CN111768863B - Infant development monitoring system and method based on artificial intelligence - Google Patents
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Abstract
The invention discloses an infant development monitoring system and a method thereof based on artificial intelligence, wherein the system comprises a development evaluation module, an infant limb movement ability identification module, a sleep monitoring module, a nutrition detection module and an doctor advice tracking module; the infant limb movement capability recognition module is used for acquiring moving pictures or videos of infants and capturing and extracting characteristic points in the moving pictures or videos through a self-adaptive least square method; forming a motion track of the characteristic points through linear prediction and iterative adjustment, comparing the motion track with the motion track of the characteristic points in the standard motion image, and observing the fitting degree of the two tracks; according to the invention, through the intelligent reminding of image recognition and doctor advice tracking in infant motion capture and the neural network analysis method for food nutrition analysis, the comprehensive monitoring of child growth and the personalized and professional guidance are finally realized, the investment of manpower and material resources is reduced, and the breeding cost is reduced.
Description
Technical Field
The invention relates to the technical field of infant growth monitoring, in particular to an infant development monitoring system and method based on artificial intelligence.
Background
In recent years, along with the progress of artificial intelligence technology, intelligence has become a development trend of electronic information industry, and more intelligent products are emerging in the living field and enter into families. Along with the continuous aggravation of the Chinese social competition, the requirements of parents on child health and education are higher and higher, and the 'no child is transported on the starting line' has become the common consensus of Chinese parents, so that the parents always attach importance to the nutrition, health, development, education and other aspects of the children from the pregnancy stage.
The field does not well apply artificial intelligence technology to infant development monitoring based on artificial intelligence. The existing infant development monitoring device based on artificial intelligence has the defects that a great deal of human intervention is still needed under the condition that medical resources are so short, so that the infant raising cost is high, and the whole-course development monitoring and health care suggestion of infants, infants and children cannot be realized.
Therefore, there is a strong need in the industry to devise a device or method that can fully utilize artificial intelligence to monitor infant growth and to provide personalized guidelines.
Disclosure of Invention
The invention aims to overcome the defects in the prior art, and provides an artificial intelligence-based infant development monitoring system and an artificial intelligence-based infant development monitoring method, which can monitor the development and growth of infants and give personalized guidance.
The aim of the invention is achieved by the following technical scheme:
an artificial intelligence based infant development monitoring system comprising: the device comprises a development evaluation module, an infant limb movement ability identification module, a sleep monitoring module, a nutrition detection module and a doctor's advice tracking module;
the development evaluation module is used for receiving child personal information input by a user, performing answering test aiming at infant capability indexes of different ages of the month according to the child personal information, obtaining capability development scores according to answering conditions of the user by combining algorithms and mathematical models containing different deep learning structures, and further giving a capability development evaluation report according to the scores;
the infant limb movement capability recognition module is used for acquiring moving pictures or videos of infants and recognizing the moving pictures or videos based on an artificial intelligence technology, and capturing and extracting characteristic points in the moving pictures or videos through a self-adaptive least square method; forming a motion track of the characteristic points through linear prediction and iterative adjustment, comparing the motion track with the motion track of the characteristic points in the standard motion image, and observing the fitting degree of the two tracks;
the sleep monitoring module is used for acquiring heart rate data and breathing data of the infants, fitting the heart rate data and the breathing data with sleep condition standard curves of corresponding age groups, and judging sleep states of the infants;
the nutrition detection system module is used for acquiring food images of infants, carrying out convolution processing on the food images based on a food image database established during early image recognition model training and by utilizing a computer vision model obtained through multi-layer training based on a convolution neural network, fitting the convolved results with the food image recognition model obtained through early training again, carrying out classification processing on the obtained features by utilizing a classifier, and outputting classification results to users to finally obtain food nutrition monitoring results;
the doctor's advice tracking module is used for obtaining doctor's advice of signing up the hospital and giving notes daily.
Preferably, the personal information includes: age, height, weight, medical history; the capability indicators include fine actions, coarse actions, social capabilities, linguistic functions, and cognitive capabilities.
