AR child health management wearable system based on artificial intelligence

Abstract

The invention discloses an AR child health management wearable system based on artificial intelligence, and relates to the technical field of child health, the system comprises a management center, and the management center is connected with a development acquisition module, a comprehensive processing module, a value analysis module and an early warning intervention module; collecting monitoring state data of the target child; performing quantitative composition on the monitoring state data to obtain an auxiliary quantitative monitoring graph, and performing data exception verification on the auxiliary quantitative monitoring graph based on the monitoring state data to obtain a variation monitoring interval; performing health grading on the abnormal change monitoring interval to obtain an abnormal change severity level; carrying out abnormity prevention according to the change severity level to obtain a prevention and alleviation instruction, carrying out virtual enhancement on the prevention and alleviation instruction through the AR wearable device, carrying out interactive recovery by the target child according to the virtual enhancement instruction, and integrating the recovery process to generate an interactive rehabilitation strategy; personalized health intervention is achieved, the understanding threshold is lowered, and health quality is improved.

Description

Technical Field

[0001] This invention relates to the field of children's health technology, specifically an AR children's health management wearable system based on artificial intelligence. Background Technology

[0002] With increasing global attention to children's health and the exponential development of digital technologies, traditional methods of children's health management are facing profound changes. Traditional models rely on parental observation and regular checkups, which have inherent limitations such as fragmented data, delayed feedback, and low child compliance. Meanwhile, with the rapid development of the Internet of Things, artificial intelligence, and augmented reality technologies, children's health management is entering a critical period of digital transformation.

[0003] In recent years, while wearable devices have become widespread, their applications in the children's field have mostly focused on single functions, lacking systematic and intelligent health intervention capabilities. Against this backdrop, a next-generation children's health management wearable system integrating artificial intelligence and advanced sensing technology has emerged, aiming to build an all-weather, immersive, and personalized digital health companion for children. To this end, we present an AI-based AR children's health management wearable system that collects children's physiological and behavioral data in real time, processes and analyzes this data using AI technology, and uses wearable AR to guide children in health management, thereby improving the health management system and fostering healthy behaviors. Summary of the Invention

[0004] The objective of this invention can be achieved through the following technical solutions: An AI-based AR wearable system for children's health management includes a management center connected to a developmental data acquisition module, a comprehensive processing module, a value analysis module, and an early warning and intervention module. The developmental acquisition module is used to collect monitoring status data and acquisition time of the target child; The integrated processing module is used to quantify and map the monitoring status data to obtain an auxiliary quantitative monitoring map, and to perform data anomaly verification on the auxiliary quantitative monitoring map based on the monitoring status data to obtain the anomaly monitoring interval. The value analysis module is used to classify the health status of the anomaly monitoring interval and obtain the severity level of the anomaly. The early warning and intervention module is used to prevent abnormalities based on the severity level of the mutation, obtain prevention and relief instructions, and virtually enhance the prevention and relief instructions through an AR wearable device. The target child interacts and recovers according to the virtual enhanced instructions, and the recovery process is integrated to generate an interactive rehabilitation strategy.

[0005] Preferably, the process of collecting monitoring status data of the target children includes: Wearable detection is performed on the target children, and a monitoring and data collection instruction is issued to the target children who pass the detection. Information is collected from the target children according to the obtained monitoring and data collection instruction to obtain monitoring status data. The obtained monitoring status data is statistically analyzed to obtain the collection time, and the obtained collection time is correlated with the corresponding monitoring status data.

[0006] Preferably, the process of obtaining the auxiliary quantitative monitoring chart includes: Quantitatively filter the monitoring status data to obtain the expected monitoring data; The interaction period is set according to the collection time, and a two-dimensional rectangular coordinate system about the collection time is constructed based on the interaction period; The obtained predicted quantity monitoring data is uploaded to a two-dimensional rectangular coordinate system. A monitoring quantification curve is generated based on the obtained predicted quantity monitoring data. The two-dimensional rectangular coordinate system is then transformed based on the obtained monitoring quantification curve to obtain an auxiliary quantification monitoring map.

