CN109859843B - Intelligent health integrated machine - Google Patents
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- CN109859843B CN109859843B CN201811373409.2A CN201811373409A CN109859843B CN 109859843 B CN109859843 B CN 109859843B CN 201811373409 A CN201811373409 A CN 201811373409A CN 109859843 B CN109859843 B CN 109859843B
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Abstract
An intelligent health all-in-one machine comprises a health detection module, a main controller, a power supply module, a face recognition module, a voice module, a data storage module and a display module; the health detection module is electrically connected with the main controller; the main controller is electrically connected with each module, analyzes and processes health data of a user, and controls signal transmission among the modules; the power module is electrically connected with the main controller and each module; the face recognition module comprises a camera, an identity recognition unit and an emotion recognition unit, wherein the camera is electrically connected with the identity recognition unit and the emotion recognition unit; the voice module comprises a voice input module, a voiceprint recognition unit and a voice recognition unit; the voice input module is electrically connected with the voiceprint recognition unit, the voice recognition unit and the voice broadcasting unit; the data storage module is used for storing user data, and the display module is used for displaying an interface. The invention can automatically recognize the identity of the user and the emotional state of the user through the human face, can finish the operation by only voice, integrates the functions of different detectors and is convenient to use.
Description
Technical Field
The invention relates to the technical field of medical equipment, in particular to an intelligent health all-in-one machine which is intelligent equipment capable of measuring physiological health parameters such as blood pressure and/or blood sugar and managing data, has a face recognition function and a voice interaction function, can monitor the blood pressure of a user for a long time and warn abnormal blood pressure states.
Background
Along with the improvement of living standard, the dietary structure and living habit of modern people are changed, and more people have chronic diseases such as hypertension, hyperglycemia, hyperlipidemia and the like. While the level of life is increasing, people are also paying attention to their health problems, and at the same time, demands for various home detectors are also increasing, such as: blood pressure meters, blood glucose meters, blood oxygen meters, and the like.
Various detectors on the market at present are very single in analysis management of the acquisition process and data, and only acquire and store the data. These instruments do not optimize the acquisition process well for different users, for example, the current blood pressure acquisition mostly involves first increasing the cuff pressure to a certain threshold and then measuring the blood pressure during deflation. The threshold value of the pressurization is set according to the threshold value which is estimated by an algorithm in the pressurization process, and the measurement process is uncomfortable when the pressurization is too large. Nor are these instruments classified and data analyzed for different users and different states of the users. Studies have shown that emotional states have an influence on some physiological parameters, in particular blood pressure, respiration, heart rate, etc. The prior art has the following defects: 1. the existing detectors on the market only have one health detection function (such as blood pressure), and cannot meet more requirements of families on health detection; 2. most detectors have a single acquisition process, and do not classify different users and different emotion states of the same user, and although the prior art applies face recognition to a sphygmomanometer, the technologies can only be used for identity authentication and data management in general; 3. most detectors are single in data analysis, and can only perform longitudinal comparison analysis with historical values, but lack transverse analysis under different emotion states of a user.
On the other hand, each detecting instrument has a different operation interface, so if a user needs to measure multiple physiological parameters, the user needs to be familiar with the operation interface of each instrument, and the using methods of the instruments are respectively mastered. However, since the users who need these tests are often older, it is difficult for them to be familiar with the interface and the process of grasping the method. Moreover, some detectors are not capable of independent operation for some elderly people and some people with physical defects (such as poor eyesight). There are therefore also the following disadvantages: 1. the operation and the use of the detector are inconvenient; for example, a sphygmomanometer needs to operate a cuff and keys at the same time, and thus the operation cannot be independently performed for partial old people and people with physical defects; 2. most detectors in the market are silent, so that for the user with poor eyesight, feedback of the detectors cannot be obtained in time, and the next operation is performed; 3. at present, the detectors in the market generally have only a single function, and each detector has different operation interfaces and key modes, so that the difficulty of the operator is high.
In addition, it is well known that blood pressure is a long-term changing process, and the fluctuation situation of blood pressure is one of important risk indexes of blood pressure, so that the health change situation of a body can be reflected. The existing household sphygmomanometer only compares blood pressure values obtained by single measurement, does not analyze long-term change conditions of blood pressure, and therefore cannot comprehensively reflect health conditions of a human body. The reason for this is that this analysis method using a comparison of the historical blood pressure average with the measured blood pressure value has the following problems: 1. the analysis conclusion is inaccurate, and sudden fluctuation of the blood pressure of a user cannot be reflected, so that potential blood pressure problems are easily ignored; 2. since the blood pressure of the user is greatly affected by other factors (such as age, etc.), the conclusion obtained by comparing with the average value is inaccurate; 3. in the face of professional numerical analysis detected by a sphygmomanometer, it is difficult for a conventional user to directly understand the meaning of health data thereof.
Therefore, how to solve the above-mentioned drawbacks of the prior art is a subject to be studied and solved by the present invention.
Disclosure of Invention
The invention aims to provide an intelligent health all-in-one machine.
In order to achieve the above purpose, the invention adopts the following technical scheme:
an intelligent health integrated machine; the system comprises a health detection module, a main controller, a power supply module, a face recognition module, a voice module, a data storage module and a display module;
the health detection module is used for collecting health data of a user, is electrically connected with the main controller and transmits the obtained health data to the main controller;
the main controller is electrically connected with each module and is used for analyzing and processing the health data of the user and controlling signal transmission among the modules;
the power module is electrically connected with the main controller and each module and is used for supplying power to the main controller and each module;
the face recognition module comprises a camera and an identity recognition unit, and the camera is electrically connected with the identity recognition unit; when the integrated machine works, the camera obtains a facial image of a user, and the identity recognition unit recognizes the identity of the user according to the facial image;
The voice module comprises a voice input module and a voice recognition unit; the voice input module is electrically connected with the voice recognition unit; when the integrated machine works, the voice input module is used for collecting voice signals of a user, and the voice recognition unit recognizes instructions of the user according to the voice signals to realize voice control;
the data storage module is used for storing user data, including user files and user health data;
the display module is used for displaying an interface of the all-in-one machine.
