CN111904400B - Electronic wrist strap - Google Patents

Electronic wrist strap Download PDF

Info

Publication number
CN111904400B
CN111904400B CN202010694099.5A CN202010694099A CN111904400B CN 111904400 B CN111904400 B CN 111904400B CN 202010694099 A CN202010694099 A CN 202010694099A CN 111904400 B CN111904400 B CN 111904400B
Authority
CN
China
Prior art keywords
early warning
decision
physiological
state
module
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
Application number
CN202010694099.5A
Other languages
Chinese (zh)
Other versions
CN111904400A (en
Inventor
晋良海
刘硕
陈述
方梅
彭爽
雷文凡
郑霞忠
陈雁高
廖成林
赵龙飞
李益
谈安健
姜桂莲
易小钰
刘涵
殷双萍
吴志鹏
李佳炘
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
China Three Gorges University CTGU
Original Assignee
China Three Gorges University CTGU
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by China Three Gorges University CTGU filed Critical China Three Gorges University CTGU
Priority to CN202010694099.5A priority Critical patent/CN111904400B/en
Publication of CN111904400A publication Critical patent/CN111904400A/en
Application granted granted Critical
Publication of CN111904400B publication Critical patent/CN111904400B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • 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
    • 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
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/68—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient
    • A61B5/6801—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient specially adapted to be attached to or worn on the body surface
    • A61B5/6802—Sensor mounted on worn items
    • A61B5/681—Wristwatch-type devices
    • 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
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01H—MEASUREMENT OF MECHANICAL VIBRATIONS OR ULTRASONIC, SONIC OR INFRASONIC WAVES
    • G01H17/00—Measuring mechanical vibrations or ultrasonic, sonic or infrasonic waves, not provided for in the preceding groups

Abstract

The electronic wrist strap comprises a physiological index data acquisition module, and is used for acquiring various human physiological index data of a user in a calm state or under a condition of making a major decision and transmitting the acquired human physiological index data to a storage module. The storage module comprises a storage unit, a storage unit and a processing unit, wherein the storage unit is used for storing parameter values of various physiological indexes in a calm state or a major decision state; and the transmission unit is used for transmitting the analysis results of all the physiological indexes to the early warning system. The early warning system comprises a Shehatt early warning module and is used for judging whether various human body physiological indexes under a major decision condition reach an early warning condition or not, and if the human body physiological indexes under the major decision condition reach the early warning condition, early warning is carried out; and the reminding terminal is used for displaying and reminding according to the abnormal physiological indexes obtained by the Shehard early warning module. The electronic wrist strap disclosed by the invention can realize monitoring and early warning of human physiological indexes, and can detect and early warn the physiological indexes when a human body makes a decision, so that the irrational errors of the decision are reduced.

