CN112168188B - Processing method and device for pressure detection data - Google Patents
Processing method and device for pressure detection data Download PDFInfo
- Publication number
- CN112168188B CN112168188B CN202011074735.0A CN202011074735A CN112168188B CN 112168188 B CN112168188 B CN 112168188B CN 202011074735 A CN202011074735 A CN 202011074735A CN 112168188 B CN112168188 B CN 112168188B
- Authority
- CN
- China
- Prior art keywords
- pressure
- information
- user
- obtaining
- output result
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Classifications
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/16—Devices for psychotechnics; Testing reaction times ; Devices for evaluating the psychological state
- A61B5/165—Evaluating the state of mind, e.g. depression, anxiety
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7235—Details of waveform analysis
- A61B5/7264—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
- A61B5/7267—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems involving training the classification device
Landscapes
- Health & Medical Sciences (AREA)
- Engineering & Computer Science (AREA)
- Life Sciences & Earth Sciences (AREA)
- Physics & Mathematics (AREA)
- Psychiatry (AREA)
- Artificial Intelligence (AREA)
- Veterinary Medicine (AREA)
- Surgery (AREA)
- Public Health (AREA)
- General Health & Medical Sciences (AREA)
- Animal Behavior & Ethology (AREA)
- Biophysics (AREA)
- Pathology (AREA)
- Biomedical Technology (AREA)
- Heart & Thoracic Surgery (AREA)
- Medical Informatics (AREA)
- Molecular Biology (AREA)
- Evolutionary Computation (AREA)
- Educational Technology (AREA)
- Hospice & Palliative Care (AREA)
- Social Psychology (AREA)
- Developmental Disabilities (AREA)
- Child & Adolescent Psychology (AREA)
- Psychology (AREA)
- Fuzzy Systems (AREA)
- Mathematical Physics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Physiology (AREA)
- Signal Processing (AREA)
- Measuring And Recording Apparatus For Diagnosis (AREA)
Abstract
The invention provides a processing method and a processing device for pressure detection data, which relate to the technical field of psychological pressure detection and are characterized in that a first preset time period is obtained; according to a first preset time period, obtaining first pressure data of a first user at a first moment and second pressure data of a second moment until N-th pressure data of an N-th moment are obtained; optimizing the second pressure data according to the first neural network model, and then obtaining a second output result; repeating the steps until an Nth output result is obtained; obtaining a first pressure lower limit value and a first pressure upper limit value; obtaining a first pressure warning value; sequentially judging whether the output result exceeds the first pressure warning value; if yes, obtaining a first warning duration; judging whether the first warning duration meets a first preset condition or not; if the first warning information does not meet the first warning information, the stability and the readability of the pressure value are improved, and the technical effect of accurately judging the psychological state of the user is achieved.
Description
Technical Field
The invention relates to the technical field of psychological stress detection, in particular to a processing method and device for stress detection data.
Background
In the context of intense competition and social evolution, people are increasingly facing the threat of various sources of stress. If the fruits are in a pressure environment for a long time, not only the psychological health is affected, but also the more serious people can threaten the life. Therefore, the pressure faced by people needs to be detected and evaluated, and the purposes of improving the life quality and prolonging the service life are achieved through scientific and comprehensive health management.
However, the applicant of the present invention has found that the prior art has at least the following technical problems:
the prior art can deduce the value of pressure through the value of a PPG or GSR sensor, but the value appears at intervals of 1 second or very small time, the stability is very poor, the difference between two continuous values is possibly very large, the true pressure level cannot be represented, and the interpretation of data by a user is very difficult.
Disclosure of Invention
The embodiment of the invention provides a processing method and a processing device for pressure detection data, which solve the technical problems that the stability of the detected pressure value in the prior art is poor, the real pressure level of a user is difficult to embody, the data interpretation is inconvenient for the user, the psychological state of the user is accurately judged and processed, the stability and the readability of the pressure value are improved, the psychological state of the user can be accurately judged, and the processing can be timely performed when the problem occurs.
In view of the foregoing, embodiments of the present application have been presented to provide a processing method and apparatus for pressure detection data.
In a first aspect, the present invention provides a processing method for pressure detection data, wherein the method comprises: step 1: obtaining a first preset time period; step 2: according to a first preset time period, acquiring first pressure data of a first user at a first moment and second pressure data of a second moment until N-th pressure data of an N-th moment are acquired, wherein the first pressure data, the second pressure data and the N-th pressure data are acquired in a PPG or GSR mode; step 3: according to the first pressure data, optimizing the second pressure data according to the first neural network model, and then obtaining a second output result; step 4: repeating the step 3 until an Nth output result is obtained; step 5: obtaining a first pressure lower limit value and a first pressure upper limit value according to the first output result, the second output result and the Nth output result; step 6: obtaining a first pressure warning value according to the first pressure lower limit value and the first pressure upper limit value; step 7: sequentially judging whether the first output result and the second output result until the Nth output result exceeds the first pressure warning value; step 8: if yes, obtaining a first warning duration; step 9: judging whether the first warning duration meets a first preset condition according to a first preset time period; step 10: if not, the first warning information is sent to the first user.
In a second aspect, the present invention provides a processing apparatus for pressure detection data, the apparatus comprising:
a first obtaining unit configured to obtain a first preset time period;
the second obtaining unit is used for obtaining first pressure data of a first user at a first moment and second pressure data of a second moment until N-th pressure data of the N-th moment are obtained according to a first preset time period, wherein the first pressure data, the second pressure data and the N-th pressure data are all acquired in a PPG or GSR mode;
the third obtaining unit is used for obtaining a second output result after optimizing the second pressure data according to the first pressure data and the first neural network model;
a fourth obtaining unit, configured to repeat the step 3 until an nth output result is obtained;
a fifth obtaining unit, configured to obtain a first pressure lower limit value and a first pressure upper limit value according to the first output result, the second output result, and the nth output result;
a sixth obtaining unit configured to obtain a first pressure warning value from the first pressure lower limit value and the first pressure upper limit value;
The first judging unit is used for sequentially judging whether the first output result and the second output result until the Nth output result exceed the first pressure warning value or not;
a seventh obtaining unit, configured to obtain a first alert duration if the first alert duration exceeds the first alert duration;
the second judging unit is used for judging whether the first warning duration meets a first preset condition according to a first preset time period;
and the first sending unit is used for sending first warning information to the first user if the first warning information is not met.
