CN115054203B - Health data intelligent online monitoring analysis management cloud platform based on digitization - Google Patents
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Abstract
The invention discloses a health data intelligent online monitoring analysis management cloud platform based on digitization, which realizes the health monitoring of behavior habits and sleep conditions of target users and evaluates the comprehensive health index and comprehensive health grade of the target users by arranging a health monitoring device setting module, a behavior habit health monitoring module, a sleep health monitoring module, a health comprehensive analysis module, a health database, a health data set building module and a health data encryption module. On one hand, the target user can realize real-time health monitoring at home, and further can know the self health condition in time and intervene health problems in advance. On the other hand, the health examination workload of medical workers is reduced, individual differences of target users are concerned, multiple dimensions of behavior postures, behavior posture duration, sleep time, brain waves and sleep posture change are covered, and a reliability basis is provided for health detection results.
Description
Technical Field
The invention belongs to the technical field of health data monitoring and analysis, and particularly relates to a health data intelligent online monitoring and analysis management cloud platform based on digitization.
Background
In recent years, along with the acceleration of modern life rhythm, a plurality of health problems are generated, researches show that the incidence rate of some chronic diseases presents an increasing trend, meanwhile, due to the increasing improvement of living standard, the attention of people to the health of the people is higher, the timely prevention and treatment of the diseases become the focus of social attention, under the situation, the monitoring and analysis of the health are particularly important, and if the real-time detection of the health of the human body can be carried out, the health problems can be found in advance and intervened.
Nowadays, the traditional offline hospital physical examination medical mode is mostly adopted for health monitoring and analysis, but the traditional offline hospital physical examination medical mode cannot meet the requirements of people for health at present, and has the problems of long time consumption, high cost and the like, and the traditional offline hospital physical examination medical mode is embodied in the following aspects:
(1) On the one hand to the patient, adopt off-line hospital physical examination medical mode to need to consume a large amount of time and money cost to drive one's body to go to the hospital and carry out health examination, and the reason of carrying out health examination is mostly because health problems have appeared to the health, there is the longer problem of waiting period in the result of health examination simultaneously, and then make the patient in time know the health status of self, can't intervene the health problem in advance, thereby can not satisfy patient's health demand well, and be unfavorable for reducing the harm that the disease brought.
(2) On the other hand, for medical workers, huge health examination workload needs to be faced every day, so that not only is great workload brought to the body, but also human subjective factors exist, and further the individual differences of patients cannot be accurately concerned, and therefore a reliability basis cannot be provided for health detection results of the patients.
Disclosure of Invention
In order to overcome the defects in the background art, the embodiment of the invention provides a health data intelligent online monitoring analysis management cloud platform based on digitization, which can effectively solve the problems in the background art.
The purpose of the invention can be realized by the following technical scheme:
the invention provides a health data intelligent online monitoring analysis management cloud platform based on digitization, which comprises a health monitoring equipment setting module, a behavior habit health monitoring module, a sleep health monitoring module, a health comprehensive analysis module, a health database, a health data set building module and a health data encryption module.
The health monitoring device setting module is respectively connected with the behavior habit health monitoring module and the sleep health monitoring module, the health database is respectively connected with the behavior habit health monitoring module, the sleep health monitoring module and the health comprehensive analysis module, and the health data set building module is respectively connected with the behavior habit health monitoring module, the sleep health monitoring module, the health comprehensive analysis module and the health data encryption module.
The health monitoring equipment setting module is used for dividing the home interior of a target user into a public area and a private area, setting a home video image monitoring terminal in the public area, setting a sleep monitor in the private area, and further mounting a brightness sensor, a brain wave sensor, a body movement sensor and a high-definition camera on the sleep monitor;
the behavior habit health monitoring module is used for carrying out health monitoring on the behavior habits of the target user, wherein the behavior habit health monitoring module comprises a behavior gesture health monitoring unit and a behavior gesture duration monitoring unit;
the sleep health monitoring module is used for carrying out health monitoring on the sleep condition of a target user, and comprises a sleep time monitoring unit, a brain wave monitoring unit and a sleep posture change monitoring unit;
the health database is used for storing posture characteristics corresponding to various behavior posture types, health indexes corresponding to various posture outlines to which the various behavior posture types belong, health duration corresponding to the various behavior posture types, health sleeping time, brain wave health change curves, health sleeping body movement frequency, health sleeping body movement intervals and comprehensive health indexes corresponding to various health levels;
the comprehensive health analysis module is used for analyzing a comprehensive health index of a target user based on monitoring results of the behavior habit health monitoring module and the sleep health monitoring module, matching the comprehensive health index of the target user with comprehensive health indexes corresponding to all health levels in a health database, and evaluating the comprehensive health level of the target user;
the health data set building module is used for building a health data set from all behavioral and posture images, behavioral and posture health indexes, behavioral and posture duration health indexes, sleep-in-time health indexes, brain wave change curves in a sleep state, brain wave health indexes in the sleep state, body movement frequency in the sleep state, sleep body movement frequency health indexes, sleep body movement three-dimensional images after each sleep body movement, initial sleep body movement three-dimensional images, sleep body movement amplitude change indexes, sleep body movement distance change indexes, comprehensive health indexes and comprehensive health levels of a target user in a monitoring period;
the health data encryption module is used for carrying out face encryption on a health data set of a target user, the user needs to unlock the health data set through face recognition when viewing the health data set, and only the health data of the user can be viewed.