[0007] Preferably, the process of obtaining the anomaly monitoring interval includes: Set health warning lines based on the target children, upload the obtained health warning lines to the auxiliary quantitative monitoring chart, and perform a preliminary verification of the auxiliary quantitative monitoring chart based on the obtained health warning lines to obtain the initial verification results; Anomalies are extracted from the initial verification results obtained based on the monitoring status data to obtain the anomaly monitoring interval.

[0008] Preferably, the process of classifying health status within the anomaly monitoring interval includes: The anomaly monitoring range is uploaded to the AR wearable device, and a health check command is issued to the AR wearable device based on the obtained anomaly monitoring range; The obtained health investigation instructions are uploaded to the management center, which then classifies the anomaly monitoring intervals into different levels to determine the severity of the anomaly. A graded early warning instruction is generated based on the severity level of the obtained anomaly.

[0009] Preferably, the process of obtaining prevention and mitigation instructions includes: Obtain graded early warning instructions, upload the obtained graded early warning instructions to the AR wearable device, generate alarms through the AR wearable device, and obtain early warning mitigation measures; Virtual perspective of early warning mitigation measures is obtained by using AR wearable devices to perform virtual vision of early warning mitigation measures; Based on the obtained virtual mitigation measures, prevention and mitigation instructions are generated and uploaded to the management center. The management center then enhances the interaction of the AR wearable device based on the received prevention and mitigation instructions. This enhanced interaction involves displaying the obtained virtual mitigation measures to the target child through the AR wearable device.

[0010] Preferably, the process of generating interactive rehabilitation strategies includes: The target children interact with and recover based on the virtual relief measures shown, and information is collected on the interaction recovery process to obtain interaction monitoring data; Perform interactive review on the obtained interactive monitoring data until the review result indicates that the target has been restored. The obtained interactive recovery process is statistically analyzed to obtain interactive rehabilitation strategies.

[0011] Compared with the prior art, the beneficial effects of the present invention are: 1. First, collect status monitoring data of children wearing AR devices to quantify the data and obtain a visual auxiliary quantitative monitoring chart. This transforms complex physiological signals into an intuitive visual map, which helps to quickly identify trends and reveal the evolution of health problems. The auxiliary quantitative monitoring chart is used to verify anomalies in the status monitoring data to obtain abnormal unhealthy data. The severity of unhealthy data is graded by abnormal intervals to obtain different severity levels of abnormality. This reduces parents' excessive anxiety about minor fluctuations, allows them to focus on medium- and high-risk events, and reduces the psychological burden on families and the strain on medical resources.

[0012] 2. By matching the severity level of the mutation with corresponding health recovery measures through the management center, we can obtain health recovery steps corresponding to different severity levels, achieve precise intervention, use AR wearable devices for virtual enhancement, obtain three-dimensional and visualized recovery measures, automatically check the compatibility of measures to prevent mutual cancellation of intervention measures, remind children to carry out health recovery, and integrate the recovery process to generate interactive rehabilitation strategies and optimize health strategies in real time. Attached Figure Description

[0013] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0014] Figure 1 This is a schematic diagram of the present invention. Detailed Implementation

[0015] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0016] like Figure 1 As shown, an AI-based AR children's health management wearable system includes a management center, which is connected to a developmental data acquisition module, a comprehensive processing module, a value analysis module, and an early warning and intervention module. The developmental acquisition module is used to collect monitoring status data and acquisition time of the target child; The integrated processing module is used to quantify and map the monitoring status data to obtain an auxiliary quantitative monitoring map, and to perform data anomaly verification on the auxiliary quantitative monitoring map based on the monitoring status data to obtain the anomaly monitoring interval. The value analysis module is used to classify the health status of the anomaly monitoring interval and obtain the severity level of the anomaly. The early warning and intervention module is used to prevent abnormalities based on the severity level of the mutation, obtain prevention and relief instructions, and virtually enhance the prevention and relief instructions through an AR wearable device. The target child interacts and recovers according to the virtual enhanced instructions, and the recovery process is integrated to generate an interactive rehabilitation strategy.