Preferably, the infant limb movement ability recognition module is further used for acquiring moving pictures or videos of infants, and capturing and extracting limb actions and behavior in the moving pictures or videos through an adaptive least square method; capturing and repairing infant motion data by a method for recovering lost characteristic points, forming a motion track of the characteristic points of a user by a linear prediction and iterative adjustment method, comparing the motion track with the motion track of the characteristic points in a standard motion image, and observing the fitting degree of the two tracks; the higher the fitting degree is, the more excellent the infant movement ability is, the retake guidance is given to the data with the extremely poor fitting condition, and the corresponding training course is recommended to the user with poor performance after multiple tests.
Preferably, the infant sleep detection pillow is used for detecting infant sleep data, and the infrared sleep detector is used for heart rate monitoring to obtain heart rate data.
Preferably, the sleep monitoring module is further used for automatically playing the cradle yeast to help sleep when the infant sleep condition is judged to be unstable; when the sleep is judged to be unstable for many times, an alarm is sent out and an improvement suggestion is provided for parents, if the problems cannot be solved by both schemes, sleep data of infants are transmitted to contracted doctors of a cooperative hospital, the doctors give on-line guidance according to the data, and if the problems cannot be solved, the doctors of the hospital can be reserved for on-line diagnosis.
Preferably, the nutrition detection system module is further configured to obtain a food picture of an infant, determine the depth of each pixel in the picture by performing visual analysis on the food picture, connect the picture of a specific food with the database, determine the food of the infant, perform nutrition analysis on the food on the basis of identifying the food, provide food heat information, and inform the user of the content of protein, fat, carbohydrate, vitamins and other components in the food; and combining the past data and basic conditions of the infants, and giving personalized diet coaching.
An artificial intelligence-based infant development monitoring method comprises the following steps:
s1, acquiring body data of an infant; the body data includes: height data, weight data, data related to sleep conditions, data related to diet, location data, data related to mental development;
s2, processing the body data;
s3, fitting the body data with a standard curve of the corresponding age index, and judging whether the body data is in a preset safety range or not to obtain a monitoring result and a corresponding suggestion; inserting the body data into a standard curve corresponding to the age corresponding index by using a Lagrange interpolation method, continuously correcting the standard curve, and finally obtaining a growth curve of the user; comparing the user data with the generated user growth curve, and giving out corresponding prediction according to the curve data;
and S4, transmitting the monitoring result, the corresponding suggestions and the corresponding predictions to the user terminal.
Preferably, the standard curve is a curve of different age and different indexes which are fitted according to the previous data set training in advance, and the standard curve formation comprises the following steps:
the method comprises the steps of obtaining infant growth and development data in advance, screening leading-edge medical indexes capable of evaluating infant growth and development, carrying out combined calculation on the obtained infant growth and development data, carrying out quantitative treatment on the obtained infant growth and development data through the leading-edge medical indexes, and then screening and fitting the data to obtain an age-height development standard curve, an age-weight development standard curve and an age-sleep volume standard curve.
Preferably, if the body data is data related to sleep conditions, S3 includes: the server compares the data related to the sleep state with the sleep volume standard curve of the physical state of the corresponding age according to the age, the physical state and the data related to the sleep state of the person to be monitored, and further obtains a training suggestion and a sleep volume range.
Preferably, step S2 comprises: cleaning the body data, deleting blank data and planning partial data; and classifying and analyzing the body data by using an SVM classifying algorithm.
Compared with the prior art, the invention has the following advantages:
the infant development monitoring system based on artificial intelligence of the invention comprises: the device comprises a development evaluation module, an infant limb movement ability identification module, a sleep monitoring module, a nutrition detection module and a doctor's advice tracking module; the artificial intelligence is mainly applied to image recognition in infant motion capture, intelligent reminding of doctor's advice tracking and a neural network analysis method for food nutrition analysis, and finally realizes comprehensive monitoring of child growth and gives personalized and professional guidance, reduces investment of manpower and material resources and lowers infant raising cost.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the invention. In the drawings:
fig. 1 is a schematic flow chart of an artificial intelligence-based infant development monitoring method in the present embodiment.
