CN116110584B - Human health risk assessment early warning system - Google Patents
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Abstract
The invention relates to the technical field of health assessment, and discloses a human health risk assessment and early warning system, which comprises the following components: the human body information acquisition module is used for acquiring basic information of a user; an evaluation action template storage module for storing an evaluation action template; the evaluation selection module is used for selecting an evaluation action template to a user, and the user imitates the selected evaluation action template in sequence to perform movement; a movement data acquisition module for acquiring movement data of a user, the movement data including physical action data and physical function data of the user; a risk assessment module for assessing human health risk; according to the invention, the body health risk is estimated based on the preset action template, the corresponding motion data is collected and compared with the historical data, the estimation range of the health risk can be enlarged, and the accuracy is higher.
Description
Technical Field
The invention relates to the technical field of health assessment, in particular to a human health risk assessment and early warning system.
Background
With the advancement of medical science and technology, we gradually progress from the past era of doctor's seeing treatment to the era of pre-doctor prevention, so that reasonable health assessment of the body is an urgent need; in the health evaluation system in the prior art, the normal value range of each body data of which the standard is matched based on age, height, weight and sex is compared with the collected body data, and whether health risks exist is judged according to whether the body data exceeds the normal value range, for example, a similar health evaluation system is adopted by a smart watch, so that the range of the health risks which can be evaluated is smaller, and the accuracy is low.
Disclosure of Invention
The invention provides a human health risk assessment early warning system, which solves the technical problem of low accuracy of health assessment in the related technology.
According to one aspect of the present invention, there is provided a human health risk assessment and early warning system, comprising:
the human body information acquisition module is used for acquiring basic information of a user;
an evaluation action template storage module for storing an evaluation action template;
the evaluation selection module is used for selecting an evaluation action template to a user, and the user imitates the selected evaluation action template in sequence to perform movement;
a movement data acquisition module for acquiring movement data of a user, the movement data including physical action data and physical function data of the user;
a history data storage module for storing movement data of a history user;
the risk assessment module is used for assessing the health risk of the human body and comprises a first data processing unit, a second data processing unit, a third data processing unit, a fourth data processing unit and a judging unit, wherein the first data processing unit is used for slicing the motion data of a user to obtain node data, and one node data corresponds to the motion data of one time point;
the second data processing unit is used for generating N first objects, each first object is mapped with a two-dimensional coordinate point, the two-dimensional coordinate points of all the first objects are distributed in a matrix array, the number of the two-dimensional coordinate points of each column is the same as that of the two-dimensional coordinate points of each row, and the minimum distance between two adjacent two-dimensional coordinate points is 1; randomly selecting N node data to map to a first object;
a third data processing unit for iteratively performing the steps of:
randomly selecting one from the node data as a second object, calculating a second distance between the second object and the first object, selecting a first object with the minimum second distance from the second object, and updating the first object and a first object adjacent to the first object;
a fourth data processing unit for calculating a first similarity of the motion data of the current user and the motion data of the historical user, sorting the motion data of the historical user in order of the first similarity with the motion data of the current user from the higher to the lower, and extracting the motion data of the previous S historical users as a neighboring set;
and a judging unit which judges the health risk of the current user based on the diagnosis result corresponding to the motion data of the historical user in the adjacent set.
Further, the basic information includes age, height, weight, body fat rate, sex.
Further, the evaluation action template is an action set comprising a plurality of independent actions arranged in a time sequence;
an independent motion describes the motion pose of a human body.
Further, the body motion data includes three-dimensional coordinate positions of human skeletal nodes of the user.
Further, the method for judging by the judging unit includes: and if the proportion of the abnormal diagnosis results corresponding to the motion data of the historical users in the adjacent set exceeds the set first proportion threshold value, judging that the health risk exists for the current user.
Further, the method for slicing the motion data of the user comprises the following steps:
extracting exercise data and body function data at the time point when exercise starts at the beginning and recording the exercise data and the body function data as node data;
the node data comprises a plurality of attributes and corresponding attribute values;
the following steps are then iteratively performed:
extracting motion data from the motion data with the latest recorded time point frame by frame, comparing the newly extracted motion data with the latest recorded time point, recording the newly extracted motion data if the body posture change of the user is judged to exceed a set threshold value, or continuing to extract the motion data frame by frame;
the method for judging that the body posture of the user is changed to exceed the set threshold value is as follows:
calculating a first distance between the body motion data of the newly extracted motion data and the body motion data of the motion data with the latest recorded time point, and judging that the body posture change of the user exceeds a set threshold value if the first distance is larger than or equal to the set first distance threshold value.
