CN105902257A - Sleep state analysis method and device and intelligent wearable equipment - Google Patents
Sleep state analysis method and device and intelligent wearable equipment Download PDFInfo
- Publication number
- CN105902257A CN105902257A CN201610495356.6A CN201610495356A CN105902257A CN 105902257 A CN105902257 A CN 105902257A CN 201610495356 A CN201610495356 A CN 201610495356A CN 105902257 A CN105902257 A CN 105902257A
- Authority
- CN
- China
- Prior art keywords
- sleep
- state
- time period
- value
- interval
- 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.)
- Granted
Links
Classifications
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/48—Other medical applications
- A61B5/4806—Sleep evaluation
- A61B5/4812—Detecting sleep stages or cycles
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
- A61B5/0205—Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
- A61B5/024—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
- A61B5/02—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
- A61B5/02438—Detecting, measuring or recording pulse rate or heart rate with portable devices, e.g. worn by the patient
-
- 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
- A61B5/02444—Details of sensor
-
- 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
- A61B5/0245—Detecting, measuring or recording pulse rate or heart rate by using sensing means generating electric signals, i.e. ECG signals
-
- 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
- A61B5/1118—Determining activity level
-
- 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
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/68—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient
- A61B5/6801—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient specially adapted to be attached to or worn on the body surface
- A61B5/6802—Sensor mounted on worn items
-
- 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
- A61B5/7225—Details of analog processing, e.g. isolation amplifier, gain or sensitivity adjustment, filtering, baseline or drift compensation
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/74—Details of notification to user or communication with user or patient ; user input means
Abstract
The invention provides a sleep state analysis method and device and intelligent wearable equipment. The method comprises the following steps: acquiring a physiological characteristic data value during user's daily activities; determining a sleep time period of a user according to the physiological characteristic data value. The intelligent wearable equipment can be used for determining sleep state analysis results, the real-time performance is high, simplicity and convenience are realized, the availability is high, and the user experience is improved while the intellectualization of the intelligent wearable equipment is improved.
Description
Technical field
It relates to the communications field, particularly relating to sleep state, to analyze method and device, intelligence wearable
Equipment.
Background technology
In correlation technique, when the sleep state of user is analyzed, need by pasting prison at human body
Survey electrode, and have professional test personnel that monitoring device operates the sleep shape that just can obtain this user
State analysis result.It is to say, the process operation obtaining the sleep state analysis result of user is complicated, no
Easily realize.
Summary of the invention
In view of this, present disclose provides sleep state and analyze method and device, intelligence wearable device,
To solve the deficiency in correlation technique.
First aspect according to disclosure embodiment, it is provided that a kind of sleep state analyzes method, for intelligence
Wearable device, described method includes:
Gathering physiological characteristic data value during user's daily routines, described physiological characteristic data value at least includes
The body of severe degree during for characterizing described user's daily routines moves data value and for characterizing described user
The heart rate value of changes in heart rate during daily routines；
The sleep period of described user is determined according to described physiological characteristic data value.
Alternatively, physiological characteristic data value during described collection user's daily routines, including:
The daily work of described user is gathered by acceleration transducer, gyro sensor or magnetic induction sensor
The described body in described physiological characteristic data value time dynamic moves data value；
Described life during described user's daily routines is gathered by EGC sensor or photoelectricity heart rate sensor
Described heart rate value in reason characteristic data value.
Alternatively, the described sleep period determining described user according to described physiological characteristic data value, bag
Include:
Described physiological characteristic data value is carried out pretreatment；
According to pretreated described physiological characteristic data value, determine the sleep period of described user.
Alternatively, described described physiological characteristic data value is carried out pretreatment, including:
Described physiological characteristic data value is carried out medium filtering process or mean filter processes.
Alternatively, described according to pretreated described physiological characteristic data value, determine sleeping of described user
Sleep the time period, including:
According to corresponding with each preset time period through pretreated described physiological characteristic data value, really
Fixed state belonging to each described preset time period is interval, and described state interval includes characterizing dormant
Sleep interval and the clear-headed interval of sign waking state；
If multiple described clear-headed intervals and target sleep interval are adjacent, by sleeping of described target sleep interval
Interval start time point of sleeping is defined as the described sleep starting point of described user；
If multiple described sleep intervals and target are clear-headed interval adjacent, the clear of interval that described target is regained consciousness
Interval start time point of waking up is defined as the described sleep termination point of described user；
When time period between described sleep starting point and described sleep termination point is defined as described sleep
Between section.
Alternatively, through pretreated described physiology special corresponding with each preset time period of described basis
Levy data value, determine that the state belonging to each described preset time period is interval, including:
To moving data value and described heart rate value through pretreated described body in described preset time period
Add up, respectively obtain body and move data statistics value and heart rate statistical value；
Described body is moved data statistics value and moves preset value less than the first body, and described heart rate statistical value is less than the
The state interval belonging to described preset time period of one preset value is defined as described sleep interval；
Described body moves data statistics value be not less than described first body and move preset value, and described heart rate statistical value
The state interval belonging to described preset time period being not less than described second preset value is defined as described clear-headed district
Between.
