child's activity status is dynamically fitted to reflect the overall intensity, rhythm fluctuation, and duration of the activity. By comparing the child's activity status assessment index AS with the health assessment status threshold T through the detection and trigger module, the system obtains the child's assessment status result Status in real time and generates corresponding prompts and suggestions. When the activity intensity SAI is low, the system can remind you to increase outdoor activity time; when the activity duration SAC is slightly insufficient, it can remind you to pay attention to the activity time arrangement; for abnormal conditions, the system not only generates a detailed abnormality report, but also can combine the performance of activity frequency fluctuation SFV to remind you to avoid fatigue accumulation. This comprehensive and dynamic activity status assessment mechanism can not only quickly discover children's potential problems in activity intensity, frequency fluctuation, and duration, but also provide accurate intervention suggestions based on real-time assessment results.

[0048] (3) Automatically generate the abnormal level standard AB based on the child's assessment status result Status, adjust the sending frequency of notifications and prompts, and the acquisition frequency of the standard feature vector set SFV and the child activity status assessment index AS, so as to dynamically match the actual needs of the child's health status. And use this to evaluate the effectiveness of the intervention suggestion generation plan. If the intervention is ineffective, the system will trigger the iterative optimization mechanism and automatically adjust the intervention content to ensure that the intervention measures can dynamically adapt to the actual status needs of the child. This optimization mechanism based on real-time feedback not only improves the scientificity and accuracy of the intervention plan, but also effectively reduces unnecessary intervention behaviors, while avoiding the accumulation of health risks, and provides a highly adaptable dynamic optimization capability for children's activity health management. Compared with static intervention methods, this system has significant advantages in real-time response and continuous optimization, making children's health management more efficient and intelligent. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 This is a schematic diagram of a block diagram of a personalized activity monitoring and full-process management system for children's health care according to the present invention. DETAILED DESCRIPTION

[0050] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0051] Example 1

[0052] This invention provides a personalized activity monitoring and full-process management system for children's health care. Figure 1 , including data monitoring module, data processing and extraction module, activity evaluation module, detection and triggering module, suggestion generation module and iterative optimization module;

[0053] The data monitoring module collects real-time data of children through wearable devices and generates a children's activity feature vector set FV;

[0054] The data processing and extraction module performs preprocessing and feature extraction on the children's activity feature vector set FV to generate a standard feature vector set SFV;

[0055] The activity assessment module evaluates the child's activity status information based on the standard feature vector set SFV, including activity intensity, frequency fluctuation, and activity duration, and establishes an activity status assessment algorithm formula for evaluation to obtain the child's activity status assessment index AS;

[0056] The detection and triggering module compares the preset health assessment state threshold T with the child activity state assessment index AS to obtain the child assessment state result, and generates the grade abnormality standard AB according to the child assessment state result;

[0057] The suggestion generation module generates a child intervention suggestion generation plan based on the abnormality level criteria AB, the activity status information, and the inactivity status information, and provides prompts and notifications based on the content of the child intervention suggestion generation plan;

[0058] The iterative optimization module performs fixed-period iterative evaluation of the child's activity status according to the child intervention suggestion generation plan, generates a new activity status evaluation index ASN, and compares it with the child activity status evaluation index AS to determine the effectiveness of the child intervention suggestion generation plan.

[0059] In this embodiment, by obtaining a child activity status assessment index (AS), it is possible to reflect a child's activity status in real time, addressing the lag and subjectivity of assessment methods such as questionnaires and interviews. The detection and triggering module compares the child activity status assessment index (AS) with the health assessment threshold (T) to generate a grade abnormality standard (AB). The recommendation generation module combines the grade abnormality standard (AB) with other relevant information to generate a specific child intervention recommendation plan. This provides parents with timely and personalized adjustment guidance, avoiding the adverse consequences caused by undetected issues. Furthermore, the iterative optimization module evaluates improvements in the child's activity status over a fixed period and generates a new activity status assessment index (ASN). This continuously optimizes the intervention recommendation plan to ensure the effectiveness of the intervention measures. This system not only enables accurate multi-dimensional assessment of children's activity and psychological state, but also generates and continuously optimizes intervention recommendations in real time. These features collectively address the shortcomings of existing systems in terms of real-time performance, comprehensiveness, and personalization, providing a more scientific and intelligent solution for child health management and significantly improving the efficiency and effectiveness of child activity status monitoring and psychological health management.

