CN102551664A - Sleep analysis method, sleep analysis table and sleep analysis system - Google Patents

Sleep analysis method, sleep analysis table and sleep analysis system Download PDF

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CN102551664A
CN102551664A CN2011100379917A CN201110037991A CN102551664A CN 102551664 A CN102551664 A CN 102551664A CN 2011100379917 A CN2011100379917 A CN 2011100379917A CN 201110037991 A CN201110037991 A CN 201110037991A CN 102551664 A CN102551664 A CN 102551664A
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sleep
sensing signal
activity density
target interval
analysis window
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CN102551664B (en
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梁胜富
刘懿哲
陈俊佑
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National Cheng Kung University NCKU
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Delta Electronics Inc
National Cheng Kung University NCKU
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Abstract

A sleep analysis method includes: sensing the action of a user through a multi-axis acceleration sensor to generate a sensing signal; processing the sensing signal to obtain an average activity density; and comparing the average activity density with a general threshold value to determine whether a large analysis window or a small analysis window is used for processing the sensing signal so as to know whether the time corresponding to at least one target interval of the sensing signal is the waking time or the sleeping time, wherein the large analysis window corresponds to a longer time, and the small analysis window corresponds to a shorter time. The accuracy of sleep analysis can be improved through two analysis windows with different sizes, and the real sleep quality of a user is reflected for reference.

Description

Sleep analysis method, sleep analysis table and sleep analysis system
Technical field
The present invention is about a kind of sleep analysis method, sleep analysis table and sleep analysis system.
Background technology
Everyone needs sleep, and sleep will account for time of 1/4th to 1/3rd in people all one's life, so the quality of sleep quality has considerable influence for people's life.And in order to detect the user sleep quality; Also have some product developments to ask the city, these can be equipped with acceleration transducer above product, dress acceleration transducer through user; And the active state of sensing user in sleep; And then analyze its sleep quality, yet existing sleep analysis device all can't react the sleep quality of user very accurately.
Fig. 1 sensing signal that to be a kind of known sleep analysis device sense according to the activity of a user through acceleration transducer, its through square processing to obtain positive number.As shown in Figure 2, known sleep analysis method comprises: signal was carried out segmentation to obtain a plurality of analystal sections (step S01) with 30 seconds; The maximum of more said analystal section and a threshold value (step S02), greater than threshold value, the time of then judging said analystal section is recovery time as if maximum, less than threshold value, the time of then judging said analystal section is the length of one's sleep as if maximum.
Yet; Known sleep analysis method is very coarse; And do not consider the relation between continuous two analystal sections, can be described as very rough analysis, and quite high probability misjudge is arranged; For example may judge recovery time and the length of one's sleep with quite high frequency fogging, and this is a manifest error according to known sleep analysis method.
Therefore, how a kind of sleep analysis method, sleep analysis table and sleep analysis system are provided, can improve the degree of accuracy of sleep analysis, and then the real sleep quality of reaction user is for reference, is one of current important topic really.
Summary of the invention
The purpose of this invention is to provide a kind of sleep analysis method, sleep analysis table and sleep analysis system, can improve the degree of accuracy of sleep analysis, and then the real sleep quality of reaction user is for reference.
The present invention can adopt following technical scheme to realize.
A kind of sleep analysis method of the present invention comprises: the action through a multiaxis acceleration transducer sensing one user produces a sensing signal; Handle said sensing signal and obtain a mean activity density; And more said mean activity density and a general threshold value and determine to use a big analysis window or a little analysis window to handle said sensing signal; The pairing time of at least one target interval to learn said sensing signal is the recovery time or the length of one's sleep; Wherein said big analysis window corresponds to a long period, and said little analysis window corresponds to a short period.
In one embodiment, mean activity density uses a signal peak of sensing signal that the peak separation characteristic is calculated.The connection of continuous two analystal sections is represented to the peak separation characteristic in the signal peak, can promote the degree of accuracy of sleep analysis by this.
In one embodiment; Big analysis window comprises a plurality of first analystal sections, said target interval and a plurality of second analystal section; Said target interval position is between said first analystal section and said second analystal section, and an activity density of said target interval uses said first analystal section and said second analystal section to calculate.Say that more specifically the activity density of said target interval uses a signal peak of said first analystal section and said second analystal section and said sensing signal that the peak separation characteristic is calculated.