[0017] In practical applications, traditional children's health management typically relies on manual recording, which is mostly one-way and lacks interactive communication. AR technology, however, can help children monitor their health status immediately and interactively, cultivating health awareness from a young age. It also helps parents identify abnormal trends and detect potential health problems early. Therefore, the first step is to collect monitoring status data of the target children through a developmental data acquisition module. The specific process includes: Wearable detection is performed on the target children, and a monitoring and data collection instruction is issued to the target children who pass the detection. Information is collected from the target children according to the obtained monitoring and data collection instruction to obtain monitoring status data. It should be further explained that, in the specific implementation process, the target child refers to a child user who wears the AR device and undergoes health management. The wear monitoring refers to detecting the device wearing status of the target child. After ensuring that the AR device is worn correctly, a monitoring and collection command is issued, indicating that the monitoring data of the target child can be collected to obtain monitoring status data. The monitoring status data includes physiological health data, behavioral and cognitive data, and environmental interaction data. Among them, physiological health data includes basic vital signs, activity and metabolic data, growth and development data, and disease indicator records; behavioral and cognitive data includes eye habits, dietary records, attention and emotions; and environmental interaction data includes environmental quality and device usage patterns. The obtained monitoring status data is statistically analyzed to obtain the collection time, and the obtained collection time is correlated with the corresponding monitoring status data.

[0018] The integrated processing module is used to quantify and map the monitoring status data to obtain an auxiliary quantitative monitoring map. Based on the monitoring status data, the auxiliary quantitative monitoring map is used to perform data anomaly verification to obtain the anomaly monitoring interval. The specific process includes: The obtained monitoring status data is quantitatively filtered to obtain the expected monitoring data; The quantitative screening refers to filtering out data that can be quantified from the obtained monitoring status data. For example, heart rate, body temperature, blood pressure, and exercise intensity in physiological health data, or eye use time and outdoor activity time in behavioral cognitive data, are all data that can be quantified. That is, the monitoring data expressed in numbers is the expected monitoring data.

[0019] The interaction period is set based on the collection time. The interaction period represents a period of time that includes several collection times. It can be the healthy usage time set by the AR device or the time period used by the target child.

[0020] A two-dimensional rectangular coordinate system is constructed based on the interaction cycle, where the horizontal axis of the two-dimensional rectangular coordinate system represents the acquisition time, which is all the acquisition times within one interaction cycle.

[0021] The obtained predicted quantity monitoring data is uploaded to a two-dimensional rectangular coordinate system. A monitoring quantification curve is generated based on the obtained predicted quantity monitoring data. The two-dimensional rectangular coordinate system is then transformed based on the obtained monitoring quantification curve to obtain an auxiliary quantification monitoring map. Furthermore, based on the expected monitoring data, including but not limited to heart rate, body temperature, blood pressure, exercise intensity, screen time, and outdoor activity time, the monitoring quantification curves include, but are not limited to, heart rate quantification curves, body temperature quantification curves, blood pressure quantification curves, exercise intensity quantification curves, screen time quantification curves, and outdoor activity time quantification curves. The curve transformation means that the two-dimensional rectangular coordinate system containing the monitoring quantification curves is recorded as an auxiliary quantification monitoring graph. The auxiliary quantification monitoring graph contains all the monitoring quantification curves. The corresponding monitoring quantification curve is displayed when the specific monitoring quantification curve to be studied is required.

