CN106606354A - Female basal body temperature measuring method based on sleeping quality analysis - Google Patents
Female basal body temperature measuring method based on sleeping quality analysis Download PDFInfo
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Abstract
The invention discloses a female basal body temperature measuring method based on sleeping quality analysis. The female basal body temperature measuring method based on sleeping quality analysis includes the following steps that sleeping quality data of a user in the set time are collected; the sleeping data are analyzed, and the sleeping state threshold value is calculated; the collected sleeping quality data are compared with the sleeping state threshold value, and the sleeping condition of the user is analyzed; and the basal body temperature of the user under the corresponding sleeping condition is analyzed according to the sleeping condition of the user. By means of the method steps, the optimal sampling time of the basal body temperature can be determined according to the sleeping quality, and the measuring precision and accuracy of the basal body temperature are improved.
Description
Technical field
The present invention relates to body temperature detection method, more particularly to a kind of female based on Analysis of sleeping quality
Property examination of basal body temperature method.
Background technology
Basal body temperature (Basal Body Temperature, BBT), also known as tranquillization body temperature, is referred to
After the sleep of 6-8 hours, body temperature is not yet affected people by motion diet or emotional change
When measured body temperature, the basal body temperature of normal women of child-bearing age as the menstrual cycle, in the cycle
Property change, the basal body temperature of long-term detection cycle change has very to the physical condition of women
Important indicative function, is in sleep 6～8 hours based on the definition of women basal body temperature BBT
Afterwards, any activity is not carried out, measured body temperature before getting up, and basal body temperature is measured for measurement
Environmental requirement is strict, it is desirable to which the measured is forbidden to get up before measurement body temperature, diet, speak.
Therefore, accurate basal body temperature measured value is obtained more difficult also cumbersome, and city at present
Basal body temperature instrument on field be substantially women morning it is clear-headed after, the body that oneself is measured with clinical thermometer
Temperature, it is evident that the body temperature for so measuring has not been in fact proper basal body temperature,
The degree of accuracy of basal body temperature itself can be affected by the sleep quality of user, existing body temperature number
There is a problem of that Data Detection precision is not high according to detection.
The content of the invention
In view of the above shortcomings of women basal body temperature measurement at present, a kind of base of present invention offer
In the women examination of basal body temperature method of Analysis of sleeping quality, can come according to the quality of sleep quality
Determine the optimum sampling time of basal body temperature, improve precision and the degree of accuracy of basal body temperature detection.
To reach above-mentioned purpose, embodiments of the invention are adopted the following technical scheme that：
A kind of women examination of basal body temperature method based on Analysis of sleeping quality, it is described based on sleep
The women examination of basal body temperature method of quality analysis comprises the steps：
Sleep quality data of the collection user in setting time；
Sleep quality data are analyzed, sleep state threshold values is calculated；
The sleep quality data of collection and sleep state threshold values are compared, user is analyzed and is slept
Dormancy situation；
According to user's sleep state, basis of the user under corresponding sleep state is analyzed
Body temperature.