[0060] Example 2

[0061] This embodiment is explained in Example 1, please refer to Figure 1 ,Specifically: the data monitoring module includes a data acquisition unit;

[0062] The data acquisition unit collects real-time data from the child through a wearable device, including using a three-axis acceleration sensor to record the acceleration change value per unit time to obtain the activity intensity AIt at time t; using a microphone to collect the child's voice signal SF; using a camera to obtain a sequence of child facial video frames to obtain the smile frequency EF within a fixed period; using a heart rate sensor and an acceleration sensor to collect the child's sleep information to obtain the resting heart rate HR and the number of turning over M, and generating a child activity feature vector set FV = {AIt, SF, EF, HR, M};

[0063] The activity intensity AIt is obtained by the following calculation formula:

[0064]

[0065] Where ax, ay and az represent the acceleration components in the horizontal, vertical and longitudinal directions respectively;

[0066] The speech signal SF is obtained by the following calculation formula:

[0067]

[0068] Where T is the length of the sampling window, s(t) is the sound signal amplitude at time t, |s(t)| is the absolute value of the sound signal amplitude, which is used to ignore the positive and negative changes of the signal and only consider the intensity of the sound. dt is a small time increment, which is determined by the sampling frequency of the device.

[0069] The smile frequency EF is obtained by the following calculation formula:

[0070]

[0071] Where Nsmile represents the number of smiles, which is obtained by counting the facial expression recognition algorithm, and T represents the length of the sampling window.

[0072] The data processing and extraction module includes a preprocessing unit and a feature extraction unit;

[0073] The preprocessing unit preprocesses the children's activity feature vector set FV, including data cleaning preprocessing, data denoising preprocessing and data normalization preprocessing, to obtain a preprocessed feature vector set FVp;

[0074] Among them, data cleaning preprocessing includes using linear interpolation method to process missing values ​​of feature vector set FV, using box plot method to process outliers of feature vector set FV, and then checking and processing outliers and missing values ​​in feature vector set FV;

[0075] Data denoising preprocessing includes using a sliding average filter method to perform time series data smoothing on the feature vector set FV after data cleaning preprocessing, and using a low-pass filter to filter high-frequency noise on the feature vector set FV after data cleaning preprocessing, thereby checking and eliminating noise caused by equipment and environmental interference in the feature vector set FV after data cleaning preprocessing;

[0076] The data normalization preprocessing includes using the minimum-maximum normalization method to normalize the feature vector set FV after data denoising preprocessing, and then mapping the feature vector set FV after data denoising preprocessing to a data range of 0 to 1 to eliminate dimensional differences. The data normalization preprocessing feature vector set FV = {AItn, SFn, EFn, HRn, Mn}.

[0077] The feature extraction unit performs feature extraction on the preprocessed feature vector set FVp to obtain activity intensity feature information, sound feature information, expression feature information and sleep feature information;

[0078] Among them, the activity intensity feature information includes the average activity intensity AIm and the activity fluctuation amplitude AIv; the sound feature information includes the sound signal energy average SFe and the sound signal peak frequency SFf; the expression feature information includes the smiling behavior ratio EFs; the sleep feature information includes the sleep score SDs, and the standard feature vector set SFV = {AIm, AIv, SFe, SFf, EFs, SDs} is generated;

[0079] The average activity intensity AIm is obtained by the following calculation formula:

[0080]

[0081] Where AItn,i represents the normalized activity intensity at the i-th moment, and N represents the total number of data points in the time window;

[0082] The activity fluctuation amplitude AIv is Obtain calculation formula;

[0083] The sound signal energy average value SFe is obtained by the following calculation formula:

[0084]

[0085] Where SFn,i represents the normalized speech signal at the i-th moment;

[0086] The peak frequency SFf of the sound signal is obtained by the following calculation formula:

[0087] SFf = argmax(FFT(SFn));

[0088] Where argmax represents the maximum function, which is used to return the frequency component with the largest intensity in the frequency domain. FFT(SFn) represents the fast Fourier transform result of the normalized speech signal, which specifically represents the intensity of each frequency component in the frequency domain.

