CN116439482A - Safety induction intelligent bracelet for diabetics - Google Patents
Safety induction intelligent bracelet for diabetics Download PDFInfo
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- CN116439482A CN116439482A CN202310471019.3A CN202310471019A CN116439482A CN 116439482 A CN116439482 A CN 116439482A CN 202310471019 A CN202310471019 A CN 202310471019A CN 116439482 A CN116439482 A CN 116439482A
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- 230000006698 induction Effects 0.000 title description 2
- 238000012544 monitoring process Methods 0.000 claims abstract description 97
- 230000036760 body temperature Effects 0.000 claims abstract description 65
- 230000002218 hypoglycaemic effect Effects 0.000 claims abstract description 42
- 208000013016 Hypoglycemia Diseases 0.000 claims abstract description 37
- 230000033001 locomotion Effects 0.000 claims abstract description 28
- 238000004891 communication Methods 0.000 claims abstract description 18
- 206010012601 diabetes mellitus Diseases 0.000 claims abstract description 15
- 239000013598 vector Substances 0.000 claims description 51
- 238000007405 data analysis Methods 0.000 claims description 33
- 238000007619 statistical method Methods 0.000 claims description 7
- 238000013136 deep learning model Methods 0.000 claims description 5
- 229910052739 hydrogen Inorganic materials 0.000 claims description 4
- 238000003062 neural network model Methods 0.000 claims description 3
- 230000004044 response Effects 0.000 claims description 3
- 230000008859 change Effects 0.000 abstract description 4
- 230000036541 health Effects 0.000 description 5
- 238000004458 analytical method Methods 0.000 description 3
- 230000006870 function Effects 0.000 description 3
- 238000000034 method Methods 0.000 description 3
- 239000008280 blood Substances 0.000 description 2
- 210000004369 blood Anatomy 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 230000003068 static effect Effects 0.000 description 2
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000007423 decrease Effects 0.000 description 1
- 230000002950 deficient Effects 0.000 description 1
- 201000010099 disease Diseases 0.000 description 1
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 229910052760 oxygen Inorganic materials 0.000 description 1
- 239000001301 oxygen Substances 0.000 description 1
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- 230000033764 rhythmic process Effects 0.000 description 1
Classifications
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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
- A44—HABERDASHERY; JEWELLERY
- A44C—PERSONAL ADORNMENTS, e.g. JEWELLERY; COINS
- A44C5/00—Bracelets; Wrist-watch straps; Fastenings for bracelets or wrist-watch straps
- A44C5/0007—Bracelets specially adapted for other functions or with means for attaching other articles
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/0002—Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network
- A61B5/0004—Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network characterised by the type of physiological signal transmitted
-
- 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
-
- 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
- A61B5/02—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
- A61B5/021—Measuring pressure in heart or blood vessels
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
Abstract
The invention relates to a safety sensing intelligent bracelet for diabetics, which comprises a body temperature monitoring module, a humidity monitoring module, a heart rate monitoring module, a movement state monitoring module, a control module, a positioning module and a communication module, wherein whether a user generates a diabetic hypoglycemia reaction is judged by monitoring the body temperature, the skin humidity, the heart rate and the movement state of the user, so that the monitoring data change of the diabetics can be found as soon as possible, and the diabetic patients can respond to the hypoglycemia reaction quickly.
Description
[ field of technology ]
The invention belongs to the field of health care equipment, and particularly relates to a safety sensing intelligent bracelet for diabetics.
[ background Art ]
With the importance of public to the concept of health management, market acceptance and acceptance of intelligent bracelets (watches) are greatly increased, and the intelligent bracelets (watches) serve as health auxiliary monitoring equipment and provide data such as heart rate, oxygen saturation and exercise in real time, so that scientific basis is provided for large health management. The existing intelligent bracelet is lack of temperature and humidity monitoring and data analysis, and is not networked with related medical institutions, so that important monitoring data are meaningless. Especially, it is important to find the data change as early as possible when diabetes mellitus is hypoglycemia.
In addition, the existing smart bracelet focuses on monitoring data, but the analysis function of the data result is deficient, so that the prediction function of disease occurrence needs to be enhanced, and the occurrence of hypoglycemia is typified by diabetics, can be discovered and intervened early through the related data change, the hazard rate of the hypoglycemia is reduced, and the blood sugar management level is improved.