According to a preferred embodiment, the behavioral gesture health monitoring unit is used for performing health monitoring on the behavioral gesture of the target user, and the specific monitoring process thereof comprises the following steps:
a1, starting a home video image monitoring terminal in a public area, and monitoring the behavior gesture of a target user in real time according to a preset time interval by using a set monitoring period to obtain a behavior gesture image of the target user corresponding to each acquisition interval;
a2, extracting attitude characteristics and attitude outlines from the behavior attitude images of the target users corresponding to the acquisition intervals, and matching the attitude characteristics with the attitude characteristics corresponding to various behavior attitude types, thereby matching the behavior attitude types of the target users corresponding to the acquisition intervals;
a3, screening out health indexes corresponding to various behavior posture profiles of the behavior posture types of the target users corresponding to the acquisition intervals from a health database based on the behavior posture types of the target users corresponding to the acquisition intervals;
a4, matching the gesture outline of the target user corresponding to each acquisition interval with the health index corresponding to each gesture outline of the behavior gesture type of the target user corresponding to the acquisition interval, and screening the health indexThe behavior posture health index of the target user corresponding to each acquisition interval is recorded as Z a A denotes the number of the acquisition interval, a =1,2.... C;
a5, counting the behavior posture health indexes corresponding to the target users according to the behavior posture health indexes corresponding to the acquisition intervals of the target users, wherein the calculation formula is as follows:
wherein ζ is expressed as a behavior posture health index corresponding to the target user, Z a And expressing the index as the behavior posture health index of the target user corresponding to the a-th collection interval.
According to a preferred embodiment, the behavioral gesture duration monitoring unit is configured to perform health monitoring on the behavioral gesture duration of the target user, and the specific monitoring process performs the following steps:
b1, counting the number of behavior posture types of the target user from the behavior posture types of the target user corresponding to each acquisition interval, numbering the behavior posture types by 1,2, a, B, a, d, and simultaneously acquiring the duration corresponding to the behavior posture types;
b2, extracting health duration corresponding to various behavior posture types of the target user from a health database based on the acquired various behavior posture types of the target user;
b3, comparing the duration time of various behavior gestures of the target user with the health duration time corresponding to the various behavior gestures, and calculating the health index of the duration time of the behavior gesture corresponding to the target user, wherein the calculation formula is as follows:
where α represents a health index, T, that is the duration of the behavioral gesture of the target user b And
respectively representing the duration corresponding to the b-th behavior gesture type and the health duration corresponding to the b-th behavior gesture type of the target user.
According to a preferred embodiment, the sleep time monitoring unit is used for health monitoring of the sleep time of the target user, and the specific monitoring process thereof comprises the following steps:
d1, starting a sleep monitor arranged in a private area, collecting the turn-off time point of indoor light by a light sensor in the sleep monitor, and further taking the turn-off time point as the turn-off time point of a target user, and meanwhile, collecting the time point of the target user when the body movement is in a stable state by a body movement sensor as the body movement stable time point;
d2, acquiring a time interval between the lamp turning-off time point and the body movement stable time point, and taking the time interval as the sleep time of the target user;
d3, comparing the sleeping time of the target user with the healthy sleeping time in the health database, and calculating the health index of the sleeping time corresponding to the target user, wherein the calculation formula is as follows:
where β is expressed as a health index, t, of the length of time the target user is asleep Sign Expressed as a healthy falling asleep time period, t' expressed as a falling asleep time period of the target user, and e expressed as a natural constant.
According to a preferred embodiment, the brain wave monitoring unit is used for performing health monitoring on the brain waves of a target user in a sleep state, and the specific monitoring process comprises the following steps:
e1, monitoring the brain waves of a target user in a sleeping state through a brain wave sensor arranged on a sleep monitor, and further acquiring a brain wave change curve of the target user in the sleeping state;
e2: based on the brain wave health change curve stored in the health database, the length of the brain wave health change curve is further extracted;
e3: the brain wave change curve of the target user in the sleeping state is superposed and compared with the brain wave health change curve stored in the health database, the length of the superposed curve is extracted, and the brain wave health index of the target user in the sleeping state, namely the brain wave health index of the target user in the sleeping state, is calculatedThe calculation formula is as follows:
wherein δ is the brain wave health index of the target user in the sleep state, L is the length of superposition of the brain wave change curves of the target user in the sleep state, and L is the length of the brain wave health change curve.
According to a preferred embodiment, the sleep posture change monitoring unit comprises a sleep body movement frequency monitoring subunit, a sleep body movement amplitude monitoring subunit and a sleep posture movement distance monitoring subunit.