According to one aspect of the present invention, sleep matter of the collection user in setting time
Amount data step specifically includes following steps：
Three direction of principal axis movable informations of the user in sleep procedure are gathered, setting data is：
X=【X1, x2, x3 ... xn】
Y=【Y1, y2, y3 ... yn】
Z=【Z1, z2, z3 ... zn】；
Obtain per minute temperature data of the user in setting time；Setting data is：
T=【T1, t2, t3 ... tn】；
According to user in sleep procedure three direction of principal axis movable informations, analyze per minute three
The sleep movement range value of user in axle all directions, sets Delta values, represents each party
Body sleep action variance values upwards：
I.e dx1=x2-x1；Dy1=y2-y1
Dx=【Dx1, dx2, dx3 ... dxn】
Dy=【Dy1, dy2, dy3 ... dyn】
Dz=【Dz1, dz2, dz3 ... dzn】；
According to per point sleep movement range value of kind user in three axle all directions, calculate
The array of the user's synthesis of the body on three direction of principal axis sleep action variance values per minute, if
Fixed number is according to being：
M1=sqtr (dx1**2, dy1**2, dz1**2)
Have：M=【M1, m2, m3 ... mn】；
According to the user's synthesis of the body on three direction of principal axis sleep action variance values per minute,
Calculate the user comprehensively mean value of sleep action variance values and root mean square numerical value, data
It is set as：
Allsum=m1+m2+ ...+mn
Allmean=allsum/n
Allstd=sqrt (m1-allmean) * * 2+ (m2-allmean) * * 2+ ...+(mn-allm
ean)**2/n；
According to the mean value and root mean square numerical value of comprehensive sleep action variance values, calculate comprehensive
The singularity of the array of sleep action variance values is closed, setting data is：
Cv=(allstd>allmean)(allstd/allmean)：(allmean/allstd)；
The array of comprehensive sleep action variance values is sorted from small to large and is newly counted
Group, and the characteristic value of new array is calculated, setting data is：
Newly array is：sortdata
Characteristic value：
(1) quartile extreme difference：Diff-4=sortdata [3n/4]-sortdata [n/4]
(2) population standard deviation：All-cv=diff-4/1.349
(3) three averages：M3=int (0.25*sortdata [n/4]+0.5*sortdata [n/2]
+0.25*sortdata[3n/4]。
It is described that sleep quality data are analyzed according to one aspect of the present invention, calculate
Sleep state thresholding step specifically includes following steps：
The deep sleep threshold values data of user's sleep quality are obtained, where it is assumed that condition：Make
User's deep sleep time in one day>The 10% of sleep total time：
DS_THRESHOLD=max (sortdata [n/10]+M3, sortdata [n/10]+int (al
lmean/cv))
DS_THRESHOLD=min (DS_THRESHOLD, sortdata [3n/4]；
If 80% data are both less than DS_THRESHOLD in the whole sleep period of user,
Judge that data are not present, then terminate analysis；
The sleep in a time period is chosen from user's sleep action variance values array m
Action amplitude of variation Value Data is analyzed, and calculate array m and, average, root mean square,
Setting data is：TIME_WINDOW data windata：
Winsum=windata [0]+windata [1]+...+windata [TIME_WINDOW-1]
Mean=winsum/wsize
Std=sqrt ((windata [- TIME_WINDOW]-mean) * * 2+...+ (windata [TI
ME_WINDOW]-mean)**2)；
According to array m and, average, root mean square calculation go out the clear-headed valve of user's sleep quality
Value Data, setting data is：
AS_THRESHOLD=(std+mean)/2
AS_THRESHOLD=max (AS_THRESHOLD, mean, int (allmean*cv), (allm
ean+allstd)/2)
AS_THRESHOLD=min (AS_THRESHOLD, sortdata [n*0.95])；
Calculated with clear-headed threshold values data according to the deep sleep threshold values data of user's sleep quality
Data cross the number of times of threshold values in TIME_WINDOW, and data setting is：
Dscnt=get_cnt (DS_THRESHOLD, " lt " ,@windata)
Ascnt=get_cnt (AS_THRESHOLD,：Gt " ,@windata).
According to one aspect of the present invention, the sleep quality data and sleep state by collection
Threshold values is compared, and is analyzed user's sleep state step and is specifically included following steps：
According to deep sleep threshold values, clear-headed threshold values in user's sleep action variance values array
And excessively deep sleep threshold values, the number of times of clear-headed threshold values are moved to judge comprehensively to be slept in setting time
Make variance values array for sound sleep or it is shallow sleep data, its deterministic process is as follows：
In the setting time comprehensively in sleep action variance values array, if 80% data it is low
In sound sleep threshold values, then this array data is sound sleep data block；
If previous state is sleeping state, the comprehensive sleep action amplitude of variation in setting time
40% data are then data block of regaining consciousness higher than clear-headed threshold values in value array, are otherwise shallow sleep number
According to block；
If previous state is deep sleep state, 50% data are needed to be higher than clear-headed threshold values, then this number
Group data judging is clear-headed data block；
If being unsatisfactory for conditions above, it is impossible to judge, then laststate is maintained.
It is described according to user's sleep state according to one aspect of the present invention, analyze and use
Basal body temperature step of the person under corresponding sleep state specifically includes following steps：
Obtain the sleep state data block in setting sleep period；
Find and sleep from per minute temperature data of the user for gathering in setting time
The corresponding temperature data of status data block.