[ invention ]
In combination with clinical and literature references, when hypoglycemia occurs, the skin surface temperature of a person decreases, perspiration increases, resulting in increased skin surface humidity and a relatively high heart rate. To this, to enlarge intelligent bracelet's functional category, can increase temperature and humidity sensing in equipment under current intelligent bracelet function to monitor rhythm of heart, temperature, humidity according to self health data, discover unusual in time send the warning. And the monitoring data are transmitted to the server for further analysis, so that an administrator is reminded of timely supervision of the patient.
Therefore, in order to solve the problems in the prior art, the invention provides a safety sensing intelligent bracelet for diabetics.
The technical scheme adopted by the invention is as follows:
a safety sensing intelligent bracelet for diabetics comprises a body temperature monitoring module, a humidity monitoring module, a heart rate monitoring module, a movement state monitoring module, a control module and a communication module;
the body temperature monitoring module is used for monitoring the body temperature of a user in real time and sending the monitored body temperature value to the control module in real time; the humidity monitoring module is used for monitoring the humidity of the skin surface of the user in real time, sending the monitored humidity value to the control module in real time, and the heart rate monitoring module is used for monitoring the heart rate of the user in real time and sending the monitored heart rate value to the control module; the motion state monitoring module is used for monitoring motion state information of a user, dividing the current motion state of the user into a plurality of levels based on the motion state information, and sending the obtained motion levels to the control module;
the control module samples the body temperature, the humidity, the heart rate and the exercise level at preset time intervals, acquires the current body temperature T, the humidity H, the heart rate R and the exercise level S, and acquires the current time, so as to acquire a five-dimensional monitoring vector < T, H, R, S and time >; the control module establishes communication connection with external data analysis equipment through the communication module and transmits data;
the smart band monitors the hypoglycemic response of a diabetic user by:
step 100: the control module acquires a current monitoring vector at intervals of preset time and sends the monitoring vector to the data analysis equipment;
step 200: the data analysis equipment stores the monitoring vector, and identifies the monitoring vector according to a preset identification model to judge whether the user has a hypoglycemia reaction or not;
step 300: if the user is judged to have the hypoglycemia reaction, a hypoglycemia alarm is sent out;
step 400: the data analysis equipment performs statistical analysis on the stored monitoring vectors for a plurality of days to acquire a hypoglycemia dangerous period of a user in one day;
the step 400 specifically includes:
step 410: dividing one day into a plurality of time periods, and counting the average value of the body temperature, the humidity, the heart rate and the exercise level in each time period according to the stored monitoring vectors of the past n days;
step 420: according to the stored monitoring vectors of the past n days, calculating the fluctuation mean value of the body temperature, the humidity and the heart rate in each time period; firstly, counting fluctuation values of body temperature, humidity and heart rate in each time period of each day in the past n days; the fluctuation value refers to half of the difference between the maximum value and the minimum value, and then the average value of the fluctuation values of the body temperature, the humidity and the heart rate in each time period is counted and used as the fluctuation average value;
step 430: for any time period A, judging whether the time period A is a hypoglycemia dangerous period according to the average value of the body temperature, the humidity, the heart rate and the exercise level in the time period A and the fluctuation average value of the body temperature, the humidity and the heart rate.
Further, the step 420 specifically includes:
for any one of the time periods A, it is assumed that the average of the body temperature, humidity, heart rate and exercise level in this time period A is divided into AT a 、AH a 、AR a And AS (application server) a And assume that the mean value of the fluctuation of body temperature, humidity and heart rate in the period is AT b 、AH b 、AR b The corresponding 8 judgment vectors are constructed through different combinations, namely:
<AT a +AT b ，AH a +AH b ，AR a +AR b ，AS a >
<AT a +AT b ，AH a +AH b ，AR a -AR b ，AS a >
<AT a +AT b ，AH a -AH b ，AR a +AR b ，AS a >
<AT a +AT b ，AH a -AH b ，AR a -AR b ，AS a >
<AT a -AT b ，AH a +AH b ，AR a +AR b ，AS a >
<AT a -AT b ，AH a +AH b ，AR a -AR b ，AS a >
<AT a -AT b ，AH a -AH b ，AR a +AR b ，AS a >
<AT a -AT b ，AH a -AH b ，AR a -AR b ，AS a >
and respectively inputting the 8 judgment vectors into a preset identification model to judge whether the hypoglycemia reaction occurs. And if one judgment vector is input into the model, judging that the hypoglycemia reaction occurs by the model output, and determining the time period A as the hypoglycemia dangerous period.