According to a preferred embodiment, the sleep body movement frequency monitoring subunit is configured to perform health monitoring on the sleep body movement frequency of the target user, and the specific monitoring process performs the following steps:
f1, monitoring and counting the body movement frequency of a target user in a sleeping state through a body movement sensor arranged on a sleep monitor;
f2, comparing the body movement frequency of the target user in the sleeping state with the healthy sleeping body movement frequency in the healthy database, and calculating the healthy index of the sleeping body movement frequency of the target user, wherein the calculation formula is as follows:
wherein epsilon represents the sleep body frequency of movement index, PC, of the target user 0 The body movement frequency of the healthy sleep is represented, and the body movement frequency of the target user in the sleep state is represented by PC'.
According to a preferred embodiment, the sleep body movement amplitude monitoring subunit is configured to perform health monitoring on the sleep body movement amplitude of the target user, and the specific monitoring process includes the following steps:
g1, numbering each sleep movement of a target user as 1,2, f, h;
g2, respectively carrying out three-dimensional image acquisition on the sleep posture and the initial sleep posture of the target user after each sleep posture movement through a high-definition camera arranged on the sleep monitor, acquiring three-dimensional images corresponding to the sleep posture and the initial sleep posture of the target user after each sleep posture movement, and further extracting the shape contour and the shape contour area corresponding to the sleep posture and the initial sleep posture of the target user after each sleep posture movement;
g3, overlapping and comparing the outline corresponding to the sleep posture after each sleep posture of the target user with the outline of the initial sleep posture, extracting the overlapping area of the outline corresponding to the sleep posture after each sleep posture of the target user and the outline of the initial sleep posture, and calculating the sleep posture amplitude change index of the target user, wherein the calculation formula is as follows:
wherein λ is expressed as the sleep movement amplitude variation index of the target user, S 0 Expressed as the initial sleeping posture outline area, S, of the target user f And the overlapping area of the outline corresponding to the sleeping posture of the target user after the f-th sleeping movement is shown.
According to a preferred embodiment, the sleep posture movement distance monitoring subunit is configured to perform health monitoring on the sleep posture movement distance of the target user, and the specific monitoring process includes the following steps:
h1, acquiring the sleeping posture of the target user after each sleeping movement and the outline center point corresponding to the initial sleeping posture based on the extracted sleeping posture of the target user after each sleeping movement and the outline center point corresponding to the initial sleeping posture;
h2, taking the outline center point of the initial sleeping posture of the target user as a reference point, further acquiring the distance between the outline center point corresponding to the sleeping posture of the target user after each sleeping posture movement and the reference point, and recording the distance as the posture movement distance corresponding to each sleeping posture movement;
h3, comparing the posture movement distance corresponding to each sleep movement with the healthy sleep posture movement distance stored in the healthy database, calculating the sleep posture movement distance change index corresponding to the target user,
wherein v is a number of a posture movement interval corresponding to each sleep movement, v =1,2 0 Expressed as healthy sleeping posture shift interval, J v The posture movement distance corresponding to the v-th sleep movement is shown.
According to a preferred embodiment, the calculation formula of the comprehensive health index of the target user is as follows:
where ψ represents a composite health index for the target user.
Compared with the prior art, the embodiment of the invention at least has the following beneficial effects:
(1) By providing the intelligent online health data monitoring, analyzing and managing cloud platform based on digitization, real-time health monitoring of target users can be achieved at home, the defect that a large amount of time and money cost are consumed to drive the patients to the hospital and conduct health examination in a traditional offline hospital examination medical mode is effectively overcome, some potential health problems can be predicted, and meanwhile the problem that the waiting period of the health examination result is long is solved, so that the patients can know the health conditions of the patients in time and intervene in the health problems in advance, the health requirements of the patients are greatly met, and harm brought by diseases is favorably reduced.
(2) According to the invention, the behavior habit and the sleep condition of the target user are subjected to health monitoring and analysis by setting the home video image monitoring terminal and the sleep monitor, and a plurality of health monitoring dimensions of behavior posture, behavior posture duration, falling asleep time, brain waves and sleep posture change are covered, so that the comprehensive health index of the target user is calculated, and the comprehensive health grade of the target user is evaluated, thereby greatly reducing the health examination workload of medical workers, relieving the workload, avoiding the situation that the individual differences of patients cannot be accurately paid attention to due to human subjective factors, and providing a reliability basis for the health detection result of the target user.
Drawings
The invention is further illustrated by means of the attached drawings, but the embodiments in the drawings do not constitute any limitation to the invention, and for a person skilled in the art, other drawings can be obtained on the basis of the following drawings without inventive effort.
Fig. 1 is a schematic view of the connection structure of the present invention.
Fig. 2 is a schematic structural diagram of a behavior habit health monitoring module according to the present invention.
Fig. 3 is a schematic structural diagram of a sleep health monitoring module according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, the invention provides a health data intelligent online monitoring analysis management cloud platform based on digitization, which comprises a health monitoring device setting module, a behavior habit health monitoring module, a sleep health monitoring module, a health comprehensive analysis module, a health database, a health data set building module and a health data encryption module.