FIG. 2 is a schematic flow chart of the artificial intelligence based capturing and extracting of human actions in image segments according to the present embodiment.
Fig. 3 is a schematic flowchart of food image analysis of the deep learning-based deep neural network method of the present embodiment.
Fig. 4 is a mobile phone screenshot of the growth assessment of the present embodiment.
FIG. 5 is a mobile phone screenshot of the height assistant of the present embodiment.
Fig. 6 is a mobile phone screenshot of a growth status question and answer in this embodiment.
Fig. 7 is a mobile phone screenshot of a growth evaluation report of the present embodiment.
Fig. 8 is a mobile phone screenshot of the nutritional assistant of this embodiment.
Fig. 9 is a mobile screenshot of order tracking of the present embodiment.
Detailed Description
The invention is further described below with reference to the drawings and examples.
Referring to fig. 1, an artificial intelligence based infant development monitoring method includes:
s1, acquiring body data of an infant based on an artificial intelligence technology and combining related equipment.
Acquiring body data of an infant; the body data includes: height data, weight data, data related to sleep conditions, data related to diet, location data, data related to mental development;
s2, processing the body data; the deep learning method in the artificial intelligence field is utilized to perform data understanding, remove irrelevant data, select proper data, combine data, discretize data and the like. Specifically, step S2 includes: cleaning the body data, deleting blank data and planning partial data; and classifying and analyzing the body data by using an SVM classifying algorithm. Further using SVM classifying algorithm to classify and analyze the data, wherein the specific analysis steps are as follows: and randomly scattering the health data and the development retardation data, classifying the retardation data by using the SVM, manually judging the screened retardation data, correcting key coefficients in multiple iterations, and improving the accuracy of the model for distinguishing the development retardation.
S3, fitting the body data with a standard curve of the corresponding age index in the early stage, and judging whether the body data is in a preset safety range or not to obtain a monitoring result and a corresponding suggestion; the body data are inserted into a standard curve corresponding to the age by using a Lagrange interpolation method in the middle period, the standard curve is continuously corrected, and finally a growth curve of the user is obtained; comparing the user data with the generated user growth curve, and giving out corresponding prediction according to the curve data;
as shown in fig. 5, the standard curve is a curve of different age and different indexes which are fitted according to the previous data set training in advance, and the standard curve formation includes the following steps: the method comprises the steps of obtaining infant growth and development data in advance, screening leading-edge medical indexes capable of evaluating infant growth and development, carrying out combined calculation on the obtained infant growth and development data, carrying out quantitative treatment on the obtained infant growth and development data through the leading-edge medical indexes, and then screening and fitting the data to obtain an age-height development standard curve, an age-weight development standard curve and an age-sleep volume standard curve.
For example, if the body data is data related to sleep conditions, S3 includes: the server compares the data related to the sleep state with the sleep volume standard curve of the physical state of the corresponding age according to the age, the physical state and the data related to the sleep state of the person to be monitored, and further obtains a training suggestion and a sleep volume range.
And S4, transmitting the monitoring result, the corresponding suggestions and the corresponding predictions to the user terminal. And step S3, judging by the server, and transmitting the monitoring result and the corresponding advice to a user terminal provided with a monitoring evaluation APP after the server finishes judging, wherein the user terminal is one of a smart phone, a tablet personal computer, a PC (personal computer) or a notebook computer. The guardian (user) can also access the server via the user terminal to query the monitoring data, as shown in fig. 4, 7, 8.