Further, the data analysis device recalculates the hypoglycemic dangerous period once per day.
Further, the intelligent bracelet further comprises a positioning module, the control module uploads the geographical positioning information acquired by the positioning module to the data analysis equipment, and the data analysis equipment sends the current geographical positioning information of the user to related personnel when alarming.
Further, the step 300 includes: the intelligent bracelet sends out alarm sound, and the volume of the alarm is intelligently set according to daytime and night.
Further, the communication module can establish communication connection with an external network or an external device and transmit data through a Bluetooth connection, a WIFI wireless local area network or a mobile network,
further, the data analysis device is a smart phone.
Further, the positioning module adopts GPS positioning or Beidou satellite positioning.
Further, the preset recognition model is a pre-trained deep learning model.
Further, the deep learning model is a deep neural network model.
The beneficial effects of the invention are as follows: the monitoring data of the diabetics can be found as soon as possible, and the diabetic patients can respond to the hypoglycemia reaction quickly; through further analysis of the monitored data, a patient's dangerous period can be discovered to be ready for treatment in advance.
[ description of the drawings ]
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate and together with the description serve to explain the invention, if necessary:
FIG. 1 is a functional block diagram of the smart band of the present invention.
[ detailed description ] of the invention
The present invention will now be described in detail with reference to the drawings and the specific embodiments thereof, wherein the exemplary embodiments and the description are for the purpose of illustrating the invention only and are not to be construed as limiting the invention.
Referring to fig. 1, a functional block diagram of the smart band of the present invention is shown, and the smart band of the present invention includes a body temperature monitoring module, a humidity monitoring module, a heart rate monitoring module, a movement state monitoring module, a control module, a positioning module, and a communication module. It should be noted that the smart band of the present invention includes not only the above module, but also other modules in the prior art, which is not limited in this aspect of the present invention.
The body temperature monitoring module is used for monitoring the body temperature of a user in real time and sending the monitored body temperature value to the control module in real time. The humidity monitoring module is used for monitoring the humidity of the skin surface of the user in real time, measuring the perspiration degree of the skin surface of the user through the humidity, and sending the monitored humidity value to the control module in real time. The heart rate monitoring module is used for monitoring the heart rate of a user in real time and sending the monitored heart rate value to the control module.
The motion state monitoring module is used for monitoring motion state information of a user, dividing the current motion state of the user into a plurality of levels based on the motion state information, and reflecting different motion amounts by different levels. For example, the motion state monitoring module may monitor the number of steps of the user, and divide the user into two motion levels 0 and 1 according to the number of steps of the user in a certain period of time, which represent static and dynamic states respectively. I.e. the number of steps in a certain time is smaller than a predetermined threshold, then it is classified as level 0, otherwise it is classified as level 1. Of course, the motion state may be divided into more levels according to different step thresholds, which is not limited by the present invention. The motion state monitoring module calculates the current motion level in real time by detecting motion information in real time, and sends the obtained motion level to the control module.
The control module is a core processing module of the intelligent bracelet, and can sample the body temperature, the humidity, the heart rate and the exercise level at preset time intervals and acquire the current time, so as to obtain a five-dimensional monitoring vector. For example, the control module acquires the current body temperature T, the humidity H, the heart rate R, the exercise level S every 10 seconds, and acquires the current time to obtain the current monitoring vector < T, H, R, S, time >.
The positioning module is used for acquiring the geographical positioning information of the intelligent bracelet, and for example, GPS positioning or Beidou satellite positioning can be adopted. The control module may obtain current geographic location information from the location module to determine a current geographic location of the user.
The communication module is used for establishing communication connection with an external network or external equipment and transmitting data, and can be connected with Bluetooth equipment, a WIFI wireless local area network, a mobile network and the like. The control module can be connected with external data analysis equipment through the communication module and transmit data. The data analysis device can be a computer in a local area network, a smart phone connected with Bluetooth, or a server on the Internet. The present invention does not limit specific data analysis devices and communication connection modes, and how to set specific data analysis devices and corresponding communication connection modes is already the prior art, and the present invention will not be repeated.