By body temperature number based on the mean value of the temperature data corresponding with sleep state data block
According to.
According to one aspect of the present invention, the user from collection is every in setting time
Find the temperature data step corresponding with sleep state data block in the temperature data of minute to perform
Front execution following steps：
Pair temperature data corresponding with sleep state data block carries out reliability and validity point
Analysis；
If the temperature data corresponding with sleep state data block is reliable effectively, body temperature number is chosen in side
According to mean value based on temperature data；
If the temperature data corresponding with sleep state data block is invalid, next sleep is chosen in side
Temperature data corresponding to status data block is analyzed.
The advantage that the present invention is implemented：By gathering sleep quality number of the user in setting time
According to；Sleep quality data are analyzed, sleep state threshold values is calculated；By the sleep matter of collection
Amount data are compared with sleep state threshold values, analyze user's sleep state；According to user
Sleep state, analyzes basal body temperature of the user under corresponding sleep state, said method step
Suddenly, the optimum sampling time of basal body temperature can be determined according to the quality of sleep quality, base is improve
The precision of plinth temperature check and the degree of accuracy.
Description of the drawings
Technical scheme in order to be illustrated more clearly that the embodiment of the present invention, below will be to embodiment
Needed for the accompanying drawing to be used be briefly described, it should be apparent that, drawings in the following description
Only some embodiments of the present invention, for those of ordinary skill in the art, are not paying
On the premise of going out creative work, can be with according to these other accompanying drawings of accompanying drawings acquisition.
Fig. 1 is a kind of women examination of basal body temperature based on Analysis of sleeping quality of the present invention
The method flow diagram of the embodiment 1 of method；
Fig. 2 is a kind of women examination of basal body temperature based on Analysis of sleeping quality of the present invention
The method flow diagram of the embodiment 2 of method.
Specific embodiment
Below in conjunction with the accompanying drawing in the embodiment of the present invention, to the technical side in the embodiment of the present invention
Case is clearly and completely described, it is clear that described embodiment is only the present invention one
Divide embodiment, rather than the embodiment of whole.Based on the embodiment in the present invention, this area is general
The every other embodiment that logical technical staff is obtained under the premise of creative work is not made,
Belong to the scope of protection of the invention.
Embodiment 1：
As shown in figure 1, a kind of women examination of basal body temperature method based on Analysis of sleeping quality,
The women examination of basal body temperature method based on Analysis of sleeping quality comprises the steps：
Step S1：Sleep quality data of the collection user in setting time；
Step S1：Sleep quality data step of the collection user in setting time is concrete
Comprise the steps：
Three direction of principal axis movable informations of the user in sleep procedure are gathered, setting data is：
X=【X1, x2, x3 ... xn】
Y=【Y1, y2, y3 ... yn】
Z=【Z1, z2, z3 ... zn】；
Obtain per minute temperature data of the user in setting time；Setting data is：
T=【T1, t2, t3 ... tn】；
According to user in sleep procedure three direction of principal axis movable informations, analyze per minute three
The sleep movement range value of user in axle all directions, sets Delta values, represents each party
Body sleep action variance values upwards：
I.e dx1=x2-x1；Dy1=y2-y1
Dx=【Dx1, dx2, dx3 ... dxn】
Dy=【Dy1, dy2, dy3 ... dyn】
Dz=【Dz1, dz2, dz3 ... dzn】；
According to per point sleep movement range value of kind user in three axle all directions, calculate
The array of the user's synthesis of the body on three direction of principal axis sleep action variance values per minute, if
Fixed number is according to being：
M1=sqtr (dx1**2, dy1**2, dz1**2)
Have：M=【M1, m2, m3 ... mn】；
According to the user's synthesis of the body on three direction of principal axis sleep action variance values per minute,
Calculate the user comprehensively mean value of sleep action variance values and root mean square numerical value, data
It is set as：
Allsum=m1+m2+ ...+mn
Allmean=allsum/n
Allstd=sqrt (m1-allmean) * * 2+ (m2-allmean) * * 2+ ...+(mn-allm
ean)**2/n；
According to the mean value and root mean square numerical value of comprehensive sleep action variance values, calculate comprehensive
The singularity of the array of sleep action variance values is closed, setting data is：
Cv=(allstd>allmean)(allstd/allmean)：(allmean/allstd)；
The array of comprehensive sleep action variance values is sorted from small to large and is newly counted
Group, and the characteristic value of new array is calculated, setting data is：
Newly array is：sortdata
Characteristic value：
(1) quartile extreme difference：Diff-4=sortdata [3n/4]-sortdata [n/4]
(2) population standard deviation：All-cv=diff-4/1.349
(3) three averages：M3=int (0.25*sortdata [n/4]+0.5*sortdata [n/2]
+0.25*sortdata[3n/4]。
User's sleep movement information includes times of exercise and the every time motion amplitude value of motion.