[0022] A health warning line is set for the target child. The health warning line is a monitoring and warning threshold within a safe range set according to the target child's age, gender, and health status. For example, the weight and height of an 8-year-old child need to meet the requirements for normal growth, that is, the health warning line for weight is Z1 and the health warning line for height is G2. The obtained health warning line is uploaded to the auxiliary quantitative monitoring chart. The auxiliary quantitative monitoring chart is then subjected to a preliminary verification based on the obtained health warning line to obtain the initial verification result, which includes the initial anomaly range and the initial safe range. It should be further explained that, in the specific implementation process, the primary verification means that in the auxiliary quantitative monitoring chart, the threshold is compared by the health warning line corresponding to each monitoring quantitative curve, and the part of the monitoring quantitative curve that does not meet the health warning line is obtained. The corresponding part of the curve is recorded as the initial anomaly interval. Here, not meeting the threshold means exceeding the safe range corresponding to the health warning line. The part of the monitoring quantitative curve that meets the health warning line is recorded as the initial safe interval, which represents the monitoring result of the safe range. For example, the health warning lines for weight and height of an 8-year-old child are Z1 and G2. That is, if the weight does not meet the Z1 range at the age of eight, the part of the monitoring quantification curve that exceeds the range will be marked as the initial abnormality interval, indicating that the growth monitoring results of the 8-year-old child are abnormal and need to be warned through AR wearable devices.

[0023] Based on the monitoring status data, anomalies are extracted from the initial verification results to obtain the anomaly monitoring interval; The anomaly extraction refers to filtering out the time period and abnormal data corresponding to the abnormal results in the monitoring status data based on the initial verification result. That is, when the initial verification result is the initial anomaly interval, the part of the collection time corresponding to this interval is recorded as the anomaly monitoring interval and the monitoring status data is marked. In other words, the monitoring status data of the abnormal time period within the interaction cycle can be obtained through the anomaly monitoring interval.

[0024] The value analysis module is used to classify the health status of the anomaly monitoring interval and obtain the severity level of the anomaly. The specific process includes: The obtained anomaly monitoring range is uploaded to the AR wearable device, and a health check command is issued to the AR wearable device based on the obtained anomaly monitoring range; The health assistance instruction indicates that an abnormal health condition has been detected in the target child's physical monitoring data during the interaction period, requiring health recovery in order to achieve health management. Therefore, the health assistance instruction is an instruction issued to the AR wearable device that health recovery is required.

[0025] The obtained health investigation instructions are uploaded to the management center, which then classifies the anomaly monitoring intervals into different levels to determine the severity of the anomaly. A graded early warning instruction is generated based on the obtained severity level of the mutation. The graded early warning instruction indicates that different severity levels of mutation have corresponding warning levels. It should be further explained that, in the specific implementation process, the grading refers to further classifying the severity of the obtained anomaly monitoring intervals to obtain different levels of anomaly severity. Furthermore, the severity classification is represented in the auxiliary quantitative monitoring chart by the maximum distance between the portion of the monitoring quantitative curve that corresponds to the initial anomaly interval and the health warning line, denoted as the abrupt change interval. The severity interval is then divided into severity intervals. For example, if the abrupt change interval is W, then it can be divided into... Therefore, based on the mutation interval falling within the corresponding severity range of different mutation monitoring intervals, different levels of mutation severity can be obtained, and graded health recovery can be carried out. In this embodiment, different graded warning instructions are dynamically prompted with color, which is conducive to guiding children to cope independently.

