CN117243576A - Alarm method and system based on individual life data - Google Patents
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- 239000008280 blood Substances 0.000 claims description 7
- 210000004369 blood Anatomy 0.000 claims description 7
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- 230000036772 blood pressure Effects 0.000 claims description 4
- 230000036760 body temperature Effects 0.000 claims description 4
- 238000013135 deep learning Methods 0.000 claims description 4
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- 238000003860 storage Methods 0.000 description 3
- 230000008667 sleep stage Effects 0.000 description 2
- 206010041235 Snoring Diseases 0.000 description 1
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
- A61B5/0205—Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
- A61B5/02055—Simultaneously evaluating both cardiovascular condition and temperature
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/01—Measuring temperature of body parts ; Diagnostic temperature sensing, e.g. for malignant or inflamed tissue
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/145—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue
- A61B5/14532—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue for measuring glucose, e.g. by tissue impedance measurement
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
- A61B5/316—Modalities, i.e. specific diagnostic methods
- A61B5/318—Heart-related electrical modalities, e.g. electrocardiography [ECG]
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/74—Details of notification to user or communication with user or patient ; user input means
- A61B5/746—Alarms related to a physiological condition, e.g. details of setting alarm thresholds or avoiding false alarms
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Abstract
The application provides an alarm method based on individual life data, and belongs to the technical field of data processing. The method comprises the following steps: acquiring individual life data at a plurality of moments based on a physiological separation information technology in a recording period; preprocessing the collected individual life data to obtain standardized individual life data; inputting the standardized individual life data into a pre-established alarm model, identifying various index values of the standardized individual life data based on the alarm model, judging whether the various index values exceed the corresponding various index normal values, and giving an alarm aiming at indexes of which the index values exceed the corresponding index normal values. The invention can monitor the life data of the individual in real time and give an alarm in time after the abnormality of the body data, so that the invention can monitor the life data, the abnormality data and the like, and can provide references for the health of the individual so as to avoid the health problem of the individual.
Description
Technical Field
The present disclosure relates to the field of data processing technologies, and in particular, to an alarm method and system based on individual life data.
Background
In the aspect of nursing for the aged, general data are adopted for normal values of heart rate and breathing, namely: all use this reference data. However, the heart rate and respiration index used for a specific group of people are not accurate enough. For example: athletes, elderly people, etc. The normal values of the respective respiration and heart rate are not exactly the same due to the difference in diet, habit, health condition, etc. of each individual. When abnormal respiration and heart rate occur, if universal indexes are adopted, the early warning accuracy rate is greatly reduced.
Disclosure of Invention
Aiming at the problems, the application provides an alarm method based on individual life data, which comprises the following steps:
acquiring individual life data at a plurality of moments based on a physiological separation information technology in a recording period;
preprocessing the collected individual life data to obtain standardized individual life data;
inputting the standardized individual life data into a pre-established alarm model, identifying various index values of the standardized individual life data based on the alarm model, judging whether the various index values exceed the corresponding various index normal values, and giving an alarm aiming at indexes of which the index values exceed the corresponding index normal values.
Alternatively, the recording period is 1-24 hours.
Optionally, during the recording period, acquiring the life data of the individual based on the physiological separation information technology includes:
acquiring individual pressure vibration and sound frequency signals which are acquired by a sensor and are above a preset frequency, and analyzing the individual pressure vibration and sound frequency signals into individual life data based on the physiological separation information technology;
the sensor is carried by an individual.
Optionally, the sensor is specifically at least one of the following: MEMS infrasonic sensors, super-sensitivity piezoelectric electronic sensors or thin film piezoelectric sensors/photosensors.
Alternatively, the preset frequency is 0.01Hz.
Optionally, the vital data of the individual includes at least one of: electrocardiogram, blood pressure, blood glucose, body temperature, respiratory rate and heart beat rate.
Optionally, preprocessing the collected life data of the individual includes: denoising the collected individual life data, filtering the denoised individual life data, and standardizing the filtered individual life data;
the filtering process specifically comprises the following steps: and continuously filtering the individual life data after denoising based on a PWM control technology.
Optionally, the alert model is pre-established based on a machine learning algorithm and a deep learning algorithm.