[0089] The smiling behavior ratio EFs is obtained by the following calculation formula:

[0090]

[0091] Where EFn,i represents the smiling frequency at the i-th moment, Osmile represents the smiling behavior threshold, and || represents an exponential function that returns 1 when the condition is true and 0 otherwise.

[0092] The sleep score SDs is obtained by the following calculation formula:

[0093] SDs=s1*(1-HRn)+s2*(1-Mn);

[0094] Wherein, HRn represents the normalized resting heart rate, Mn represents the normalized number of turns, s1 and s2 represent the preset weight values ​​of the normalized resting heart rate HRn and the normalized number of turns Mn, respectively, and s1 + s2 = 1. The specific value is set by the user.

[0095] In this embodiment, an efficient data processing link is established from raw data to a standard feature vector set SFV, making the acquisition of children's activity, emotion, and sleep information more comprehensive, accurate, and dynamic. With the help of multimodal devices such as a three-axis accelerometer, microphone, and camera, real-time data including activity intensity AIt, voice signal SF, smile frequency EF, sleep information, resting heart rate HR, and number of turns M are collected to form the children's activity feature vector set FV. In the preprocessing stage, the system generates a normalized feature vector set FVp through data cleaning, denoising, and normalization to ensure the integrity, smoothness, and consistency of the data, and effectively eliminate the interference caused by equipment noise and dimensional differences. Finally, through the feature extraction unit, a standard feature vector set SFV is generated, including the average activity intensity AIm, the activity fluctuation amplitude AIv, the average sound signal energy SFe, the sound signal peak frequency SFf, the smile behavior ratio EFs, and the sleep score SDs, providing high-quality multi-dimensional basic data for subsequent activity status assessment. The system not only enables refined monitoring of children's activity status and related characteristics, but also significantly improves the reliability and availability of data, especially in dynamic environments, ensuring the real-time and accuracy of monitoring results, and providing more scientific data support for children's health assessment and behavior analysis.

[0096] Example 3

[0097] This embodiment is explained in Example 2, please refer to Figure 1 ,Specifically: the activity evaluation module includes a state evaluation unit and an evaluation index unit;

[0098] The state evaluation unit evaluates the activity state information and inactivity state information of the child based on the standard feature vector set SFV, including activity intensity, frequency fluctuation and activity continuity, and obtains activity intensity SAI, frequency fluctuation SFV and activity continuity SAC marking information;

[0099] The activity intensity is fitted by the average activity intensity AIm, the average sound signal energy SFe and the smiling behavior ratio EFs, and is marked as activity intensity SAI, which reflects the activity intensity of children. The greater the activity, the higher the score will be, and the activity in a high emotional state will further improve the score.

[0100] The frequency fluctuation is fitted by the average intensity of activity AIm, the peak frequency of the sound signal SFf and the proportion of smiling behavior EFs, and is marked as frequency fluctuation SFV, which reflects the frequency fluctuation of children's activities. The more intense the value is, the stronger the sense of rhythm is. The lower the value is, the smaller the activity fluctuation is and the more stable the rhythm is.

[0101] The activity persistence is fitted by the sleep score SDs and the activity fluctuation amplitude AIv, and is marked as activity persistence SAC;

[0102] The activity intensity SAI is Obtain calculation formula;

[0103] The frequency fluctuation SFV is obtained by the calculation formula SFV=ln(1+AIm*SFf)*(1+EFs), where ln represents a logarithmic function;

[0104] The activity persists through SAC The calculation formula is obtained, where e represents an exponential function, which is specifically used to map the activity fluctuation amplitude AIv to the range of 0 to 1.