Description

Electronic wrist strap
Technical Field
The invention relates to the technical field of intelligent wearable equipment, in particular to an electronic wrist strap.
Background
With the development of scientific technology, electronic products for monitoring various physiological indexes of human bodies are successively released in the market, for example: sports bracelet, smart phone, smart watch, etc. However, in the current research of intelligent wearable equipment, more physiological indexes such as heart rate, body temperature, voice, respiration and blood pressure are related to health, and the intelligent wearable equipment is applied to clinical work such as medical treatment and health care, has a single application layer, and cannot store and analyze historical data to achieve the early warning effect.
In fact, the specific cognitive theory indicates that many economic behaviors are specific in nature, and emphasizes that physical signals, body states, actions or displacements of others have certain influence on economic decisions. Therefore, the variation and fluctuation of various physiological indexes are not only related to the health of the body, but also have certain influence on decision makers. When making a great decision, a decision maker is required to have stable and clear mind and thinking without being influenced by other factors. But the variation and fluctuation of various physiological indexes can cause the decision maker to generate risk preference for risk seeking or risk evasion, and finally, the decision made by the decision maker is not the result expected to be achieved, thereby causing certain loss. Therefore, it is an urgent need to solve the problem of developing a wearable physiological index detection and early warning wearable device which not only focuses on medical health but also plays a role in decision making.
Disclosure of Invention
The invention provides an electronic wrist strap, which can monitor and early warn physiological indexes when a human body makes a decision while realizing monitoring and early warning of the physiological indexes of the human body, is beneficial to improving the rationality level of a decision maker facing a major decision event and reducing the irrational errors of the decision.
The technical scheme adopted by the invention is as follows:
the electronic wrist strap comprises a watch strap and a wrist strap body, wherein the wrist strap body is connected with the watch strap and is provided with a display module, a processor module, a physiological index data acquisition module, a storage module and an early warning system;
the processor module is used for interpreting instructions and processing data, and comprises: the system comprises a control module, a physiological index data acquisition module, a storage module and an early warning system.
The control module includes:
the instruction register unit is used for storing an executing instruction which is read out from the storage module currently;
the instruction decoding unit is used for analyzing the current instruction and determining the operation to be completed;
the operation control unit is used for generating a control signal obtained by decoding and sending the control signal to the storage module and the Shehatt early warning module;
the Shehard early warning module is used for judging whether various human body physiological indexes under a major decision condition reach an early warning condition or not, if so, early warning is carried out, and TMS320LF2407ADSP is used as a main control chip.
The physiological index data acquisition module is used for acquiring various human physiological index data of a user in a calm rational state or under a condition of making a major decision and transmitting the acquired human physiological index data to the storage module;
the memory module includes:
the storage unit is used for storing parameter values of various physiological indexes in a calm state or a major decision state, and adopts a read-only memory of 512 kilobytes and a random access memory of 384 kilobytes;
and the transmission unit is used for transmitting the analysis results of various physiological indexes to the early warning system and adopts Bluetooth 4.0BLE low-power-consumption intelligent hardware.
The early warning system includes:
and the reminding terminal is used for displaying and reminding according to the abnormal physiological indexes obtained by the Shehard early warning module.
The physiological index data acquisition module comprises:
the heart rate acquisition unit is used for acquiring heart rate parameter values of a decision maker in a calm rational state, wherein each day is a large period, each hour is a small period, and each two minutes is a node; in a major decision state, measuring in units of every minute;
the voice acquisition unit is used for acquiring voice parameter values of a decision maker in a calm rational state, wherein each day is a large period, each hour is a small period, and each two minutes is a node; in a major decision state, measuring in units of every minute;
the blood pressure acquisition unit is used for acquiring blood pressure parameter values of a decision maker in a calm rational state, wherein each day is a large period, each hour is a small period, and each two minutes is a node; measured in units of minutes in a critical decision state.
The Shehatt early warning module comprises:
the early warning analysis unit is used for forming a complete waveform by the change of the index data along with time by using the obtained data information of various human physiological indexes under the important decision condition;
the early warning threshold value calculating unit is used for calculating the weight ratio of the change of the heart rate, the voice and the blood pressure to the risk preference change of a decision maker, calculating threshold values according to various data in a calm rational state obtained by the physiological index data acquisition module and providing basis for judging the abnormality of the physiological index;
the early warning judgment unit is used for judging the waveform of each human body physiological index along a time axis under the condition of a heavy decision according to the calculated early warning threshold;