[0026] The early warning and intervention module is used to prevent abnormalities based on the severity level of the mutation, obtain prevention and mitigation instructions, and virtually enhance these instructions using an AR wearable device. The target child then interacts and recovers based on the enhanced instructions, and the recovery process is integrated to generate an interactive rehabilitation strategy. The specific process includes: Obtain graded early warning instructions, upload the obtained graded early warning instructions to the AR wearable device, generate alarms through the AR wearable device, and obtain early warning mitigation measures; It should be further explained that, in the specific implementation process, the alarm generation means that the graded early warning instructions generated according to the different severity of the anomaly monitoring interval have corresponding different health recovery measures. That is, different health recovery measures are set for different levels. For example, the early warning relief measures for the anomaly monitoring interval with the mildest abnormality are relatively mild. For the anomaly monitoring interval with the highest severity, it is necessary to notify the caregiver to conduct a health check and send the patient to the hospital for professional treatment if necessary. In particular, the early warning and mitigation measures were designed with the participation of medical experts to avoid misleading parents due to knowledge gaps.

[0027] Virtual perspective of early warning mitigation measures is obtained by using AR wearable devices to perform virtual vision of early warning mitigation measures; Furthermore, the virtual perspective refers to generating a three-dimensional, directly displayable relief measure in the visual area of ​​the AR wearable device based on the steps corresponding to the warning and relief measures. This is the virtual relief measure. For example, when monitoring the abnormality range caused by excessive eye strain, the warning and relief measure is a health exercise to relieve eye fatigue. The corresponding virtual perspective generates three-dimensional virtual health exercise steps, allowing the target child to intuitively understand the steps. This is the virtual relief measure. It can transform abstract knowledge into an AR dynamic model, helping children intuitively understand health principles, reducing the risk of operational errors in real-world scenarios, reducing fear of diagnosis and treatment, and improving treatment cooperation.

[0028] Based on the obtained virtual mitigation measures, prevention and mitigation instructions are generated and uploaded to the management center. The management center then enhances the interaction of the AR wearable device based on the received prevention and mitigation instructions. This enhanced interaction involves displaying the obtained virtual mitigation measures to the target child through the AR wearable device.

[0029] The target children interact with and recover based on the virtual relief measures shown, and information is collected on the interaction recovery process to obtain interaction monitoring data; The information collection refers to the continued collection and monitoring of status data after the target child has completed the repair process of abnormal behavior according to the virtual relief measures, and this data is recorded as interactive monitoring data. The "repair process" means that the target child is instructed to exercise or recover according to the displayed virtual relief measures. If the abnormality is related to physical conditions, the virtual relief measures will notify the caregiver that the child needs to be taken to the hospital for professional medical recovery. The collection of interactive monitoring data is to determine whether the target child has exercised and recovered in accordance with the requirements of the virtual relief measures. For example, if there is an abnormality in weight, whether the child has exercised in accordance with the virtual relief measures can be determined through the collected interactive monitoring data.

[0030] Perform interactive review on the obtained interactive monitoring data until the review result indicates that the target has been restored. It should be further explained that, in the specific implementation process, the interactive review means to perform data anomaly verification on the interactive monitoring data, so that the finally obtained anomaly monitoring range disappears, then the interactive review is completed, and the review result is recorded as the target completion recovery; the target completion recovery means that after the target child completes the virtual relief measures displayed by the AR wearable device, the current unhealthy abnormal state of the body is relieved, that is, it is not judged as an abnormal situation. Specifically, if an anomaly monitoring zone still appears after interactive review, then a higher level of virtual mitigation measures need to be generated based on the severity of the anomaly. These measures should then be deployed to the target child via AR wearable devices for continued interactive recovery until the final interactive review result indicates that the target has completed recovery.

[0031] The obtained interactive recovery process is statistically analyzed to obtain an interactive rehabilitation strategy. The interactive rehabilitation strategy represents the interactive measures and processes in the statistical interactive recovery process, and generates a rehabilitation strategy to repair the unhealthy abnormal state of the target child. This interactive rehabilitation strategy addresses the hidden and developmental nature of children's health problems and enables early detection and early intervention.