[0105] The evaluation index unit evaluates the activity intensity SAI, frequency fluctuation SFV and activity duration SAC based on the acquired activity intensity SAI, frequency fluctuation SFV and activity duration SAC marking information by establishing an activity state evaluation algorithm formula to reflect the comprehensive performance of activity intensity, frequency fluctuation and duration in children's activities, and obtain the child activity state evaluation index AS;

[0106] The children's activity status assessment index AS is Obtain the calculation formula, where e represents the exponential function.

[0107] The detection and triggering module includes an anomaly detection unit and an intervention suggestion generation unit;

[0108] The abnormality detection unit compares the preset health assessment state threshold T with the child activity state assessment index AS to obtain the child assessment state result;

[0109] The child assessment status results are obtained by comparing:

[0110] When the child's activity status assessment index AS is less than the health assessment status threshold T, the child's assessment status result is obtained as a qualified state, marked as Status = 0, indicating that the child's activity status is normal and no special intervention is required. The child should continue to maintain his activity habits and routine.

[0111] When the child's activity status assessment index AS ≥ the health assessment status threshold T, the child's assessment status result is unqualified and marked as Status = 1, indicating that the child's activity status is unqualified and that activity arrangements and content need to be adjusted. Activity habits and schedules should also be adjusted based on the activity intensity SAI, frequency fluctuation SFV, and activity duration SAC marking information. When the activity intensity SAI is low, it is recommended to increase outdoor activity time. When the frequency fluctuation SFV is normal but the activity duration SAC is slightly insufficient, attention should be paid to the child's activity time arrangement.

[0112] When the child's activity status assessment index AS ≥ twice the health assessment status threshold T, the child's assessment status result is abnormal and marked as Status = 2, indicating that the child's activity is abnormal. Parents or management personnel are asked to adjust the activity content and the child's rest time. There is an abnormal activity intensity, and an abnormality report is generated based on the activity intensity SAI, frequency fluctuation SFV, and activity duration SAC marking information. When the activity intensity SAI is low, it may indicate insufficient activity; when the frequency fluctuation SFV is high, it may lead to fatigue accumulation; when the activity duration SAC performance is poor, attention should be paid to the child's daily routine.

[0113] In this embodiment, a child's activity status is dynamically approximated by combining multiple dimensions, including average activity intensity (AIm), average sound signal energy (SFe), and the proportion of smiling behaviors (EFs). This reflects the overall intensity, rhythm fluctuation, and duration of activity. By comparing the child's activity status assessment index (AS) with the health assessment threshold (T) through the detection and triggering module, the system obtains the child's assessment status (Status) in real time and generates corresponding prompts and recommendations. When activity intensity (SAI) is low, the system can prompt users to increase outdoor activity time; when activity duration (SAC) is slightly insufficient, it can prompt users to adjust their activity schedule. For abnormal activity status, the system not only generates detailed exception reports but also uses activity frequency fluctuation (SFV) to remind users to avoid fatigue accumulation. This comprehensive and dynamic activity status assessment mechanism not only quickly identifies potential issues with children's activity intensity, frequency fluctuation, and duration, but also provides precise intervention recommendations based on real-time assessment results, helping parents and managers effectively manage children's activity habits and schedules. Compared to traditional single-dimensional monitoring methods, this system achieves a deep linkage between activity status and intervention measures, providing an efficient, scientific, and intelligent solution for children's activity health management.