In a third aspect, the present invention provides a processing device for pressure detection data, comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the method of any one of the preceding aspects when the program is executed.
The above-mentioned one or more technical solutions in the embodiments of the present application at least have one or more of the following technical effects:
the embodiment of the invention provides a processing method and a device for pressure detection data, wherein the searching method comprises the following steps: step 1: obtaining a first preset time period; step 2: according to a first preset time period, acquiring first pressure data of a first user at a first moment and second pressure data of a second moment until N-th pressure data of an N-th moment are acquired, wherein the first pressure data, the second pressure data and the N-th pressure data are acquired in a PPG or GSR mode; step 3: according to the first pressure data, optimizing the second pressure data according to the first neural network model, and then obtaining a second output result; step 4: repeating the step 3 until an Nth output result is obtained; step 5: obtaining a first pressure lower limit value and a first pressure upper limit value according to the first output result, the second output result and the Nth output result; step 6: obtaining a first pressure warning value according to the first pressure lower limit value and the first pressure upper limit value; step 7: sequentially judging whether the first output result and the second output result until the Nth output result exceeds the first pressure warning value; step 8: if yes, obtaining a first warning duration; step 9: judging whether the first warning duration meets a first preset condition according to a first preset time period; step 10: if the data are not satisfied, the first warning information is sent to the first user, so that the technical effects that the stability of the detected pressure value in the prior art is poor, the real pressure level of the user is difficult to embody, the user is inconvenient to read the data, the psychological state of the user is accurately judged and processed are achieved, the stability and the readability of the pressure value are improved, the psychological state of the user can be accurately judged, and the processing can be timely performed when the problem occurs are achieved.
The foregoing description is only an overview of the present invention, and is intended to be implemented in accordance with the teachings of the present invention in order that the same may be more clearly understood and to make the same and other objects, features and advantages of the present invention more readily apparent.
Drawings
FIG. 1 is a flow chart of a method for processing pressure detection data according to an embodiment of the invention;
FIG. 2 is a flow chart of a first training model for pressure detection data according to an embodiment of the present invention;
FIG. 3 is a flowchart of a method for processing pressure detection data to obtain an alert level of a first user according to an embodiment of the present invention;
fig. 4 is a schematic flow chart of a processing method for pressure detection data in order to obtain first image information of the first user according to an embodiment of the present invention;
FIG. 5 is a flowchart illustrating an effect of correcting the first warning information in a processing method for pressure detection data according to an embodiment of the present invention;
fig. 6 is a schematic flow chart of an effect of providing health guidance for a user in time in a processing method for pressure detection data according to an embodiment of the present invention;
FIG. 7 is a schematic flow chart of a method for obtaining a first warning duration in pressure detection data according to an embodiment of the present invention;
FIG. 8 is a schematic diagram of a device for processing pressure detection data according to an embodiment of the present invention;
fig. 9 is a schematic structural diagram of another exemplary electronic device according to an embodiment of the present invention.
Reference numerals illustrate: the device comprises a first obtaining unit 11, a second obtaining unit 12, a third obtaining unit 13, a fourth obtaining unit 14, a fifth obtaining unit 15, a sixth obtaining unit 16, a first judging unit 17, a seventh obtaining unit 18, a second judging unit 19, a first transmitting unit 20, a bus 300, a receiver 301, a processor 302, a transmitter 303, a memory 304 and a bus interface 306.
Detailed Description
The embodiment of the invention provides a processing method and a processing device for pressure detection data, which are used for solving the technical problems that the stability of the detected pressure value in the prior art is poor, the real pressure level of a user is difficult to embody, the data interpretation is inconvenient for the user, the psychological state of the user is accurately judged and processed, the stability and the readability of the pressure value are improved, the psychological state of the user can be accurately judged, and the processing can be timely performed when the problem occurs. Hereinafter, example embodiments according to the present application will be described in detail with reference to the accompanying drawings. It should be apparent that the described embodiments are only some of the embodiments of the present application and not all of the embodiments of the present application, and it should be understood that the present application is not limited by the example embodiments described herein.
Summary of the application
In the context of intense competition and social evolution, people are increasingly facing the threat of various sources of stress. If the fruits are in a pressure environment for a long time, not only the psychological health is affected, but also the more serious people can threaten the life. Therefore, the pressure faced by people needs to be detected and evaluated, and the purposes of improving the life quality and prolonging the service life are achieved through scientific and comprehensive health management. The prior art can deduce the value of pressure through the value of a PPG or GSR sensor, but the value appears at intervals of 1 second or very small time, the stability is very poor, the difference between two continuous values is possibly very large, the true pressure level cannot be represented, and the interpretation of data by a user is very difficult.
Aiming at the technical problems, the technical scheme provided by the invention has the following overall thought:
the embodiment of the application provides a processing method for pressure detection data, wherein the method comprises the following steps: step 1: obtaining a first preset time period; step 2: according to a first preset time period, acquiring first pressure data of a first user at a first moment and second pressure data of a second moment until N-th pressure data of an N-th moment are acquired, wherein the first pressure data, the second pressure data and the N-th pressure data are acquired in a PPG or GSR mode; step 3: according to the first pressure data, optimizing the second pressure data according to the first neural network model, and then obtaining a second output result; step 4: repeating the step 3 until an Nth output result is obtained; step 5: obtaining a first pressure lower limit value and a first pressure upper limit value according to the first output result, the second output result and the Nth output result; step 6: obtaining a first pressure warning value according to the first pressure lower limit value and the first pressure upper limit value; step 7: sequentially judging whether the first output result and the second output result until the Nth output result exceeds the first pressure warning value; step 8: if yes, obtaining a first warning duration; step 9: judging whether the first warning duration meets a first preset condition according to a first preset time period; step 10: if not, the first warning information is sent to the first user.