The health monitoring device setting module is respectively connected with the behavior habit health monitoring module and the sleep health monitoring module, the health database is respectively connected with the behavior habit health monitoring module, the sleep health monitoring module and the health comprehensive analysis module, and the health data set building module is respectively connected with the behavior habit health monitoring module, the sleep health monitoring module, the health comprehensive analysis module and the health data encryption module.
The health monitoring device setting module is used for dividing the home interior of a target user into a public area and a private area, setting a home video image monitoring terminal in the public area, setting a sleep monitor in the private area, and further installing a brightness sensor, a brain wave sensor, a body movement sensor and a high-definition camera on the sleep monitor;
the public areas include a living room, a dining room, and a balcony, and the private areas include a bedroom.
The behavior habit health monitoring module is used for carrying out health monitoring on the behavior habits of the target users.
Referring to fig. 2, the behavior habit health monitoring module includes a behavior gesture health monitoring unit and a behavior gesture duration monitoring unit;
specifically, the behavior posture health monitoring unit is used for performing health monitoring on the behavior posture of the target user, and the specific monitoring process executes the following steps:
a1, starting a home video image monitoring terminal in a public area, and monitoring the behavior posture of a target user in real time according to a set monitoring period and preset time intervals to obtain behavior posture images of the target user corresponding to each acquisition interval;
a2, extracting attitude characteristics and attitude outlines from the target user behavior attitude images corresponding to the acquisition intervals, and matching the attitude characteristics with the attitude characteristics corresponding to various behavior attitude types, thereby matching the target user behavior attitude types corresponding to the acquisition intervals;
it should be noted that the posture characteristics refer to behavior posture profiles based on target users, the head central point and the foot central point are selected to be in linear connection, and are marked as behavior posture profile median lines, the ground is used as a reference horizontal plane, an included angle formed between the behavior posture profile median line and the reference horizontal plane is further obtained, meanwhile, the distance between the head central point and the reference horizontal plane is obtained, and behavior posture types of the target users are further judged according to the included angle formed between the behavior posture profile median line and the reference horizontal plane and the distance between the head central point and the reference horizontal plane, wherein the behavior posture types include standing posture, sitting posture, sleeping posture, squatting posture and the like.
Illustratively, if the included angle formed between the median line of the behavior posture outline of the target user and the reference horizontal plane is 180 degrees, and meanwhile, the distance between the head center point in the behavior posture outline of the target user and the reference horizontal plane is close to the height of the target user, the behavior posture type of the target user is judged to be the standing posture.
A3, screening out health indexes corresponding to various behavior posture profiles of the behavior posture types of the target users corresponding to the acquisition intervals from a health database based on the behavior posture types of the target users corresponding to the acquisition intervals;
a4, matching the target user posture profile corresponding to each acquisition interval with the health index corresponding to each posture profile to which the target user behavior posture type corresponding to the acquisition interval belongs, screening the target user behavior posture health index corresponding to each acquisition interval from the health indexes, and recording the target user behavior posture health index as Z a A denotes the number of acquisition intervals, a =1,2.... C;
a5, counting the behavior posture health indexes corresponding to the target users according to the behavior posture health indexes corresponding to the acquisition intervals of the target users, wherein the calculation formula is as follows:
wherein ζ is expressed as a behavior posture health index corresponding to the target user, Z a And expressing the index as the behavior posture health index of the target user corresponding to the a-th collection interval.
Specifically, the behavioral gesture duration monitoring unit is configured to perform health monitoring on the behavioral gesture duration of the target user, and the specific monitoring process performs the following steps:
b1, counting the number of behavior gesture types of the target user from the behavior gesture types of the target user corresponding to each acquisition interval, numbering the behavior gesture types by 1,2, B, d, and simultaneously acquiring the duration corresponding to the behavior gesture types;
b2, extracting health duration corresponding to various behavior gesture types of the target user from a health database based on the acquired various behavior gesture types of the target user;
b3. Various types of target usersThe duration time of the behavior gesture is compared with the health duration time corresponding to various behavior gestures, and the health index of the duration time of the behavior gesture corresponding to the target user is calculated, wherein the calculation formula is as follows:
where α represents a health index, T, that is the duration of the behavioral gesture of the target user b And
respectively representing the duration corresponding to the b-th behavior gesture type and the health duration corresponding to the b-th behavior gesture type of the target user.
It should be noted that, in the above health index calculation formula of the behavior gesture duration corresponding to the target user, the larger the ratio between the health duration corresponding to a certain behavior gesture type and the duration corresponding to the behavior gesture type of the target user is, the larger the health index of the behavior gesture duration corresponding to the target user is, which indicates that the behavior gesture duration of the target user conforms to the health standard.
The sleep health monitoring module is used for carrying out health monitoring on the sleep condition of a target user.