and the alarm unit is used for alarming abnormal human body physiological indexes and reporting the abnormal human body physiological indexes to the reminding terminal.
The reminding terminal comprises:
the early warning prompting unit is used for ringing or vibrating to prompt the abnormal physiological indexes;
the Bluetooth communication unit is used for connecting the mobile phone with the electronic wrist strap so that the electronic wrist strap can access various large websites;
and the emergency plan unit is used for searching for the specific measures for the abnormal physiological indexes and transmitting the measures to the display module 2, so that the decision maker takes corresponding measures to recover to normal.
The invention relates to an electronic wrist strap, which comprises a physiological index monitoring system and an early warning system: (1) a reliable physiological index monitoring system is designed, and is used for collecting data of various physiological indexes in a calm state and a major decision state and storing, analyzing and sorting the data. (2) And designing an early warning system, calculating an early warning threshold according to data acquired by the monitoring system, and judging whether early warning is needed or not according to the early warning threshold, so that a decision maker can make a cold-static decision under a major decision condition.
The electronic wrist strap reminds a terminal to transmit physical signals such as sound and vibration to a decision maker according to the judgment and early warning analysis result of the Houtt early warning module, is communicated with a network to search a corresponding abnormity solving method, and is displayed to the decision maker through a display screen, so that the decision maker is in a rational level as much as possible.
The invention relates to an electronic wrist strap, wherein a Houttte chart is used as a metering value control chart, stable upper and lower control limits are calculated, and the judgment can be carried out according to a judgment criterion. The invention is different from the traditional psychology 'leaving' rational level measuring scale method, which is an innovative method for monitoring and early warning decision behaviors under a specific theory framework, and adopts blood pressure, heart rate, voice and the like to represent the direct influence of specific information on decision psychology and behaviors.
The electronic wrist strap is beneficial to improving the rationality level of a decision maker facing major decision events and reducing the irrational errors of decision making.
Drawings
Fig. 1 is a schematic view of an electronic wristband according to the present invention.
Wherein: the voice monitoring system comprises a watch band 1, a display module 2, a voice acquisition unit 3, a blood pressure acquisition unit 4, a loudspeaker 5 and a heart rate acquisition unit 6.
FIG. 2(1) is a schematic diagram of Huoht's criterion 1 in the embodiment;
FIG. 2(2) is a schematic diagram of Huoht criterion 2 in an embodiment;
FIG. 2(3) is a schematic diagram of Huoht's criterion 3 in the embodiment;
FIG. 2(4) is a schematic diagram of Huoht criterion 4 in an embodiment;
FIG. 2(5) is a schematic diagram of Huoht's criterion 5 in an embodiment;
FIG. 2(6) is a schematic diagram of an embodiment of Houtt's criterion 6;
FIG. 2(7) is a schematic diagram of an embodiment of Houtt's criterion 7;
FIG. 2(8) is a schematic diagram of the Huoht criterion 8 in the embodiment.
Detailed Description
Electronic wrist strap, including watchband 1, wrist strap body, watchband 1 is connected to the wrist strap body, the wrist strap body is equipped with display module 2, processor module, physiological index data acquisition module, storage module, early warning system.
The processor module comprises a control module, a physiological index detection system and an early warning system.
The physiological index monitoring system comprises a physiological index data acquisition module and a storage module. The physiological index data acquisition module acquires physiological indexes in a calm state and a major decision state.
The physiological index data acquisition module comprises:
the heart rate acquisition unit is used for acquiring heart rate parameter values of a decision maker in a calm rational state, wherein each day is a large period, each hour is a small period, and each two minutes is a node; in a major decision state, measuring in units of every minute; the heart rate acquisition unit can use an HRB6708 heart rate pulse chip.
The voice acquisition unit is used for acquiring voice parameter values of a decision maker in a calm rational state, wherein each day is a large period, each hour is a small period, and each two minutes is a node; in a major decision state, measuring in units of every minute; the voice acquisition unit may use an AIC3104 audio acquisition chip.
The blood pressure acquisition unit is used for acquiring blood pressure parameter values of a decision maker in a calm rational state, wherein each day is a large period, each hour is a small period, and each two minutes is a node; measured in units of minutes in a critical decision state. The blood pressure acquisition unit can use a YKB1712 blood pressure sensor chip, and the chip can be upgraded along with the technical progress.
And the collected data is sorted, stored and transmitted to the early warning system through the storage module.
The data acquisition steps are as follows:
1) distinguishing physiological index data in a calm state and a major decision state;
2) dividing data collected in a quiet rational state in 24 subgroups, wherein each subgroup measures 30 times, namely 30 nodes;
3) measuring the frequency of each node in a calm state by 2-frequency per minute, wherein: the frequency of the heart rate per minute is recorded as XiVoice is marked as YiBlood pressure Zi；
4) Mean value of heart rate in calm state
Figure GDA0003587606790000041
Mean value of voice
Figure GDA0003587606790000042
Mean value of blood pressure
Figure GDA0003587606790000043
m represents the number of minutes of each physiological index measurement in a calm state;