[0032] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to any specific implementation. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. An AI-based AR wearable system for children's health management, comprising a management center, characterized in that, The management center is connected to a developmental data acquisition module, a comprehensive processing module, a value analysis module, and an early warning and intervention module. The developmental acquisition module is used to collect monitoring status data and acquisition time of the target child; The integrated processing module is used to quantify and map the monitoring status data to obtain an auxiliary quantitative monitoring map, and to perform data anomaly verification on the auxiliary quantitative monitoring map based on the monitoring status data to obtain the anomaly monitoring interval. The value analysis module is used to classify the health status of the anomaly monitoring interval and obtain the severity level of the anomaly. The early warning and intervention module is used to prevent abnormalities based on the severity level of the mutation, obtain prevention and relief instructions, and virtually enhance the prevention and relief instructions through an AR wearable device. The target child interacts and recovers according to the virtual enhanced instructions, and the recovery process is integrated to generate an interactive rehabilitation strategy.

2. The AR-based children's health management wearable system according to claim 1, characterized in that, The process of collecting monitoring status data of the target children includes: Wearable detection is performed on the target children, and a monitoring and data collection instruction is issued to the target children who pass the detection. Information is collected from the target children according to the obtained monitoring and data collection instruction to obtain monitoring status data. The obtained monitoring status data is statistically analyzed to obtain the collection time, and the obtained collection time is correlated with the corresponding monitoring status data.

3. The AR-based children's health management wearable system according to claim 1, characterized in that, The process of obtaining auxiliary quantitative monitoring charts includes: Quantitatively filter the monitoring status data to obtain the expected monitoring data; The interaction period is set according to the collection time, and a two-dimensional rectangular coordinate system about the collection time is constructed based on the interaction period; The obtained predicted quantity monitoring data is uploaded to a two-dimensional rectangular coordinate system. A monitoring quantification curve is generated based on the obtained predicted quantity monitoring data. The two-dimensional rectangular coordinate system is then transformed based on the obtained monitoring quantification curve to obtain an auxiliary quantification monitoring map.

4. The AR-based children's health management wearable system according to claim 1, characterized in that, The process of obtaining the anomaly monitoring range includes: Set health warning lines based on the target children, upload the obtained health warning lines to the auxiliary quantitative monitoring chart, and perform a preliminary verification of the auxiliary quantitative monitoring chart based on the obtained health warning lines to obtain the initial verification results; Anomalies are extracted from the initial verification results obtained based on the monitoring status data to obtain the anomaly monitoring interval.

5. The AR-based children's health management wearable system according to claim 4, characterized in that, The process of classifying health status within anomaly monitoring intervals includes: The anomaly monitoring range is uploaded to the AR wearable device, and a health check command is issued to the AR wearable device based on the obtained anomaly monitoring range; The obtained health investigation instructions are uploaded to the management center, which then classifies the anomaly monitoring intervals into different levels to determine the severity of the anomaly. A graded early warning instruction is generated based on the severity level of the obtained anomaly.

6. The AR-based children's health management wearable system according to claim 5, characterized in that, The process of obtaining prevention and mitigation instructions includes: Obtain graded early warning instructions, upload the obtained graded early warning instructions to the AR wearable device, generate alarms through the AR wearable device, and obtain early warning mitigation measures; Virtual perspective of early warning mitigation measures is obtained by using AR wearable devices to perform virtual vision of early warning mitigation measures; Based on the obtained virtual mitigation measures, prevention and mitigation instructions are generated and uploaded to the management center. The management center then enhances the interaction of the AR wearable device based on the received prevention and mitigation instructions. This enhanced interaction involves displaying the obtained virtual mitigation measures to the target child through the AR wearable device.

7. The AR-based children's health management wearable system according to claim 6, characterized in that, The process of generating interactive rehabilitation strategies includes: The target children interact with and recover based on the virtual relief measures shown, and information is collected on the interaction recovery process to obtain interaction monitoring data; Perform interactive review on the obtained interactive monitoring data until the review result indicates that the target has been restored. The obtained interactive recovery process is statistically analyzed to obtain interactive rehabilitation strategies.

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