The relevant content explanation in the technical scheme is as follows:
1. in the scheme, the blood pressure value measured by the user is analyzed by a dynamic blood pressure analysis method; the method comprises the following steps:
respectively measuring the blood pressure values of the user on different continuous or intermittent dates according to the positive sequence of the dates, wherein the measured blood pressure values are used as historical blood pressure values;
and
performing curve fitting on the historical systolic pressure and the historical diastolic pressure in the historical blood pressure values to respectively obtain a change trend function of the historical systolic pressure and a change trend function of the historical diastolic pressure;
and
measuring a current blood pressure value of a user;
and
comparing the current systolic pressure in the current blood pressure value with the change trend function of the historical systolic pressure, or/and simultaneously comparing the current diastolic pressure in the current blood pressure value with the change trend function of the historical diastolic pressure;
If at least one of the current systolic pressure and the current diastolic pressure is not matched with the corresponding function, judging that the current blood pressure value is abnormal;
and if the current systolic pressure and the current diastolic pressure are matched with the respective corresponding functions, judging that the current blood pressure value is normal.
2. In the above-mentioned scheme, the fitting modes include polynomial fitting, exponential fitting, gaussian fitting (the fitting modes are not limited to the above three modes, and are only illustrative);
assume that: historical time point x i The method comprises the following steps: x is x 1 ，x 2 ，...，x n Historical blood pressure value y i The method comprises the following steps: y is 1 ，y 2 ，...y n Expressed as a discrete function y=f (x); y is i For the historical systolic pressure or the historical diastolic pressure, i is any natural number from 1 to n, and n is a natural number and represents the number of historical blood pressure values.
3. In the above scheme, the fitting mode is a polynomial fitting method; the model is as follows: p (x) =a m x m +a m-1 x m-1 +…+a 1 x+a 0 ∈П m (m+1＜n)；
Fitting the above discrete function y=f (x) with a function y '=p (x), where y' represents the value of the predicted systolic or diastolic pressure;
with a j Representation a 0 ，a 1 ，...，a m Coefficients of a polynomial; j is any natural number from 1 to m, m is a natural number; the coefficient a j All (x i ，y i ) And (5) value determination.
Coefficient a j From the known historical time points and the historical blood pressure value (x i ，y i ) And carrying out model function and solving.
4. In the above scheme, the fitting mode is an exponential function fitting method; the model is as follows:
fitting the above discrete function y=f (x) with a function y '=p (x), where y' represents the value of the predicted systolic or diastolic pressure;
with b j Representation b 0 ，b 1 ，...，b m By lambda j Represented by lambda 1 ，λ 2 ，...，λ m All are exponential function coefficients; j is any natural number from 1 to m, m is a natural number; the coefficient b j And the coefficient lambda j Is a function corresponding to all (x i ，y i ) And (5) value determination.
Coefficient b j Sum coefficient lambda j Is obtained from the known historical time points and the historical blood pressure value (x i ，y i ) And carrying out model function and solving.
5. In the above scheme, the fitting mode is a Gaussian fitting method; the model is as follows:
fitting the above discrete function y=f (x) with a function y '=p (x), where y' represents the value of the predicted systolic or diastolic pressure;
with c j Representation c 0 ，c 1 ，...，c m Mu is used as j Representation mu 1 ，μ 2 ，...，μ m By sigma j Representation sigma 1 ，σ 2 ，...，σ m Coefficients of gaussian functions; j is any natural number from 1 to m, m is a natural number; the coefficient c j The coefficient mu j And the coefficient sigma j Is a function corresponding to all (x i ，y i ) And (5) value determination.
Coefficient c j Coefficient mu j Sum coefficient sigma j Is obtained from the known historical time points and the historical blood pressure value (x i ，y i ) And carrying out model function and solving.
6. In the above scheme, e is a base number of natural logarithms, is an infinite non-cyclic fraction, and has a value of about: 2.71828182845904523536.
7. in the above scheme, the method for solving the fitting function model coefficient may be a least square method:
fitting y=f (x) using a p (x) function model
The least squares method is such that the fitting function y=p (x), at any x i The points, the distance between the fitting function point and the fitted function point is the smallest, i.e. all (x i ，p(x i ) (x) i ，y i ) The sum of the distances is the smallest;
namely:
and the two sides calculate the bias guide of the coefficients carried by the function model at the same time.
8. In the above scheme, if the function model is a polynomial, the two sides are simultaneously applied to a j Obtaining a deflection guide;
namely: is provided with
Pair a j The deviation is calculated as follows:
all (x i ，y i ) To solve the equation set to obtain a 0 ，a 1 ，...，a m 。
9. In the above scheme, if the function model is an exponential function, two sides of the function model are simultaneously matched with b j And lambda (lambda) j Obtaining a deflection guide;
namely: is provided with
Pair b j The deviation is calculated as follows:
for lambda j The deviation is calculated as follows:
all (x i ，y i ) To solve the equation set to obtain b 0 ，b 1 ，...，b m Lambda (lambda) 1 ，λ 2 ，…，λ m 。
10. In the above scheme, if the function model is a gaussian function, c is simultaneously applied to both sides of each j ，μ j Sum sigma j Obtaining a deflection guide;
namely: is provided with
Pair c j The deviation is calculated as follows:
for mu j The deviation is calculated as follows:
for sigma j The deviation is calculated as follows:
all (x i ，y i ) To solve the equation set to obtain c 0 ，c 1 ，...，c m Mu and mu 1 ，μ 2 ，…，μ m Sigma (sigma) 1 ，σ 2 ，…，σ m 。
11. In the above scheme, after measuring the current blood pressure value of the user, the current blood pressure value is compared with a preset value, and whether the measured current blood pressure value is an effective value is judged. That is, the erroneous measurement value is clearly detected in the abnormal state is excluded.
12. In the above scheme, the blood pressure values of the user are respectively measured on different continuous or intermittent dates according to the positive sequence of the dates, and each measurement day is measured at least once, and the measured blood pressure values are used as historical blood pressure values. For example, a time-divided measurement is performed.