[0114] Example 4

[0115] This embodiment is explained in Example 3, please refer to Figure 1Specifically: the intervention suggestion generation unit automatically generates a grade abnormality standard AB according to the child assessment status result, and generates a child intervention suggestion generation plan according to the grade abnormality standard AB, adjusts the frequency of notification and prompt of the child assessment status result, and adjusts the acquisition frequency of the standard feature vector set SFV and the child activity status assessment index AS;

[0116] The child intervention suggestion generation plan is generated in the following way;

[0117] When the child assessment status result Status = 0, the abnormal level standard AB = 0 is generated, and the sending frequency of notifications and prompts, as well as the acquisition frequency of the standard feature vector set SFV and the child activity status assessment index AS are not adjusted;

[0118] When the child assessment status result status is Status = 1, the abnormal level standard AB = 2 is generated, the sending frequency of notifications and reminders is adjusted to 2 times the initial frequency, and the standard feature vector set SFV and the child activity status assessment index AS are adjusted to 2 + 1 times the initial acquisition frequency;

[0119] When the child assessment status result status Status = 2, the level abnormality standard AB = 4 is generated, the sending frequency of notifications and prompts is adjusted to 4 times the initial frequency, and the standard feature vector set SFV and the child activity status assessment index AS are adjusted to 4+2 times the initial acquisition frequency.

[0120] The iterative optimization module includes an activity status updating unit and an intervention effect evaluation unit;

[0121] The activity status update unit performs a fixed-period iterative evaluation of the child's activity status according to the child intervention suggestion generation plan, including a fixed-period iterative evaluation of the child's activity status at a frequency of 2+1 times and a fixed-period iterative evaluation of the child's activity status at a frequency of 4+2 times, generates a new activity status evaluation index ASN, and reflects the real-time activity status after the child intervention suggestion generation plan is executed.

[0122] The intervention effect evaluation unit compares the activity state assessment index ASN with the child activity state assessment index AS to obtain the difference △AS, and judges the effectiveness of the child intervention suggestion generation plan based on the difference △AS;

[0123] The effectiveness of the child intervention recommendation generation plan is judged by the following methods:

[0124] When the difference △AS ≥ 0, the child intervention suggestion generation plan is invalid, triggering the iterative optimization mechanism to adjust the content of the child intervention suggestion generation plan;

[0125] When the difference △AS is less than 0, the generation plan for obtaining child intervention suggestions is effective and the iterative optimization mechanism is not triggered.

[0126] In this embodiment, the intervention recommendation generation unit automatically generates a grade abnormality standard AB based on the child's assessment status result (Status), adjusts the frequency of sending notifications and reminders, as well as the frequency of obtaining the standard feature vector set SFV and the child's activity status assessment index AS, thereby dynamically matching the actual needs of the child's health status. For unqualified and abnormal status, the system conducts fixed-cycle activity status assessments at a frequency of 2+1 times and 4+2 times, respectively, to generate a new activity status assessment index ASN that reflects the real-time activity status after the intervention plan is implemented. In the iterative optimization module, the intervention effect evaluation unit compares the new activity status assessment index ASN with the original activity status assessment index AS, calculates the difference ΔAS, and uses this to evaluate the effectiveness of the intervention recommendation generation plan. If the intervention is ineffective, the system triggers an iterative optimization mechanism to automatically adjust the intervention content to ensure that the intervention measures can dynamically adapt to the child's actual health needs. This optimization mechanism based on real-time feedback not only improves the scientific nature and accuracy of the intervention plan, but also effectively reduces unnecessary intervention behaviors and avoids the accumulation of health risks, providing highly adaptable dynamic optimization capabilities for children's activity health management. Compared with static intervention methods, this system has significant advantages in real-time response and continuous optimization, making children's health management more efficient and intelligent.