Based on the above functional modules of the smart band of the present invention, specific working steps of the smart band of the present invention will be described in detail below.
Step 100: the control module acquires current five-dimensional monitoring vectors (T, H, R, S and time) at intervals of preset time, and sends the monitoring vectors to the data analysis equipment.
Specifically, as mentioned above, the control module may determine a five-dimensional monitoring vector < body temperature T, humidity H, heart rate R, exercise level S, time period > at predetermined intervals according to real-time monitoring values of the body temperature monitoring module, the humidity monitoring module, the heart rate monitoring module, and the exercise state monitoring module; after obtaining the monitoring vector, the control module sends the monitoring vector to a data analysis device through a communication module, and the data analysis device can store the monitoring vector. The preset time can be set by a manufacturer as a default value or can be set by a manager according to specific requirements.
For example, the smart band may send the acquired monitoring vector to a smart phone in real time via a smart phone near the bluetooth connection.
Step 200: and the data analysis equipment identifies the monitoring vector according to a preset identification model and judges whether the user has a hypoglycemia reaction or not.
Specifically, after receiving the monitoring vector, the data analysis device inputs the monitoring vector into a preset recognition model. The recognition model can judge whether the recognition model has the characteristic of hypoglycemia response or not according to the input monitoring vector. The identification model may be any identification method in the prior art, and the invention is not limited thereto. Specifically, a simple identification method can be adopted, that is, based on the motion level S in the monitoring vector, whether the body temperature, the humidity and the heart rate are higher or lower than a preset value range is judged. The preset value range is a normal value range related to the exercise level, for example, the value ranges of the body temperature, the humidity and the heart rate are different when the user is in static state and dynamic state. The monitoring vector may also be identified using a pre-trained deep learning model, such as a deep neural network model.
According to one embodiment of the present invention, a smart phone may be used as the data analysis device to identify the monitoring vector through a preset identification model. According to another preferred embodiment of the present invention, the smart phone may also forward the monitoring vector to a designated web server, which identifies the monitoring vector.
Step 300: and if the user is judged to have a hypoglycemia reaction, a hypoglycemia alarm is sent out.
The low blood sugar alarm can adopt different alarm modes, for example, the data analysis equipment can immediately inform related personnel through telephone, short messages, instant messaging and the like. The control module of the intelligent bracelet can upload the geographic positioning information acquired by the positioning module when uploading data to the data analysis equipment, so that the data analysis equipment can also send the current geographic positioning information of the user to related personnel when alarming.
The data analysis equipment can also send alarm information to the intelligent bracelet, and the intelligent bracelet can send alarm sound, and alarm volume is set up according to daytime/night intelligence, and the warning sound through the bracelet helps the user in time discover hypoglycemia, makes countermeasure fast.
Step 400: the data analysis equipment performs statistical analysis on the stored monitoring vectors for a plurality of days to acquire the hypoglycemia dangerous period of the user in one day.
Specifically, the data analysis device receives and stores the monitoring vector of the smart bracelet in real time, and after storing the monitoring vector of enough days, the data analysis device can perform statistical analysis on the stored monitoring vector of the past n days, namely, the change condition of the body temperature, the humidity and the heart rate in one day is subjected to statistical analysis, so that a time period in which the user is most likely to generate hypoglycemia in one day, namely, a hypoglycemia dangerous period is found.
The step 400 specifically includes:
step 410: dividing one day into a plurality of time periods, and counting the average value of the body temperature, the humidity, the heart rate and the exercise level in each time period according to the stored monitoring vectors of the plurality of days.
Specifically, from point 0, each half hour may be divided into a period of time, i.e., a first period of time from point 0 to point 0:30, a second period of time from point 0:30 to point 1, and so on.
Assuming statistical analysis of the monitoring vectors over the past n days, the average of body temperature, humidity, heart rate and exercise level over each time period of each day may be counted first, and then the average over n days of each time period. For example, for the body temperature of the first period, the average value T of the body temperature of the first period in the first day is counted 1 Counting the average value T of the body temperature in the first time period in the second day 2 And so on, obtaining n average body temperatures T in the first time period of the past n days 1 ，T 2 ，……，T n Then calculate the average T of the n average body temperatures ave ＝(T 1 +T 2 +……+T n ) And/n is the average value of the body temperature in the first time period. In the same way as described above,the average of body temperature, humidity, heart rate and exercise level over each time period can be calculated (the average of exercise level can be rounded to an integer level).