5) making statistics on physiological index data collected in a major decision state according to the statistics per minute, and recording the frequency of heart rate per minute as xiVoice is recorded as yiBlood pressure is recorded as zi。
The early warning system calculates the weight ratio of the influence of the heart rate, the voice and the blood pressure on the decision risk preference of a decision maker according to the data collected by the physiological index monitoring system, and the weight ratio is calculated by the following steps:
firstly, calculating and analyzing a correlation coefficient matrix between every two indexes of heart rate, voice and blood pressure under a major decision state, and calculating a characteristic value lambda through matrix transformationiAnd a feature vector uij；
According to lambdaiSelecting the number n of the principal components, taking the number of the eigenvalues larger than 1, if the calculated lambda is larger thaniIf 3 of the components are more than 1, taking 3 main components;
thirdly according to the formula
Figure GDA0003587606790000051
Calculating an initial factor matrix, aijIs the correlation coefficient of the ith index and the jth principal component;
fourthly, calculating the weight coefficient T of the ith indexiThe calculation formula is as follows:
Figure GDA0003587606790000052
ai1…ainis the correlation coefficient of the ith index and the 1 st 1 … n principal components, ak1…aknA correlation coefficient indicating the kth index and the 1 st 1 … n principal component;
fifthly, TiNormalized into percentThe heart rate weight is ω1(ii) a Voice weight is omega2(ii) a The blood pressure weight is omega3。
The early warning system receives the data of the physiological index monitoring system and then calculates an early warning threshold value, so that the early warning threshold value is taken as a judgment basis for abnormity, and the early warning threshold value calculation steps are as follows:
1: and calculating the statistical control quantity. Average value of each subgroup
Figure GDA0003587606790000053
The calculation formula is as follows:
Figure GDA0003587606790000054
the standard deviation s for each subgroup, calculated as:
Figure GDA0003587606790000055
average of each subgroup mean
Figure GDA0003587606790000056
Is calculated by the formula
Figure GDA0003587606790000057
Mean of standard deviation of each subgroup
Figure GDA0003587606790000061
Is calculated by the formula
Figure GDA0003587606790000062
2: and calculating an early warning central line. The calculation formula of the early warning center line CL is as follows,
Figure GDA0003587606790000063
3: and calculating early warning and getting on line. Average of early warning on-line UCL, early warning central line CL and standard deviationValue of
Figure GDA0003587606790000064
Related to a control parameter k, and the calculation formula is
Figure GDA0003587606790000065
4: and calculating early warning offline. Average value of pre-warning offline LCL, pre-warning central line CL and standard deviation
Figure GDA0003587606790000066
Related to a control parameter k, and the calculation formula is
Figure GDA0003587606790000067
5: the area between the early warning center line CL and the early warning up-down lines UCL and LCL is equally divided into three parts, which are designated as A, B, C.
Physiological index change value q of decision maker in every minute under important decision stateiThe calculation formula is as follows:
Figure GDA0003587606790000068
physiological index data q of the collected important decisioniThe formed waveform is comprehensively compared with an early warning threshold value to judge whether early warning is needed or not, and the early warning judgment criteria are as follows:
criterion 1: the waveform formed by various human body physiological indexes in a major decision state has 1 point
Figure GDA0003587606790000069
Figure GDA00035876067900000610
When in range of (A), i.e., a point is outside of zone A;
criterion 2: the waveform formed by various human body physiological indexes in a major decision state has 9 continuous points which fall on the same side of the early warning center line CL, namely 9 points are in or outside the C area;
criterion 3: the waveform formed by various human body physiological indexes in a major decision state is continuously increased or decreased by 6 points;
criterion 4: adjacent points in the continuous 14 points of the waveform formed by various human body physiological indexes in a major decision state are alternately arranged up and down;
criterion 5: 2 points of continuous 3 points of the waveform formed by various human body physiological indexes in a major decision state fall in an area A;
criterion 6: 4 points of 5 continuous waveforms formed by various human physiological indexes in a major decision state fall in a B area;
criterion 7: 15 continuous points of a waveform formed by various human body physiological indexes in a major decision state fall above and below a region C on two sides of a central line;
criterion 8: the continuous 8 points of the waveform formed by various human body physiological indexes in a major decision state fall on two sides of the early warning central line, and none of the points is in the C area.
When the early warning judging unit judges that the early warning is not needed, no operation is executed; when the early warning judgment unit judges that early warning is needed, relevant measures which can enable the abnormal physiological indexes of the decision maker to be recovered to a normal range can be retrieved through a network which is connected with the mobile phone through the Bluetooth module, and the relevant measures are displayed in the display module 2, so that the decision maker can make an accurate decision. The emotional state of the decision maker can cause the change of the physiological indexes, and under the positive emotional state, the decision maker tends to take risk, underestimates the risk and makes a high-risk and high-compensation decision; in a passive emotional state, the decision maker tends to avoid the risk, overestimates the risk and makes a low-risk and low-compensation decision.