An artificial intelligence-based infant development monitoring system based on the artificial intelligence-based infant development monitoring method, comprising: the device comprises a development evaluation module, an infant limb movement ability identification module, a sleep monitoring module, a nutrition detection module and a doctor's advice tracking module;
as shown in FIG. 6, the development evaluation module is configured to receive personal information (age, height, weight, medical history) of a child input by a user, perform an answer test for infant ability indexes (fine actions, coarse actions, social abilities, language functions, cognitive abilities) of different ages according to the personal information of the child, combine algorithms and mathematical models (for example, 1. Training with a support vector machine (sVI M) algorithm, and output dependent variables to determine whether the patient is developing slowly;
referring to fig. 2, the infant limb movement capability recognition module is configured to obtain a moving picture or video of an infant, and capture and extract feature points in the moving picture or video by an adaptive least square method; forming a motion track of the characteristic points through linear prediction and iterative adjustment, comparing the motion track with the motion track of the characteristic points in the standard motion image, and observing the fitting degree of the two tracks; the method can iteratively extract more complex and higher-level features from low-level features by utilizing the advantage that a deep convolutional neural network (CNN network) can automatically learn, thereby better establishing a computer vision model.
Specifically, the infant limb movement ability recognition module is further used for acquiring a moving picture or video of an infant, and capturing and extracting limb actions and behavior in the moving picture or video through a self-adaptive least square method; capturing and repairing infant motion data by a method for recovering lost characteristic points, forming a motion track of the characteristic points of a user by a linear prediction and iterative adjustment method, comparing the motion track with the motion track of the characteristic points in a standard motion image, and observing the fitting degree of the two tracks; the higher the fitting degree is, the more excellent the infant movement ability is, the retake guidance is given to the data with the extremely poor fitting condition, and the corresponding training course is recommended to the user with poor performance after multiple tests.
The sleep monitoring module is used for acquiring heart rate data and breathing data of the infants, fitting the heart rate data and the breathing data with sleep condition standard curves of corresponding age groups, and judging sleep states of the infants;
in this embodiment, infant sleep data is detected by the infant sleep detection pillow, and heart rate data is obtained by performing heart rate monitoring by the infrared sleep detector. The sleep monitoring module is also used for automatically playing the cradle yeast to help sleep when the infant sleep condition is judged to be unstable; when the sleep is judged to be unstable for many times, an alarm is sent out and an improvement suggestion is provided for parents, if the problems cannot be solved by both schemes, sleep data of infants are transmitted to contracted doctors of a cooperative hospital, the doctors give on-line guidance according to the data, and if the problems cannot be solved, the doctors of the hospital can be reserved for on-line diagnosis.
Referring to fig. 3, the nutrition detection system module is configured to obtain a food image of an infant, perform convolution processing on the food image based on a food image database established during training of a pre-image recognition model, and utilize a computer vision model obtained through multi-layer training based on a convolutional neural network, fit the convolved result with the food image recognition model obtained through the pre-training again, perform classification processing on the obtained features (such as types, heat and nutrition components of food) by using a classifier, and output the classification result to a user, so as to finally obtain a food nutrition monitoring result; the convolutional neural network (CNN network) is continuously perfected mainly through multi-layer training of an input layer, a hidden layer and an output layer, and the recognition hit rate is improved. The input layer is mainly responsible for cutting, scaling and dividing an original image input by a user and preprocessing the original image. Specifically, the nutrition detection system module is further used for obtaining food pictures of infants, judging the depth of each pixel in the pictures by adopting a pattern recognition technology through visual analysis of the food pictures, connecting the pictures of specific foods with a database, judging the foods of the infants, carrying out nutrition analysis on the foods on the basis of recognizing the foods, providing food heat information, and informing users of the contents of proteins, fats, carbohydrates, vitamins and other components in the foods; and combining the past data and basic conditions of the infants, and giving personalized diet coaching.
As shown in fig. 9, the order tracking module is configured to obtain orders and daily attention points of a doctor in a contracted hospital.