Optionally, each index value includes: individual heart rate values, individual breath values, individual temperature values, and individual blood glucose values.
Optionally, the calculation method of the normal values of each index comprises the following steps:
acquiring life data of individual histories at multiple moments corresponding to various indexes in a preset period, and classifying the life data of the individual histories at multiple moments to obtain a life data set taking a day as a unit;
taking the average value of the daily life data set as an index value of the individual day;
calculating normal values of various indexes according to the daily index values of the individual;
wherein xm is 1…n For individuals 1 toIndex value of n days, n is total number of index values of the individual per day.
Compared with the prior art, the beneficial effects of this application are:
the application provides an alarm method based on individual life data, which comprises the following steps: acquiring individual life data at a plurality of moments based on a physiological separation information technology in a recording period; preprocessing the collected individual life data to obtain standardized individual life data; inputting the standardized individual life data into a pre-established alarm model, identifying various index values of the standardized individual life data based on the alarm model, judging whether the various index values exceed the corresponding various index normal values, and giving an alarm aiming at indexes of which the index values exceed the corresponding index normal values. The invention can monitor the life data of the individual in real time and give an alarm in time after the abnormality of the body data, so that the invention can monitor the life data, the abnormality data and the like, and can provide references for the health of the individual so as to avoid the health problem of the individual.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following description will briefly explain the drawings used in the embodiments of the present application, and it is obvious that the drawings described below are only specific embodiments of the present application, and that a person skilled in the art may obtain other embodiments according to the following drawings without inventive effort.
FIG. 1 is a schematic flow chart of embodiment 1 of the method of the present application;
FIG. 2 is a schematic flow chart of embodiment 2 of the method of the present application;
FIG. 3 is a schematic illustration of sleep stages of an individual monitored in method example 2 of the present application;
FIG. 4 is a schematic representation of respiration of an individual monitored in accordance with example 2 of the method of the present application;
FIG. 5 is a schematic representation of heart rate of an individual monitored in method example 2 of the present application;
FIG. 6 is a schematic diagram of body movements of an individual monitored in method example 2 of the present application;
the accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the application and together with the description, serve to explain the principles of the application.
Detailed Description
The exemplary embodiments of the present application will now be described with reference to the accompanying drawings, however, the present application may be embodied in many different forms and is not limited to the examples described herein, which are provided to fully and completely disclose the present application and fully convey the scope of the application to those skilled in the art. The terms used in the exemplary embodiments shown in the drawings are not intended to be limiting of the present application. In the drawings, like elements/components are referred to by like reference numerals.
Unless otherwise indicated, terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art. In addition, it will be understood that terms defined in commonly used dictionaries should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense.
Example 1:
the application provides an alarm method s100 based on individual life data, as shown in fig. 1, comprising the following steps:
step s101, acquiring individual life data at a plurality of moments based on a physiological separation information technology in a recording period;
step s102, preprocessing the collected individual life data to obtain standardized individual life data;
step s103, inputting the standardized individual life data into a pre-established alarm model, identifying each index value of the standardized individual life data based on the alarm model, judging whether each index value exceeds the corresponding normal value of each index, and giving an alarm aiming at the index of which the index value exceeds the corresponding normal value of the index.
Wherein the recording period is 1-24 hours.
Wherein, in the recording period, based on the physiological separation information technology, obtain individual life data, include:
acquiring individual pressure vibration and sound frequency signals which are acquired by a sensor and are above a preset frequency, and analyzing the individual pressure vibration and sound frequency signals into individual life data based on the physiological separation information technology;
the sensor is carried by an individual.
Wherein the sensor is specifically at least one of the following: MEMS infrasonic sensors, super-sensitivity piezoelectric electronic sensors or thin film piezoelectric sensors/photosensors.
Wherein the preset frequency is 0.01Hz.
Wherein the vital data of the individual comprises at least one of: electrocardiogram, blood pressure, blood glucose, body temperature, respiratory rate and heart beat rate.
Wherein preprocessing the collected vital data of the individual comprises: denoising the collected individual life data, filtering the denoised individual life data, and standardizing the filtered individual life data;
the filtering process specifically comprises the following steps: and continuously filtering the individual life data after denoising based on a PWM control technology.