The embodiment of the application provides a processing method for pressure detection data, which is applied to a central pressure data platform of intelligent electronic equipment, wherein the pressure data platform is used for carrying out pressure data association with mobile phone software of a user, such as pressure monitoring APP and the like. All kinds of pressure data obtained in the embodiment of the invention are automatically matched and correlated from the pressure database in the pressure monitoring APP through a computer communication technology, and are obtained after processing. Furthermore, the pressure data can be efficiently and automatically matched, correlated and processed through a computer technology, so that the technical problem to be solved by the invention is solved, and the technical effect of the invention is realized.
Having described the basic principles of the present application, the following detailed description of the technical solutions of the present application will be made by using the accompanying drawings and specific embodiments, and it should be understood that the specific features of the embodiments and embodiments of the present application are detailed descriptions of the technical solutions of the present application, and not limiting the technical solutions of the present application, and the technical features of the embodiments and embodiments of the present application may be combined with each other without conflict.
Example 1
Fig. 1 is a flow chart of a processing method for pressure detection data according to an embodiment of the invention. As shown in fig. 1, an embodiment of the present invention provides a processing method for pressure detection data, where the method includes:
step 1: a first preset time period is obtained.
Step 2: according to a first preset time period, acquiring first pressure data of a first user at a first moment and second pressure data of a second moment until N-th pressure data of the N-th moment are acquired, wherein the first pressure data, the second pressure data and the N-th pressure data are acquired in a PPG or GSR mode.
Specifically, the first preset time period is the time of a task for monitoring the pressure of the user, for example, the first preset time period is one day when the pressure change of the user in one day needs to be collected, and the first preset time period is one week when the pressure change of the user in one week needs to be collected. Further, according to a first preset time period and a preset acquisition time interval, pressure data of the first user at each moment are acquired. In this embodiment, the pressure raw data at each moment is collected by a sensor such as PPG or GSR, where PPG (Photo Plethysmo Graphy) is a photoplethysmography pulse wave signal and GSR (Galvanic skin response) is a skin electroresponse physiological signal. In other words, the first pressure data of the first user at the first moment and the second pressure data of the second moment are obtained until the nth pressure data of the nth moment is obtained, and the value of the pressure is deduced after the data is acquired by the sensors such as the PPG or the GSR.
Step 3: and according to the first pressure data, optimizing the second pressure data according to the first neural network model, and obtaining a second output result.
Further, in order to obtain the second output result, as shown in fig. 2, step 3 of the embodiment of the present application further includes:
step 301: inputting the first pressure data and the second pressure data into a first training model, wherein the first training model is obtained through training of multiple sets of training data, and each set of training data in the multiple sets of training data comprises: the first pressure data, the second pressure data, and identification information for identifying a second output result of the second pressure data;
step 302: and obtaining output information of the first training model, wherein the output information comprises a second output result of the second pressure data.
Specifically, the first training model is a neural network model in a machine learning model, and the machine learning model can continuously learn a large amount of pressure data, further continuously correct the model, and finally obtain satisfactory experience to process other pressure data. The machine model is obtained through training of multiple sets of training pressure data, and the neural network model is essentially a supervised learning process through training of the training pressure data. The first training model in the embodiment of the application is obtained by training multiple sets of training pressure data by machine learning, and each set of training pressure data in the multiple sets of training pressure data comprises: the first pressure data, the second pressure data, and identification information for identifying a second output result of the second pressure data.
Wherein identification information of a second output result of the second pressure data is taken as the supervisory pressure data. And inputting each group of training pressure data, performing supervised learning on the first pressure data and the second pressure data, and determining that the output information of the first training model reaches a convergence state. Comparing the second output result information of the second pressure data with the output result of the first training model, and performing the next set of pressure data supervised learning after the set of pressure data supervised learning is completed when the second output result information of the second pressure data is consistent with the output result of the first training model; when the first pressure data is inconsistent with the second pressure data, the first training model carries out self-correction until the output result of the first training model is consistent with the second output result information of the identified second pressure data, the supervision learning of the first group is completed, and the supervision learning of the next group of pressure data is carried out; and through the supervised learning of a large amount of pressure data, the output result of the machine learning model reaches a convergence state, and the supervised learning is completed. Through the process of supervised learning the first training model, the second output result information of the second pressure data output by the first training model is more accurate, and the effects of accurately monitoring the user pressure in real time, optimizing the pressure data and improving the stability of the data are achieved.
Further, the second output result at the second moment is output result information after optimization according to the first pressure data and the second pressure data, when the first moment is the starting point of the first preset time period, the first output result is set as the first pressure data, that is, the original data of the first moment acquired by the PPG/GSR sensor. The optimization method is to perform data stabilization processing by an exponential algorithm, for example, the first second output value of the original data (PPG/GSR) is n1, the second output result of the second output result r2=n1×80% +n2×20% is calculated after the optimization, and the data stabilization processing is performed by the exponential method, so that the output data result is more stable and convenient to read.
Step 4: repeating the step 3 until an Nth output result is obtained.
Step 5: and obtaining a first pressure lower limit value and a first pressure upper limit value according to the first output result, the second output result and the Nth output result.
Step 6: and obtaining a first pressure warning value according to the first pressure lower limit value and the first pressure upper limit value.
Specifically, after optimizing the pressure data at each moment, N output result information can be obtained, and further, after comparing the first output result and the second output result to the nth output result, the first pressure lower limit value and the first pressure upper limit value can be obtained, that is, the highest point and the lowest point of the first user pressure value in the N output results can be further determined. Then, the pressure warning value may be calculated from the first pressure lower limit value and the first pressure upper limit value, for example, a point with 75% high may be used as a high risk line according to the highest point and the lowest point of the pressure value, and further, the first pressure warning value may be set according to the specific situation, which is not particularly limited in this embodiment. Therefore, the purpose that the pressure data are more conveniently read by a user and the detection result is more accurate is achieved.