Further, the first distance is calculated by the following formula:
wherein the method comprises the steps of、/>、/>Three-dimensional coordinates of the ith human body skeleton node of the body motion data are respectively +.>、、/>Three-dimensional coordinates of the ith human body skeleton node of one body action data are respectively.
Further, the formula for updating the first object and the attribute value of the first object associated therewith is as follows:
wherein the method comprises the steps ofValues representing the jth item attribute of the first object after updating，/>Value of the j-th item attribute representing the first object before update,/>The value of the j-th item attribute representing the second object, t represents the number of times all the first objects are updated.
Further, the method for calculating the first similarity comprises the following steps:
given weighted bipartite graphWherein set->The vertexes of the set Y respectively represent the first object of the motion data of the historical user, and the maximum weight perfect match of the weighted bipartite graph G is solved through a Kuhn-Munkres algorithm;
the initial top-scalar value when solving for a perfect match of the maximum weights of the weighted bipartite graph G is determined as follows:
top label assignment of vertex of set Y is 0, setThe vertex label of the vertex is assigned as the maximum value of the second similarity of the first object mapped by the vertex and the first object mapped by the vertex of the set Y;
second similarity degreeThe calculation formula of (2) is as follows:
wherein, the liquid crystal display device comprises a liquid crystal display device,for the collection->The value of the f-th item attribute of the first object of the vertex of (2), a->For the collection->The value of the f-th attribute of the first object of the vertex of (a), z being the total number of attributes of the first object;
and obtaining the weight sum after matching based on the maximum weight perfect matching as the first similarity.
Further, the human health risk assessment early warning system further comprises a diagnosis prediction module, wherein the diagnosis prediction module recommends corresponding diagnosis items for the current user based on the assessment result of the risk assessment module;
the diagnosis prediction module comprises a first prediction module and a second prediction module, wherein the first prediction module selects a diagnosis result corresponding to the motion data of the historical user with the largest first similarity of the motion data of the current user from the adjacent set;
the second prediction module selects a diagnosis item for diagnosing abnormality from the diagnosis results selected by the first prediction module, and recommends the selected diagnosis item as a diagnosis item for the current user.
The invention has the beneficial effects that:
according to the invention, the physical health risk is estimated based on the preset action template, the corresponding motion data is acquired and compared with the historical data, so that the estimation range of the health risk can be enlarged, the health risk in the medical and surgical fields can be estimated, the accuracy is higher, the recommendation of the corresponding diagnosis item can be provided, and the accurate medical seeking of a user is facilitated.
Drawings
FIG. 1 is a schematic diagram of a system for early warning of human health risk assessment according to the present invention;
FIG. 2 is a schematic block diagram of a risk assessment module of the present invention;
FIG. 3 is a schematic diagram of a second module of the human health risk assessment and early warning system according to the present invention;
FIG. 4 is a block diagram of a diagnostic prediction module of the present invention.
In the figure: the system comprises a human body information acquisition module 101, an evaluation action template storage module 102, an evaluation selection module 103, a motion data acquisition module 104, a history data storage module 105, a risk evaluation module 106, a first data processing unit 1061, a second data processing unit 1062, a third data processing unit 1063, a fourth data processing unit 1064, a judgment unit 1065, a diagnosis prediction module 107, a first prediction module 1071 and a second prediction module 1072.
Detailed Description
The subject matter described herein will now be discussed with reference to example embodiments. It is to be understood that these embodiments are merely discussed so that those skilled in the art may better understand and implement the subject matter described herein and that changes may be made in the function and arrangement of the elements discussed without departing from the scope of the disclosure herein. Various examples may omit, replace, or add various procedures or components as desired. In addition, features described with respect to some examples may be combined in other examples as well.
Example 1
As shown in fig. 1-2, a human health risk assessment and early warning system includes:
a human body information acquisition module 101 for acquiring basic information of a user;
the basic information includes age, height, weight, body fat rate and sex;
an evaluation action template storage module 102 for storing an evaluation action template;
an evaluation action template is an action set that contains a plurality of independent actions arranged in a time sequence.