Claims (7)

1. Electronic wrist strap, including watchband (1), wrist strap body connects watchband (1), its characterized in that: the wrist strap body is provided with a display module (2), a physiological index data acquisition module, a storage module, a processor module and an early warning system;
the processor module is used for interpreting instructions and processing data;
the processor module, comprising:
the instruction register unit is used for storing an executing instruction which is read out from the storage module currently;
the instruction decoding unit is used for analyzing the current instruction and determining the operation to be completed;
the operation control unit is used for generating a control signal obtained by decoding and sending the control signal to the storage module and the Huohtt early warning module;
the Shehatt early warning module is used for judging whether various human body physiological indexes under the major decision condition reach an early warning condition or not, and if the human body physiological indexes under the major decision condition reach the early warning condition, early warning is carried out;
the physiological index data acquisition module is used for acquiring various human physiological index data of a user in a calm state or under a condition of making a major decision and transmitting the acquired human physiological index data to the storage module;
the memory module includes:
the storage unit is used for storing parameter values of various physiological indexes in a calm state or a major decision state;
the transmission unit is used for transmitting the analysis results of various physiological indexes to the early warning system;
the early warning system includes:
the reminding terminal is used for displaying and reminding according to the abnormal physiological indexes obtained by the Shehard early warning module;
the early warning system calculates the weight ratio of the influence of the heart rate, the voice and the blood pressure on the decision risk preference of a decision maker according to the data collected by the physiological index monitoring system, and the weight ratio is calculated by the following steps:
firstly, calculating and analyzing correlation coefficient matrix between every two indexes of heart rate, voice and blood pressure under the condition of major decision making, and then calculating characteristic value lambda by means of matrix transformationiAnd a feature vector uij；
According to lambdaiSelecting the number n of the principal components, taking the number of the eigenvalues larger than 1, if the calculated lambda is larger thaniIf 3 of the components are more than 1, taking 3 main components;
thirdly according to the formula
Figure FDA0003587606780000011
Calculating an initial factor matrix, aijIs the correlation coefficient of the ith index and the jth principal component;
fourthly, calculating the weight coefficient T of the ith indexiThe calculation formula is as follows:
Figure FDA0003587606780000021
ai1…aina correlation coefficient, a, representing the ith index and the 1 st 1 … n principal componentsk1…aknA correlation coefficient indicating the kth index and the 1 st 1 … nth principal component;
fifthly, TiNormalized to percentage heart rate weight of ω1(ii) a Voice weight is omega2(ii) a The blood pressure weight is omega3。
2. The electronic wristband of claim 1, wherein: the physiological index data acquisition module comprises:
the heart rate acquisition unit is used for acquiring heart rate parameter values of a decision maker in a calmness state, wherein in the calmness state, a large period is set every day, a small period is set every hour, and a node is set every two minutes; in a major decision state, measuring in units of every minute;
the voice acquisition unit is used for acquiring voice parameter values of a decision maker in a calm rational state, wherein each day is a large period, each hour is a small period, and each two minutes is a node; measured in units of minutes in critical decision state;
the blood pressure acquisition unit is used for acquiring blood pressure parameter values of a decision maker in a calm rational state, wherein each day is a large period, each hour is a small period, and each two minutes is a node; measured in units of minutes in a critical decision state.
3. The electronic wristband of claim 1, wherein: the Shehatt early warning module comprises:
the early warning analysis unit is used for forming a complete waveform by the change of the index data along with time by using the obtained data information of various human physiological indexes under the important decision condition;
the early warning threshold value calculating unit is used for calculating the weight ratio of the change of the heart rate, the voice and the blood pressure to the risk preference change of a decision maker, calculating threshold values according to various data in a calm rational state obtained by the physiological index data acquisition module and providing basis for judging the abnormality of the physiological index;
the early warning judgment unit is used for judging the waveform of each human body physiological index along a time axis under the condition of a heavy decision according to the calculated early warning threshold;
and the alarm unit is used for alarming abnormal human body physiological indexes and reporting the abnormal human body physiological indexes to the reminding terminal.