Referring to fig. 3, the sleep health monitoring module includes a sleep time monitoring unit, a brain wave monitoring unit and a sleep posture change monitoring unit;
specifically, the sleep time monitoring unit is configured to perform health monitoring on sleep time of a target user, and the specific monitoring process executes the following steps:
d1, starting a sleep monitor arranged in a private area, collecting the turn-off time point of indoor light by a light sensor in the sleep monitor, and further taking the turn-off time point as the turn-off time point of a target user, and meanwhile, collecting the time point of the target user when the body movement is in a stable state by a body movement sensor as the body movement stable time point;
d2, acquiring a time interval between the lamp turning-off time point and the body movement stable time point, and taking the time interval as the sleep time of the target user;
d3, comparing the sleeping time of the target user with the healthy sleeping time in the health database, and calculating the health index of the sleeping time corresponding to the target user, wherein the calculation formula is as follows:
where β is expressed as a health index, t, of the length of time the target user is asleep Sign board Expressed as a healthy falling asleep time period, t' expressed as a falling asleep time period of the target user, and e expressed as a natural constant.
It should be noted that, in the above formula for calculating the health index of the time period of falling asleep corresponding to the target user, the larger the ratio between the healthy time period of falling asleep and the time period of falling asleep of the target user is, the larger the health index of the time period of falling asleep corresponding to the target user is, the more the time period of falling asleep of the target user meets the health standard.
Specifically, the brain wave monitoring unit is used for performing health monitoring on the brain waves of a target user in a sleep state, and the specific monitoring process of the brain wave monitoring unit comprises the following steps:
e1, monitoring the brain waves of a target user in a sleeping state through a brain wave sensor arranged on a sleep monitor, and further acquiring a brain wave change curve of the target user in the sleeping state;
e2: based on brain wave health change curves stored in a health database, the length of the brain wave health change curve is further extracted;
e3: the brain wave change curve of the target user in the sleeping state is superposed and compared with the brain wave health change curve stored in the health database, the length of the superposed curve is extracted, and the brain wave health index of the target user in the sleeping state is calculated, wherein the calculation formula is as follows:
wherein δ is the brain wave health index of the target user in the sleeping state, L is the length of coincidence of the brain wave change curves of the target user in the sleeping state, and L is the length of the brain wave health change curve.
It should be noted that, in the above formula for calculating the brain wave health index of the target user in the sleep state, the larger the ratio between the length of the coincidence of the brain wave variation curves of the target user in the sleep state and the length of the brain wave health variation curves of the target user is, the larger the brain wave health index of the target user in the sleep state is, which indicates that the brain waves of the target user in the sleep state meet the health standard.
Specifically, the sleep posture change monitoring unit comprises a sleep posture movement frequency monitoring subunit, a sleep posture movement amplitude monitoring subunit and a sleep posture movement distance monitoring subunit.
Further, the sleep body movement frequency monitoring subunit is configured to perform health monitoring on the sleep body movement frequency of the target user, and the specific monitoring process includes the following steps:
f1, monitoring and counting the body movement frequency of a target user in a sleeping state through a body movement sensor arranged on a sleep monitor;
f2, comparing the body movement frequency of the target user in the sleeping state with the healthy sleeping body movement frequency in the healthy database, and calculating the healthy index of the sleeping body movement frequency of the target user, wherein the calculation formula is as follows:
wherein epsilon represents the sleep body frequency of movement index, PC, of the target user 0 The body movement frequency of the healthy sleep is represented, and the body movement frequency of the target user in the sleep state is represented by PC'.
It should be noted that, in the above formula for calculating the sleep movement frequency health index of the target user, the larger the ratio between the healthy sleep movement frequency and the movement frequency of the target user in the sleep state is, the larger the sleep movement frequency health index of the target user is, which indicates that the movement frequency of the target user in the sleep state meets the health standard.
Further, the sleep body movement amplitude monitoring subunit is configured to perform health monitoring on the sleep body movement amplitude of the target user, and the specific monitoring process includes the following steps:
g1, numbering each sleep movement of a target user as 1,2, f, h;
g2, respectively carrying out three-dimensional image acquisition on the sleep posture and the initial sleep posture of the target user after each sleep posture movement through a high-definition camera arranged on the sleep monitor, acquiring three-dimensional images corresponding to the sleep posture and the initial sleep posture of the target user after each sleep posture movement, and further extracting the shape contour and the shape contour area corresponding to the sleep posture and the initial sleep posture of the target user after each sleep posture movement;
g3, overlapping and comparing the outline corresponding to the sleep posture after each sleep posture of the target user with the outline of the initial sleep posture, extracting the overlapping area of the outline corresponding to the sleep posture after each sleep posture of the target user and the outline of the initial sleep posture, and calculating the sleep posture amplitude change index of the target user, wherein the calculation formula is as follows:
wherein λ is expressed as the sleep movement amplitude variation index of the target user, S 0 Expressed as the initial sleeping posture outline area, S, of the target user f And the overlapping area of the outline corresponding to the sleeping posture after the f-th sleeping posture of the target user is shown.
It should be noted that, in the above calculation formula of the target user sleep body movement amplitude variation index, the smaller the difference between the overlapping area of the outline corresponding to the sleep body state after the f-th sleep body movement of the target user and the outline area of the initial sleep body state of the target user is, the smaller the target user sleep body movement amplitude variation index is, which indicates that the target user sleep body movement amplitude variation conforms to the health standard.