Further, in order to ensure the safety of pressure warning data storage, generating a first verification code according to the first pressure warning value, wherein the first verification code and the first pressure warning value are in one-to-one correspondence; and generating a second verification code … according to the second pressure warning value and the first verification code, so as to push the second verification code …, taking the first pressure warning value and the first verification code as a first storage unit, taking the second pressure warning value and the second verification code as a second storage unit …, and so on, so as to obtain M storage units in total. The verification code information is used as main body identification information, and the main body identification information is used for distinguishing the main body from other main bodies. When the training data is required to be called, each next node receives the data stored by the previous node, checks and stores the data through a consensus mechanism, and concatenates each storage unit through a hash technology, so that the training data is not easy to lose and damage, and the safety and accuracy of the pressure warning data are improved through a data information processing technology based on a block chain, the accuracy of calling the pressure warning value through a verification code is ensured, and the accuracy of acquiring the pressure warning value is ensured.
Step 7: and judging whether the first output result, the second output result and the Nth output result exceed the first pressure warning value or not in sequence.
Step 8: if yes, obtaining a first warning duration.
Further, in order to obtain the first alert duration, as shown in fig. 7, step 8 of the embodiment of the present application further includes:
step 801: recording all output results exceeding the first pressure warning value in the first output result and the second output result until the Nth output result;
step 802: and accumulating the moments corresponding to all the output results exceeding the first pressure warning value to obtain a first warning duration.
Specifically, after a task is finished, that is, after the pressure value in a first preset time period is collected, all the pressure values (after optimization) in the task interval are calculated, the lowest point and the highest point of data are confirmed, then 75% high points are found, further, according to the first pressure warning value, whether a first output result and a second output result until an Nth output result exceed the first pressure warning value or not is judged in sequence, if the output result exceeding the first pressure warning value exists, all the times corresponding to the output result exceeding the first pressure warning value are accumulated, and a first warning duration is obtained, wherein the first warning duration is the time when the pressure output result exceeds the warning value in the first preset time period. Recording all the output results exceeding the first pressure warning value in the first output result and the second output result until the Nth output result, and accumulating the time corresponding to the output result exceeding the first pressure warning value to obtain the first warning duration. For example, after all the optimized pressure values are obtained, a pressure graph can be drawn according to the data, and then a line with 75% high is found in the graph to be used as a high-risk line, the data above the high-risk line, that is, the representative pressure value is high, and then all the moments corresponding to the pressure values above the high-risk line are accumulated, so that the first warning duration can be obtained.
Step 9: judging whether the first warning duration meets a first preset condition according to a first preset time period;
step 10: if not, the first warning information is sent to the first user.
Specifically, after the first warning duration is obtained, whether the first warning duration meets a first preset condition or not can be judged according to a first preset time period, wherein the first preset condition is a preset threshold range of the ratio of the first warning duration to the first preset time period. When the first warning duration does not meet the first preset condition, namely the ratio of the first warning duration to the first preset time period exceeds a preset ratio range, the pressure value of the first user exceeding the warning line exceeds a certain duty ratio, and the first warning information is required to be further sent to the first user so as to achieve the purpose of high-risk reminding of the user and prevent the user from influencing physical and mental health due to the fact that the user is under high pressure for a long time.
Further, in order to obtain the alert level of the first user, as shown in fig. 3, step 10 of the embodiment of the present application further includes:
step 101: obtaining first proportional relation information according to the first warning duration and the first preset time period;
Step 102: obtaining first image information of the first user;
step 103: inputting the first proportional relation information and the first image information into a second training model, wherein the second training model is obtained through training of multiple groups of training data, and each group of training data in the multiple groups of training data comprises: the first proportional relation information, the first portrait information and identification information for identifying the alert level of the user;
step 104: obtaining output information of the second training model, wherein the output information comprises alert level information of the first user;
step 105: and obtaining first instruction information according to the warning level information, wherein the first instruction information is used for sending the first warning information to the first user after obtaining the first warning information from a preset warning information list.
Specifically, as mentioned above, the second training model is also a neural network model in a machine learning model, and the machine learning model can continuously learn a large amount of pressure data, and further continuously correct the model, so as to finally obtain satisfactory experience to process other pressure data. The machine model is obtained through training of multiple sets of training pressure data, and the neural network model is essentially a supervised learning process through training of the training pressure data. The second training model in the embodiment of the present application is obtained by training multiple sets of training pressure data by machine learning, where each set of training pressure data in the multiple sets of training pressure data includes: first proportional relation information, first portrait information, and identification information for identifying an alert level of a user.
Wherein, the identification information of the warning level of the user is used as the supervision pressure data. And inputting each group of training pressure data, performing supervised learning on the first proportional relation information and the first image information, and determining that the output information of the second training model reaches a convergence state. Comparing the warning grade information of the user with the output result of the second training model, and performing the next set of pressure data supervised learning after the set of pressure data supervised learning is completed when the warning grade information of the user is consistent with the output result of the second training model; when the first training model is inconsistent, the second training model carries out self-correction until the output result of the second training model is consistent with the warning level information of the identified user, the supervision learning of the second training model is completed, and the next supervision learning of the pressure data is carried out; and through the supervised learning of a large amount of pressure data, the output result of the machine learning model reaches a convergence state, and the supervised learning is completed. Through the process of supervised learning the second training model, the warning grade information of the user output by the second training model is more accurate, and the effects of accurately monitoring the pressure of the user in real time and timely finding and processing the abnormal value are achieved.
Further, the first proportional relation information is the ratio between the first warning duration and the first preset time period, the first image information is personal tag information of the first user, after the warning level information of the first user is obtained, the first instruction information is generated according to the warning level information, and further, the first warning information corresponding to the warning level is obtained from a preset warning information list, and then the first warning information is sent to the first user. The preset warning information list is a list of the corresponding relation between preset warning levels and warning information, that is, different warning levels, and the sent warning information is different. For example, when the alert level is high, it is indicated that the high pressure data of the user is heavy, the user needs to be reminded in time, and after the response of the user is obtained, corresponding measures can be taken, if the user does not respond within a preset time, alert reminding needs to be continuously performed at certain frequency intervals. Thereby further achieving the effects of guaranteeing the personal health of the user and timely reminding the discovery of the problems.