4. The electronic wristband of claim 1 or 3, wherein: the reminding terminal comprises:
the early warning prompting unit is used for ringing or vibrating to prompt the abnormal physiological indexes;
the Bluetooth communication unit is used for connecting the mobile phone with the electronic wrist strap so that the electronic wrist strap can access all large websites;
and the emergency plan unit is used for searching the abnormal physiological indexes for the targeted measures and transmitting the abnormal physiological indexes to the display module (2) so that the decision maker can take corresponding measures to restore to normal.
5. The electronic wristband of claim 1, wherein:
the data acquisition steps are as follows:
1) distinguishing physiological index data in a calm state and a major decision state;
2) dividing data collected in a quiet rational state in 24 subgroups, wherein each subgroup measures 30 times, namely 30 nodes;
3) measuring the frequency of each node in a calm state by 2-frequency per minute, wherein: the frequency of the heart rate per minute is recorded as XiVoice is marked as YiBlood pressure Zi；
4) Mean value of heart rate in calm state
Figure FDA0003587606780000031
Mean value of voice
Figure FDA0003587606780000032
Mean value of blood pressure
Figure FDA0003587606780000033
m represents the number of minutes of each physiological index measurement in a calm state;
5) making statistics on physiological index data collected in a major decision state according to the statistics per minute, and recording the frequency of heart rate per minute as xiVoice is recorded as yiBlood pressure is recorded as zi。
6. The electronic wristband of claim 1, wherein:
the early warning system receives the data of the physiological index monitoring system and then calculates an early warning threshold value, so that the early warning threshold value is taken as a judgment basis for abnormity, and the early warning threshold value calculation steps are as follows:
1): calculating a statistical control quantity; average value of each subgroup
Figure FDA0003587606780000034
The calculation formula is as follows:
Figure FDA0003587606780000035
the standard deviation s for each subgroup, calculated as:
Figure FDA0003587606780000036
average of each subgroup mean
Figure FDA0003587606780000037
Is calculated by the formula
Figure FDA0003587606780000038
Mean of standard deviation of each subgroup
Figure FDA0003587606780000039
Is calculated by the formula
Figure FDA00035876067800000310
2): calculating an early warning central line CL, wherein the calculation formula of the early warning central line CL is as follows,
Figure FDA0003587606780000041
3): calculating the average value of the early warning on-line UCL, the early warning central line CL and the standard deviation
Figure FDA0003587606780000042
Related to a control parameter k, and the calculation formula is
Figure FDA0003587606780000043
4): calculating the average value of the early warning offline LCL, the early warning offline central line CL and the standard deviation
Figure FDA0003587606780000044
Related to the control parameter k, the calculation formula is
Figure FDA0003587606780000045
5): the area between the early warning center line CL and the early warning up-down lines UCL and LCL is equally divided into three parts, which are designated as A, B, C.
7. The electronic wristband of claim 1, wherein:
physiological index change value q of decision maker in every minute under important decision stateiThe calculation formula is as follows:
Figure FDA0003587606780000046
physiological index data q of the collected important decisioniThe formed waveform is comprehensively compared with an early warning threshold value to judge whether early warning is needed or not, and the early warning judgment criteria are as follows:
criterion 1: the waveform formed by various human body physiological indexes in a major decision state has 1 point
Figure FDA0003587606780000047
Figure FDA0003587606780000048
Within (3), i.e., a point outside zone A;
criterion 2: the waveform formed by various human body physiological indexes in a major decision state has 9 continuous points which fall on the same side of the early warning center line CL, namely 9 points are in or outside the C area;
criterion 3: the waveform formed by various human body physiological indexes in a major decision state is continuously increased or decreased by 6 points;
criterion 4: adjacent points in the continuous 14 points of the waveform formed by various human body physiological indexes in a major decision state are alternately arranged up and down;
criterion 5: 2 points of continuous 3 points of the waveform formed by various human body physiological indexes in a major decision state fall in an area A;
criterion 6: 4 points of 5 continuous waveforms formed by various human physiological indexes in a major decision state fall in a B area;
criterion 7: 15 continuous points of a waveform formed by various human body physiological indexes in a major decision state fall above and below a region C on two sides of a central line;
criterion 8: the continuous 8 points of the waveform formed by various human body physiological indexes in a major decision state fall on two sides of the early warning central line, and none of the points is in the C area.
CN202010694099.5A 2020-07-17 2020-07-17 Electronic wrist strap Active CN111904400B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202010694099.5A CN111904400B (en) 2020-07-17 2020-07-17 Electronic wrist strap

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202010694099.5A CN111904400B (en) 2020-07-17 2020-07-17 Electronic wrist strap

Publications (2)

Publication Number Publication Date
CN111904400A CN111904400A (en) 2020-11-10
CN111904400B true CN111904400B (en) 2022-06-03

Family

ID=73281040

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202010694099.5A Active CN111904400B (en) 2020-07-17 2020-07-17 Electronic wrist strap

Country Status (1)

Country Link
CN (1) CN111904400B (en)

Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116269355B (en) * 2023-05-11 2023-08-01 江西珉轩智能科技有限公司 Safety monitoring system based on figure gesture recognition

Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPH1063635A (en) * 1996-08-23 1998-03-06 Gijutsu Kenkyu Kumiai Shinjoho Shiyori Kaihatsu Kiko Device for selecting feature of multi-dimensional input data
CA2245607A1 (en) * 1997-08-18 1999-02-18 Allen J. Hinkle Interactive health care system and method
CN105913199A (en) * 2016-05-13 2016-08-31 华北电力大学 Energy, economy and environment coordination degree calculation method and device based on ellipsoidal model
CN108090657A (en) * 2017-12-05 2018-05-29 大连理工大学 Oil ＆ Gas Storage facility risk assessment based on Xiu Hate control theories and probabilistic neural network manages system and method with on-line early warning
CN109171771A (en) * 2018-08-08 2019-01-11 互通金融科技有限公司 Internet of Things dealer prior-warning device and method for early warning
CN109745046A (en) * 2019-01-22 2019-05-14 北京航空航天大学 A kind of electrical impedance imaging electrode and system suitable under motion state
US10653368B1 (en) * 2013-09-09 2020-05-19 Cerner Innovation, Inc. Determining when to emit an alarm

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPH1063635A (en) * 1996-08-23 1998-03-06 Gijutsu Kenkyu Kumiai Shinjoho Shiyori Kaihatsu Kiko Device for selecting feature of multi-dimensional input data
CA2245607A1 (en) * 1997-08-18 1999-02-18 Allen J. Hinkle Interactive health care system and method
US10653368B1 (en) * 2013-09-09 2020-05-19 Cerner Innovation, Inc. Determining when to emit an alarm
CN105913199A (en) * 2016-05-13 2016-08-31 华北电力大学 Energy, economy and environment coordination degree calculation method and device based on ellipsoidal model
CN108090657A (en) * 2017-12-05 2018-05-29 大连理工大学 Oil ＆ Gas Storage facility risk assessment based on Xiu Hate control theories and probabilistic neural network manages system and method with on-line early warning
CN109171771A (en) * 2018-08-08 2019-01-11 互通金融科技有限公司 Internet of Things dealer prior-warning device and method for early warning
CN109745046A (en) * 2019-01-22 2019-05-14 北京航空航天大学 A kind of electrical impedance imaging electrode and system suitable under motion state

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
症状监测预警数据分析及方法研究;刘永召;《中国优秀硕士学位论文全文数据库 医药卫生科技辑》;20180115;第三章第3.2节 *

Also Published As

Publication number Publication date
CN111904400A (en) 2020-11-10

Similar Documents

Publication Publication Date Title
US11457822B2 (en) Methods and systems for arrhythmia tracking and scoring
US9801553B2 (en) System, method, and computer program product for the real-time mobile evaluation of physiological stress
KR20190050693A (en) Method and apparatus for high accuracy photolethysmogram based atrial fibrillation detection using wearable device
CN103876711A (en) Wearable electronic device and human body health monitoring and managing system
CN101248984A (en) Information management system and information management server
US11432778B2 (en) Methods and systems for patient monitoring
US20180310867A1 (en) System and method for stress level management
US20230233152A1 (en) Methods, apparatus and systems for adaptable presentation of sensor data
CN111904400B (en) Electronic wrist strap
JP2008253727A (en) Monitor device, monitor system and monitoring method
US20220313147A1 (en) Miscarriage identification and prediction from wearable-based physiological data
CN107221128B (en) A kind of evaluation of portable body fall risk and early warning system and its method
EP0906057A1 (en) Apparatus for analyzing hrv signals (heart rate variation)
Mohammadzadeh et al. Prediction of physiological response over varying forecast lengths with a wearable health monitoring platform
CN112120715A (en) Pressure monitoring and relieving system
JP2022517096A (en) Systems, devices, and methods for identifying brain conditions from cranial movements due to intracerebral blood flow
CN114098729A (en) Emotional state objective measurement method based on cardiac interval
GB2512305A (en) Apparatus and method for estimating energy expenditure
CN211834368U (en) Athlete fatigue detection system
AU2021103601A4 (en) System and method for monitoring post covid patient using machine learning and block chain
CN108175405A (en) A kind of system and its equipment for being used to calculate heart stabilizer degree
US20230210503A1 (en) Systems and Methods for Generating Menstrual Cycle Cohorts and Classifying Users into a Cohort
Tóth-Laufer et al. A personal profile based patient-specific anytime risk calculation model
CN110833391A (en) Human body sensing device
WO2023220245A2 (en) Method and apparatus for determining abnormal cardiac conditions non-invasively

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