In one embodiment, when said activity density during, use big analysis window and use a signal maximum characteristic of sensing signal to recomputate said activity density greater than a big window threshold value.Signal maximum characteristic is represented the maximum activity amount of analystal section.After the signal peak of the connection of utilizing continuous two analystal sections of representative comes computational activity density to the peak separation characteristic; The signal maximum characteristic that re-uses the sensing signal recomputates activity density; So can obtain the effect of double acknowledge, and thereby promote the degree of accuracy of sleep analysis.
In one embodiment, when said activity density during, judge that the pairing time of said target interval is recovery time, otherwise judge that then the pairing time of said target interval is the length of one's sleep greater than big window threshold value.
Below the situation of little analysis window is used in explanation.
In one embodiment; Little analysis window comprises a plurality of the 3rd analystal sections, said target interval and a plurality of the 4th analystal section; Said target interval position is between said the 3rd analystal section and said the 4th analystal section, and an activity density of said target interval uses said the 3rd analystal section and said the 4th analystal section to calculate.Say that more specifically the said activity density of target interval uses a signal peak of said the 3rd analystal section and said the 4th analystal section and said sensing signal that the peak separation characteristic is calculated.
In one embodiment, when said activity density during, use little analysis window and use a signal maximum characteristic of said sensing signal to recomputate said activity density greater than a wicket threshold value.Signal maximum characteristic is represented the maximum activity amount of analystal section.After the signal peak of the connection of utilizing continuous two analystal sections of representative comes computational activity density to the peak separation characteristic; The signal maximum characteristic that re-uses the sensing signal recomputates activity density; So can obtain the effect of double acknowledge, and thereby promote the degree of accuracy of sleep analysis.
In one embodiment, when said activity density during, judge that the pairing time of target interval is recovery time, otherwise judge that then the pairing time of target interval is the length of one's sleep greater than the wicket threshold value.
A kind of sleep analysis table of the present invention, it is worn on a user and analyzes its sleep state on one's body, and comprises a multiaxis acceleration transducer, a processing module and a timing unit.The action of multiaxis acceleration transducer sensing user and produce a sensing signal.Processing module and multiaxis acceleration transducer couple, and receive the sensing signal.Timing unit and processing module couple, and one time of timing.Wherein, Processing module is handled the sensing signal and is obtained a mean activity density; And relatively mean activity density and a general threshold value and to determine to use a big analysis window or a little analysis window to handle the sensing signal be the recovery time or the length of one's sleep with the pairing time of at least one target interval of learning the sensing signal; Wherein big analysis window corresponds to a long period, and little analysis window corresponds to a short period.
A kind of sleep analysis of the present invention system comprises an analytic unit and a hypnograph table.The hypnograph table is worn on a user and comprise a multiaxis acceleration transducer, a control unit, a timing unit and a transmission unit on one's body.The action of the said user of multiaxis acceleration transducer sensing and produce a sensing signal.Control unit and multiaxis acceleration transducer couple, and receive the sensing signal.Timing unit and control unit couple, and one time of timing.Transmission unit transmits said time and said sensing signal to analytic unit.Wherein, Analytic unit is handled the sensing signal and is obtained a mean activity density; And relatively mean activity density and a general threshold value and to determine to use a big analysis window or a little analysis window to handle the sensing signal be the recovery time or the length of one's sleep with the pairing time of at least one target interval of learning the sensing signal; Wherein big analysis window corresponds to a long period, and little analysis window corresponds to a short period.
Hold the above, the sensing signal that the action that the present invention handles multiaxis acceleration transducer sensing one user through two kinds of different big or small analysis window produces.When mean activity density during greater than general threshold value; Represent user possibly be in the sleep of big mobility; Under this situation, use the big analysis window of corresponding long period to handle the sensing signal, because discover if user is in the sleep of big mobility; One period long period can be continued, so if use the big analysis window of corresponding long period can confirm then whether user is in waking state.On the contrary; When mean activity degree during, represent user possibly be in the sleep (for example deep sleep) of less mobility, under this situation less than general threshold value; Use the little analysis window of corresponding short period to handle the sensing signal; Because discover that it all can be in less mobility one period short period if user is in the sleep of less mobility, so if use the little analysis window of corresponding short period can confirm then whether user is in sleep state.By this, the present invention utilizes two kinds of different big or small analysis window can promote the precision of sleep analysis, and then the real sleep quality of reaction user is for reference.