13. In the above scheme, after measuring the current blood pressure value of the user, checking the time period of the moment corresponding to the current blood pressure value, and storing the current blood pressure value in the corresponding time period of the current day.
The time period at least comprises the morning and/or evening of each measuring day, and can specifically comprise: 6:00-9:00, 17:00-20:00, 9:00-17:00, 20:00-24:00, 0:00-6:00, etc.
14. In the above scheme, judging whether the historical blood pressure value of the user reaches a preset number in the corresponding time period; and if the number of the historical blood pressure values is greater than or equal to a preset number, analyzing the current blood pressure value of the user.
15. In the above scheme, curve fitting is performed on the historical systolic pressure and the historical diastolic pressure in the historical blood pressure values in each time period, so as to obtain a change trend function curve of the historical systolic pressure and a change trend function curve of the historical diastolic pressure, which are consistent with the number of the time periods, respectively.
16. In the above scheme, the predicted systolic pressure and the predicted diastolic pressure (i.e., the systolic pressure and the diastolic pressure which should be theoretically assumed by the current blood pressure) of the current blood pressure value are calculated through the curve fitting, and a first deviation value (SD) of the current systolic pressure and the predicted systolic pressure and a second deviation value (DD) of the current diastolic pressure and the predicted diastolic pressure are calculated respectively;
then, the first deviation value (SD) and the second deviation value (DD) are analyzed according to a preset threshold value to obtain blood pressure fluctuation information of the user, namely whether the current blood pressure value of the user is normal or abnormal or the degree of abnormality is graded, and parameters of the preset threshold value can be obtained through long-term clinical data analysis or can be set manually.
17. In the above aspect, the first deviation value (SD) and the second deviation value (DD) are absolute values, and the blood pressure fluctuation information includes:
The first deviation value (SD) is larger than a first systolic pressure preset threshold value (SL 1) or the second deviation value (DD) is larger than a first diastolic pressure preset threshold value (DL 1), and the blood pressure fluctuation of the user is judged to be of a first grade;
the first deviation value (SD) is larger than a second systolic pressure preset threshold value (SL 2) or the second deviation value (DD) is larger than a second diastolic pressure preset threshold value (DL 2), and the blood pressure fluctuation of the user is judged to be of a second grade;
the first deviation value (SD) is larger than a third systolic pressure preset threshold value (SL 3) or the second deviation value (DD) is larger than a third diastolic pressure preset threshold value (DL 3), and the blood pressure fluctuation of the user is judged to be of a third grade.
The first systolic pressure preset threshold (SL 1) is smaller than the second systolic pressure preset threshold (SL 2) is smaller than the third systolic pressure preset threshold (SL 3), and the first diastolic pressure preset threshold (DL 1) is smaller than the second diastolic pressure preset threshold (DL 2) and smaller than the third diastolic pressure preset threshold (DL 3).
18. In the above scheme, the classification of the test results can be illustrated as follows:
if the blood pressure fluctuation is the first grade, the blood pressure fluctuation is indicated to be slightly abnormal;
if the blood pressure fluctuation is the second grade, the blood pressure fluctuation is indicated to be moderate abnormality;
if the blood pressure fluctuation is the third level, the blood pressure fluctuation is serious abnormality.
The number of gradations is not limited to three levels as disclosed herein, and may be more practically required to increase or decrease in order to indicate the severity of blood pressure fluctuations, such as mild abnormalities meaning that blood pressure needs to be noticed by the user, recommended dietary work and rest by the user, and moderate and severe abnormalities meaning that the user needs to be diagnosed by a doctor.
19. In the above scheme, the first deviation value (SD) is a positive value or a negative value, and the second deviation value (DD) is a positive value or a negative value; the preset threshold comprises a positive preset threshold and a negative preset threshold, when the two deviation values are positive, the positive preset threshold and the negative preset threshold are analyzed and compared, and when the two deviation values are negative, the negative preset threshold and the positive preset threshold are analyzed and compared, so that blood pressure fluctuation information of a user is obtained.
If the threshold value is within the threshold value interval, the abnormality is determined to be normal, and if the threshold value is outside the threshold value interval, the degree of abnormality may be classified.
20. In the above scheme, a value may be preset as the historical blood pressure value, or the first measured blood pressure value is taken as the historical blood pressure value, and since the number of the historical blood pressure values is single, the condition of direct fitting into a curve is not provided, and therefore, the curve fitting condition is provided after multiple times of measurement are needed to obtain multiple times of blood pressure values of the user.
21. In the above scheme, the curve fitting may be performed before or after the current blood pressure value of the user is measured.
22. In the above scheme, after obtaining the blood pressure fluctuation information of the user, the system informs the user of the current blood pressure value obtained by the measurement at this time in one or more modes of display screen display, lamplight prompt, voice broadcast or beeping, or/and prompts the user whether the current blood pressure value is normal or not, and can also inform the user of the fluctuation information of the current blood pressure value.
23. In the above scheme, the face recognition module further comprises an emotion recognition unit, and the camera is electrically connected with the emotion recognition unit; when the integrated machine works, the camera obtains a facial image of the user, and the emotion recognition unit recognizes emotion of the user according to the facial image.
24. In the above scheme, the identification unit is used for identifying facial features of the user so as to confirm the identity of the user and store the health data into the user file; if the user is identified as a new user, registering the new user through the facial features and storing the health data into a file of the new user.
25. In the above scheme, the emotion recognition technology is the prior art, namely, emotion recognition can be realized through both machine learning and deep learning technologies, and learning can be performed through a large number of pictures, and the principle of the emotion recognition technology is similar to that of face recognition.
26. In the above scheme, the health data includes data such as blood pressure value, blood glucose value, blood oxygen value, etc.
27. In the above scheme, the health detection module comprises one or more of the following modules:
the blood pressure measuring module, the blood sugar measuring module, the blood oxygen measuring module and the blood fat measuring module.