Further, the sleep posture movement distance monitoring subunit is configured to perform health monitoring on the sleep posture movement distance of the target user, and the specific monitoring process includes the following steps:
h1, acquiring the sleeping posture of the target user after each sleeping movement and the outline center point corresponding to the initial sleeping posture based on the extracted sleeping posture of the target user after each sleeping movement and the outline center point corresponding to the initial sleeping posture;
h2, taking the outline center point of the initial sleeping posture of the target user as a reference point, further acquiring the distance between the outline center point corresponding to the sleeping posture of the target user after each sleeping posture movement and the reference point, and recording the distance as the posture movement distance corresponding to each sleeping posture movement;
h3, comparing the posture movement distance corresponding to each sleep movement with the healthy sleep posture movement distance stored in the healthy database, calculating the sleep posture movement distance change index corresponding to the target user,
wherein v is a number of a posture movement interval corresponding to each sleep movement, v =1,2 0 Expressed as healthy sleeping posture shift interval, J v The posture movement distance corresponding to the v-th sleep movement is shown.
It should be noted that, in the above formula for calculating the sleep posture movement interval change index corresponding to the target user, the smaller the ratio between the posture movement interval corresponding to the nth sleep posture movement and the healthy sleep posture movement interval, the smaller the sleep posture movement interval change index corresponding to the target user is, which indicates that the sleep posture movement interval change corresponding to the target user meets the health standard.
The health database is used for storing posture characteristics corresponding to various behavior posture types, health indexes corresponding to various posture outlines to which the various behavior posture types belong, health duration corresponding to the various behavior posture types, health sleeping time, brain wave health change curves, health sleeping body movement frequency, health sleeping body movement intervals and comprehensive health indexes corresponding to various health levels.
The comprehensive health analysis module is used for analyzing a comprehensive health index of a target user based on monitoring results of the behavior habit health monitoring module and the sleep health monitoring module, matching the comprehensive health index of the target user with comprehensive health indexes corresponding to all health levels in a health database, and evaluating the comprehensive health level of the target user;
according to the embodiment of the invention, by evaluating the comprehensive health grade of the target user, a reliable basis can be provided for the target user to formulate a health improvement plan.
Specifically, the calculation formula of the comprehensive health index of the target user is as follows:
where ψ represents a composite health index for the target user.
According to the embodiment of the invention, the behavior habit and the sleep condition of the target user are subjected to health monitoring and analysis by setting the home video image monitoring terminal and the sleep monitor, and the health monitoring dimensionalities of behavior posture, behavior posture duration, sleep time, brain waves and sleep posture change are covered, so that the comprehensive health index of the target user is calculated, and the comprehensive health grade of the target user is evaluated, therefore, the health physical examination workload of medical workers is greatly reduced, the workload is relieved, the situation that the individual difference of patients cannot be accurately paid attention to due to human subjective factors is avoided, and a reliability basis can be provided for the health detection result of the target user.
The health data set building module is used for building a health data set from all behavioral and posture images, behavioral and posture health indexes, behavioral and posture duration health indexes, sleep-in-time health indexes, brain wave change curves in sleep states, brain wave health indexes in sleep states, body movement frequency in sleep states, sleep body movement frequency health indexes, sleep body movement three-dimensional images after various sleep body movements, initial sleep body movement three-dimensional images, sleep body movement amplitude change indexes, sleep body movement distance change indexes, comprehensive health indexes and comprehensive health levels of a target user in a monitoring period.
The health data encryption module is used for encrypting the face of a health data set of a target user, the user needs to unlock through face recognition when checking the health data, and only the health data of the user can be checked;
according to the embodiment of the invention, the health data of the target user is encrypted, so that the personal health data of the target user is effectively prevented from being leaked, and the personal privacy safety of the target user is further ensured.
The embodiment of the invention provides a health data intelligent online monitoring analysis management cloud platform based on digitization, which can realize real-time health monitoring of a target user at home, effectively overcomes the defects that a large amount of time and money cost are consumed to drive to a hospital and carry out health examination in a traditional offline hospital examination medical mode, can predict some potential health problems, and simultaneously avoids the problem that the waiting period of the health examination result is long, so that a patient can timely know the self health condition and intervene the health problem in advance, the health requirement of the patient is greatly met, and the harm caused by diseases is favorably reduced.
The foregoing is merely exemplary and illustrative of the present invention and various modifications, additions and substitutions may be made by those skilled in the art to the specific embodiments described without departing from the scope of the invention as defined in the following claims.