Further, in order to obtain the first image information of the first user, as shown in fig. 4, step 102 in the embodiment of the present application further includes:
step 1021: obtaining basic attribute information of the first user;
step 1022: obtaining personal morphology information of the first user;
step 1023: acquiring health condition information of the first user;
step 1024: and obtaining first image information of the first user according to the basic attribute information, the health condition information and the personal morphology information.
Specifically, the basic attribute information is basic information related to the first user, including but not limited to age, occupation, work, sex, etc. of the first user; the personal shape information is the shape information of the first user, such as the height condition, the fat and thin condition and the like of the user; the health status information is the personal health status of the first user, such as healthy, sub-healthy, the presence of a certain disease, etc. After the basic attribute information, the health condition information and the personal form information of the first user are collected, the three types of information can be combined to form first image information, for example, the image information of the first user is: a male computer programmer aged 30 years, physically healthy, and lean. After the basic attribute information, the health condition information and the personal form information of the first user are acquired, the related information can be further analyzed and processed, so that the model is more convenient to learn, the accuracy of the model in learning the portrait information is further improved, the data processing speed is improved, and the effect of data accuracy is realized.
Further, in order to achieve the effect of correcting the first warning information, as shown in fig. 5, step 10 in the embodiment of the present application further includes:
step 106: acquiring historical pressure data of the first user in a second preset time period;
step 107: acquiring historical behavior data of the first user in the second preset time period;
step 108: obtaining a first influence coefficient of the first user according to the historical pressure data and the historical behavior data;
step 109: obtaining second instruction information according to the first influence coefficient, wherein the second instruction information is used for obtaining second warning information after adjusting the first warning information;
step 110: and sending the second warning information to the first user.
Specifically, the historical pressure data of the first user in a second preset time period is obtained, wherein the second preset time period can be selected according to actual needs, and the embodiment is not particularly limited. For example, the second preset time period may be one day, two days, one week, etc. Further, historical behavior data of the first user in a second preset time period can be correspondingly collected, namely, whether the user participates in some more stimulated activities or whether the user has a change in the home or not, and whether the user is subject to unexpected conditions such as examination disfavored, loving and the like in the second preset time period. Further, according to the historical pressure data and the historical behavior data, a first influence coefficient for the pressure value of the user can be obtained, then second instruction information is generated according to the first influence coefficient, after the first warning information is adjusted according to the first influence coefficient and the second instruction information, second warning information after adjustment is obtained, and then the second warning information is sent to the first user, so that the effects of accurately monitoring, adjusting and early warning the pressure of the user in real time and improving the accuracy of psychological pressure data of the user are achieved.
Further, in order to realize real-time monitoring of user pressure data, avoid causing injury and influence to physical and mental health of the user, timely provide health guidance effect for the user, as shown in fig. 6, step 5 of the embodiment of the present application further includes:
step 501: drawing a pressure curve graph of the first user according to the first output result, the second output result and the Nth output result;
step 502: obtaining a pressure standard curve graph;
step 503: obtaining pressure deviation information of the first user according to the pressure curve graph and the pressure standard curve graph;
step 504: judging whether the pressure deviation information of the first user meets a second preset condition or not;
step 505: if not, acquiring the first hospital information, wherein the first hospital information is a hospital within a preset distance from the first user;
step 506: obtaining first doctor information according to the first hospital information, wherein the first doctor information has a first association degree with the first user;
step 507: and sending the first hospital information and the first doctor information to the first user or a first contact of the first user.
Specifically, after all the optimized output results in the first preset time period are obtained, a pressure curve graph of the first user can be drawn according to all the output results, then a pressure standard curve graph corresponding to the first user is obtained according to the image information of the first user, and then the pressure curve graph is compared with the pressure standard curve graph to obtain pressure deviation information of the first user, and whether the pressure deviation information meets a second preset condition or not is judged, namely whether the pressure deviation exceeds a preset deviation range or not is judged, if so, the fact that the pressure deviation information does not meet the second preset condition is indicated. Furthermore, the pressure value of the user is high, and medical treatment is needed so as to obtain the optimal diagnosis and treatment result. Therefore, the first hospital information needs to be further obtained, wherein the first hospital information is a hospital within a preset distance from the first user, then the first doctor information can be obtained, the first doctor information and the first user have a first association degree, namely, psychological symptoms treated by the first doctor are associated with the first user correspondingly, and finally, the first hospital information, the first doctor information and route information can be sent to the first user or a first contact person of the first user, so that the user pressure data is further monitored in real time, injuries and influences on physical and mental health of the user are avoided, and the health guidance effect is provided for the user timely.
Example two
Based on the same inventive concept as one of the processing methods for pressure detection data in the foregoing embodiments, the present invention also provides a processing method apparatus for pressure detection data, as shown in fig. 8, the apparatus comprising:
a first obtaining unit 11, the first obtaining unit 11 being configured to obtain a first preset time period;
the second obtaining unit 12 is configured to obtain, according to a first preset period of time, first pressure data of a first user at a first moment and second pressure data of a second moment until nth pressure data of the nth moment is obtained, where the first pressure data, the second pressure data and the nth pressure data are all collected by means of PPG or GSR;
a third obtaining unit 13, where the third obtaining unit 13 is configured to obtain a second output result after optimizing the second pressure data according to the first pressure data and the first neural network model;
a fourth obtaining unit 14, where the fourth obtaining unit 14 is configured to repeat the step 3 until an nth output result is obtained;
a fifth obtaining unit 15, where the fifth obtaining unit 15 is configured to obtain a first pressure lower limit value and a first pressure upper limit value according to the first output result, the second output result, and the nth output result;
A sixth obtaining unit 16, wherein the sixth obtaining unit 16 is configured to obtain a first pressure warning value according to the first pressure lower limit value and the first pressure upper limit value;
a first judging unit 17, where the first judging unit 17 is configured to sequentially judge whether the first output result, the second output result, and the nth output result exceed the first pressure warning value;
a seventh obtaining unit 18, where the seventh obtaining unit 18 is configured to obtain the first alert duration if the first alert duration is exceeded;
a second judging unit 19, where the second judging unit 19 is configured to judge whether the first warning duration meets a first preset condition according to a first preset time period;
and the first sending unit 20 is configured to send first alert information to the first user if the first alert information is not satisfied by the first sending unit 20.