Wherein, the alarm model is established in advance based on a machine learning algorithm and a deep learning algorithm.
Wherein each index value includes: individual heart rate values, individual breath values, individual temperature values, and individual blood glucose values.
The calculation method of the normal values of all indexes comprises the following steps:
acquiring life data of individual histories at multiple moments corresponding to various indexes in a preset period, and classifying the life data of the individual histories at multiple moments to obtain a life data set taking a day as a unit;
taking the average value of the daily life data set as an index value of the individual day;
calculating normal values of various indexes according to the daily index values of the individual;
wherein xm is 1…n For index values of 1 to n days for an individual, n is the total number of index values for each day for the individual.
Example 2:
the application provides an alarm method s200 based on individual life data, as shown in fig. 2, comprising the following steps:
step s201, acquiring individual life data at a plurality of moments based on a physiological separation information technology in a recording period;
step S202, preprocessing the collected individual life data to obtain standardized individual life data;
step s303, inputting the standardized individual life data into a pre-established alarm model, identifying each index value of the standardized individual life data based on the alarm model, judging whether each index value exceeds the corresponding normal value of each index, and giving an alarm aiming at the index of which the index value exceeds the corresponding normal value of the index.
The steps s201 to 203 specifically include the following steps:
for step s201, acquiring life data of the individual at a plurality of moments, namely data acquisition, specifically includes:
vital data of the individual is collected including, but not limited to, electrocardiogram, blood pressure, blood glucose, body temperature, respiratory rate, heart beat rate, etc. One recording period is 24 hours (from 12 pm to 12 pm the next day), such a recording period covers a continuous sleep time. And the statistical analysis is convenient. The physiological information separation technology is realized by adopting an Active control technology
Heart rate (0.8 Hz-1.5 Hz), respiration ((0.2 Hz-0.8 Hz)), snoring, body movement and other vital sign information separation.
The sensing technology adopts MEMS infrasonic wave sensing/super-sensitivity piezoelectric electronic sensing/film piezoelectric sensing/photoelectric sensing
Pressure vibration and sound frequency signals above 0.01Hz are collected.
Preprocessing the collected life data of the individual, namely preprocessing the data, specifically including:
the collected data is pre-processed, such as denoising, filtering, normalization, etc., to reduce noise and bias of the data.
The spectrum analysis adopts a comb type filtering technology and a PWM control technology to realize continuous filtering control,
aiming at step s203, judging whether the index values exceed the corresponding normal values of the indexes, namely, data analysis, specifically including:
the preprocessed data is analyzed using machine learning or deep learning algorithms (i.e., algorithms provided by the alert model) to identify possible anomalies.
When the vital sign value of the user deviates from the normal value twice within 5 minutes, a general alarm is sent out to remind relevant personnel to pay attention to; when abnormal values appear 3 times within 5 minutes or 4 times within 10 minutes, an important alarm is sent out to prompt the related personnel to confirm with the user face to face. When the abnormality occurs for 5 times or more, an emergency alarm is sent out, and corresponding emergency measures are carried out.
The acquired sleep stage index, respiratory index, heart rate index and body movement index of the individual are shown in figures 3, 4, 5 and 6 respectively;
in the data preprocessing in step s203, in embodiment 2 of the present application, the heart rate data and the respiration data of the individual are taken as examples to illustrate, and the normal values of the heart rate index and the respiration index are calculated, and the calculation process is as follows:
after data preprocessing, data are grouped according to time, the data are divided into a day group, a week group and a month group, and each group of data are ordered from small to large, so that abnormal values can be distributed at two ends, and data interference is avoided; then the median is used for calculation:
day set data analysis process:
when the data amount N is odd, the heart rate hm=x (n+1)/2
Hm is the (n+1)/2 data in the data group, namely the current individual heart rate value
When N is even, rm= [ X (N/2) +x (N/2+1) ]/2
Hm is the sum of the data of the N/2 th and the data of the 1+N/2 th in the data group, and then average is taken to obtain the current body heart rate value.