Description of drawings
Fig. 1 is the sensing signal that a kind of known sleep analysis device senses;
Fig. 2 is a kind of flow chart of known sleep analysis method;
Fig. 3 is the block schematic diagram of a kind of sleep analysis system of the preferred embodiment of the present invention;
Fig. 4 is the flow chart of a kind of sleep analysis method of the preferred embodiment of the present invention;
Fig. 5 is the sketch map of a kind of sensing signal of the preferred embodiment of the present invention;
Fig. 6 is the conceptual schematic view of big analysis window of the present invention; And
Fig. 7 is the block schematic diagram of the sleep analysis table of another embodiment of the present invention.
The main element symbol description:
1: the sleep analysis system
11: analytic unit
12,12 ': the hypnograph table
121: the multiaxis acceleration transducer
122: control unit
123: timing unit
124: transmission unit
125: high pass filter
126: analog-digital converter
127: memorizer
13: processing module
S01, S02: the step of known sleep analysis method
S101~S115: the step of sleep analysis method of the present invention
The specific embodiment
Below will a kind of sleep analysis method, sleep analysis table and sleep analysis system according to the preferred embodiment of the present invention be described with reference to correlative type, wherein components identical will be explained with the components identical symbol.
Fig. 3 is the block schematic diagram of a kind of sleep analysis system 1 of the preferred embodiment of the present invention.As shown in Figure 3, sleep analysis system 1 comprises an analytic unit 11 and a hypnograph table 12.The hypnograph table is worn on a user and comprise a multiaxis acceleration transducer 121, a control unit 122, a timing unit 123 and a transmission unit 124 on one's body.
The action of multiaxis acceleration transducer 121 sensing user and produce a sensing signal.The sensing signal for example comprises the acceleration information of multiaxis (like X, Y, Z axle).Multiaxis acceleration transducer 121 can for example be G pick off (G sensor).In addition, multiaxis acceleration transducer 121 also can refer to can obtain after treatment the pick off of acceleration information, for example gyroscope (gyroscope).
Control unit 122 couples with multiaxis acceleration transducer 121, and receives the sensing signal.Control unit 122 for example is a microcontroller (micro-controller).
Timing unit 123 couples with control unit 122, and one time of timing, to learn real-time time.Transmission unit 124 transmits said time and sensing signal to analytic unit 11, and analytic unit 11 is handled the sensing signal then.Transmission unit 124 can be wire transmission unit (for example USB, IEEE 1394) or wireless transmission unit (for example blue bud, Wireless USB).
The assembly that above-mentioned hypnograph table 12 is had only is for example; In addition; Hypnograph table 12 also can have other electronic building brick; For example, the sensing signal of multiaxis acceleration transducer 121 can be earlier through a high pass filter 125 so that low-frequency noise is removed, be digital signal via an analog-digital converter 126 with the signal conversion again.Hypnograph table 12 can have a memorizer 127 to store the information and the time of sensing signal.
Fig. 4 is the flow chart of a kind of sleep analysis method of the preferred embodiment of the present invention, below please with reference to Fig. 3 and start and the sleep analysis method of Fig. 4 to further specify sleep analysis system 1.
At first, the sleep analysis method comprises step S101, and its action through a multiaxis acceleration transducer 121 sensings one user produces a sensing signal.Because this step is specified in, so repeat no more at this.
Then, the sleep analysis method comprises through handling the sensing signal and obtains a mean activity density.Fig. 5 is the sketch map of a kind of sensing signal of present embodiment.In this step, for making things convenient for the subsequent treatment of sensing signal, earlier will be because of there not being the signal zero padding of movable non-registered period, and whole signal square is made numerical value all is positive number.