Further, after the optimizing the second pressure data according to the first output result and the first neural network model, obtaining a second output result includes:
the first training unit is used for inputting the first pressure data and the second pressure data into a first training model, wherein the first training model is obtained through training of multiple groups of training data, and each group of training data in the multiple groups of training data comprises: the first pressure data, the second pressure data, and identification information for identifying a second output result of the second pressure data;
An eighth obtaining unit configured to obtain output information of the first training model, where the output information includes a second output result of the second pressure data.
Further, the device further comprises:
a ninth obtaining unit, configured to obtain first proportional relation information according to the first alert duration and the first preset time period;
a tenth obtaining unit configured to obtain first image information of the first user;
the second training unit is used for inputting the first proportional relation information and the first image information into a second training model, wherein the second training model is obtained through training of multiple groups of training data, and each group of training data in the multiple groups of training data comprises: the first proportional relation information, the first portrait information and identification information for identifying the alert level of the user;
an eleventh obtaining unit configured to obtain output information of the second training model, where the output information includes alert level information of the first user;
A twelfth obtaining unit, configured to obtain first instruction information according to the alert level information, where the first instruction information is used to send the first alert information to the first user after obtaining the first alert information from a preset alert information list.
Further, obtaining the first image information of the first user includes:
a thirteenth obtaining unit configured to obtain basic attribute information of the first user;
a fourteenth obtaining unit configured to obtain personal form information of the first user;
a fifteenth obtaining unit configured to obtain health condition information of the first user;
a sixteenth obtaining unit configured to obtain first image information of the first user based on the basic attribute information, the health status information, and the personal morphology information.
Further, the device further comprises:
a seventeenth obtaining unit configured to obtain historical pressure data of the first user over a second preset period of time;
an eighteenth obtaining unit configured to obtain historical behavior data of the first user in the second preset period of time;
A nineteenth obtaining unit, configured to obtain a first influence coefficient of the first user according to the historical pressure data and the historical behavior data;
a twentieth obtaining unit, configured to obtain second instruction information according to the first influence coefficient, where the second instruction information is used to obtain second warning information after the first warning information is adjusted;
and the second sending unit is used for sending the second warning information to the first user.
Further, the device further comprises:
the first execution unit is used for drawing a pressure curve graph of the first user according to the first output result, the second output result and the Nth output result;
a twenty-first obtaining unit for obtaining a pressure standard graph;
a twenty-second obtaining unit, configured to obtain pressure deviation information of the first user according to the pressure graph and the pressure standard graph;
the third judging unit is used for judging whether the pressure deviation information of the first user meets a second preset condition or not;
A twenty-third obtaining unit, configured to obtain the first hospital information if the first hospital information does not meet the first preset distance, where the first hospital information is a hospital within a preset distance from the first user;
a twenty-fourth obtaining unit, configured to obtain first doctor information according to the first hospital information, where the first doctor information has a first association degree with the first user;
and the third sending unit is used for sending the first hospital information and the first doctor information to the first user or the first contact person of the first user.
Further, the obtaining the first warning duration includes:
the second execution unit is used for recording all output results exceeding the first pressure warning value in the first output result and the second output result until an Nth output result;
a twenty-fifth obtaining unit, configured to accumulate the moments corresponding to all the output results exceeding the first pressure warning value, to obtain a first warning duration.
The foregoing various modifications and specific examples of a processing method for pressure detection data in the first embodiment of fig. 1 are equally applicable to a processing apparatus for pressure detection data in this embodiment, and from the foregoing detailed description of a processing method for pressure detection data, those skilled in the art will clearly know the implementation method of a processing apparatus for pressure detection data in this embodiment, so that the details of this embodiment will not be described in detail herein for brevity.
Example III
Based on the same inventive concept as one of the processing methods for pressure detection data in the foregoing embodiments, the present invention further provides an exemplary electronic device, as shown in fig. 9, including a memory 304, a processor 302, and a computer program stored on the memory 304 and executable on the processor 302, where the processor 302 implements the steps of any of the foregoing methods for processing pressure detection data when executing the program.
Where in FIG. 9, a bus architecture (represented by bus 300), bus 300 may comprise any number of interconnected buses and bridges, with bus 300 linking together various circuits, including one or more processors, represented by processor 302, and memory, represented by memory 304. Bus 300 may also link together various other circuits such as peripheral devices, voltage regulators, power management circuits, etc., as are well known in the art and, therefore, will not be described further herein. Bus interface 306 provides an interface between bus 300 and receiver 301 and transmitter 303. The receiver 301 and the transmitter 303 may be the same element, i.e. a transceiver, providing a means for communicating with various other apparatus over a transmission medium. The processor 302 is responsible for managing the bus 300 and general processing, while the memory 304 may be used to store pressure data used by the processor 302 in performing operations.