[0127] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A personalized activity monitoring and full-process management system for children's health care, characterized by: It includes data monitoring module, data processing and extraction module, activity evaluation module, detection and triggering module, suggestion generation module and iterative optimization module; The data monitoring module collects real-time data of children through wearable devices and generates a children's activity feature vector set FV; The data processing and extraction module performs preprocessing and feature extraction on the children's activity feature vector set FV to generate a standard feature vector set SFV; The activity assessment module evaluates the child's activity status information based on the standard feature vector set SFV, including activity intensity, frequency fluctuation, and activity duration, and establishes an activity status assessment algorithm formula for evaluation to obtain the child's activity status assessment index AS; The activity evaluation module includes a state evaluation unit and an evaluation index unit; The state evaluation unit evaluates the activity state information and inactivity state information of the child based on the standard feature vector set SFV, including activity intensity, frequency fluctuation and activity continuity, and obtains activity intensity SAI, frequency fluctuation SFV and activity continuity SAC marking information; The activity intensity is fitted by the average activity intensity AIm, the average sound signal energy SFe and the smiling behavior ratio EFs, and marked as activity intensity SAI, which reflects the activity intensity of children; The frequency fluctuation is fitted by the average activity intensity AIm, the peak frequency SFf of the sound signal and the proportion of smiling behavior EFs, and is marked as frequency fluctuation SFV, which reflects the fluctuation of children's activity frequency; The activity persistence is fitted by the sleep score SDs and the activity fluctuation amplitude AIv, and is marked as activity persistence SAC; The evaluation index unit evaluates the activity intensity SAI, frequency fluctuation SFV and activity duration SAC based on the acquired activity intensity SAI, frequency fluctuation SFV and activity duration SAC marking information by establishing an activity state evaluation algorithm formula to reflect the comprehensive performance of activity intensity, frequency fluctuation and duration in children's activities, and obtain the child activity state evaluation index AS; The children's activity status assessment index AS is Calculation formula is obtained, where e represents the exponential function; The detection and triggering module compares the preset health assessment state threshold T with the child activity state assessment index AS to obtain the child assessment state result, and generates the grade abnormality standard AB according to the child assessment state result; The detection and triggering module includes an anomaly detection unit and an intervention suggestion generation unit; The abnormality detection unit compares the preset health assessment state threshold T with the child activity state assessment index AS to obtain the child assessment state result; The child assessment status results are obtained by comparing: When the child's activity status assessment index AS is less than the health assessment status threshold T, the child's assessment status result is obtained as a qualified state, marked as Status=0, indicating that the child's activity status is normal and no special intervention is required. The child should continue to maintain his activity habits and routine. When the child's activity status assessment index AS ≥ the health assessment status threshold T, the child's assessment status result is obtained as unqualified and marked as Status = 1, indicating that the child's activity status is unqualified and that the activity arrangement and content need to be adjusted. Activity habits and routines should also be adjusted based on the activity intensity SAI, frequency fluctuation SFV, and activity duration SAC marking information; When the child's activity status assessment index AS ≥ twice the health assessment status threshold T, the child's assessment status result is abnormal and marked as Status = 2, indicating that the child's activity is abnormal. Parents or management personnel are requested to adjust the activity content and the child's rest time. If there is abnormal activity intensity, an abnormality report will be generated based on the activity intensity SAI, frequency fluctuation SFV, and activity duration SAC tag information; The intervention suggestion generation unit automatically generates a grade abnormality standard AB based on the child assessment status result, and generates a child intervention suggestion generation plan based on the grade abnormality standard AB, adjusts the frequency of notification and prompt of the child assessment status result, and adjusts the frequency of obtaining the standard feature vector set SFV and the child activity status assessment index AS; The child intervention suggestion generation plan is generated in the following way; When the child assessment status result status Status=0, the abnormal level standard AB=0 is generated, and the sending frequency of notifications and prompts, as well as the acquisition frequency of the standard feature vector set SFV and the child activity status assessment index AS are not adjusted; When the child assessment status result status is Status=1, the abnormal level standard AB=2 is generated, the sending frequency of notifications and reminders is adjusted to 2 times the initial frequency, and the standard feature vector set SFV and the child activity status assessment index AS are adjusted to 2+1 times the initial acquisition frequency; When the child assessment status result status is Status=2, the abnormal level standard AB=4 is generated, the sending frequency of notifications and reminders is adjusted to 4 times the initial frequency, and the standard feature vector set SFV and the child activity status assessment index AS are adjusted to 4+2 times the initial acquisition frequency; The suggestion generation module generates a child intervention suggestion generation plan based on the abnormality level criteria AB, the activity status information, and the inactivity status information, and provides prompts and notifications based on the content of the child intervention suggestion generation plan; The iterative optimization module performs fixed-period iterative evaluation of the child's activity status according to the child intervention suggestion generation plan, generates a new activity status evaluation index ASN, and compares it with the child activity status evaluation index AS to determine the effectiveness of the child intervention suggestion generation plan.