In this embodiment, the infant development monitoring system based on artificial intelligence further includes a diet module, wherein the diet module is configured to form a training set by collecting food picture data provided by a user, extract feature vectors (such as the amount of food, the food components, etc.) in the pictures by using an SVM algorithm, calculate data provided to a terminal by the user according to the feature vectors, and divide the diet structure of the user into two types according to the calculation result, one type is a diet structure beneficial to healthy development of an infant, the other type is a diet structure not beneficial to healthy development of an infant, and a scheme suitable for the age of an infant and the growth condition is customized for the user on heat intake and diet advice.
In the infant development monitoring system based on artificial intelligence, body data necessary for realizing a growth and development monitoring function are distinguished, and the data are divided into data which can be directly acquired by a monitoring terminal and data which cannot be directly acquired. The data (such as age, height, weight and the like) which can be directly acquired are selected to be directly collected when information is collected for a user, and the data (such as exercise capacity, social capacity and the like) which cannot be directly acquired are subjected to learning of the conventional indexes of the existing medicine, so that an intelligent data acquisition algorithm is developed, such as an algorithm for capturing actions of infants, analyzing voice and the like, so that the data acquisition is realized, and advanced papers in the fields of image recognition, voice recognition and the like are fully algorithmicized in the process.
In this embodiment, optimization and inspection of the above modules are also performed: and finishing deep learning when the model tends to be stable, and performing test and check on a final algorithm given by the server. And inputting a batch of latest complete data (such as age, height, weight, sleeping amount, diet structure and the like) of a hospital, substituting a final fitting curve to be transmitted to a server for calculation, outputting evaluation data of physical signs, exercise capacity and the like of a user, and giving reference suggestions for sleeping time and diet structure of the user. Comparing the algorithm test result with the doctor judgment result, checking whether the algorithm test result is consistent with the doctor judgment result, and if the algorithm test result is not consistent with the doctor judgment result, improving the learning algorithm to learn again; if yes, embedding an app into the obtained algorithm to perform internal measurement on part of users.
In this embodiment, development of WeChat applet and application is also performed: the early development centers on APK, android applications use jdk as an environment, android sdk and adt as tools, eclipse as an editor to develop, the middle stage beautifies and improves the UI of APK, and later, android is used as a reference to establish WeChat public signals and develop on an IOS platform.
The above embodiments are preferred examples of the present invention, and the present invention is not limited thereto, and any other modifications or equivalent substitutions made without departing from the technical aspects of the present invention are included in the scope of the present invention.
Claims (7)
1. An artificial intelligence based infant development monitoring system, comprising: the infant limb movement monitoring system comprises a development evaluation module, an infant limb movement identification module, a sleep monitoring module, a nutrition detection module, a doctor's advice tracking module and a diet module;
the development evaluation module is used for receiving child personal information input by a user, performing answering test aiming at infant capability indexes of different ages of the month according to the child personal information, obtaining capability development scores according to answering conditions of the user by combining algorithms and mathematical models containing different deep learning structures, and further giving a capability development evaluation report according to the scores;
the infant limb movement capability recognition module is used for acquiring moving pictures or videos of infants and recognizing the moving pictures or videos based on an artificial intelligence technology, and capturing and extracting characteristic points in the moving pictures or videos through a self-adaptive least square method; capturing and repairing infant motion data by a method for recovering lost characteristic points, forming a motion track of the characteristic points of a user by linear prediction and iterative adjustment, comparing the motion track with the motion track of the characteristic points in a standard motion image, and observing the fitting degree of the two tracks; the higher the fitting degree is, the more excellent the infant movement ability is, the retake guidance is given to the data with the extremely poor fitting condition, and the corresponding training course is recommended to the user with poor performance after multiple tests;
the sleep monitoring module is used for acquiring heart rate data and breathing data of the infants, fitting the heart rate data and the breathing data with sleep condition standard curves of corresponding age groups, and judging sleep states of the infants;
the nutrition detection module is used for acquiring food images of infants, carrying out convolution processing on the food images based on a food image database established during training of a pre-image recognition model and by utilizing a computer vision model obtained through multi-layer training based on a convolution neural network, fitting the convolved result with the food image recognition model obtained through the pre-training again, carrying out classification processing on the obtained characteristics by utilizing a classifier, and outputting the classification result to a user to finally obtain a food nutrition monitoring result;
the nutrition detection module is also used for obtaining food pictures of infants, judging the depth of each pixel in the pictures by performing visual analysis on the food pictures, connecting the pictures of specific foods with the database, judging the foods of the infants, performing nutrition analysis on the foods on the basis of identifying the foods, providing food heat information, and informing the user of the contents of protein, fat, carbohydrate, vitamins and other components in the foods; giving individualized diet coaching by combining infant passing data and basic conditions;
the doctor's advice tracking module is used for acquiring doctor's advice and daily notice of signing up with a hospital;
the diet module is used for forming a training set by collecting food picture data provided by a user, extracting feature vectors in pictures by using an SVM algorithm, calculating data provided to a terminal by the user according to the feature vectors, and classifying diet structures of the user into two types according to calculation results, wherein one type is a diet structure beneficial to healthy development of infants, and the other type is a diet structure not beneficial to healthy development of infants, and a scheme suitable for infant age groups and growth conditions is customized for the user on heat intake and diet suggestions.