An independent motion describes a motion gesture of a human body;
for example, the motion gesture of individually lifting the right leg in a standing state is described for an independent motion.
The independent action may be expressed in text or picture.
The action set may be expressed in a video.
Different assessment action templates focus on assessment of different aspects of the health risk, e.g. assessment action templates close to jogging focus on assessing health risk in the lungs, heart, legs, feet, respiratory tract;
for example, assessment action templates containing a large number of bending and stretching movements focus on health risk assessment in the spine and cervical spine;
more than one evaluation action template can be selected for evaluation according to the condition of a user, and all evaluation action templates can be selected for omnibearing evaluation.
An evaluation selection module 103 for selecting an evaluation action template to a user, the user performing exercise by sequentially mimicking the selected evaluation action template;
a motion data acquisition module 104 for acquiring motion data of a user, the motion data including physical action data and physical function data of the user;
the body motion data comprises three-dimensional coordinate positions of human skeleton nodes of the user;
the physical function data includes the age, height, weight, body fat rate, heart rate, blood pressure, body temperature, respiratory rate, etc. of the user.
A history data storage module 105 for storing movement data of a history user;
a historical user's motion data is for an evaluation action template.
In one embodiment of the invention, a piece of historical user's movement data corresponds to a diagnostic result.
A risk assessment module 106 for assessing health risk, which includes a first data processing unit 1061, a second data processing unit 1062, a third data processing unit 1063, a fourth data processing unit 1064, and a determination unit 1065, where the first data processing unit 1061 is configured to slice exercise data of a user to obtain node data, and one node data corresponds to exercise data of one time point;
the method for slicing the motion data of the user comprises the following steps:
extracting exercise data and body function data at the time point when exercise starts at the beginning and recording the exercise data and the body function data as node data;
the node data comprises a plurality of attributes and corresponding attribute values;
in one embodiment of the invention, coordinates of a dimension of a human skeleton node are used as an attribute, and blood pressure is used as an attribute.
The following steps are then iteratively performed:
extracting motion data from the motion data with the latest recorded time point frame by frame, comparing the newly extracted motion data with the latest recorded time point, recording the newly extracted motion data if the body posture change of the user is judged to exceed a set threshold value, or continuing to extract the motion data frame by frame;
the method for judging that the body posture of the user is changed to exceed the set threshold value is as follows:
calculating a first distance between the body motion data of the newly extracted motion data and the body motion data of the motion data with the latest recorded time point, and judging that the body posture change of the user exceeds a set threshold value if the first distance is larger than or equal to the set first distance threshold value.
The time between two adjacent frames of motion data is the acquisition interval of the motion data.
In one embodiment of the present invention, a method for calculating a first distance includes:
extracting limb units formed by connecting a plurality of human body skeleton nodes, calculating the included angle of the same limb unit of two body motion data, and taking the maximum included angle as a value of a first distance;
in one embodiment of the invention, the first distance is calculated by the following formula:
wherein, the liquid crystal display device comprises a liquid crystal display device,、/>、/>three-dimensional coordinates of the ith human body skeleton node of the body motion data are respectively +.>、/>、/>Three-dimensional coordinates of the ith human body skeleton node of the body action data are respectively;
for a standard human skeleton model diagram, 18 human skeleton nodes are provided
The second data processing unit 1062 is configured to generate N first objects, where each first object maps a two-dimensional coordinate point, and the two-dimensional coordinate points of all the first objects are distributed in a matrix array, where the number of two-dimensional coordinate points in each column is the same as the number of two-dimensional coordinate points in each row, and a minimum distance between two adjacent two-dimensional coordinate points is 1;
randomly selecting N node data to map to a first object;
N=where M is the total number of node data of the motion data of the current user.
A third data processing unit 1063 for iteratively performing the following steps:
randomly selecting one from the node data as a second object, calculating a second distance between the second object and the first object, selecting a first object with the minimum second distance from the second object, and updating the first object and a first object adjacent to the first object;
updating a first object and a first object associated therewithThe formula for the attribute values of (a) is as follows:
wherein the method comprises the steps ofValue of the j-th item attribute representing the first object after updating,/>Value of the j-th item attribute representing the first object before update,/>The value representing the j-th attribute of the second object, t represents the number of times all the first objects are updated (one attribute value of one first object is updated as one number of times).