Step 420: and counting the fluctuation mean value of the body temperature, the humidity and the heart rate in each time period according to the stored monitoring vectors for a plurality of days.
Specifically, assuming that statistical analysis is performed on monitoring vectors of the past n days, fluctuation values of body temperature, humidity and heart rate in each time period of each day can be counted first; the fluctuation value refers to half of the difference between the maximum value and the minimum value. And then counting the average value of the fluctuation values of the body temperature, the humidity and the heart rate in n days in each time period as a fluctuation average value.
For example, for the body temperature of the first period, the fluctuation value TB of the body temperature of the first period in the first day is counted 1 I.e. TB 1 ＝(TMax 1 -TMin 1 ) 2, wherein TMax 1 Is the maximum value of body temperature, TMin, in the first time period of the first day 1 Is the minimum value of the body temperature in the first time period of the first day, and the like, can calculate the body temperature fluctuation value TB in the first time period in the past n days 1 ，TB 2 ，……，TB n . Then calculate the average value TB of the n body temperature fluctuation values ave ＝(TB 1 +TB 2 +……+TB n ) And/n, which is the mean value of the fluctuation of the body temperature in the first time period. In the same way, the mean value of the fluctuation of the body temperature, the humidity and the heart rate in each time period can be calculated.
Step 430: for any time period A, judging whether the time period A is a hypoglycemia dangerous period according to the average value of the body temperature, the humidity, the heart rate and the exercise level in the time period A and the fluctuation average value of the body temperature, the humidity and the heart rate.
Specifically, for any one of the time periods a, it is assumed that the average values of the body temperature, humidity, heart rate and exercise level in the time period a are classified as AT a 、AH a 、AR a And AS (application server) a And assume that the mean value of the fluctuation of body temperature, humidity and heart rate in the period is AT b 、AH b 、AR b Then go throughThe corresponding 8 judgment vectors can be constructed by the same combination, namely:
<AT a +AT b ，AH a +AH b ，AR a +AR b ，AS a >
<AT a +AT b ，AH a +AH b ，AR a -AR b ，AS a >
<AT a +AT b ，AH a -AH b ，AR a +AR b ，AS a >
<AT a +AT b ，AH a -AH b ，AR a -AR b ，AS a >
<AT a -AT b ，AH a +AH b ，AR a +AR b ，AS a >
<AT a -AT b ，AH a +AH b ，AR a -AR b ，AS a >
<AT a -AT b ，AH a -AH b ，AR a +AR b ，AS a >
<AT a -AT b ，AH a -AH b ，AR a -AR b ，AS a >
and respectively inputting the 8 judgment vectors into a preset identification model to judge whether the hypoglycemia reaction occurs. And if one judgment vector is input into the model, judging that the hypoglycemia reaction occurs by the model output, and determining the time period A as the hypoglycemia dangerous period.
By the method, the hypoglycemia dangerous period of the user in one day can be identified, and corresponding preparation can be made in advance.
The hypoglycemic risk period may vary over time, so the hypoglycemic risk period may be recalculated once per day.
The foregoing description is only of the preferred embodiments of the invention, and all changes and modifications that come within the meaning and range of equivalency of the structures, features and principles of the invention are therefore intended to be embraced therein.