Step S2：Sleep quality data are analyzed, sleep state threshold values is calculated；
Step S2：Sleep quality data are analyzed, sleep state threshold values step is calculated
Suddenly following steps are specifically included：
The deep sleep threshold values data of user's sleep quality are obtained, where it is assumed that condition：Make
User's deep sleep time in one day>The 10% of sleep total time：
DS_THRESHOLD=max (sortdata [n/10]+M3, sortdata [n/10]+int (al
lmean/cv))
DS_THRESHOLD=min (DS_THRESHOLD, sortdata [3n/4]；
If 80% data are both less than DS_THRESHOLD in the whole sleep period of user,
Judge that data are not present, then terminate analysis；
The sleep in a time period is chosen from user's sleep action variance values array m
Action amplitude of variation Value Data is analyzed, and calculate array m and, average, root mean square,
Setting data is：TIME_WINDOW data windata：
Winsum=windata [0]+windata [1]+...+windata [TIME_WINDOW-1]
Mean=winsum/wsize
Std=sqrt ((windata [- TIME_WINDOW]-mean) * * 2+...+ (windata [TI
ME_WINDOW]-mean)**2)；
According to array m and, average, root mean square calculation go out the clear-headed valve of user's sleep quality
Value Data, setting data is：
AS_THRESHOLD=(std+mean)/2
AS_THRESHOLD=max (AS_THRESHOLD, mean, int (allmean*cv), (allm
ean+allstd)/2)
AS_THRESHOLD=min (AS_THRESHOLD, sortdata [n*0.95])；
Calculated with clear-headed threshold values data according to the deep sleep threshold values data of user's sleep quality
Data cross the number of times of threshold values in TIME_WINDOW, and data setting is：
Dscnt=get_cnt (DS_THRESHOLD, " lt " ,@windata)
Ascnt=get_cnt (AS_THRESHOLD,：Gt " ,@windata).
Step S3：The sleep quality data of collection and sleep state threshold values are compared, is analyzed
User's sleep state；
Step S3：The sleep quality data of collection and sleep state threshold values are compared,
Analyze user's sleep state step and specifically include following steps：
According to deep sleep threshold values, clear-headed threshold values in user's sleep action variance values array
And excessively deep sleep threshold values, the number of times of clear-headed threshold values are moved to judge comprehensively to be slept in setting time
Make variance values array for sound sleep or it is shallow sleep data, its deterministic process is as follows：
In the setting time comprehensively in sleep action variance values array, if 80% data it is low
In sound sleep threshold values, then this array data is sound sleep data block；
If previous state is sleeping state, the comprehensive sleep action amplitude of variation in setting time
40% data are then data block of regaining consciousness higher than clear-headed threshold values in value array, are otherwise shallow sleep number
According to block；
If previous state is deep sleep state, 50% data are needed to be higher than clear-headed threshold values, then this number
Group data judging is clear-headed data block；
If being unsatisfactory for conditions above, it is impossible to judge, then laststate is maintained.
Step S4：According to user's sleep state, user is analyzed under corresponding sleep state
Basal body temperature；
Step S4：According to user's sleep state, user is analyzed in corresponding sleep shape
Basal body temperature step under condition specifically includes following steps：
Obtain the sleep state data block in setting sleep period；
Find and sleep from per minute temperature data of the user for gathering in setting time
The corresponding temperature data of status data block.