2. The artificial intelligence based infant development monitoring system and method according to claim 1, wherein the personal information includes: age, height, weight, medical history; the capability indicators include fine actions, coarse actions, social capabilities, linguistic functions, and cognitive capabilities.
3. The artificial intelligence based infant development monitoring system of claim 1, wherein infant sleep data is detected by an infant sleep detection pillow, and heart rate data is obtained by heart rate monitoring by an infrared sleep detector.
4. The infant development monitoring system based on artificial intelligence according to claim 1, wherein the sleep monitoring module is further configured to automatically play a bassinet to help sleep when it is determined that the infant is not sleeping stably; when the sleep is judged to be unstable for many times, an alarm is sent out and an improvement suggestion is provided for parents, if the problems cannot be solved by both schemes, sleep data of infants are transmitted to contracted doctors of a cooperative hospital, the doctors give on-line guidance according to the data, and if the problems cannot be solved, the doctors of the hospital can be reserved for on-line diagnosis.
5. An artificial intelligence based infant development monitoring method, characterized in that it is applied to the artificial intelligence based infant development monitoring system according to any one of claims 1 to 4, comprising:
s1, acquiring body data of an infant; the body data includes: height data, weight data, data related to sleep conditions, data related to diet, location data, data related to mental development;
s2, processing the body data, specifically: cleaning the body data, deleting blank data and planning partial data; classifying and analyzing the body data by using an SVM classifying algorithm;
s3, fitting the body data with a standard curve of the corresponding age index, and judging whether the body data is in a preset safety range or not to obtain a monitoring result and a corresponding suggestion; inserting the body data into a standard curve corresponding to the age corresponding index by using a Lagrange interpolation method, continuously correcting the standard curve, and finally obtaining a growth curve of the user; comparing the user data with the generated growth curve of the user, and giving out corresponding predictions according to the curve data;
and S4, transmitting the monitoring result, the corresponding suggestions and the corresponding predictions to the user terminal.
6. The artificial intelligence based infant development monitoring method of claim 5, wherein the standard curve is a curve of different ages and different indexes which are fitted in advance according to previous data set training, and the standard curve formation comprises the following steps:
the method comprises the steps of obtaining infant growth and development data in advance, screening leading-edge medical indexes capable of evaluating infant growth and development, carrying out combined calculation on the obtained infant growth and development data, carrying out quantitative treatment on the obtained infant growth and development data through the leading-edge medical indexes, and then screening and fitting the data to obtain an age-height development standard curve, an age-weight development standard curve and an age-sleep volume standard curve.
7. The artificial intelligence based infant development monitoring method of claim 6, wherein if the physical data is data related to sleep conditions, S3 comprises: the server compares the data related to the sleep state with the sleep volume standard curve of the physical state of the corresponding age according to the age, the physical state and the data related to the sleep state of the person to be monitored, and further obtains a training suggestion and a sleep volume range.
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