Claims (10)
1. The safety sensing intelligent bracelet for the diabetics is characterized by comprising a body temperature monitoring module, a humidity monitoring module, a heart rate monitoring module, a movement state monitoring module, a control module and a communication module;
the body temperature monitoring module is used for monitoring the body temperature of a user in real time and sending the monitored body temperature value to the control module in real time; the humidity monitoring module is used for monitoring the humidity of the skin surface of the user in real time, sending the monitored humidity value to the control module in real time, and the heart rate monitoring module is used for monitoring the heart rate of the user in real time and sending the monitored heart rate value to the control module; the motion state monitoring module is used for monitoring motion state information of a user, dividing the current motion state of the user into a plurality of levels based on the motion state information, and sending the obtained motion levels to the control module;
the control module samples the body temperature, the humidity, the heart rate and the exercise level at preset time intervals, acquires the current body temperature T, the humidity H, the heart rate R and the exercise level S, and acquires the current time, so as to acquire a five-dimensional monitoring vector < T, H, R, S and time >; the control module establishes communication connection with external data analysis equipment through the communication module and transmits data;
the smart band monitors the hypoglycemic response of a diabetic user by:
step 100: the control module acquires a current monitoring vector at intervals of preset time and sends the monitoring vector to the data analysis equipment;
step 200: the data analysis equipment stores the monitoring vector, and identifies the monitoring vector according to a preset identification model to judge whether the user has a hypoglycemia reaction or not;
step 300: if the user is judged to have the hypoglycemia reaction, a hypoglycemia alarm is sent out;
step 400: the data analysis equipment performs statistical analysis on the stored monitoring vectors for a plurality of days to acquire a hypoglycemia dangerous period of a user in one day;
the step 400 specifically includes:
step 410: dividing one day into a plurality of time periods, and counting the average value of the body temperature, the humidity, the heart rate and the exercise level in each time period according to the stored monitoring vectors of the past n days;
step 420: according to the stored monitoring vectors of the past n days, calculating the fluctuation mean value of the body temperature, the humidity and the heart rate in each time period; firstly, counting fluctuation values of body temperature, humidity and heart rate in each time period of each day in the past n days; the fluctuation value refers to half of the difference between the maximum value and the minimum value, and then the average value of the fluctuation values of the body temperature, the humidity and the heart rate in each time period is counted and used as the fluctuation average value;
step 430: for any time period A, judging whether the time period A is a hypoglycemia dangerous period according to the average value of the body temperature, the humidity, the heart rate and the exercise level in the time period A and the fluctuation average value of the body temperature, the humidity and the heart rate.
2. The diabetes patient safety sensing smart band of claim 1, wherein the step 420 specifically comprises:
for any one of the time periods A, it is assumed that the average of the body temperature, humidity, heart rate and exercise level in this time period A is divided into AT a 、AH a 、AR a And AS (application server) a And assume that the mean value of the fluctuation of body temperature, humidity and heart rate in the period is AT b 、AH b 、AR b The corresponding 8 judgment vectors are constructed through different combinations, namely:
<AT a +AT b ，AH a +AH b ，AR a +AR b ，AS a >
<AT a +AT b ，AH a +AH b ，AR a -AR b ，AS a >
<AT a +AT b ，AH a -AH b ，AR a +AR b ，AS a >
<AT a +AT b ，AH a -AH b ，AR a -AR b ，AS a >
<AT a -AT b ，AH a +AH b ，AR a +AR b ，AS a >
<AT a -AT b ，AH a +AH b ，AR a -AR b ，AS a >
<AT a -AT b ，AH a -AH b ，AR a +AR b ，AS a >
<AT a -AT b ，AH a -AH b ，AR a -AR b ，AS a >
and respectively inputting the 8 judgment vectors into a preset identification model to judge whether the hypoglycemia reaction occurs. And if one judgment vector is input into the model, judging that the hypoglycemia reaction occurs by the model output, and determining the time period A as the hypoglycemia dangerous period.
3. The diabetes patient safety sensing smart band of claim 1, wherein the period of hypoglycemia risk is recalculated once a day.
4. The diabetes patient safety sensing smart bracelet according to claim 1, further comprising a positioning module, wherein the control module uploads the geographical positioning information acquired by the positioning module to the data analysis device, and the data analysis device sends the current geographical positioning information of the user to related personnel when alarming.
5. The diabetes patient safety sensing smart band of claim 1, wherein the step 300 comprises: the intelligent bracelet sends out alarm sound, and the volume of the alarm is intelligently set according to daytime and night.
6. The diabetes patient safety sensing smart bracelet according to claim 1, wherein the communication module can establish a communication connection with an external network or an external device and transmit data through a bluetooth connection, a WIFI wireless local area network or a mobile network.
7. The diabetes patient safety sensing smart band of claim 1, wherein the data analysis device is a smart phone.
8. The diabetes patient safety sensing smart bracelet of claim 4, wherein the positioning module adopts GPS positioning or Beidou satellite positioning.
9. The diabetes patient safety sensing smart bracelet of claim 1, wherein the preset recognition model is a pre-trained deep learning model.
10. The diabetes patient safety sensing smart bracelet of claim 9, wherein the deep learning model is a deep neural network model.
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