By body temperature number based on the mean value of the temperature data corresponding with sleep state data block
According to.
By gathering sleep quality data of the user in setting time；Sleep quality data are entered
Row analysis, calculates sleep state threshold values；By the sleep quality data of collection and sleep state threshold values
Compare, analyze user's sleep state；According to user's sleep state, analyze and use
Basal body temperature of the person under corresponding sleep state, said method step can be according to sleep quality
Quality improves the precision and standard of basal body temperature detection determining the optimum sampling time of basal body temperature
Exactness.
Embodiment 2：
As shown in Fig. 2 a kind of women examination of basal body temperature method based on Analysis of sleeping quality,
The women examination of basal body temperature method based on Analysis of sleeping quality comprises the steps：
Step S1：Sleep quality data of the collection user in setting time；
Step S1：Sleep quality data step of the collection user in setting time is concrete
Comprise the steps：
Three direction of principal axis movable informations of the user in sleep procedure are gathered, setting data is：
X=【X1, x2, x3 ... xn】
Y=【Y1, y2, y3 ... yn】
Z=【Z1, z2, z3 ... zn】；
Obtain per minute temperature data of the user in setting time；Setting data is：
T=【T1, t2, t3 ... tn】；
According to user in sleep procedure three direction of principal axis movable informations, analyze per minute three
The sleep movement range value of user in axle all directions, sets Delta values, represents each party
Body sleep action variance values upwards：
I.e dx1=x2-x1；Dy1=y2-y1
Dx=【Dx1, dx2, dx3 ... dxn】
Dy=【Dy1, dy2, dy3 ... dyn】
Dz=【Dz1, dz2, dz3 ... dzn】；
According to per point sleep movement range value of kind user in three axle all directions, calculate
The array of the user's synthesis of the body on three direction of principal axis sleep action variance values per minute, if
Fixed number is according to being：
M1=sqtr (dx1**2, dy1**2, dz1**2)
Have：M=【M1, m2, m3 ... mn】；
According to the user's synthesis of the body on three direction of principal axis sleep action variance values per minute,
Calculate the user comprehensively mean value of sleep action variance values and root mean square numerical value, data
It is set as：
Allsum=m1+m2+ ...+mn
Allmean=allsum/n
Allstd=sqrt (m1-allmean) * * 2+ (m2-allmean) * * 2+ ...+(mn-allm
ean)**2/n；
According to the mean value and root mean square numerical value of comprehensive sleep action variance values, calculate comprehensive
The singularity of the array of sleep action variance values is closed, setting data is：
Cv=(allstd>allmean)(allstd/allmean)：(allmean/allstd)；
The array of comprehensive sleep action variance values is sorted from small to large and is newly counted
Group, and the characteristic value of new array is calculated, setting data is：
Newly array is：sortdata
Characteristic value：
(1) quartile extreme difference：Diff-4=sortdata [3n/4]-sortdata [n/4]
(2) population standard deviation：All-cv=diff-4/1.349
(3) three averages：M3=int (0.25*sortdata [n/4]+0.5*sortdata [n/2]
+0.25*sortdata[3n/4]。
User's sleep movement information includes times of exercise and the every time motion amplitude value of motion.