The above-mentioned one or more technical solutions in the embodiments of the present application at least have one or more of the following technical effects:
the embodiment of the invention provides a processing method and a device for pressure detection data, wherein the method comprises the following steps: step 1: obtaining a first preset time period; step 2: according to a first preset time period, acquiring first pressure data of a first user at a first moment and second pressure data of a second moment until N-th pressure data of an N-th moment are acquired, wherein the first pressure data, the second pressure data and the N-th pressure data are acquired in a PPG or GSR mode; step 3: according to the first pressure data, optimizing the second pressure data according to the first neural network model, and then obtaining a second output result; step 4: repeating the step 3 until an Nth output result is obtained; step 5: obtaining a first pressure lower limit value and a first pressure upper limit value according to the first output result, the second output result and the Nth output result; step 6: obtaining a first pressure warning value according to the first pressure lower limit value and the first pressure upper limit value; step 7: sequentially judging whether the first output result and the second output result until the Nth output result exceeds the first pressure warning value; step 8: if yes, obtaining a first warning duration; step 9: judging whether the first warning duration meets a first preset condition according to a first preset time period; step 10: if the data are not satisfied, the first warning information is sent to the first user, so that the technical effects that the stability of the detected pressure value in the prior art is poor, the real pressure level of the user is difficult to embody, the user is inconvenient to read the data, the psychological state of the user is accurately judged and processed are achieved, the stability and the readability of the pressure value are improved, the psychological state of the user can be accurately judged, and the processing can be timely performed when the problem occurs are achieved.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable pressure data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable pressure data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable pressure data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (8)
1. A processing method for pressure detection data, wherein the method comprises:
step 1: obtaining a first preset time period;
step 2: according to a first preset time period, acquiring first pressure data of a first user at a first moment and second pressure data of a second moment until N-th pressure data of an N-th moment are acquired, wherein the first pressure data, the second pressure data and the N-th pressure data are acquired in a PPG or GSR mode;
step 3: according to a first output result, optimizing the second pressure data according to a first neural network model, and obtaining a second output result, wherein the first output result is obtained after the first pressure data is optimized according to the first neural network model;
step 4: repeating the step 3 until an Nth output result is obtained;
step 5: obtaining a first pressure lower limit value and a first pressure upper limit value according to the first output result, the second output result and the Nth output result;
step 6: obtaining a first pressure warning value according to the first pressure lower limit value and the first pressure upper limit value;
step 7: sequentially judging whether the first output result and the second output result until the Nth output result exceeds the first pressure warning value;
Step 8: if yes, obtaining a first warning duration;
step 9: judging whether the first warning duration meets a first preset condition according to a first preset time period;
step 10: if not, sending first warning information to the first user;
drawing a pressure curve graph of the first user according to the first output result, the second output result and the Nth output result;
obtaining a pressure standard curve graph;
obtaining pressure deviation information of the first user according to the pressure curve graph and the pressure standard curve graph;
judging whether the pressure deviation information of the first user meets a second preset condition or not;
if not, obtaining first hospital information, wherein the first hospital information is a hospital within a preset distance from the first user;
obtaining first doctor information according to the first hospital information, wherein the first doctor information has a first association degree with the first user;
the first hospital information and the first doctor information are sent to the first user or a first contact person of the first user;
generating a first verification code according to the first pressure warning value, wherein the first verification code and the first pressure warning value are in one-to-one correspondence;
Generating a second verification code … according to the second pressure warning value and the first verification code, so as to generate an Nth verification code according to the Nth pressure warning value and the N-1 th verification code;
taking the first pressure warning value and the first verification code as a first storage unit, taking the second pressure warning value and the second verification code as a second storage unit … and so on to obtain M storage units in total.
2. The method of claim 1, wherein said optimizing said second pressure data according to said first output result and said first neural network model, to obtain a second output result, comprises:
inputting the first pressure data and the second pressure data into a first training model, wherein the first training model is obtained through training of multiple sets of training data, and each set of training data in the multiple sets of training data comprises: the first pressure data, the second pressure data, and identification information for identifying a second output result of the second pressure data;
and obtaining output information of the first training model, wherein the output information comprises a second output result of the second pressure data.
3. The method of claim 1, wherein the if not, transmitting first alert information to the first user, the method further comprising:
Obtaining first proportional relation information according to the first warning duration and the first preset time period;
obtaining first image information of the first user;
inputting the first proportional relation information and the first image information into a second training model, wherein the second training model is obtained through training of multiple groups of training data, and each group of training data in the multiple groups of training data comprises: the first proportional relation information, the first portrait information and identification information for identifying the alert level of the user;
obtaining output information of the second training model, wherein the output information comprises alert level information of the first user;
and obtaining first instruction information according to the warning level information, wherein the first instruction information is used for sending the first warning information to the first user after obtaining the first warning information from a preset warning information list.
4. The method of claim 3, wherein the obtaining the first image information of the first user comprises:
obtaining basic attribute information of the first user;
obtaining personal morphology information of the first user;
Acquiring health condition information of the first user;
and obtaining first image information of the first user according to the basic attribute information, the health condition information and the personal morphology information.
5. A method as claimed in claim 3, wherein, prior to said sending said first alert information to said first user, said method further comprises:
acquiring historical pressure data of the first user in a second preset time period;
acquiring historical behavior data of the first user in the second preset time period;
obtaining a first influence coefficient of the first user according to the historical pressure data and the historical behavior data;
obtaining second instruction information according to the first influence coefficient, wherein the second instruction information is used for obtaining second warning information after adjusting the first warning information;
and sending the second warning information to the first user.
6. The method of claim 1, wherein the obtaining a first alert duration comprises:
recording all output results exceeding the first pressure warning value in the first output result and the second output result until the Nth output result;
And accumulating the moments corresponding to all the output results exceeding the first pressure warning value to obtain a first warning duration.