Claims (8)
1. The utility model provides a health data intelligence on-line monitoring analysis management cloud platform based on digitization which characterized in that includes:
the system comprises a health monitoring equipment setting module, a brightness sensor, a brain wave sensor, a body movement sensor and a high-definition camera, wherein the health monitoring equipment setting module is used for dividing the home interior of a target user into a public area and a private area, setting a home video image monitoring terminal in the public area, setting a sleep monitor in the private area and further installing the brightness sensor, the brain wave sensor, the body movement sensor and the high-definition camera on the sleep monitor;
the behavior habit health monitoring module is used for carrying out health monitoring on the behavior habits of the target user, and comprises a behavior posture health monitoring unit and a behavior posture duration time monitoring unit;
the sleep health monitoring module is used for carrying out health monitoring on the sleep condition of a target user, and comprises a sleep time monitoring unit, a brain wave monitoring unit and a sleep posture change monitoring unit;
the health database is used for storing posture characteristics corresponding to various behavior posture types, health indexes corresponding to various posture outlines to which the various behavior posture types belong, health duration time corresponding to the various behavior posture types, health sleeping time, brain wave health change curves, health sleeping body movement frequency, health sleeping body movement intervals and comprehensive health indexes corresponding to various health levels;
the comprehensive health analysis module is used for analyzing a comprehensive health index of the target user based on monitoring results of the behavioral habit health monitoring module and the sleep health monitoring module, matching the comprehensive health index of the target user with comprehensive health indexes corresponding to all health levels in a health database, and evaluating the comprehensive health level of the target user;
the health data set building module is used for building a health data set from all behavioral and posture images, behavioral and posture health indexes, behavioral and posture duration health indexes, sleep-in-time health indexes, brain wave change curves in a sleep state, brain wave health indexes in the sleep state, body movement frequency in the sleep state, sleep body movement frequency health indexes, sleep body movement three-dimensional images after each sleep body movement, initial sleep body movement three-dimensional images, sleep body movement amplitude change indexes, sleep body movement distance change indexes, comprehensive health indexes and comprehensive health levels of a target user in a monitoring period;
the health data encryption module is used for carrying out face encryption on a health data set of a target user, unlocking the health data set by face recognition when the user checks the health data, and checking only the health data of the user;
the sleep posture change monitoring unit comprises a sleep body movement frequency monitoring subunit, a sleep body movement amplitude monitoring subunit and a sleep posture movement distance monitoring subunit;
the sleep posture movement distance monitoring subunit is used for performing health monitoring on the sleep posture movement distance of the target user, and the specific monitoring process executes the following steps:
h1, acquiring the sleeping posture of the target user after each sleeping movement and the outline central point corresponding to the initial sleeping posture based on the extracted outline corresponding to the sleeping posture of the target user after each sleeping movement and the initial sleeping posture;
h2, taking the outline center point of the initial sleeping posture of the target user as a reference point, further acquiring the distance between the outline center point corresponding to the sleeping posture of the target user after each sleeping posture movement and the reference point, and recording the distance as the posture movement distance corresponding to each sleeping posture movement;
h3, comparing the posture movement distance corresponding to each sleep movement with the healthy sleep posture movement distance stored in the healthy database, calculating the sleep posture movement distance change index corresponding to the target user,
wherein v is a number of a posture movement distance corresponding to each sleep body movement, v =1,2 0 Expressed as healthy sleeping posture movement interval, J v The posture movement distance corresponding to the v-th sleep movement is shown.
2. The cloud platform for intelligent online monitoring, analysis and management of health data based on digitization according to claim 1, wherein the cloud platform comprises: the behavior posture health monitoring unit is used for carrying out health monitoring on the behavior posture of a target user, and the specific monitoring process executes the following steps:
a1, starting a home video image monitoring terminal in a public area, and monitoring the behavior gesture of a target user in real time according to a preset time interval by using a set monitoring period to obtain a behavior gesture image of the target user corresponding to each acquisition interval;
a2, extracting attitude characteristics and attitude outlines from the behavior attitude images of the target users corresponding to the acquisition intervals, and matching the attitude characteristics with the attitude characteristics corresponding to various behavior attitude types, thereby matching the behavior attitude types of the target users corresponding to the acquisition intervals;
a3, screening out health indexes corresponding to various behavior posture profiles of the behavior posture types of the target users corresponding to the acquisition intervals from a health database based on the behavior posture types of the target users corresponding to the acquisition intervals;
a4, matching the target user posture profile corresponding to each acquisition interval with the health index corresponding to each posture profile to which the target user behavior posture type corresponding to the acquisition interval belongs, screening the health index of the target user behavior posture corresponding to each acquisition interval from the health index, and recording the health index as Z a A denotes the number of the acquisition interval, a =1,2.... C;
a5, counting the behavior posture health indexes corresponding to the target users according to the behavior posture health indexes corresponding to the acquisition intervals of the target users, wherein the calculation formula is as follows:
wherein ζ is expressed as a behavior posture health index corresponding to the target user, Z a And expressing the target user behavior posture health index corresponding to the a-th collection interval.