Step S2：Sleep quality data are analyzed, sleep state threshold values is calculated；
Step S2：Sleep quality data are analyzed, sleep state threshold values step is calculated
Suddenly following steps are specifically included：
The deep sleep threshold values data of user's sleep quality are obtained, where it is assumed that condition：Make
User's deep sleep time in one day>The 10% of sleep total time：
DS_THRESHOLD=max (sortdata [n/10]+M3, sortdata [n/10]+int (al
lmean/cv))
DS_THRESHOLD=min (DS_THRESHOLD, sortdata [3n/4]；
If 80% data are both less than DS_THRESHOLD in the whole sleep period of user,
Judge that data are not present, then terminate analysis；
The sleep in a time period is chosen from user's sleep action variance values array m
Action amplitude of variation Value Data is analyzed, and calculate array m and, average, root mean square,
Setting data is：TIME_WINDOW data windata：
Winsum=windata [0]+windata [1]+...+windata [TIME_WINDOW-1]
Mean=winsum/wsize
Std=sqrt ((windata [- TIME_WINDOW]-mean) * * 2+...+ (windata [TI
ME_WINDOW]-mean)**2)；
According to array m and, average, root mean square calculation go out the clear-headed valve of user's sleep quality
Value Data, setting data is：
AS_THRESHOLD=(std+mean)/2
AS_THRESHOLD=max (AS_THRESHOLD, mean, int (allmean*cv), (allm
ean+allstd)/2)
AS_THRESHOLD=min (AS_THRESHOLD, sortdata [n*0.95])；
Calculated with clear-headed threshold values data according to the deep sleep threshold values data of user's sleep quality
Data cross the number of times of threshold values in TIME_WINDOW, and data setting is：
Dscnt=get_cnt (DS_THRESHOLD, " lt " ,@windata)
Ascnt=get_cnt (AS_THRESHOLD,：Gt " ,@windata).
Step S3：The sleep quality data of collection and sleep state threshold values are compared, is analyzed
User's sleep state；
Step S3：The sleep quality data of collection and sleep state threshold values are compared,
Analyze user's sleep state step and specifically include following steps：
According to deep sleep threshold values, clear-headed threshold values in user's sleep action variance values array
And excessively deep sleep threshold values, the number of times of clear-headed threshold values are moved to judge comprehensively to be slept in setting time
Make variance values array for sound sleep or it is shallow sleep data, its deterministic process is as follows：
In the setting time comprehensively in sleep action variance values array, if 80% data it is low
In sound sleep threshold values, then this array data is sound sleep data block；
If previous state is sleeping state, the comprehensive sleep action amplitude of variation in setting time
40% data are then data block of regaining consciousness higher than clear-headed threshold values in value array, are otherwise shallow sleep number
According to block；
If previous state is deep sleep state, 50% data are needed to be higher than clear-headed threshold values, then this number
Group data judging is clear-headed data block；
If being unsatisfactory for conditions above, it is impossible to judge, then laststate is maintained.
Step S4：According to user's sleep state, user is analyzed under corresponding sleep state
Basal body temperature；
Step S4：According to user's sleep state, user is analyzed in corresponding sleep shape
Basal body temperature step under condition specifically includes following steps：
Obtain the sleep state data block in setting sleep period；
Find and sleep from per minute temperature data of the user for gathering in setting time
The corresponding temperature data of status data block.
By body temperature number based on the mean value of the temperature data corresponding with sleep state data block
According to.
Step S5：Pair temperature data corresponding with sleep state data block carries out reliability and has
The analysis of effect property；
If the temperature data corresponding with sleep state data block is reliable effectively, temperature data is chosen in side
Mean value based on temperature data；
If the temperature data corresponding with sleep state data block is invalid, next sleep shape is chosen in side
Temperature data corresponding to state data block is analyzed.
Wherein, after finding the corresponding basal body temperature data of next sleep state data block, repeat
Above-mentioned steps, if not finding next sleep state data block, then it is assumed that do not find or do not have
The data of validity are present, and analysis terminates.
The advantage that the present invention is implemented：By gathering sleep quality number of the user in setting time
According to；Sleep quality data are analyzed, sleep state threshold values is calculated；By the sleep matter of collection
Amount data are compared with sleep state threshold values, analyze user's sleep state；According to user
Sleep state, analyzes basal body temperature of the user under corresponding sleep state, said method step
Suddenly, the optimum sampling time of basal body temperature can be determined according to the quality of sleep quality, base is improve
The precision of plinth temperature check and the degree of accuracy.
The above, specific embodiment only of the invention, but protection scope of the present invention is simultaneously
This is not limited to, any those skilled in the art is in technology model disclosed by the invention
In enclosing, the change or replacement that can be readily occurred in all should be included within the scope of the present invention.
Therefore, protection scope of the present invention should be defined by the scope of the claims.