The first object of the neighborhood of the first object means that the linear distance of two-dimensional coordinate points mapped by two first objects is smaller than。
The termination condition for the third data processing unit 1063 to iteratively perform the above steps is: all node data is selected as the first object and each node data is selected as the first object only once.
A fourth data processing unit 1064 for calculating a first similarity between the motion data of the current user and the motion data of the historical user, sorting the motion data of the historical user in order of the first similarity with the motion data of the current user from the top to the bottom, and extracting the motion data of the previous S historical users as a neighbor set;
the first similarity calculation method comprises the following steps:
given weighted bipartite graphWherein set->Are respectively represented by the vertexes of (1)The method comprises the steps that a first object of motion data of a current user, vertexes of a set Y respectively represent the first object of motion data of a historical user, and the maximum weight of a weighted bipartite graph G is perfectly matched through a Kuhn-Munkres algorithm;
the initial top-scalar value when solving for a perfect match of the maximum weights of the weighted bipartite graph G is determined as follows: the top label of the vertex of the set Y is assigned as 0, and the top label of the vertex of the set is assigned as the maximum value of the second similarity between the first object mapped by the vertex and the first object mapped by the vertex of the set Y;
the calculation formula of the second similarity is as follows:
wherein, the liquid crystal display device comprises a liquid crystal display device,for the collection->The value of the f-th item attribute of the first object of the vertex of (2), a->For the collection->The value of the f-th attribute of the first object of the vertex of (a), z being the total number of attributes of the first object;
obtaining the matched weight sum based on maximum weight perfect matching and taking the weight sum as a first similarity;
a judging unit 1065 that judges the health risk of the current user based on the diagnosis results corresponding to the motion data of the historical user in the proximity set;
as a method of judgment, if the proportion of the abnormal diagnosis results corresponding to the motion data of the historical users in the adjacent set exceeds the set first proportion threshold, judging that the health risk exists for the current user.
As shown in fig. 3 to 4, in one embodiment of the present invention, a human health risk assessment early warning system further includes a diagnosis prediction module 107, which recommends a corresponding diagnosis item for a current user based on the assessment result of the risk assessment module 106;
specifically, the diagnostic prediction module 107 includes a first prediction module 1071 and a second prediction module 1072, where the first prediction module 1071 selects a diagnostic result corresponding to the motion data of the historical user having the greatest first similarity to the motion data of the current user from the neighboring set;
the second prediction module 1072 selects a diagnosis item for diagnosing abnormality from the diagnosis results selected by the first prediction module 1071, and recommends the selected diagnosis item as a diagnosis item for the current user.
In one embodiment of the present invention, since the human body structures of users of different genders are greatly different, only the exercise data of the historical user of the same genders as the current user is selected to be input into the risk assessment module 106 if the sample is sufficient.
The embodiment has been described above with reference to the embodiment, but the embodiment is not limited to the above-described specific implementation, which is only illustrative and not restrictive, and many forms can be made by those of ordinary skill in the art, given the benefit of this disclosure, are within the scope of this embodiment.
Claims (9)
1. A human health risk assessment and early warning system, comprising:
the human body information acquisition module is used for acquiring basic information of a user; an evaluation action template storage module for storing an evaluation action template; the evaluation selection module is used for selecting an evaluation action template to a user, and the user imitates the selected evaluation action template in sequence to perform movement;
a movement data acquisition module for acquiring movement data of a user, the movement data including physical action data and physical function data of the user;
a history data storage module for storing movement data of a history user;
the risk assessment module is used for assessing the health risk of the human body and comprises a first data processing unit, a second data processing unit, a third data processing unit, a fourth data processing unit and a judging unit, wherein the first data processing unit is used for slicing the motion data of a user to obtain node data, and one node data corresponds to the motion data of one time point;
the second data processing unit is used for generating N first objects, each first object is mapped with a two-dimensional coordinate point, the two-dimensional coordinate points of all the first objects are distributed in a matrix array, the number of the two-dimensional coordinate points of each column is the same as that of the two-dimensional coordinate points of each row, and the minimum distance between two adjacent two-dimensional coordinate points is 1; randomly selecting N node data to map to a first object;
a third data processing unit for iteratively performing the steps of: randomly selecting one from the node data as a second object, calculating a second distance between the second object and the first object, selecting a first object with the minimum second distance from the second object, and updating the first object and a first object adjacent to the first object;