Alternatively, after the described sleep period determining described user according to described physiological characteristic data value,
Described method also includes:
According to described physiological characteristic data value, the sleep state of user described in described sleep period is entered
Row is analyzed, and determines sleep state analysis result；
Wherein, described sleep state analysis result includes that described user is in described sleep period deeply
Dormant sound sleep time period and sound sleep duration, it is in sleeping state shallow and sleeps the time period and shallow when sleeping
Long and be in rapid eye movement time period of rapid-eye-movement sleep (REM sleep) state and rapid eye movement duration at least one.
Alternatively, described according to described physiological characteristic data value to user's described in described sleep period
Sleep state is analyzed, and determines sleep state analysis result, including:
According to described physiological characteristic data value, determine the described user each institute in described sleep period
State the sleep state that preset time period is corresponding；
Calculate the accumulation duration of described preset time period corresponding to identical described sleep state, obtain described in sleep
Dormancy state analysis result.
Alternatively, described according to described physiological characteristic data value, determine that described user is in the described length of one's sleep
The sleep state that each described preset time period in Duan is corresponding, including:
If the described heart rate value in described preset time period is above the first heart rate threshold, determine described use
The family described sleep state in described preset time period is described rapid-eye-movement sleep (REM sleep) state；
If the described heart rate value in described preset time period is below the second heart rate threshold, and described default
Described body in time period moves data value and moves preset value less than the second body, determines that described user presets described
Described sleep state in time period is described deep sleep state；
If the described sleep state in described preset time period is not belonging to described rapid-eye-movement sleep (REM sleep) state
And it is not belonging to described deep sleep state, determine the described user described sleep shape in described preset time period
State is described sleeping state.
Alternatively, after the physiological characteristic data value when described collection user's daily routines, described method
Also include:
After described physiological characteristic data value input preset model, carry out model training；
Sleep model is set up according to model training result；
Described user is marked out in the described sleep residing for each preset time period by described sleep model
State.
Second aspect according to disclosure embodiment, it is provided that a kind of sleep state analytical equipment, for intelligence
Wearable device, described device includes:
Data acquisition module, physiological characteristic data value during for gathering user's daily routines, described physiology
The body of severe degree when characteristic data value at least includes for characterizing described user's daily routines moves data value
Heart rate value with changes in heart rate during for characterizing described user's daily routines；
Sleep period determines module, for determining sleeping of described user according to described physiological characteristic data value
Sleep the time period.
Alternatively, described data acquisition module includes:
First gathers submodule, for being sensed by acceleration transducer, gyro sensor or magnetic induction
The described body in described physiological characteristic data value when device gathers described user's daily routines moves data value；
Second gathers submodule, for gathering described user by EGC sensor or photoelectricity heart rate sensor
The described heart rate value in described physiological characteristic data value during daily routines.
Alternatively, described sleep period determines that module includes:
Pretreatment submodule, for carrying out pretreatment to described physiological characteristic data value；
Sleep period determines submodule, is used for according to pretreated described physiological characteristic data value, really
The sleep period of fixed described user.
Alternatively, described pretreatment submodule includes:
Filter processing unit, for carrying out medium filtering process or average filter to described physiological characteristic data value
Ripple processes.
Alternatively, described sleep period determines that submodule includes:
State interval determination unit, for according to corresponding with each preset time period through pretreated
Described physiological characteristic data value, determines that the state belonging to each described preset time period is interval, described state
Interval includes characterizing dormant sleep interval and characterizing the clear-headed interval of waking state；
Sleep starting point determines unit, if adjacent for multiple described clear-headed intervals and target sleep interval,
The described sleep that the sleep interval start time point of described target sleep interval is defined as described user is initiateed
Point；
Sleep termination point determines unit, if clear-headed interval adjacent for multiple described sleep intervals and target,
Clear-headed for described target interval clear-headed interval start time point is defined as the described sleep termination of described user
Point；
Sleep period determines unit, for by between described sleep starting point and described sleep termination point
Time period is defined as described sleep period.
Alternatively, described state interval unit includes:
Statistics subelement, for moving data through pretreated described body in described preset time period
Value and described heart rate value are added up, and respectively obtain body and move data statistics value and heart rate statistical value；
First state interval determines subelement, dynamic less than the first body pre-for described body moves data statistics value
If value, and described heart rate statistical value is interval less than the state belonging to described preset time period of the first preset value
It is defined as described sleep interval；
Second state interval determines subelement, is not less than described first for described body is moved data statistics value
Body moves preset value, and described heart rate statistical value is not less than the described preset time period institute of described second preset value
The state interval belonged to is defined as described clear-headed interval.
Alternatively, described device also includes:
Sleep state analysis result determines module, is used for according to described physiological characteristic data value described sleep
In time period, the sleep state of described user is analyzed, and determines sleep state analysis result；
Wherein, described sleep state analysis result includes that described user is in described sleep period deeply
Dormant sound sleep time period and sound sleep duration, it is in sleeping state shallow and sleeps the time period and shallow when sleeping
Long and be in rapid eye movement time period of rapid-eye-movement sleep (REM sleep) state and rapid eye movement duration at least one.