Claims (6)
1. a kind of women examination of basal body temperature method based on Analysis of sleeping quality, its feature exists
In the women examination of basal body temperature method based on Analysis of sleeping quality comprises the steps：
Sleep quality data of the collection user in setting time；
Sleep quality data are analyzed, sleep state threshold values is calculated；
The sleep quality data of collection and sleep state threshold values are compared, user is analyzed and is slept
Dormancy situation；
According to user's sleep state, base body of the user under corresponding sleep state is analyzed
Temperature.
2. the women examination of basal body temperature based on Analysis of sleeping quality according to claim 1
Method, it is characterised in that sleep quality data step of the collection user in setting time
Suddenly following steps are specifically included：
Three direction of principal axis movable informations of the user in sleep procedure are gathered, setting data is：
X=【X1, x2, x3 ... xn】
Y=【Y1, y2, y3 ... yn】
Z=【Z1, z2, z3 ... zn】；
Obtain per minute temperature data of the user in setting time；Setting data is：
T=【T1, t2, t3 ... tn】；
According to user in sleep procedure three direction of principal axis movable informations, analyze per minute three
The sleep movement range value of user in axle all directions, sets Delta values, represents each party
Body sleep action variance values upwards：
I.e dx1=x2-x1；Dy1=y2-y1
Dx=【Dx1, dx2, dx3 ... dxn】
Dy=【Dy1, dy2, dy3 ... dyn】
Dz=【Dz1, dz2, dz3 ... dzn】；
According to per point sleep movement range value of kind user in three axle all directions, calculate
The array of the user's synthesis of the body on three direction of principal axis sleep action variance values per minute, if
Fixed number is according to being：
M1=sqtr (dx1**2, dy1**2, dz1**2)
Have：M=【M1, m2, m3 ... mn】；
According to the user's synthesis of the body on three direction of principal axis sleep action variance values per minute,
Calculate the user comprehensively mean value of sleep action variance values and root mean square numerical value, data
It is set as：
Allsum=m1+m2+ ...+mn
Allmean=allsum/n
Allstd=sqrt (m1-allmean) * * 2+ (m2-allmean) * * 2+ ...+(mn-allm
ean)**2/n；
According to the mean value and root mean square numerical value of comprehensive sleep action variance values, calculate comprehensive
The singularity of the array of sleep action variance values is closed, setting data is：
Cv=(allstd>allmean)(allstd/allmean)：(allmean/allstd)；
The array of comprehensive sleep action variance values is sorted from small to large and is newly counted
Group, and the characteristic value of new array is calculated, setting data is：
Newly array is：sortdata
Characteristic value：
(1) quartile extreme difference：Diff-4=sortdata [3n/4]-sortdata [n/4]
(2) population standard deviation：All-cv=diff-4/1.349
(3) three averages：M3=int (0.25*sortdata [n/4]+0.5*sortdata [n/2]
+0.25*sortdata[3n/4]。
3. the women examination of basal body temperature based on Analysis of sleeping quality according to claim 2
Method, it is characterised in that described to be analyzed to sleep quality data, calculates sleep state
Thresholding step specifically includes following steps：
The deep sleep threshold values data of user's sleep quality are obtained, where it is assumed that condition：Make
User's deep sleep time in one day>The 10% of sleep total time：
DS_THRESHOLD=max (sortdata [n/10]+M3, sortdata [n/10]+int (al
lmean/cv))
DS_THRESHOLD=min (DS_THRESHOLD, sortdata [3n/4]；
If 80% data are both less than DS_THRESHOLD in the whole sleep period of user,
Judge that data are not present, then terminate analysis；
The sleep in a time period is chosen from user's sleep action variance values array m
Action amplitude of variation Value Data is analyzed, and calculate array m and, average, root mean square,
Setting data is：TIME_WINDOW data windata：
Winsum=windata [0]+windata [1]+...+windata [TIME_WINDOW-1]
Mean=winsum/wsize
Std=sqrt ((windata [- TIME_WINDOW]-mean) * * 2+...+ (windata [TI
ME_WINDOW]-mean)**2)；
According to array m and, average, root mean square calculation go out the clear-headed valve of user's sleep quality
Value Data, setting data is：
AS_THRESHOLD=(std+mean)/2
AS_THRESHOLD=max (AS_THRESHOLD, mean, int (allmean*cv), (allm
ean+allstd)/2)
AS_THRESHOLD=min (AS_THRESHOLD, sortdata [n*0.95])；
Calculated with clear-headed threshold values data according to the deep sleep threshold values data of user's sleep quality
Data cross the number of times of threshold values in TIME_WINDOW, and data setting is：
Dscnt=get_cnt (DS_THRESHOLD, " lt " ,@windata)
Ascnt=get_cnt (AS_THRESHOLD,：Gt " ,@windata).