a fourth data processing unit for calculating a first similarity of the motion data of the current user and the motion data of the historical user, sorting the motion data of the historical user in order of the first similarity with the motion data of the current user from the higher to the lower, and extracting the motion data of the previous S historical users as a neighboring set;
the first similarity calculation method comprises the following steps:
given a weighted bipartite graph:
G＝(X，Y)：X＝{x 1 ，x 2 ，...x n }，Y＝{y 1 ，y 2 ，...y m }，
the vertex of the set X respectively represents a first object of motion data of a current user, the vertex of the set Y respectively represents a first object of motion data of a historical user, and the maximum weight perfect matching of the weighted bipartite graph G is solved through a Kuhn-Munkres algorithm;
the initial top-scalar value when solving for a perfect match of the maximum weights of the weighted bipartite graph G is determined as follows:
the top label of the vertex of the set Y is assigned as 0, and the top label of the vertex of the set X is assigned as the maximum value of the second similarity between the first object mapped by the vertex and the first object mapped by the vertex of the set Y;
second similarity S 2 The calculation formula of (2) is as follows:
wherein D is f Value of the f-th item attribute of the first object being the vertex of set X, E f The value of the f-th item attribute of the first object which is the vertex of the set Y, and z is the total number of the attributes of the first object;
obtaining the matched weight sum based on maximum weight perfect matching and taking the weight sum as a first similarity;
and a judging unit which judges the health risk of the current user based on the diagnosis result corresponding to the motion data of the historical user in the adjacent set.
2. The human health risk assessment and pre-warning system according to claim 1, wherein the basic information includes age, height, weight, body fat rate, sex.
3. The human health risk assessment and early warning system according to claim 1, wherein the assessment action template is an action set comprising a plurality of independent actions arranged in time sequence;
an independent motion describes the motion pose of a human body.
4. The system of claim 1, wherein the physical activity data comprises three-dimensional coordinate locations of skeletal nodes of the user's body.
5. The human health risk assessment and early warning system according to claim 1, wherein the motion data of a historical user corresponds to a diagnosis result, and the judging unit judges the method comprises: and if the proportion of the abnormal diagnosis results corresponding to the motion data of the historical users in the adjacent set exceeds the set first proportion threshold value, judging that the health risk exists for the current user.
6. The human health risk assessment and early warning system according to claim 1, wherein the method for slicing the exercise data of the user comprises:
extracting exercise data and body function data at the time point when exercise starts at the beginning and recording the exercise data and the body function data as node data;
the node data comprises a plurality of attributes and corresponding attribute values;
the following steps are then iteratively performed:
extracting motion data from the motion data with the latest recorded time point frame by frame, comparing the newly extracted motion data with the latest recorded time point, recording the newly extracted motion data if the body posture change of the user is judged to exceed a set threshold value, or continuing to extract the motion data frame by frame;
the method for judging that the body posture of the user is changed to exceed the set threshold value is as follows:
calculating a first distance between the body motion data of the newly extracted motion data and the body motion data of the motion data with the latest recorded time point, and judging that the body posture change of the user exceeds a set threshold value if the first distance is larger than or equal to the set first distance threshold value.
7. The human health risk assessment and pre-warning system according to claim 6, wherein the first distance D 1 Calculated by the following formula:
wherein A is i1 、B i1 、C i1 Three-dimensional coordinates of the ith human body skeleton node of the body motion data respectively, A i2 、B i2 、C i2 Three-dimensional coordinates of the ith human body skeleton node of one body action data are respectively.
8. The human health risk assessment and pre-warning system according to claim 1, wherein the formula for updating the attribute values of the first object and the first object associated therewith is as follows:
wherein the method comprises the steps ofValue of the j-th item attribute representing the first object after updating,/>Value of the j-th item attribute representing the first object before update,/>The value of the j-th item attribute representing the second object, t represents the number of times all the first objects are updated.
9. The human health risk assessment and early warning system according to claim 1, further comprising a diagnosis prediction module for recommending a corresponding diagnosis item to the current user based on the assessment result of the risk assessment module;
the diagnosis prediction module comprises a first prediction module and a second prediction module, wherein the first prediction module selects a diagnosis result corresponding to the motion data of the historical user with the largest first similarity of the motion data of the current user from the adjacent set;
the second prediction module selects a diagnosis item for diagnosing abnormality from the diagnosis results selected by the first prediction module, and recommends the selected diagnosis item as a diagnosis item for the current user.
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