28. In the above scheme, the health detection module is a blood pressure measurement module, and the blood pressure measurement module comprises an inflation module, a deflation module, a controller, a signal acquisition module and a data processing module; the controller is electrically connected with the inflation module, the deflation module, the signal acquisition module and the data processing module. The blood pressure measuring module is used for measuring blood pressure after receiving the instruction of the main controller and transmitting measured data to the main controller for analysis, and is of the prior art, and can be mastered by a person skilled in the art and adjusted adaptively according to requirements, so that the blood pressure measuring module is not repeated.
29. In the above scheme, the health detection module is a blood glucose measurement module, and the blood glucose measurement module comprises an electrochemical analysis module, a signal acquisition module and a data processing module; the signal acquisition module is electrically connected with the electrochemical analysis module, and the electrochemical analysis module is electrically connected with the data processing module. The blood sugar measuring module is used for measuring blood sugar after receiving the instruction of the main controller and transmitting measured data to the main controller for analysis, and is of the prior art, and can be mastered by a person skilled in the art and adjusted adaptively as required, so that the blood sugar measuring module is not repeated.
30. In the above scheme, the voice module further comprises a voiceprint recognition unit; the voice input module is electrically connected with the voiceprint recognition unit; when the integrated machine works, the voice input module is used for collecting voice signals of a user, and the voiceprint recognition unit recognizes the identity of the user according to the voice signals.
31. In the above scheme, the voiceprint recognition unit is configured to recognize a voiceprint feature of a user, so as to confirm an identity of the user, and store the health data into an archive of the user; if the user is identified as a new user, registering the new user through voiceprint, and storing the health data into the file of the new user.
32. In the above scheme, the voice recognition unit is configured to convert voice information into text information, then perform matching analysis on the text information and keywords stored in the database, analyze an instruction corresponding to the keywords after matching the text information with the corresponding keywords, and finally send the instruction to the main controller to implement execution of the instruction.
33. In the above scheme, the voice control includes a voice start integrated machine, voice control executing each test command, a display interface and other control operations.
34. In the above scheme, the voice input module comprises a microphone and a filter, and the microphone and the filter are electrically connected.
35. In the above scheme, the voice module further comprises a voice broadcasting unit, wherein the voice broadcasting unit is used for receiving an instruction of a user and searching a corresponding voice file according to the instruction to play.
36. In the scheme, the system further comprises a data transmission module, wherein the data transmission module is electrically connected with the power supply module and the main controller;
the data transmission module is wire transmission or/and wireless transmission.
37. In the above scheme, the display module is a touch screen display and a non-touch screen display.
The working principle and the advantages of the invention are as follows:
the invention has the following advantages through the arrangement of the face recognition module: 1. the identity can be automatically identified through the face to log in, so that the user does not need to log in manually, and the operation mode is simplified; 2. identifying the identity of a user before health detection, and calling file data of a corresponding user; taking blood pressure detection as an example, analyzing historical systolic pressure to obtain a threshold value of air bag pressurization so as to provide the user with the best experience effect in the process of measuring blood pressure; 3. analyzing facial features of a user before health detection to acquire the current emotion state of the user; taking blood pressure detection as an example, after the blood pressure measurement is finished, the blood pressure change condition and health condition of a user can be analyzed through the emotion state and the blood pressure data so as to obtain more accurate and reasonable blood pressure change information; 4. various common health detectors are integrated on one device and controlled by one main controller, so that the user is familiar with the simplest operation flow, and the health detector is convenient for home use and saves cost.
The invention has the following advantages through the arrangement of the voice module: 1. the whole operation process of detection can be separated from the keyboard, keys and screen interfaces, and all operations can be completed by only voice; for example: the user says "measure blood pressure", the system prompts by voice whether the cuff is bound or not ", the user replies yes, the system starts to measure blood pressure, and the measured blood pressure is stored in the corresponding user file; the user says "view health data", the system opens the corresponding user file, the display system displays the health data of the user which is automatically analyzed by the system, the system prompts whether to report the health data by voice, the user replies yes, and the system starts to report the health data of the user; the user speaks "modify file", the display displays user information, the user can modify user information according to the voice prompt of the system; 2. various detectors are integrated on one device and controlled by one main controller, so that only one simplest operation flow is needed to be familiar, the family use is convenient, and the cost is saved; 3. the detection operation of all detectors can be completed by voice, and the system automatically records and stores; 4. for the solitary old people, the invention provides the health management equipment capable of carrying out voice interaction, and the fun and convenience of life can be added for the solitary old people.
According to the invention, the historical blood pressure value is analyzed in a curve fitting mode, so that the change trend of the blood pressure value of the user can be effectively predicted, on one hand, a judgment conclusion about whether the blood pressure is normal or not can be given when the user performs current measurement, on the other hand, the dynamic state of the blood pressure change of the user can be displayed, and a health prompt can be provided for the user, so that people can know the health state of the user more clearly, and meanwhile, the blood pressure data which is helpful for medication guidance can be provided when the user makes a doctor visit in a hospital; when the blood pressure measuring device is used, a user does not need to strictly follow a certain specific measuring rule to measure, the user can measure at any time, and each measurement can be compared with the change trend function of the historical blood pressure value to obtain a conclusion that whether the blood pressure value is normal or not. However, based on the technical solution of the present invention, in addition to the above discrete measurement mode, the dynamic blood pressure analysis method of the present invention further includes, but is not limited to, the following professional functions: 1. the invention has the function of fitting the change trend function of the blood pressure value of each day, the measuring points are different time periods of each measuring day, after a plurality of measuring days are measured, the invention is beneficial to grasping the change trend of the blood pressure value of each measuring day, and the invention can also carry out the comparison of the change trend function between each day, and because the change trend function can be displayed in the form of a curve, the invention is beneficial to judging whether the blood pressure has abnormal fluctuation compared with the prior measuring day or not; 2. the invention has the function of fitting the change trend function of the blood pressure value of each week, the measuring point is seven measuring days of each week, after the measurement of a plurality of weeks, the change trend function of each week can be mastered, and the comparison of the change trend function between weeks can be carried out, and as the change trend function can be displayed in a curve form, the invention is helpful for judging whether the blood pressure has abnormal fluctuation compared with the prior measuring week or not; 3. the invention has the function of fitting the blood pressure value change trend function in a specific time period, and the measuring point is the specific time period of each measuring day, such as 6 in the morning: 00-9:00, after a plurality of measurements, helps to grasp the change dynamics of the blood pressure value in the time period every day, and thus helps to judge whether the current measurement has abnormal fluctuation of blood pressure compared with the previous measurement. The dynamic blood pressure analysis method provided by the invention can acquire more blood pressure data along with longer service time, so as to obtain more accurate analysis conclusion.