7. A processing device for pressure detection data, the device comprising:
a first obtaining unit configured to obtain a first preset time period;
the second obtaining unit is used for obtaining first pressure data of a first user at a first moment and second pressure data of a second moment until N-th pressure data of the N-th moment are obtained according to a first preset time period, wherein the first pressure data, the second pressure data and the N-th pressure data are all acquired in a PPG or GSR mode;
the third obtaining unit is used for obtaining a second output result after optimizing the second pressure data according to a first output result and a first neural network model, wherein the first output result is obtained after optimizing the first pressure data according to the first neural network model;
a fourth obtaining unit, configured to repeat the step 3 until an nth output result is obtained;
A fifth obtaining unit, configured to obtain a first pressure lower limit value and a first pressure upper limit value according to the first output result, the second output result, and the nth output result;
a sixth obtaining unit configured to obtain a first pressure warning value from the first pressure lower limit value and the first pressure upper limit value;
the first judging unit is used for sequentially judging whether the first output result and the second output result until the Nth output result exceed the first pressure warning value or not;
a seventh obtaining unit, configured to obtain a first alert duration if the first alert duration exceeds the first alert duration;
the second judging unit is used for judging whether the first warning duration meets a first preset condition according to a first preset time period;
the first sending unit is used for sending first warning information to the first user if the first warning information is not met;
the first execution unit is used for drawing a pressure curve graph of the first user according to the first output result, the second output result and the Nth output result;
A twenty-first obtaining unit for obtaining a pressure standard graph;
a twenty-second obtaining unit, configured to obtain pressure deviation information of the first user according to the pressure graph and the pressure standard graph;
the third judging unit is used for judging whether the pressure deviation information of the first user meets a second preset condition or not;
a twenty-third obtaining unit, configured to obtain first hospital information if the first hospital information does not meet the first preset distance, where the first hospital information is a hospital within the first preset distance from the first user;
a twenty-fourth obtaining unit, configured to obtain first doctor information according to the first hospital information, where the first doctor information has a first association degree with the first user;
the third sending unit is used for sending the first hospital information and the first doctor information to the first user or a first contact person of the first user;
the first generation unit is used for generating a first verification code according to the first pressure warning value, wherein the first verification code and the first pressure warning value are in one-to-one correspondence;
A second generating unit for generating a second verification code … according to the second pressure warning value and the first verification code, and generating an nth verification code according to the nth pressure warning value and the nth-1 verification code;
and the third execution unit is used for taking the first pressure warning value and the first verification code as a first storage unit, taking the second pressure warning value and the second verification code as a second storage unit … and so on to obtain M storage units in total.
8. A processing device for pressure detection data, comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the steps of the method according to any one of claims 1-6 when the program is executed by the processor.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011074735.0A CN112168188B (en) | 2020-10-09 | 2020-10-09 | Processing method and device for pressure detection data |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011074735.0A CN112168188B (en) | 2020-10-09 | 2020-10-09 | Processing method and device for pressure detection data |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112168188A CN112168188A (en) | 2021-01-05 |
| CN112168188B true CN112168188B (en) | 2023-07-25 |
Family
ID=73948787
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011074735.0A Active CN112168188B (en) | 2020-10-09 | 2020-10-09 | Processing method and device for pressure detection data |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112168188B (en) |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113297021B (en) * | 2021-05-21 | 2023-01-17 | 联想长风科技(北京)有限公司 | Autonomous detection method and system for server equipment pulled out of rack |
| CN113241178B (en) * | 2021-05-28 | 2023-06-27 | 温州康宁医院股份有限公司 | Device for determining severity of depression of tested person |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108601566A (en) * | 2016-11-17 | 2018-09-28 | 华为技术有限公司 | A kind of stress evaluating method and device |
| CN109758141A (en) * | 2019-03-06 | 2019-05-17 | 清华大学 | A kind of psychological pressure monitoring method, apparatus and system |
| CN111513730A (en) * | 2020-03-20 | 2020-08-11 | 合肥工业大学 | Psychological stress prediction method and system based on multi-channel physiological data |
| CN111513732A (en) * | 2020-04-29 | 2020-08-11 | 山东大学 | Intelligent psychological stress assessment early warning system for various groups of people under epidemic disease condition |
-
2020
- 2020-10-09 CN CN202011074735.0A patent/CN112168188B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108601566A (en) * | 2016-11-17 | 2018-09-28 | 华为技术有限公司 | A kind of stress evaluating method and device |
| CN109758141A (en) * | 2019-03-06 | 2019-05-17 | 清华大学 | A kind of psychological pressure monitoring method, apparatus and system |
| CN111513730A (en) * | 2020-03-20 | 2020-08-11 | 合肥工业大学 | Psychological stress prediction method and system based on multi-channel physiological data |
| CN111513732A (en) * | 2020-04-29 | 2020-08-11 | 山东大学 | Intelligent psychological stress assessment early warning system for various groups of people under epidemic disease condition |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112168188A (en) | 2021-01-05 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US11834052B2 (en) | Estimator generation apparatus, monitoring apparatus, estimator generation method, and computer-readable storage medium storing estimator generation program | |
| CN112168188B (en) | Processing method and device for pressure detection data | |
| KR20190105163A (en) | Patient condition predicting apparatus based on artificial intelligence and predicting method using the same | |
| US11571169B2 (en) | Probability-based detector and controller apparatus, method, computer program | |
| US10037412B2 (en) | Patient health state compound score distribution and/or representative compound score based thereon | |
| CN106407699A (en) | Coronary heart disease prediction method and prediction system based on incremental neural network model | |
| CN112289423A (en) | Method and system for double-diagnosis and rehabilitation based on intelligent community patients | |
| CN106446560A (en) | Hyperlipidemia prediction method and prediction system based on incremental neural network model | |
| CN106821349A (en) | For the event generation method and device of wearable custodial care facility | |
| CN109712708B (en) | Health condition prediction method and device based on data mining | |
| CN112022172B (en) | Pressure detection method and device based on multi-modal physiological data | |
| CN111317458A (en) | Blood pressure detection system based on deep learning | |
| CN112489806A (en) | Intelligent management method and system for disease state information of diabetic foot patient | |
| CN106384005A (en) | Incremental neural network model-based depression prediction method and prediction system | |
| CN106384012A (en) | Incremental neural network model-based allergic dermatitis prediction method and prediction system | |
| CN112992312B (en) | Qualified monitoring method and system for spinal cord injury rehabilitation training | |
| CN113205053A (en) | Epileptic ward safety monitoring method and system | |
| CN106250715A (en) | A kind of chronic pharyngolaryngitis Forecasting Methodology based on increment type neural network model and prognoses system | |
| Ahmed | A personalized health-monitoring system for elderly by combining rules and case-based reasoning | |
| CN112244882B (en) | Disease early warning method and device based on multi-mode physiological data | |
| CN113057597B (en) | Method and system for monitoring physiological state of puerpera in real time in production process | |
| CN117838072B (en) | Cardiovascular disease monitoring method, device, equipment and storage medium of intelligent mattress | |
| CN114694848B (en) | Electronic information acquisition system for epidemic situation prevention and control | |
| Kolomvatsos et al. | Intelligent contextual data stream monitoring | |
| CN112699317B (en) | System and method for improving valvular disease screening quality |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |