CN111481215B - Mental disorder patient remote early warning system based on risk level judgment - Google Patents

Mental disorder patient remote early warning system based on risk level judgment Download PDF

Info

Publication number
CN111481215B
CN111481215B CN202010275290.6A CN202010275290A CN111481215B CN 111481215 B CN111481215 B CN 111481215B CN 202010275290 A CN202010275290 A CN 202010275290A CN 111481215 B CN111481215 B CN 111481215B
Authority
CN
China
Prior art keywords
patient
grade
computer
data
matrix
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
CN202010275290.6A
Other languages
Chinese (zh)
Other versions
CN111481215A (en
Inventor
向静
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.)
Southwest University Of Political Science & Law
Original Assignee
Southwest University Of Political Science & Law
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 Southwest University Of Political Science & Law filed Critical Southwest University Of Political Science & Law
Priority to CN202010275290.6A priority Critical patent/CN111481215B/en
Publication of CN111481215A publication Critical patent/CN111481215A/en
Application granted granted Critical
Publication of CN111481215B publication Critical patent/CN111481215B/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
    • A61B5/024—Detecting, measuring or recording pulse rate or heart rate
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
    • A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
    • A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
    • A61B5/1126—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb using a particular sensing technique
    • A61B5/1128—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb using a particular sensing technique using image analysis
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/16—Devices for psychotechnics; Testing reaction times ; Devices for evaluating the psychological state
    • A61B5/165—Evaluating the state of mind, e.g. depression, anxiety
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
    • G10L25/00—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00
    • G10L25/48—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 specially adapted for particular use
    • G10L25/51—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 specially adapted for particular use for comparison or discrimination
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N7/00—Television systems
    • H04N7/18—Closed-circuit television [CCTV] systems, i.e. systems in which the video signal is not broadcast
    • 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
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
    • Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

Abstract

The invention provides a mental disorder patient remote early warning system based on risk level judgment, which comprises a computer and wearable equipment, wherein the computer is used for: the computer is respectively connected with the wearable equipment; the computer is connected with the mobile terminal of the family of the patient; the computer acquires real-time geographic position information of the patient through the wearable equipment and sends the information to the family mobile terminal; the computer calculates the final dangerous state coefficient of the mental disorder patient and sends the final dangerous state coefficient to the family member mobile terminal. The beneficial effects of the invention are as follows: the physiological information, the activity video and the voice of the patient are monitored, so that the mental disease state evaluation data of the patient are automatically acquired, and the family members of the patient can master the dynamic changes of the illness state and the danger of the mental disorder person at the first time.

Description

Mental disorder patient remote early warning system based on risk level judgment
Technical Field
The application relates to the technical field of information, in particular to a mental disorder patient remote early warning system based on risk level judgment.
Background
The risk assessment standard published by the working standard of severe mental disorder management and treatment (2018 edition) is simpler, only the behavior is considered, the multi-factor risk is ignored, the multi-factor risk is seriously separated from pathological mental symptoms, the multi-factor risk can be only used for simple grading and convenient follow-up visit management, whether the disease is happened or not and whether the disease is light or heavy can not be accurately assessed, most of basal management staff have limited hands and can take a plurality of roles at the same time, follow-up visit is carried out by a plurality of telephones, and the effective supervision function can not be achieved.
Disclosure of Invention
In order to solve the problem of assessing the mental state of a mental disorder patient through physiological signal data and action image data of the mental disorder patient, the invention provides a mental disorder patient remote early warning system based on risk level judgment, which comprises a computer and a wearable device, wherein the computer is used for:
the computer is respectively connected with the wearable equipment;
the computer is connected with the mobile terminal of the family of the patient;
the computer acquires real-time geographic position information of the patient through the wearable equipment and sends the information to the family mobile terminal;
the computer calculates the final dangerous state coefficient of the mental disorder patient and sends the final dangerous state coefficient to the family member mobile terminal.
Further, the computer performs the steps of:
s1, acquiring heartbeat data, somatosensory gesture data, voice data and monitoring video data of a patient through wearable equipment worn by the patient and security monitoring equipment in the action range of the patient;
s2, analyzing patient behaviors according to the heartbeat data, the voice data, the somatosensory gesture data and the monitoring video data, automatically generating patient behavior data, and acquiring patient risk grade data by the patient behavior data;
s3: and acquiring a final risk state coefficient of the patient according to the risk level data of the user.
Further, the method comprises the steps of,
the danger level comprises three levels of low danger, medium danger and high danger.
Further, the method comprises the steps of,
the step S3 includes the steps of:
s31, determining a grade score according to the following rule:
the rank is low with a score of 1 for each item,
each item in the ranking has a score of 10,
each item with a high ranking score of 100,
the target column is categorized as to the type of the target column,
y corresponds to a matrix of 17X1, corresponds to features 1-17, and is marked 1 if there are corresponding features, or is marked 0 if there are
Figure GDA0004183378130000021
Let the final score be Y fen Then there is
Figure GDA0004183378130000022
If Y fen =0, then the patient is normal and no calculation is needed;
if Y fen ∈(0,10]The patient's corresponding grade is low;
if Y fen E (10, 100), the corresponding grade of the patient is medium;
if Y fen And more than or equal to 100, the corresponding grade of the patient is high.
S321: according to the setting in the step S31, the contrast matrixes established by the low, medium and high levels are respectively determined as follows:
Figure GDA0004183378130000023
where i=1, 2,3,4,5,6, x 1i =1, other x 1i =0. (i is an integer of 1-17)
Figure GDA0004183378130000024
Where i=7, 8,9, 10, 11, x 2i =1, other x 2i =0. (i is an integer of 1-17)
Figure GDA0004183378130000025
Where i=12, 13, 14, 15, 16, 17, x 3i =1, other x 3i =0. (i is an integer of 1-17)
S322, making:
Figure GDA0004183378130000026
when x is a1i =1, other x a1i =0. (i is an integer from 1 to 17)
Figure GDA0004183378130000031
When x is a2i =1, other x a2i =0. (i is an integer from 1 to 17)
Figure GDA0004183378130000032
When x is a3i =1, other x a3i =0. (i is an integer from 1 to 17)/(i)>
Order the
Figure GDA0004183378130000033
Wherein:
when the patient grade is low, let
Figure GDA0004183378130000034
Carrying out gray correlation calculation;
when the patient grade is middle, let X 1 ＝X' zhong Let X 01 ＝X a1 ·Y、X 02 ＝X a2 Y, let X respectively 0 ＝X 01 And X 0 ＝X 02 Carry over X (1) = [ X ] 0 X 1 ]Gray correlation degree calculation is carried out in the process, and finally two correlation degree coefficients r are respectively obtained 01 And r 02 ；
When the patient grade is high, there is X 1 ＝X' ga o
Let X 01 ＝X a1 ·Y、X 02 ＝X a2 Y and X 03 ＝X a3 Y, let X respectively 0 ＝X 01 、X 0 ＝X 02 And X 0 ＝X 03 The method comprises the steps of carrying out a first treatment on the surface of the Carry over X (1) = [ X ] 0 X 1 ]Gray correlation degree calculation is carried out in the process, and three correlation degree coefficients r are finally obtained 01 、r 02 And r 03 ；
S323: the step of calculating the gray correlation degree comprises the steps of,
S3231：
and (3) pairing:
Figure GDA0004183378130000041
normalization is performed row by row, and the formula is as follows:
Figure GDA0004183378130000042
each new x after normalization is calculated one by one through the formula ij Composing a new matrix X (2), then:
Figure GDA0004183378130000043
s3232: the matrix X (2) is subjected to difference sequence delta, delta max And delta min
And (3) making:
Δ＝[Δ i1 ],i＝1,...,17
Δ ij ＝|x i0 -x i1 |,i＝1,…,17；j＝0,1；
then:
Figure GDA0004183378130000044
Figure GDA0004183378130000045
s3233 adopts the following formula to calculate gray correlation coefficient matrix xi
ξ＝[ξ i1 ],i＝1,…,17
Figure GDA0004183378130000046
Wherein, beta is the association coefficient;
S3234：
let the gray correlation degree of the comparison sequence X and the target sequence Y be r, and calculate r by adopting the following formula
Figure GDA0004183378130000051
S324 calculates a state coefficient R, including,
let W= [ W ] 1 w 2 w 3 ]Is a weight matrix, wherein W1 corresponds to a low weight coefficient of a patient, W2 corresponds to a weight coefficient in the patient, W3 corresponds to a high weight coefficient of a patient, the weight coefficients respectively represent the proportion of low, medium and high-level components in psychological diseases of the patient, the weight coefficients are preset by a user, and W 1 w 2 w 3 Satisfy w 1 +w 2 +w 3 ＝1。
The state coefficient R is calculated as follows:
Figure GDA0004183378130000052
when the patient level is low,
R＝w 1 ·r 01
when the patient's grade is medium,
R＝w 1 ·r 01 +w 2 ·r 02
when the patient's grade is high,
R＝w 1 ·r 01 +w 2 ·r 02 +w 3 ·r 03 。
the beneficial effects of the invention are as follows: through monitoring the physiological information, the activity video and the voice of the patient, the mental disorder state evaluation data of the patient are automatically acquired and sent to the server, so that the family members of the patient can master the dynamic changes of the illness state and the danger of the mental disorder people at the first time.
Drawings
FIG. 1 is a flow chart of the present invention.
Fig. 2 is a diagram of the present invention.
Detailed Description
The invention provides a mental disorder patient remote early warning system based on risk level judgment as shown in fig. 1, which comprises a computer and wearable equipment:
the computer is respectively connected with the wearable equipment;
the computer is connected with the mobile terminal of the family of the patient;
the patient's mobile terminal may be a smart phone, which is connected to the internet with the computer.
The computer acquires real-time geographic position information of the patient through the wearable equipment and sends the information to the family mobile terminal;
further, the computer performs the steps of:
s1, acquiring heartbeat data, somatosensory gesture data, voice data and monitoring video data of a patient through wearable equipment worn by the patient and security monitoring equipment in the action range of the patient;
s2, analyzing patient behaviors according to the heartbeat data, the voice data, the somatosensory gesture data and the monitoring video data, automatically generating patient behavior data, and acquiring patient risk grade data by the patient behavior data;
s3: and acquiring a final risk state coefficient of the patient according to the risk level data of the user.
In the implementation process of the invention, the dangerous grade is classified into three grades of low risk, medium risk and high risk according to the following table.
Figure GDA0004183378130000061
/>
Figure GDA0004183378130000071
The behavior and symptom expression are automatically analyzed by a computer, and the voice data, the somatosensory gesture data and the monitoring video data are automatically obtained.
In the implementation process of the invention, the wearable device can be an intelligent watch with a communication function and a heartbeat sensor and a somatosensory gesture sensor, and the intelligent watch detects heartbeat data and somatosensory gesture data of a user and intelligently analyzes sleep conditions (deep sleep time, shallow sleep time and number of times of getting up) and sends the sleep conditions to the computer. The smart watch has a microphone that can acquire voice data of the patient and send the voice data to the computer. In one embodiment of the invention, an apple smartwatch is used, and in another embodiment of the invention, the smartwatch uses a microphone-integrated watch gt2.
The video data of the patient can be acquired by the monitoring equipment in the environment where the patient is located, and in one embodiment of the invention, the computer is connected with the security monitoring equipment, and the security monitoring equipment of the residential district of the patient acquires the video data of the patient.
Somatosensory data can be obtained through an accelerometer and a gyroscope integrated within the smart watch.
The technical scheme that the computer analyzes the behavior of the patient through the video data and the voice data is known in the prior art.
The process of obtaining the final risk status coefficient of the patient based on the user risk level data will be described in detail.
The first step: determining a ranking
Step1 determining the rank score
Grade Each score of
Low and low 1
In (a) 10
High height 100
Step2: categorizing target columns
Y corresponds to a matrix of 17X1, corresponds to features 1-17, and is marked 1 if there are corresponding features, or is marked 0 if there are
Figure GDA0004183378130000081
Let the final score be Y fen Then there is
Figure GDA0004183378130000082
If Y fen =0, then the patient is normal and no calculation is needed;
if Y fen ∈(0,10]The patient's corresponding grade is low;
if Y fen E (10, 100), the corresponding grade of the patient is medium;
if Y fen And more than or equal to 100, the corresponding grade of the patient is high.
And a second step of: determining severity scores within different classes
Step1: determining a contrast matrix
According to the above settings, there are three contrast matrixes established by low, medium and high respectively:
Figure GDA0004183378130000083
where i=1, 2,3,4,5,6, x 1i =1, other x 1i =0. (i is an integer of 1-17)
Figure GDA0004183378130000084
Where i=7, 8,9, 10, 11, x 2i =1, other x 2i =0. (i is an integer of 1-17)
Figure GDA0004183378130000085
Where i=12, 13, 14, 15, 16, 17, x 3i =1, other x 3i =0. (i is an integer of 1-17)
Step2 determines the input matrix
And (3) making:
Figure GDA0004183378130000091
when x is a1i =1, other x a1i =0. (i is an integer from 1 to 17)
Figure GDA0004183378130000092
When x is a2i =1, other x a2i =0. (i is an integer from 1 to 17)/(i)>
Figure GDA0004183378130000093
When x is a3i =1, other x a3i =0. (i is an integer from 1 to 17)
Order the
Figure GDA0004183378130000094
Wherein:
when the patient grade is low, let
Figure GDA0004183378130000095
When the patient grade is middle, let X 1 ＝X' zhong
And because y=x a1 ·Y+X a2 ·Y
Let X 01 ＝X a1 ·Y、X 02 ＝X a2 Y, let X respectively 0 ＝X 01 And X 0 ＝X 02 Carry over X (1) = [ X ] 0 X 1 ]Gray correlation degree calculation is carried out in the process, and finally two correlation degree coefficients r are respectively obtained 01 And r 02 。
When the patient grade is high, there is X 1 ＝X' ga o
And because y=x a1 ·Y+X a2 ·Y+X a3 ·Y
Let X 01 ＝X a1 ·Y、X 02 ＝X a2 Y and X 03 ＝X a3 Y, let X respectively 0 ＝X 01 、X 0 ＝X 02 And X 0 ＝X 03 The method comprises the steps of carrying out a first treatment on the surface of the Carry over X (1) = [ X ] 0 X 1 ]Gray correlation degree calculation is carried out in the process, and three correlation degree coefficients r are finally obtained 01 、r 02 And r 03 。
From the above formula, r when the patient grade is medium 03 =0; when the patient grade is low, r 03 ＝r 02 ＝0。
Then for a mental patient there must be three coefficients r 01 r 02 r 03 And a characteristic matrix R of the patient bingren Let R be bingren ＝[r 01 r 02 r 03 ]。
Step3: calculating gray correlation
Step3.1 normalization
And (3) pairing:
Figure GDA0004183378130000101
normalization is performed row by row, and the formula is as follows:
Figure GDA0004183378130000102
each new x after normalization is calculated one by one through the formula ij Composing a new matrix X (2), then:
Figure GDA0004183378130000103
step3.2 differencing sequence delta, delta for matrix X (2) max And delta min
And (3) making:
Δ＝[Δ i1 ],i＝1,…,17
Δ ij ＝|x i0 -x i1 |,i＝1,…,17,j＝0,1
then:
Figure GDA0004183378130000104
Figure GDA0004183378130000105
step3.3 grey correlation coefficient matrix xi
ξ＝[ξ i1 ],i＝1,…,17
Figure GDA0004183378130000111
Wherein beta is a correlation coefficient, and under the condition of no special requirement, the value of beta is generally 0.5.
Step3.4: calculating gray correlation degree r
Let the gray correlation degree between the comparison sequence X and the target sequence Y be r, the following are:
Figure GDA0004183378130000112
step4 calculating the state coefficient R
The gray correlation r obtained in step3.4 reflects the correlation between the characteristics of the patient and the most serious current level, and in order to obtain a characteristic quantity which can completely reflect the state of the patient, further processing is required for the gray correlation r.
Let W= [ W ] 1 w 2 w 3 ]Is a weight matrix, wherein w 1 w 2 w 3 The weight coefficients respectively correspond to the low, medium and high grades of the patient, and represent the proportion of the low, medium and high grade components in the psychological diseases of the patient, and the weight coefficients are generally judged by experts. And w is 1 w 2 w 3 Satisfy w 1 +w 2 +w 3 ＝1。
As known from step2, a patient's feature matrix R bingren ＝[r 01 r 02 r 03 ]Wherein r is 01 r 02 r 03 Respectively correspond to three correlation coefficients r obtained in step2 01 、r 02 And r 03
Then there is an R formula as follows:
Figure GDA0004183378130000113
/>
X' di representation matrix X di Transpose of X ', X' zhong Representation matrix X zhong Transpose of X ', X' gao Representation matrix X gao W' represents the transpose of matrix W
The final factor R is the severity score at different levels, and R is a number between 0 and 1, the greater R indicates the more severe the patient is at the current level.
And has the following steps:
when the patient level is low,
R＝w 1 ·r 01
when the patient's grade is medium,
R＝w 1 ·r 01 +w 2 ·r 02
when the patient's grade is high,
R＝w 1 ·r 01 +w 2 ·r 02 +w 3 ·r 03 。
according to the invention, through three preset weight coefficients and calculating three relevance coefficients r01, r02 and r03, more accurate mental state numerical quantification of the mental disorder patient is realized.
According to the invention, the disease state of the mental disorder patient can be accurately estimated by calculating the final dangerous state coefficient R, and the mental disorder state estimation data of the patient can be automatically acquired and sent to the server by monitoring the physiological information, the activity video and the voice of the patient, so that the family members of the patient can master the state of illness and the dynamic change of the dangers of the mental disorder patient at the first time.

Claims (1)

1. Mental disorder patient remote early warning system based on dangerous level judgment, which is characterized by comprising a computer and wearable equipment:
the computer is respectively connected with the wearable equipment;
the computer is connected with the mobile terminal of the family of the patient;
the computer acquires real-time geographic position information of the patient through the wearable equipment and sends the information to the family mobile terminal;
the computer calculates the final dangerous state coefficient of the mental disorder patient and sends the final dangerous state coefficient to the family member mobile terminal;
the computer performs the steps of:
s1, acquiring heartbeat data, somatosensory gesture data, voice data and monitoring video data of a patient through wearable equipment worn by the patient and security monitoring equipment in the action range of the patient;
s2, analyzing patient behaviors according to the heartbeat data, the voice data, the somatosensory gesture data and the monitoring video data, automatically generating patient behavior data, and acquiring patient risk grade data by the patient behavior data;
s3: acquiring a final dangerous state coefficient of a patient according to the dangerous grade data of the user;
the danger level comprises three levels of low risk, medium risk and high risk;
the step S3 includes the steps of:
s31, determining a grade score according to the following rule:
the rank is low with a score of 1 for each item,
each item in the ranking has a score of 10,
each item with a high ranking score of 100,
the target column is categorized as to the type of the target column,
y corresponds to a matrix of 17X1, corresponds to features 1-17, and is marked 1 if there are corresponding features, or is marked 0 if there are
Figure FDA0004092226250000011
Let the final score be Y fen Then there is
Figure FDA0004092226250000012
If Y fen =0, then the patient is normal and no calculation is needed;
if Y fen ∈(0,10]The patient's corresponding grade is low;
if Y fen E (10, 100), the corresponding grade of the patient is medium;
if Y fen More than or equal to 100, the corresponding grade of the patient is high;
s321: according to the setting in the step S31, the contrast matrixes established by the low, medium and high levels are respectively determined as follows:
Figure FDA0004092226250000021
where i=1, 2,3,4,5,6,
Figure FDA0004092226250000022
others->
Figure FDA0004092226250000023
The value is an integer of 1-17;
Figure FDA0004092226250000024
where i=7, 8,9, 10, 11,
Figure FDA0004092226250000025
others->
Figure FDA0004092226250000026
The value is an integer of 1-17; />
Figure FDA0004092226250000027
Where i=12, 13, 14, 15, 16, 17,
Figure FDA0004092226250000028
others->
Figure FDA0004092226250000029
The value is an integer of 1-17;
s322, making:
Figure FDA00040922262500000210
i=1, 2, … 6, x a1i =1, other x a1i ＝0
Figure FDA00040922262500000211
i=7, 8, … 11, x a2i =1, other x a2i ＝0
Figure FDA00040922262500000212
i=12, 13, … 17, x a3i =1, other x a3i ＝0
i is an integer of 1-17;
order the
Figure FDA00040922262500000213
Wherein:
when the patient grade is low, let
X 1 ＝X' di ，
Figure FDA0004092226250000031
Figure FDA0004092226250000032
Gray correlation calculation is carried out, and finally a correlation coefficient r is obtained 01 ；
When the patient grade is middle, let X 1 ＝X' zhong Let X 01 ＝X a1 ·Y、X 02 ＝X a2 Y, let X respectively 0 ＝X 01 And X 0 ＝X 02 Carry over X (1) = [ X ] 0 X 1 ]The gray level is calculated to finally obtain two association coefficients r respectively 01 And r 02 ；
X' di Representation matrix X di Transpose of X ', X' zhong Representation matrix X zhong Transpose of X ', X' gao Representation matrix X gao Is a transpose of (2);
when the patient grade is high, there is X 1 ＝X' gao
Let X 01 ＝X a1 ·Y、X 02 ＝X a2 Y and X 03 ＝X a3 Y, let X respectively 0 ＝X 01 、X 0 ＝X 02 And X 0 ＝X 03 The method comprises the steps of carrying out a first treatment on the surface of the Carry over X (1) = [ X ] 0 X 1 ]Gray correlation degree calculation is carried out in the process, and three correlation degree coefficients r are finally obtained 01 、r 02 And r 03 ；
S323: the step of calculating the gray correlation degree comprises the steps of,
S3231：
and (3) pairing:
Figure FDA0004092226250000033
normalization is performed row by row, and the formula is as follows:
Figure FDA0004092226250000034
each new x after normalization is calculated one by one through the formula ij Composing a new matrix X (2), then:
Figure FDA0004092226250000035
s3232: the matrix X (2) is subjected to difference sequence delta, delta max And delta min
And (3) making:
Δ＝[Δ i1 ],i＝1,…,17
Δ ij ＝|x i0 -x i1 |,i＝1,…,17,j＝0,1
then:
Figure FDA0004092226250000041
Figure FDA0004092226250000042
s3233 adopts the following formula to calculate gray correlation coefficient matrix xi
ξ＝[ξ i1 ],i＝1,…,17
Figure FDA0004092226250000043
Wherein, beta is the association coefficient;
S3234：
let the gray correlation degree of the comparison sequence X and the target sequence Y be r, and calculate r by adopting the following formula
Figure FDA0004092226250000044
S324 calculates a state coefficient R, including,
let W= [ W ] 1 w 2 w 3 ]Is a weight matrix, wherein w 1 Corresponding to low weight coefficient, w, of patient 2 Corresponding to the weight coefficient, w, in the patient 3 The weight coefficients corresponding to the high level of the patient are respectively indicative of the specific weights of the low, medium and high level components in the psychological diseases of the patient, the coefficients are preset by the user, and w 1 w 2 w 3 Satisfy w 1 +w 2 +w 3 ＝1；
The state coefficient R is calculated as follows:
Figure FDA0004092226250000045
w' represents the transpose of matrix W;
when the patient level is low,
R＝w 1 ·r 01
when the patient's grade is medium,
R＝w 1 ·r 01 +w 2 ·r 02
when the patient's grade is high,
R＝w 1 ·r 01 +w 2 ·r 02 +w 3 ·r 03 。
CN202010275290.6A 2020-04-09 2020-04-09 Mental disorder patient remote early warning system based on risk level judgment Active CN111481215B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202010275290.6A CN111481215B (en) 2020-04-09 2020-04-09 Mental disorder patient remote early warning system based on risk level judgment

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202010275290.6A CN111481215B (en) 2020-04-09 2020-04-09 Mental disorder patient remote early warning system based on risk level judgment

Publications (2)

Publication Number Publication Date
CN111481215A CN111481215A (en) 2020-08-04
CN111481215B true CN111481215B (en) 2023-05-23

Family

ID=71789829

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202010275290.6A Active CN111481215B (en) 2020-04-09 2020-04-09 Mental disorder patient remote early warning system based on risk level judgment

Country Status (1)

Country Link
CN (1) CN111481215B (en)

Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114677826B (en) * 2022-03-24 2023-10-31 中南大学湘雅二医院 Mental patient violence early warning system based on individual behaviors and physiological characteristics

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CA2599148A1 (en) * 2005-02-22 2006-08-31 Health-Smart Limited Methods and systems for physiological and psycho-physiological monitoring and uses thereof
CN107517375A (en) * 2017-07-25 2017-12-26 天津大学 To the evaluation analysis method of stereos copic viewing fatigue caused by three-dimensional Depth Motion
CN110189500A (en) * 2019-06-21 2019-08-30 西南政法大学 A kind of intelligent remote mentally disturbed's risk early warning system based on bluetooth positioning
CN110276929A (en) * 2019-06-21 2019-09-24 西南政法大学 A kind of intelligent remote mentally disturbed's risk early warning system based on mobile intelligent terminal
CN110522983A (en) * 2018-05-23 2019-12-03 深圳先进技术研究院 Brain stimulation system, method, equipment and storage medium based on artificial intelligence
GB202000242D0 (en) * 2020-01-08 2020-02-19 Limbic Ltd Dynamic user response data collection system & method

Family Cites Families (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US9427185B2 (en) * 2013-06-20 2016-08-30 Microsoft Technology Licensing, Llc User behavior monitoring on a computerized device
CN109394203A (en) * 2017-08-18 2019-03-01 广州市惠爱医院 The monitoring of phrenoblabia convalescence mood and interference method
CN110200643A (en) * 2019-06-21 2019-09-06 西南政法大学 A kind of long-distance intelligent mentally disturbed risk early warning system

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CA2599148A1 (en) * 2005-02-22 2006-08-31 Health-Smart Limited Methods and systems for physiological and psycho-physiological monitoring and uses thereof
CN107517375A (en) * 2017-07-25 2017-12-26 天津大学 To the evaluation analysis method of stereos copic viewing fatigue caused by three-dimensional Depth Motion
CN110522983A (en) * 2018-05-23 2019-12-03 深圳先进技术研究院 Brain stimulation system, method, equipment and storage medium based on artificial intelligence
CN110189500A (en) * 2019-06-21 2019-08-30 西南政法大学 A kind of intelligent remote mentally disturbed's risk early warning system based on bluetooth positioning
CN110276929A (en) * 2019-06-21 2019-09-24 西南政法大学 A kind of intelligent remote mentally disturbed's risk early warning system based on mobile intelligent terminal
GB202000242D0 (en) * 2020-01-08 2020-02-19 Limbic Ltd Dynamic user response data collection system & method

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
Ambulatory assessment in panic disorder and specific phobia;Alpers G W.;《Psychological assessment》;全文 *
安全心理尺度的三角模糊数学表达方法研究;刘琛,等;《中国安全科学学报》;全文 *

Also Published As

Publication number Publication date
CN111481215A (en) 2020-08-04

Similar Documents

Publication Publication Date Title
Di Rosa et al. Concurrent validation of an index to estimate fall risk in community dwelling seniors through a wireless sensor insole system: A pilot study
US20180008191A1 (en) Pain management wearable device
CN107798210B (en) Multi-model landslide displacement prediction method and system
CN106127594B (en) User room interest degree calculation method and system based on principal component analysis
WO2021051944A1 (en) Automatic sleep aid music pushing method and apparatus, computer device, and storage medium
CN111481215B (en) Mental disorder patient remote early warning system based on risk level judgment
WO2015018780A4 (en) Method, device and system for annotated capture of sensor data and crowd modelling of activities
JPWO2013128972A1 (en) Motion determination device, motion determination system, and motion determination method
CN116311539B (en) Sleep motion capturing method, device, equipment and storage medium based on millimeter waves
EP3424411A1 (en) Method and system for postural stability assessment
CN111481182B (en) Mental disorder patient risk level judging system
CN117133464B (en) Intelligent monitoring system and monitoring method for health of old people
CN111466929B (en) Violent mental disorder patient prevention system based on risk level judgment
DeMasi et al. Detecting change in depressive symptoms from daily wellbeing questions, personality, and activity
KR20190058289A (en) Detecting respiratory rates in audio using an adaptive low-pass filter
CN111481214B (en) Mental disorder patient risk level judgment method
CN111369763B (en) Attack prevention system for mental disorder patient
CN111466928B (en) Mental disorder patient carry-on early warning system based on risk level judgment
WO2021161314A1 (en) System and method for determining and predicting of a misstep
CN111210070A (en) Data analysis method and device, electronic equipment and readable storage medium
Hemmatpour et al. Polynomial classification model for real-time fall prediction system
JP2019187492A (en) Estimation method and estimation device
CN114847949A (en) Cognitive fatigue recovery method based on electroencephalogram characteristics and relaxation indexes
Chawan et al. Person Identification And Tinetti Score Prediction Using Balance Parameters: A Machine Learning Approach To Determine Fall Risk
CN113205363A (en) Service index monitoring method and device based on big data

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