3. The cloud platform for intelligent online monitoring, analysis and management of health data based on digitization according to claim 2, wherein: the behavior gesture duration monitoring unit is used for carrying out health monitoring on the behavior gesture duration of a target user, and the specific monitoring process comprises the following steps:
b1, counting the number of behavior posture types of the target user from the behavior posture types of the target user corresponding to each acquisition interval, numbering the behavior posture types by 1,2, a, B, a, d, and simultaneously acquiring the duration corresponding to the behavior posture types;
b2, extracting health duration corresponding to various behavior posture types of the target user from a health database based on the acquired various behavior posture types of the target user;
b3, comparing the duration time of various behavior gestures of the target user with the health duration time corresponding to the various behavior gestures, and calculating the health index of the duration time of the behavior gesture corresponding to the target user, wherein the calculation formula is as follows:
where α represents a health index, T, that is the duration of the behavioral gesture of the target user b And
respectively representing the duration corresponding to the b-th behavior gesture type and the health duration corresponding to the b-th behavior gesture type of the target user.
4. The cloud platform for intelligent online monitoring, analysis and management of health data based on digitization according to claim 3, wherein: the sleep time monitoring unit is used for carrying out health monitoring on the sleep time of a target user, and the specific monitoring process executes the following steps:
d1, starting a sleep monitor arranged in a private area, collecting the turn-off time point of indoor light by a light sensor in the sleep monitor, and further taking the turn-off time point as the turn-off time point of a target user, and meanwhile, collecting the time point of the target user when the body movement is in a stable state by a body movement sensor as the body movement stable time point;
d2, acquiring a time interval between the light-off time point and the body movement stable time point, and taking the time interval as the sleeping time of the target user;
d3, comparing the sleeping time of the target user with the healthy sleeping time in the health database, and calculating the health index of the sleeping time corresponding to the target user, wherein the calculation formula is as follows:
wherein beta isHealth index, t, expressed as the length of time a target user has fallen asleep Sign Expressed as a healthy falling asleep time period, t' expressed as a falling asleep time period of the target user, and e expressed as a natural constant.
5. The cloud platform for intelligent online monitoring, analysis and management of health data based on digitization as claimed in claim 4, wherein: the brain wave monitoring unit is used for carrying out health monitoring on the brain waves of a target user in a sleeping state, and the specific monitoring process comprises the following steps:
e1, monitoring the brain waves of a target user in a sleeping state through a brain wave sensor arranged on a sleep monitor, and further acquiring a brain wave change curve of the target user in the sleeping state;
e2: based on brain wave health change curves stored in a health database, the length of the brain wave health change curve is further extracted;
e3: the brain wave change curve of the target user in the sleeping state is superposed and compared with the brain wave health change curve stored in the health database, the length of the superposed curve is extracted, and the brain wave health index of the target user in the sleeping state is calculated, wherein the calculation formula is as follows:
wherein δ is the brain wave health index of the target user in the sleep state, L is the length of superposition of the brain wave change curves of the target user in the sleep state, and L is the length of the brain wave health change curve.
6. The cloud platform for intelligent online monitoring, analysis and management of health data based on digitization according to claim 5, wherein: the sleep body movement frequency monitoring subunit is used for performing health monitoring on the sleep body movement frequency of a target user, and the specific monitoring process executes the following steps:
f1, monitoring and counting the body movement frequency of a target user in a sleeping state through a body movement sensor arranged on a sleep monitor;
f2, comparing the body movement frequency of the target user in the sleeping state with the healthy sleeping body movement frequency in the health database, and calculating the sleeping body movement frequency health index of the target user, wherein the calculation formula is as follows:
wherein epsilon is expressed as the sleep body movement frequency health index, PC, of the target user 0 The body movement frequency of the healthy sleep is represented, and the body movement frequency of the target user in the sleep state is represented by PC'.
7. The cloud platform for intelligent online monitoring, analysis and management of health data based on digitization according to claim 6, wherein: the sleep body movement amplitude monitoring subunit is used for performing health monitoring on the sleep body movement amplitude of the target user, and the specific monitoring process executes the following steps:
g1, numbering each sleep movement of a target user as 1,2, f, h;
g2, respectively carrying out three-dimensional image acquisition on the sleep posture and the initial sleep posture of the target user after each sleep posture movement through a high-definition camera arranged on the sleep monitor, acquiring three-dimensional images corresponding to the sleep posture and the initial sleep posture of the target user after each sleep posture movement, and further extracting the shape contour and the shape contour area corresponding to the sleep posture and the initial sleep posture of the target user after each sleep posture movement;
g3, the outline corresponding to the sleep posture of the target user after each sleep posture movement is superposed and compared with the outline of the initial sleep posture, the superposed area of the outline corresponding to the sleep posture of the target user after each sleep posture movement and the outline of the initial sleep posture is extracted, and the change index of the sleep posture amplitude of the target user is calculated, wherein the calculation formula is as follows:
wherein λ is expressed as the sleep movement amplitude variation index of the target user, S 0 RepresentIs the initial sleep posture outline area, S, of the target user f And the overlapping area of the outline corresponding to the sleeping posture after the f-th sleeping posture of the target user is shown.
8. The cloud platform for intelligent online monitoring, analysis and management of health data based on digitization according to claim 7, wherein: the comprehensive health index calculation formula of the target user is as follows:
where ψ represents a composite health index for the target user.
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