4. the women examination of basal body temperature based on Analysis of sleeping quality according to claim 3
Method, it is characterised in that the sleep quality data by collection are carried out with sleep state threshold values
Compare, analyze user's sleep state step and specifically include following steps：
According to deep sleep threshold values, clear-headed threshold values in user's sleep action variance values array
And excessively deep sleep threshold values, the number of times of clear-headed threshold values are moved to judge comprehensively to be slept in setting time
Make variance values array for sound sleep or it is shallow sleep data, its deterministic process is as follows：
In the setting time comprehensively in sleep action variance values array, if 80% data it is low
In sound sleep threshold values, then this array data is sound sleep data block；
If previous state is sleeping state, the comprehensive sleep action amplitude of variation in setting time
40% data are then data block of regaining consciousness higher than clear-headed threshold values in value array, are otherwise shallow sleep number
According to block；
If previous state is deep sleep state, 50% data are needed to be higher than clear-headed threshold values, then this number
Group data judging is clear-headed data block；
If being unsatisfactory for conditions above, it is impossible to judge, then laststate is maintained.
5. the women examination of basal body temperature based on Analysis of sleeping quality according to claim 4
Method, it is characterised in that described according to user's sleep state, analyzes user corresponding
Sleep state under basal body temperature step specifically include following steps：
Obtain the sleep state data block in setting sleep period；
Find and sleep from per minute temperature data of the user for gathering in setting time
The corresponding temperature data of status data block.
By body temperature number based on the mean value of the temperature data corresponding with sleep state data block
According to.
6. the women examination of basal body temperature based on Analysis of sleeping quality according to claim 5
Method, it is characterised in that the body per minute from the user of collection in setting time
Find in warm data perform before the temperature data step corresponding with sleep state data block is performed with
Lower step：
Pair temperature data corresponding with sleep state data block carries out reliability and validity point
Analysis；
If the temperature data corresponding with sleep state data block is reliable effectively, body temperature number is chosen in side
According to mean value based on temperature data；
If the temperature data corresponding with sleep state data block is invalid, next sleep is chosen in side
Temperature data corresponding to status data block is analyzed.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10060802B1 (en) * | 2013-12-02 | 2018-08-28 | Summer Merie Ragosta | Intelligent digital thermometer |
| CN115956886A (en) * | 2022-01-29 | 2023-04-14 | 张哲� | Female physiological health monitoring method |
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|---|---|---|---|---|
| US20120238900A1 (en) * | 2011-03-14 | 2012-09-20 | Natalie Rechberg | Portable Preprogrammed Thermometer For Indicating Fertility Status |
| CN103815878A (en) * | 2014-02-21 | 2014-05-28 | 深圳清华大学研究院 | Basal body temperature detection device and method for detecting basal body temperature |
| CN204636322U (en) * | 2015-05-17 | 2015-09-16 | 派凡科技（上海）有限公司 | A kind of clinical thermometer measured for women's basal body temperature |
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2015
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Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20120238900A1 (en) * | 2011-03-14 | 2012-09-20 | Natalie Rechberg | Portable Preprogrammed Thermometer For Indicating Fertility Status |
| CN103815878A (en) * | 2014-02-21 | 2014-05-28 | 深圳清华大学研究院 | Basal body temperature detection device and method for detecting basal body temperature |
| CN204636322U (en) * | 2015-05-17 | 2015-09-16 | 派凡科技（上海）有限公司 | A kind of clinical thermometer measured for women's basal body temperature |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10060802B1 (en) * | 2013-12-02 | 2018-08-28 | Summer Merie Ragosta | Intelligent digital thermometer |
| CN115956886A (en) * | 2022-01-29 | 2023-04-14 | 张哲� | Female physiological health monitoring method |
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