When the data amount N is odd, the breath rm=x (n+1)/2
Rm is the (n+1)/2 data in the data group, namely the current individual respiratory value
When N is even, rm= [ X (N/2) +x (N/2+1) ]/2
Rm is the sum of the data of the N/2 th and the data of the 1+N/2 th in the data group, and then average is the current individual respiratory value
Rm, hm represent a value in a breath, heart rate sample distribution.
Week and month group data analysis process:
the final individual respiration value R is calculated as follows:
the heart rate value H is calculated as follows:
the vital sign values of the individual are updated every half year or one year according to the calculation of the algorithm process so as to realize the self-adaptive adjustment of the vital sign data.
If the heart rate, respiration and apnea time during sleeping exceeds the normal value R, H of the current user in one recording period, corresponding alarm information is generated and is notified to the relevant person.
The alarm model can monitor the life data of an individual in real time and discover abnormality in time.
The model has high self-adaptability and can be adjusted and optimized in real time according to the life data of an individual.
The model can be seamlessly integrated with the existing medical system, such as the existing HIS system of a hospital, and provides the daily vital sign data of the patient for doctor diagnosis as a reference; the system is integrated with the existing information system of the pension institution, so that the house patrol efficiency and the like can be improved.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application 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 solutions in the embodiments of the present application may be implemented in various computer languages, for example, object-oriented programming language Java, and an transliterated scripting language JavaScript, etc.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. 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 data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable 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 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 data processing 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.
While preferred embodiments of the present application have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the application.
It will be apparent to those skilled in the art that various modifications and variations can be made in the present application without departing from the spirit or scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the claims and the equivalents thereof, the present application is intended to cover such modifications and variations.
Claims (10)
1. An alarm method based on individual life data, the alarm method comprising:
acquiring individual life data at a plurality of moments based on a physiological separation information technology in a recording period;
preprocessing the collected individual life data to obtain standardized individual life data;
inputting the standardized individual life data into a pre-established alarm model, identifying various index values of the standardized individual life data based on the alarm model, judging whether the various index values exceed the corresponding various index normal values, and giving an alarm aiming at indexes of which the index values exceed the corresponding index normal values.
2. The alarm method of claim 1, wherein the recording period is 1-24 hours.
3. The method of claim 1, wherein the acquiring life data of the individual based on the physiological separation information technique during the recording period comprises:
acquiring individual pressure vibration and sound frequency signals which are acquired by a sensor and are above a preset frequency, and analyzing the individual pressure vibration and sound frequency signals into individual life data based on the physiological separation information technology;
the sensor is carried by an individual.
4. A method of alerting as claimed in claim 3, wherein the sensor is in particular at least one of the following: MEMS infrasonic sensors, super-sensitivity piezoelectric electronic sensors or thin film piezoelectric sensors/photosensors.
5. A method of alerting according to claim 3, wherein the preset frequency is 0.01Hz.
6. The alarm method of claim 1, wherein the individual's vital data comprises at least one of: electrocardiogram, blood pressure, blood glucose, body temperature, respiratory rate and heart beat rate.
7. The alarm method of claim 1, wherein the preprocessing of the collected vital data of the individual comprises: denoising the collected individual life data, filtering the denoised individual life data, and standardizing the filtered individual life data;
the filtering process specifically comprises the following steps: and continuously filtering the individual life data after denoising based on a PWM control technology.
8. The alert method of claim 1, wherein the alert model is pre-established based on a machine learning algorithm and a deep learning algorithm.
9. The alarm method according to claim 1, wherein the index values include: individual heart rate values, individual breath values, individual temperature values, and individual blood glucose values.
10. The alarm method according to claim 1, wherein the calculation method of the normal values of the respective indexes comprises:
acquiring life data of individual histories at multiple moments corresponding to various indexes in a preset period, and classifying the life data of the individual histories at multiple moments to obtain a life data set taking a day as a unit;
taking the average value of the daily life data set as an index value of the individual day;
calculating normal values of various indexes according to the daily index values of the individual;
wherein xm is 1…n For index values of 1 to n days for an individual, n is the total number of index values for each day for the individual.
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| CN202311335719.6A CN117243576A (en) | 2023-10-16 | 2023-10-16 | Alarm method and system based on individual life data |
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| CN202311335719.6A CN117243576A (en) | 2023-10-16 | 2023-10-16 | Alarm method and system based on individual life data |
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