Drawings
Fig. 1 is a schematic block diagram of a circuit according to an embodiment of the present invention.
Detailed Description
The invention is further described below with reference to the accompanying drawings and examples:
examples: referring to fig. 1, an intelligent health integrated machine; the system comprises a health detection module, a main controller, a power supply module, a face recognition module, a voice module, a data storage module and a display module;
the health detection module comprises a blood pressure measurement module, a blood sugar measurement module, a blood oxygen measurement module and a blood fat measurement module, and is used for collecting health data of a user, such as a blood pressure value, a blood sugar value, a blood oxygen value and the like, and is electrically connected with the main controller, and the obtained health data is transmitted to the main controller;
the main controller is electrically connected with each module and is used for analyzing and processing the health data of the user and controlling signal transmission among the modules;
the power module is electrically connected with the main controller and each module and is used for supplying power to the main controller and each module;
the face recognition module comprises a camera, an identity recognition unit and an emotion recognition unit, wherein the camera is electrically connected with the identity recognition unit and the emotion recognition unit; when the integrated machine works, after a user opens a measurement interface, the camera is triggered to work, the camera obtains a facial image of the user, the identity recognition unit recognizes the identity of the user according to the facial image, and the identity recognition unit is used for recognizing the facial features of the user so as to confirm the identity of the user and store the health data into a file of the user; if the user is identified as a new user, registering the new user through the facial features and storing the health data into a file of the new user. The emotion recognition unit recognizes the emotion of the user according to the facial image, and the emotion recognition technology is the prior art, namely, emotion recognition can be realized through both machine learning and deep learning technologies, and learning can be performed through a large number of pictures, and the principle of the emotion recognition technology is similar to that of face recognition.
The voice module comprises a voice input module, a voiceprint recognition unit and a voice recognition unit; the voice input module is electrically connected with the voiceprint recognition unit and the voice recognition unit; the voice input module comprises a microphone and a filter, and the microphone and the filter are electrically connected. When the integrated machine works, the voice input module is used for collecting voice signals of a user, the voiceprint recognition unit is used for recognizing the identity of the user according to the voice signals, the voiceprint recognition unit is used for recognizing voiceprint characteristics of the user so as to confirm the identity of the user, and the health data are stored in an archive of the user; if the user is identified as a new user, registering the new user through voiceprint, and storing the health data into the file of the new user. The voice recognition unit recognizes the instruction of the user according to the voice signal, and realizes voice control, wherein the voice control comprises a voice starting integrated machine, voice control execution of each test command, a display interface and the like. The voice recognition unit is used for converting voice information into text information, then carrying out matching analysis on the text information and keywords stored in the database, after matching the corresponding keywords, analyzing an instruction corresponding to the keywords, and finally sending the instruction to the main controller to realize the execution of the instruction.
The voice module further comprises a voice broadcasting unit, wherein the voice broadcasting unit is used for receiving an instruction of a user and searching a corresponding voice file according to the instruction to play.
The data storage module is used for storing user data, including user files and user health data; the system comprises a power supply module, a main controller, a data transmission module and a control module, wherein the data transmission module is electrically connected with the power supply module and the main controller; the data transmission module is wire transmission or/and wireless transmission.
The display module is a touch screen display or a non-touch screen display and is used for displaying an interface of the all-in-one machine; the interface includes an interface when in use, and may also display a user's health report when not in use, etc.
The blood pressure measuring module comprises an inflation module, a deflation module, a controller, a signal acquisition module and a data processing module; the controller is electrically connected with the inflation module, the deflation module, the signal acquisition module and the data processing module. The blood pressure measuring module is used for measuring blood pressure after receiving the instruction of the main controller and transmitting measured data to the main controller for analysis, and is of the prior art, and can be mastered by a person skilled in the art and adjusted adaptively according to requirements, so that the blood pressure measuring module is not repeated.
The blood glucose measuring module comprises an electrochemical analysis module, a signal acquisition module and a data processing module; the signal acquisition module is electrically connected with the electrochemical analysis module, and the electrochemical analysis module is electrically connected with the data processing module. The blood sugar measuring module is used for measuring blood sugar after receiving the instruction of the main controller and transmitting measured data to the main controller for analysis, and is of the prior art, and can be mastered by a person skilled in the art and adjusted adaptively as required, so that the blood sugar measuring module is not repeated.
The invention also analyzes the blood pressure value measured by the user by a dynamic blood pressure analysis method; the method comprises the following steps:
respectively measuring the blood pressure values of the user on different continuous or intermittent dates according to the positive sequence of the dates, wherein the measured blood pressure values are used as historical blood pressure values;
and
performing curve fitting on the historical systolic pressure and the historical diastolic pressure in the historical blood pressure values to respectively obtain a change trend function of the historical systolic pressure and a change trend function of the historical diastolic pressure;
and
measuring a current blood pressure value of a user;
and
comparing the current systolic pressure in the current blood pressure value with the change trend function of the historical systolic pressure, or/and simultaneously comparing the current diastolic pressure in the current blood pressure value with the change trend function of the historical diastolic pressure;
If at least one of the current systolic pressure and the current diastolic pressure is not matched with the corresponding function, judging that the current blood pressure value is abnormal;
and if the current systolic pressure and the current diastolic pressure are matched with the corresponding functions, judging that the current blood pressure value is normal, and simultaneously, counting the current blood pressure value into the historical blood pressure value.
The fitting modes include polynomial fitting, exponential function fitting and Gaussian fitting (the fitting modes are not limited to the three modes and are only illustrative);
assume that: historical time point x i The method comprises the following steps: x is x 1 ，x 2 ，...，x n Historical blood pressure value y i The method comprises the following steps: y is 1 ，y 2 ，...y n Expressed as a discrete function y=f (x); y is i For the historical systolic pressure or the historical diastolic pressure, i is any natural number from 1 to n, and n is a natural number and represents the number of historical blood pressure values.
If the fitting mode is a polynomial fitting method;
the model is: p (x) =a m x m +a m-1 x m-1 +…+a 1 x+a 0 ∈П m (m+1＜n)；
Fitting the above discrete function y=f (x) with a function y '=p (x), where y' represents the value of the predicted systolic or diastolic pressure;
with a j Representation a 0 ，a 1 ，...，a m Coefficients of a polynomial; j is any natural number from 1 to m, m is a natural number; the coefficient a j All (x i ，y i ) And (5) value determination.
Coefficient a j From the known historical time points and the historical blood pressure value (x i ，y i ) And carrying out model function and solving.
If the fitting mode is an exponential function fitting method;
the model is:
fitting the above discrete function y=f (x) with a function y '=p (x), where y' represents the value of the predicted systolic or diastolic pressure;
with b j Representation b 0 ，b 1 ，...，b m By lambda j Represented by lambda 1 ，λ 2 ，...，λ m All are exponential function coefficients; j is any natural number from 1 to m, m is a natural number; the coefficient b j And the coefficient lambda j Is a function corresponding to all (x i ，y i ) And (5) value determination.
Wherein e is a natural logarithm base, is an infinite non-cyclic fraction, and has a value of about: 2.71828182845904523536.
coefficient b j Sum coefficient lambda j Is obtained from the known historical time points and the historical blood pressure value (x i ，y i ) And carrying out model function and solving.
If the fitting mode is Gaussian fitting;
the model is:
fitting the above discrete function y=f (x) with a function y '=p (x), where y' represents the value of the predicted systolic or diastolic pressure;
with c j Representation c 0 ，c 1 ，...，c m Mu is used as j Representation mu 1 ，μ 2 ，...，μ m By sigma j Representation sigma 1 ，σ 2 ，...，σ m Coefficients of gaussian functions; j is any natural number from 1 to m, m is a natural number; the coefficient c j The coefficient mu j And the coefficient sigma j Is a function corresponding to all (x i ，y i ) And (5) value determination.
Coefficient c j Coefficient mu j Sum coefficient sigma j Is obtained from the known historical time points and the historical blood pressure value (x i ，y i ) And carrying out model function and solving.
The method for solving the fitting function model coefficient can be a least square method:
fitting y=f (x) using a p (x) function model
The least squares method is such that the fitting function y=p (x), at any x i The points, the distance between the fitting function point and the fitted function point is the smallest, i.e. all (x i ，p(x i ) (x) i ，y i ) The sum of the distances is the smallest;
namely:
and the two sides calculate the bias guide of the coefficients carried by the function model at the same time.
Wherein if the function model is a polynomial, the two sides are simultaneously opposite to a j Obtaining a deflection guide;
namely: is provided with
Pair a j The deviation is calculated as follows:
all (x i ，y i ) To solve the equation set to obtain a 0 ，a 1 ，...，a m 。
If the function model is an exponential function, two sides of the function model are respectively and simultaneously matched with b j And lambda (lambda) j Obtaining a deflection guide;
namely: is provided with
Pair b j The deviation is calculated as follows:
for lambda j The deviation is calculated as follows:
all (x i ，y i ) To solve the equation set to obtain b 0 ，b 1 ，...，b m Lambda (lambda) 1 ，λ 2 ，…，λ m 。
If the function model is Gaussian, the two sides are simultaneously matched with c j ，μ j Sum sigma j Obtaining a deflection guide;
namely: is provided with/>
Pair c j The deviation is calculated as follows:
for mu j The deviation is calculated as follows:
For sigma j The deviation is calculated as follows:
all (x i ，y i ) To solve the equation set to obtain c 0 ，c 1 ，...，c m Mu and mu 1 ，μ 2 ，…，μ m Sigma (sigma) 1 ，σ 2 ，…，σ m 。
After measuring the current blood pressure value of the user, comparing the current blood pressure value with a preset value, and judging whether the measured current blood pressure value is an effective value or not. That is, the erroneous measurement value is clearly detected in the abnormal state is excluded.
The blood pressure values of the user can be measured respectively on different continuous or intermittent dates according to the positive sequence of the dates, and the measured blood pressure values are used as historical blood pressure values at least once in each measurement day. For example, a time-divided measurement is performed.
After measuring the current blood pressure value of the user, checking the time period of the moment corresponding to the current blood pressure value, and storing the current blood pressure value into the corresponding time period of the current day.
The time period at least comprises the morning and/or evening of each measuring day, and can specifically comprise: 6:00-9:00, 17:00-20:00, 9:00-17:00, 20:00-24:00, 0:00-6:00, etc.
After measuring the current blood pressure value of a user, judging whether the historical blood pressure value of the user reaches a preset number in the corresponding time period; and if the number of the historical blood pressure values is greater than or equal to a preset number, analyzing the current blood pressure value of the user.
And then, performing curve fitting on the historical systolic pressure and the historical diastolic pressure in the historical blood pressure values in each time period to respectively obtain a change trend function curve of the historical systolic pressure and a change trend function curve of the historical diastolic pressure, which are consistent with the number of the time periods.
Then, calculating a predicted systolic pressure and a predicted diastolic pressure (i.e., a systolic pressure and a diastolic pressure which should be theoretically assumed by the current blood pressure) of the current blood pressure value by the curve fitting, and calculating a first deviation value (SD) of the current systolic pressure and the predicted systolic pressure and a second deviation value (DD) of the current diastolic pressure and the predicted diastolic pressure, respectively;
then, the first deviation value (SD) and the second deviation value (DD) are analyzed according to a preset threshold value to obtain blood pressure fluctuation information of the user, namely whether the current blood pressure value of the user is normal or abnormal or the degree of abnormality is graded, and parameters of the preset threshold value can be obtained through long-term clinical data analysis or can be set manually.
Wherein the first deviation value (SD) and the second deviation value (DD) are absolute values, and the blood pressure fluctuation information includes:
the first deviation value (SD) is larger than a first systolic pressure preset threshold value (SL 1) or the second deviation value (DD) is larger than a first diastolic pressure preset threshold value (DL 1), and the blood pressure fluctuation of the user is judged to be of a first grade;
The first deviation value (SD) is larger than a second systolic pressure preset threshold value (SL 2) or the second deviation value (DD) is larger than a second diastolic pressure preset threshold value (DL 2), and the blood pressure fluctuation of the user is judged to be of a second grade;
the first deviation value (SD) is larger than a third systolic pressure preset threshold value (SL 3) or the second deviation value (DD) is larger than a third diastolic pressure preset threshold value (DL 3), and the blood pressure fluctuation of the user is judged to be of a third grade.
The first systolic pressure preset threshold (SL 1) is smaller than the second systolic pressure preset threshold (SL 2) is smaller than the third systolic pressure preset threshold (SL 3), and the first diastolic pressure preset threshold (DL 1) is smaller than the second diastolic pressure preset threshold (DL 2) and smaller than the third diastolic pressure preset threshold (DL 3).
Wherein, for the classification explanation of the test result, the following can be indicated:
if the blood pressure fluctuation is the first grade, the blood pressure fluctuation is indicated to be slightly abnormal;
if the blood pressure fluctuation is the second grade, the blood pressure fluctuation is indicated to be moderate abnormality;
if the blood pressure fluctuation is the third level, the blood pressure fluctuation is serious abnormality.
The number of gradations is not limited to three levels as disclosed herein, and may be more practically required to increase or decrease in order to indicate the severity of blood pressure fluctuations, such as mild abnormalities meaning that blood pressure needs to be noticed by the user, recommended dietary work and rest by the user, and moderate and severe abnormalities meaning that the user needs to be diagnosed by a doctor.
Wherein the first deviation value (SD) is a positive or negative value and the second deviation value (DD) is a positive or negative value; the preset threshold comprises a positive preset threshold and a negative preset threshold, when the two deviation values are positive, the positive preset threshold and the negative preset threshold are analyzed and compared, and when the two deviation values are negative, the negative preset threshold and the positive preset threshold are analyzed and compared, so that blood pressure fluctuation information of a user is obtained.
If the threshold value is within the threshold value interval, the abnormality is determined to be normal, and if the threshold value is outside the threshold value interval, the degree of abnormality may be classified.
In addition, a value can be preset as a historical blood pressure value, or the first measured blood pressure value is taken as the historical blood pressure value, and because the number of the historical blood pressure values is single, the condition of direct fitting into a curve is not provided, and therefore the curve fitting condition is provided after a plurality of times of blood pressure values of a user are obtained through a plurality of times of measurement.
The curve fitting may be performed before or after the current blood pressure value of the user is measured.
After the blood pressure fluctuation information of the user is obtained, the system informs the user of the current blood pressure value obtained by the measurement at this time in one or more modes of display screen display, lamplight prompt, voice broadcast or beeping, or/and prompts the user whether the current blood pressure value is normal or not, and can also inform the user of the fluctuation information of the current blood pressure value.
According to the invention, the historical blood pressure value is analyzed in a curve fitting mode, so that the change trend of the blood pressure value of the user can be effectively predicted, on one hand, a judgment conclusion about whether the blood pressure is normal or not can be given when the user performs current measurement, on the other hand, the dynamic state of the blood pressure change of the user can be displayed, and a health prompt can be provided for the user, so that people can know the health state of the user more clearly, and meanwhile, the blood pressure data which is helpful for medication guidance can be provided when the user makes a doctor visit in a hospital; and the dynamic blood pressure analysis method can acquire more accurate analysis conclusion along with the longer use time, and the more blood pressure data are acquired.
The above embodiments are provided to illustrate the technical concept and features of the present invention and are intended to enable those skilled in the art to understand the content of the present invention and implement the same, and are not intended to limit the scope of the present invention. All equivalent changes or modifications made in accordance with the spirit of the present invention should be construed to be included in the scope of the present invention.
Claims (8)
1. An intelligent health integrated machine; the method is characterized in that:
the system comprises a health detection module, a main controller, a power supply module, a face recognition module, a voice module, a data storage module and a display module;
The health detection module is used for collecting health data of a user, is electrically connected with the main controller and transmits the obtained health data to the main controller;
the main controller is electrically connected with each module and is used for analyzing and processing the health data of the user and controlling signal transmission among the modules;
the power module is electrically connected with the main controller and each module and is used for supplying power to the main controller and each module;
the face recognition module comprises a camera and an identity recognition unit, and the camera is electrically connected with the identity recognition unit; when the integrated machine works, the camera obtains a facial image of a user, and the identity recognition unit recognizes the identity of the user according to the facial image;
the voice module comprises a voice input module and a voice recognition unit; the voice input module is electrically connected with the voice recognition unit; when the integrated machine works, the voice input module is used for collecting voice signals of a user, and the voice recognition unit recognizes instructions of the user according to the voice signals to realize voice control;
the data storage module is used for storing user data, including user files and user health data;
The display module is used for displaying an interface of the all-in-one machine;
analyzing the blood pressure value measured by the user by a dynamic blood pressure analysis method; the method comprises the following steps:
respectively measuring the blood pressure values of the user on different continuous or intermittent dates according to the positive sequence of the dates, wherein the measured blood pressure values are used as historical blood pressure values;
and
performing curve fitting on the historical systolic pressure and the historical diastolic pressure in the historical blood pressure values to respectively obtain a change trend function of the historical systolic pressure and a change trend function of the historical diastolic pressure;
and
measuring a current blood pressure value of a user;
and
comparing the current systolic pressure in the current blood pressure value with the change trend function of the historical systolic pressure, or/and simultaneously comparing the current diastolic pressure in the current blood pressure value with the change trend function of the historical diastolic pressure;
if at least one of the current systolic pressure and the current diastolic pressure is not matched with the corresponding function, judging that the current blood pressure value is abnormal, and sending a prompt by a system;
if the current systolic pressure and the current diastolic pressure are matched with the corresponding functions, judging that the current blood pressure value is normal;
After measuring the current blood pressure value of a user, checking a time period in which the moment corresponding to the current blood pressure value is located, and storing the current blood pressure value into the corresponding time period of the current day;
judging whether the historical blood pressure value of the user reaches a preset number in the corresponding time period; if the number of the historical blood pressure values is greater than or equal to a preset number, analyzing the current blood pressure value of the user;
and performing curve fitting on the historical systolic pressure and the historical diastolic pressure in the historical blood pressure values in each time period to respectively obtain a change trend function curve of the historical systolic pressure and a change trend function curve of the historical diastolic pressure, which are consistent with the number of the time periods.
2. The all-in-one machine according to claim 1, wherein: the fitting mode comprises polynomial fitting, exponential function fitting and Gaussian fitting;
assume that: historical time point x i The method comprises the following steps: x is x 1 ,x 2 ,…,x n Historical blood pressure value y i The method comprises the following steps: y is 1 ,y 2 ,…y n Expressed as a discrete function y=f (x); historical blood pressure value y i Specifically, the number of the historical systolic pressure or the historical diastolic pressure is adopted, i is any natural number from 1 to n, n is a natural number, and the number of the historical blood pressure values is represented.
3. The all-in-one machine according to claim 2, wherein: the fitting mode is a polynomial fitting method; the model is as follows: p (x) =a m x m +a m-1 x m-1 +…+a 1 x+a 0 ∈∏ m (m+1<n)；
Fitting the above discrete function y=f (x) with a function y '=p (x), where y' represents the value of the predicted systolic or diastolic pressure;
with a j Representation a 0 ,a 1 ,…,a m Coefficients of a polynomial; j is any natural number from 1 to m, m is a natural number; coefficient a j All (x i ,y i ) And (5) value determination.
4. The all-in-one machine according to claim 2, wherein: the fitting mode is an exponential function fitting method; the model is as follows:
fitting the above discrete function y=f (x) with a function y '=p (x), where y' represents the value of the predicted systolic or diastolic pressure;
with b j Representation b 0 ,b 1 ,…,b m By lambda j Represented by lambda 1 ,λ 2 ,…,λ m All are exponential function coefficients; j is any natural number from 1 to m, m is a natural number; coefficient b j Sum coefficient lambda j Is a function corresponding to all (x i ,y i ) And (5) value determination.
5. The all-in-one machine according to claim 2, wherein: the fitting mode is a Gaussian fitting method; the model is as follows:
fitting the above discrete function y=f (x) with a function y '=p (x), where y' represents the value of the predicted systolic or diastolic pressure;
With c j Representation c 0 ,c 1 ,…,c m Mu is used as j Representation mu 1 ,μ 2 ,…,μ m By sigma j Representation sigma 1 ,σ 2 ,…,σ m Coefficients of gaussian functions; j is any natural number from 1 to m, m is a natural number; coefficient c j Coefficient mu j Sum coefficient sigma j Is a function corresponding to all (x i ,y i ) And (5) value determination.
6. The all-in-one machine according to claim 1, wherein: the blood pressure values of the user are measured respectively on different continuous or intermittent dates according to the positive sequence of the dates, and the measured blood pressure values are measured at least once every measuring day and serve as historical blood pressure values.
7. The all-in-one machine according to claim 1, wherein: calculating a predicted systolic pressure and a predicted diastolic pressure of a current blood pressure value through the curve fitting, and respectively calculating a first deviation value (SD) of the current systolic pressure and the predicted systolic pressure and a second deviation value (DD) of the current diastolic pressure and the predicted diastolic pressure;
and then, analyzing the first deviation value (SD) and the second deviation value (DD) according to a preset threshold value to obtain the blood pressure fluctuation information of the user.
8. The all-in-one machine according to claim 7, wherein:
the first deviation value (SD) and the second deviation value (DD) are absolute values, and the blood pressure fluctuation information includes:
The first deviation value (SD) is larger than a first systolic pressure preset threshold value (SL 1) or the second deviation value (DD) is larger than a first diastolic pressure preset threshold value (DL 1), and the blood pressure fluctuation of the user is judged to be of a first grade;
the first deviation value (SD) is larger than a second systolic pressure preset threshold value (SL 2) or the second deviation value (DD) is larger than a second diastolic pressure preset threshold value (DL 2), and the blood pressure fluctuation of the user is judged to be of a second grade;
the first deviation value (SD) is larger than a third systolic pressure preset threshold value (SL 3) or the second deviation value (DD) is larger than a third diastolic pressure preset threshold value (DL 3), and the blood pressure fluctuation of the user is judged to be of a third grade.
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| EP4016524A4 (en) * | 2019-08-15 | 2022-08-24 | Shenzhen Mindray Bio-Medical Electronics Co., Ltd. | Medical device and medical device system |
| CN112672120B (en) * | 2019-10-15 | 2022-09-09 | 许桂林 | Projector with voice analysis function and personal health data generation method |
| CN111202503A (en) * | 2020-01-21 | 2020-05-29 | 江苏乾元通信息科技有限公司 | Household physical sign monitoring device and control method thereof |
| CN112075928A (en) * | 2020-09-21 | 2020-12-15 | 浙江奥智网络科技有限公司 | Health state recognition all-in-one machine and control system thereof |
| CN112366005A (en) * | 2020-11-17 | 2021-02-12 | 徐晓民 | Four basic life family health management systems based on people |
| CN112447284A (en) * | 2020-11-26 | 2021-03-05 | 北京沃东天骏信息技术有限公司 | Information generation method and device, electronic equipment and computer readable medium |
| CN113192635A (en) * | 2020-12-09 | 2021-07-30 | 夏灵林 | Intelligent system design and application for assisting blood pressure standardized measurement |
| CN116807426A (en) * | 2023-07-24 | 2023-09-29 | 山东山科智心科技有限公司 | System based on photoelectric sensor vital sign signal acquisition and analysis |
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