Then, handle the sensing signal through analytic unit 11 and obtain mean activity density.The mean activity density of present embodiment uses a signal peak of sensing signal that the peak separation characteristic is calculated, and the connection of continuous two analystal sections is represented to the peak separation characteristic in the signal peak, can promote the degree of accuracy of sleep analysis by this.As shown in Figure 5; The sensing signal is for example to be segmented into a plurality of analystal sections (step S102) in 30 seconds; So-called " the signal peak is to peak separation " characteristic is promptly when the maximum of continuous two analystal sections during all greater than a basic threshold value, the peak value of said two analystal sections time apart.Above-mentioned basic threshold value can for example be calculated by following formula:
T=0.1*STD(ACC)
Wherein, ACC represents whole sensing signal, and the STD representative is got standard deviation to the sensing signal, and T is basic threshold value.Is example with 0.1 times of standard deviation as basic threshold value at this.
When the signal peak to peak separation during less than a threshold values; The signal peak that then is designated as a tool activeness is to peak separation, and wherein threshold values for example is 11 seconds, that is to say two analystal sections of 30 seconds; During less than 11 seconds, the signal peak that is designated as a tool activeness is to peak separation to peak separation at its signal peak.Then mean activity density promptly is that the signal peak of tool activeness is to the quantity of the peak separation value (step S103) divided by the analystal section quantity of whole sensing signal.
After obtaining mean activity density; The sleep analysis method comprises: relatively a mean activity density and a general threshold value and determine to use a big analysis window or a little analysis window to handle said sensing signal; The pairing time of at least one target interval to learn the sensing signal is the recovery time or the length of one's sleep; Wherein, big analysis window corresponds to a long period, and little analysis window corresponds to a short period.
Shown in step S104, compare a mean activity density and a general threshold value, at this; General threshold value for example is 0.0575; When mean activity density during, use big analysis window, and the peak separation characteristic is calculated the activity density (S105) of a target interval with the signal peak greater than general threshold value.
The notion of big analysis window is described with Fig. 6.Big analysis window can comprise a plurality of first analystal sections, target interval and a plurality of second analystal section, and the target interval position is between said first analystal section and said second analystal section.First analystal section, target interval and second analystal section all are 30 seconds sectional analystal sections, and wherein the activity density of target interval is calculated according to said first analystal section and said second analystal section.Big analysis window can for example comprise 69 analystal sections, comprising 34 first analystal sections and 34 second analystal sections and a target interval.
Say that further the activity density of target interval can use the signal peak of said first analystal section and said second analystal section that the peak separation characteristic is calculated.Be activity density be the signal peak of tool activeness of said first analystal section, target interval and said second analystal section to the quantity of peak separation divided by the value of the analystal section quantity of whole sensing signal (for example 69) (for example 35/69).Repeat aforesaid way, can obtain the activity density of a plurality of target intervals.
After obtaining the activity density of a target interval; The sleep analysis method can also comprise: compare an activity density and a big window threshold value (S106); When activity density during, use big analysis window and use a signal maximum characteristic of sensing signal to recomputate activity density (S107) greater than big window threshold value.Big window threshold value for example is 0.1.
" signal maximum " characteristic refers to the signal maximum in the analystal section.Utilize process that signal maximum characteristic comes computational activity density and utilize the signal peak similar the special little account form of peak separation.If the signal maximum greater than basic threshold value (T), then is designated as the signal maximum of a tool activeness.And activity density is the peaked quantity of the signal of tool activeness divided by the value of the analystal section quantity of big analysis window (for example 23/69).
After obtaining the activity density that goes out with signal maximum feature calculation; Compare activity density and big window threshold value (S108) again; When activity density during greater than big window threshold value; Judge that the pairing time of said target interval is recovery time, otherwise judge that then the pairing time of said target interval is the length of one's sleep.
The person of noting; After on behalf of the signal peak of the connection of continuous two analystal sections, the present embodiment utilization peak separation characteristic is come computational activity density; The signal maximum characteristic that re-uses the sensing signal recomputates activity density; So can obtain the effect of double acknowledge, and thereby promote the degree of accuracy of sleep analysis.Certainly, in other embodiments, can omit step S105, S106 or omit step S107, S108.