2. The personalized activity monitoring and full-process management system for children's health care according to claim 1, characterized in that: The data monitoring module includes a data acquisition unit; The data acquisition unit collects real-time data on the child through a wearable device, including using a three-axis accelerometer to record the acceleration change value per unit time to obtain the activity intensity AIt at time t; using a microphone to collect the child's voice signal SF; using a camera to obtain a sequence of child facial video frames to obtain the smile frequency EF within a fixed period; using a heart rate sensor and an accelerometer to collect the child's sleep information to obtain the resting heart rate HR and the number of turning over M, and generate a child activity feature vector set FV={AIt, SF, EF, HR, M}.

3. The personalized activity monitoring and full-process management system for children's health care according to claim 2, characterized in that: The data processing and extraction module includes a preprocessing unit and a feature extraction unit; The preprocessing unit preprocesses the children's activity feature vector set FV, including data cleaning preprocessing, data denoising preprocessing and data normalization preprocessing, to obtain a preprocessed feature vector set FVp; Among them, data cleaning preprocessing includes using linear interpolation method to process missing values ​​of feature vector set FV, using box plot method to process outliers of feature vector set FV, and then checking and processing outliers and missing values ​​in feature vector set FV; Data denoising preprocessing includes using a sliding average filter method to perform time series data smoothing on the feature vector set FV after data cleaning preprocessing, and using a low-pass filter to filter high-frequency noise on the feature vector set FV after data cleaning preprocessing, thereby checking and eliminating noise caused by equipment and environmental interference in the feature vector set FV after data cleaning preprocessing; Data normalization preprocessing includes using the minimum-maximum normalization method to normalize the feature vector set FV after data denoising preprocessing, and then mapping the feature vector set FV after data denoising preprocessing to the data range of 0 to 1 to eliminate dimensional differences. The data normalization preprocessing feature vector set FV={AItn, SFn, EFn, HRn, Mn}.

4. The personalized activity monitoring and full-process management system for children's health care according to claim 3, characterized in that: The feature extraction unit performs feature extraction on the preprocessed feature vector set FVp to obtain activity intensity feature information, sound feature information, expression feature information and sleep feature information; Among them, the activity intensity feature information includes the average activity intensity AIm and the activity fluctuation amplitude AIv; the sound feature information includes the sound signal energy average SFe and the sound signal peak frequency SFf; the expression feature information includes the smiling behavior proportion EFs; the sleep feature information includes the sleep score SDs, generating a standard feature vector set SFV={AIm, AIv, SFe, SFf, EFs, SDs}.

5. The personalized activity monitoring and full-process management system for children's health care according to claim 1, characterized in that: The iterative optimization module includes an activity status updating unit and an intervention effect evaluation unit; The activity status update unit performs a fixed-period iterative evaluation of the child's activity status according to the child intervention suggestion generation plan, including a fixed-period iterative evaluation of the child's activity status at a frequency of 2+1 times and a fixed-period iterative evaluation of the child's activity status at a frequency of 4+2 times, generates a new activity status evaluation index ASN, and reflects the real-time activity status after the child intervention suggestion generation plan is executed.

6. The personalized activity monitoring and full-process management system for children's health care according to claim 5, characterized in that: The intervention effect evaluation unit compares the activity state assessment index ASN with the child activity state assessment index AS to obtain the difference △AS, and judges the effectiveness of the child intervention suggestion generation plan based on the difference △AS; The effectiveness of the child intervention recommendation generation plan is judged by the following methods: When the difference △AS ≥ 0, the child intervention suggestion generation plan is invalid, triggering the iterative optimization mechanism to adjust the content of the child intervention suggestion generation plan; When the difference △AS is less than 0, the generation plan for obtaining child intervention suggestions is effective and the iterative optimization mechanism is not triggered.

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