In addition, shown in step S104,, use little analysis window, and the peak separation characteristic is calculated the activity density (S109) of a target interval with the signal peak when mean activity density during less than general threshold value.Little analysis window corresponds to a short period, and 15 analystal sections for example are comprising 7 the 3rd analystal sections, a target interval and 7 the 4th analystal sections.Because the account form of said activity density is identical with the account form of utilizing big analysis window, only be used in the quantity difference of the analystal section of calculating, so repeat no more at this.
After obtaining the activity density of a target interval; The sleep analysis method can also comprise: compare an activity density and a wicket threshold value (S110); When activity density during, use little analysis window and use a signal maximum characteristic of sensing signal to recomputate activity density (S111) greater than the wicket threshold value.The wicket threshold value for example is 0.2.Account form by in said activity density is identical with the account form of utilizing big analysis window, and only the quantity of analystal section is different, so repeat no more at this.
After obtaining the activity density that goes out with signal maximum feature calculation; Compare activity density and wicket threshold value (S112) again; When activity density during greater than the wicket threshold value; Judge that the pairing time of said target interval is recovery time, otherwise judge that then the pairing time of said target interval is the length of one's sleep.
The person of noting; After on behalf of the signal peak of the connection of continuous two analystal sections, the present embodiment utilization peak separation characteristic is come computational activity density; The signal maximum characteristic that re-uses the sensing signal recomputates activity density; So can obtain the effect of double acknowledge, and thereby promote the degree of accuracy of sleep analysis.Certainly, in other embodiments, can omit step S109, S110 or omit step S111, S112.
In addition; In the step S106 and S110 of the sleep analysis method of present embodiment, when activity density is not more than big window threshold value, and when being not more than the wicket threshold value; The sleep analysis method also comprises step S113: comparison signal maximum characteristic and basic threshold value; When signal maximum characteristic during, then judge it is recovery time, otherwise then judge it is the length of one's sleep greater than basic threshold value.
Then, step S114 judges whether the sensing signal of next analystal section, if having, then continues step S105 or S109, then can not calculate sleep quality or each item sleep pointer (step S115) if having.
In addition; Fig. 7 is the sleep analysis table 12 of another embodiment of the present invention ' sketch map; The difference of the sleep analysis table 12 of the sleep analysis system 1 of itself and Fig. 3 is; The sleep analysis table 12 of sleep analysis system 1 needs extra analytic unit carry out the analysis of sensing signal, and sleep analysis table 12 ' can independently accomplish the analysis of sensing signal.Wherein, sleep analysis table 12 ' analytic unit 11, control unit 122, high pass filter 125 analog-digital converters 126 and memorizer 127 can be included in the processing module 13.
At sleep analysis table 12 ' in, the action of multiaxis acceleration transducer 121 sensing user and produce a sensing signal.Processing module and multiaxis acceleration transducer couple, and receive the sensing signal.Timing unit and processing module couple, and one time of timing.Wherein, Processing module is handled the sensing signal and is obtained a mean activity density; And relatively mean activity density and a general threshold value and to determine to use a big analysis window or a little analysis window to handle the sensing signal be the recovery time or the length of one's sleep with the pairing time of at least one target interval of learning the sensing signal; Wherein big analysis window corresponds to a long period, and little analysis window corresponds to a short period.Because sleep analysis table 12 ' the sleep analysis method of doing flowing mode and use thereof detail at the foregoing description, so repeat no more at this.
In sum, the present invention handles the sensing signal that the action of multiaxis acceleration transducer sensing one user produces through the analysis window of two kinds of different sizes.When mean activity density during greater than general threshold value; Represent user possibly be in the sleep of big mobility; Under this situation, use the big analysis window of corresponding long period to handle the sensing signal, because discover if user is in the sleep of big mobility; One period long period can be continued, so if use the big analysis window of corresponding long period can confirm then whether user is in waking state.On the contrary; When mean activity degree during, represent user possibly be in the sleep (for example deep sleep) of less mobility, under this situation less than general threshold value; Use the little analysis window of corresponding short period to handle the sensing signal; Because discover that it all can be in less mobility one period short period if user is in the sleep of less mobility, so if use the little analysis window of corresponding short period can confirm then whether user is in sleep state.By this, the present invention utilizes two kinds of different big or small analysis window can promote the precision of sleep analysis, and then the real sleep quality of reaction user is for reference.
The above only is an illustrative, and non-limiting.Anyly do not break away from spirit of the present invention and category, and, all should be included in the claim institute restricted portion its equivalent modifications of carrying out or change.

Claims (12)

1. a sleep analysis method is characterized in that, comprising:
Action through a multiaxis acceleration transducer sensing one user produces a sensing signal;
Handle said sensing signal and obtain a mean activity density; And
A more said mean activity density and a general threshold value and determine to use a big analysis window or a little analysis window to handle said sensing signal; The pairing time of at least one target interval to learn said sensing signal is the recovery time or the length of one's sleep; Wherein said big analysis window corresponds to a long period, and said little analysis window corresponds to a short period.
2. sleep analysis method according to claim 1 is characterized in that, said mean activity density uses a signal peak of said sensing signal that the peak separation characteristic is calculated.
3. sleep analysis method according to claim 1; It is characterized in that; Said big analysis window comprises a plurality of first analystal sections, said target interval and a plurality of second analystal section; Said target interval position is between said first analystal section and said second analystal section, and an activity density of said target interval uses said first analystal section and said second analystal section to calculate.
4. sleep analysis method according to claim 3 is characterized in that, the said activity density of said target interval uses a signal peak of said first analystal section and said second analystal section and said sensing signal that the peak separation characteristic is calculated.
5. sleep analysis method according to claim 4 is characterized in that, also comprises:
When said activity density during, use said big analysis window and use a signal maximum characteristic of said sensing signal to recomputate said activity density greater than a big window threshold value.
6. according to claim 4 or 5 described sleep analysis methods; It is characterized in that; When said activity density during greater than said big window threshold value, judge that the pairing time of said target interval is recovery time, opposite situation judges that then the pairing time of said target interval is the length of one's sleep.
7. sleep analysis method according to claim 1; It is characterized in that; Said little analysis window comprises a plurality of the 3rd analystal sections, said target interval and a plurality of the 4th analystal section; Said target interval position is between said the 3rd analystal section and said the 4th analystal section, and an activity density of said target interval uses said the 3rd analystal section and said the 4th analystal section to calculate.
8. sleep analysis method according to claim 7 is characterized in that, the said activity density of said target interval uses a signal peak of said the 3rd analystal section and said the 4th analystal section and said sensing signal that the peak separation characteristic is calculated.
9. sleep analysis method according to claim 8 is characterized in that, also comprises:
When said activity density during, use said little analysis window and use a signal maximum characteristic of said sensing signal to recomputate said activity density greater than a wicket threshold value.
10. according to Claim 8 or 9 described sleep analysis methods; It is characterized in that; When said activity density during greater than said wicket threshold value, judge that the pairing time of said target interval is recovery time, opposite situation judges that then the pairing time of said target interval is the length of one's sleep.
11. a sleep analysis table is worn on a user and analyzes its sleep state on one's body, it is characterized in that, comprising:
One multiaxis acceleration transducer, the action of the said user of sensing and produce a sensing signal;
One processing module couples with said multiaxis acceleration transducer, and receives said sensing signal; And
One timing unit; Couple with said processing module; And one time of timing; Wherein said processing module is handled said sensing signal and is obtained a mean activity density; And more said mean activity density and a general threshold value and to determine to use a big analysis window or a little analysis window to handle said sensing signal be the recovery time or the length of one's sleep with the pairing time of at least one target interval of learning said sensing signal, wherein said big analysis window corresponds to a long period, and said little analysis window corresponds to a short period.
12. a sleep analysis system is characterized in that, comprising:
One analytic unit; And
One hypnograph table is worn on one's body the user, comprising:
One multiaxis acceleration transducer, the action of the said user of sensing and produce a sensing signal;
One control unit couples with said multiaxis acceleration transducer, and receives said sensing signal;
One timing unit couples with said control unit, and one time of timing; And
One transmission unit transmits said time and said sensing signal to said analytic unit,
Wherein said analytic unit is handled said sensing signal and is obtained a mean activity density; And more said mean activity density and a general threshold value and to determine to use a big analysis window or a little analysis window to handle said sensing signal be the recovery time or the length of one's sleep with the pairing time of at least one target interval of learning said sensing signal; Wherein said big analysis window corresponds to a long period, and said little analysis window corresponds to a short period.
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