Alternatively, described sleep state analysis result determines that module includes:
Sleep state determines submodule, for according to described physiological characteristic data value, determines that described user exists
The sleep state that each described preset time period in described sleep period is corresponding；
Calculating sub module, for calculating the accumulation of described preset time period corresponding to identical described sleep state
Duration, obtains described sleep state analysis result.
Alternatively, described sleep state determines that submodule includes:
First sleep state determines unit, if the described heart rate value in described preset time period is the highest
In the first heart rate threshold, determine that the described user described sleep state in described preset time period is described
Rapid-eye-movement sleep (REM sleep) state；
Second sleep state determines unit, if the described heart rate value in described preset time period is the lowest
It is dynamic less than the second body default that described body in the second heart rate threshold, and described preset time period moves data value
Value, determines that the described user described sleep state in described preset time period is described deep sleep state；
3rd sleep state determines unit, if the described sleep state in described preset time period is not
Belong to described rapid-eye-movement sleep (REM sleep) state and be not belonging to described deep sleep state, determining that described user is described
Described sleep state in preset time period is described sleeping state.
Alternatively, described device also includes:
Model training module, for by after described physiological characteristic data value input preset model, carrying out model
Training；
Sleep model building module, for setting up sleep model according to model training result；
Labeling module, for marking out described user in each preset time period institute by described sleep model
The described sleep state at place.
The third aspect according to disclosure embodiment, it is provided that a kind of intelligence wearable device, including:
Processor；
For storing the memorizer of processor executable；
Wherein, described processor is configured to the sleep state analysis method performed described in above-mentioned first aspect.
From above technical scheme, intelligence wearable device can be when collecting user's daily routines
After physiological characteristic data, it is analyzed, so that it is determined that go out the sleep period of described user.Pass through
Said process, user has only to wear intelligence wearable device, it is possible to by described intelligence wearable device
Determining described sleep state analysis result, real-time is high, it is achieved easy, and availability is high, improves intelligence
The while that energy wearable device being intelligentized, improve Consumer's Experience.
It should be appreciated that it is only exemplary and explanatory that above general description and details hereinafter describe,
The disclosure can not be limited.
Accompanying drawing explanation
Accompanying drawing herein is merged in description and constitutes the part of this specification, it is shown that meet the disclosure
Embodiment, and for explaining the principle of the disclosure together with description.
Fig. 1 is that the disclosure analyzes method flow diagram according to a kind of sleep state shown in an exemplary embodiment；
Fig. 2 is that the disclosure analyzes method flow according to the another kind of sleep state shown in an exemplary embodiment
Figure；
Fig. 3 is that the disclosure analyzes method flow according to the another kind of sleep state shown in an exemplary embodiment
Figure；
Fig. 4 is that the disclosure analyzes method flow according to the another kind of sleep state shown in an exemplary embodiment
Figure；
Fig. 5 is the scene during the disclosure is analyzed according to the another kind of sleep state shown in an exemplary embodiment
Schematic diagram；
Fig. 6 is that the disclosure is shown according to the scene in a kind of sleep state analysis shown in an exemplary embodiment
It is intended to；
Fig. 7 is the scene during the disclosure is analyzed according to the another kind of sleep state shown in an exemplary embodiment
Schematic diagram；
Fig. 8 is that the disclosure is according to a kind of sleep state analytical equipment block diagram shown in an exemplary embodiment；
Fig. 9 is that the disclosure is according to the another kind of sleep state analytical equipment block diagram shown in an exemplary embodiment；
Figure 10 is that the disclosure is according to the another kind of sleep state analytical equipment frame shown in an exemplary embodiment
Figure；
Figure 11 is that the disclosure is according to a kind of intelligence analyzed for sleep state shown in an exemplary embodiment
One structural representation of energy wearable device.
Detailed description of the invention
Here will illustrate exemplary embodiment in detail, its example represents in the accompanying drawings.Following
When description relates to accompanying drawing, unless otherwise indicated, the same numbers in different accompanying drawings represents same or analogous
Key element.Embodiment described in following exemplary embodiment does not represent the institute consistent with the disclosure
There is embodiment.On the contrary, they only with as appended claims describes in detail, the one of the disclosure
The example of the apparatus and method that a little aspects are consistent.
The term used in the disclosure is only merely for describing the purpose of specific embodiment, and is not intended to be limiting
The disclosure." a kind of " of singulative used in disclosure and the accompanying claims book, " institute
State " and " being somebody's turn to do " be also intended to include most form, unless context clearly shows that other implications.Also should
Work as understanding, term "and/or" used herein refer to and comprise one or more be associated list item
Any or all possible combination of purpose.
Although should be appreciated that may use term first, second, third, etc. various to describe in the disclosure
Information, but these information should not necessarily be limited by these terms.These terms only be used for by same type of information that
This distinguishes.Such as, without departing from the scope of this disclosure, the first information can also be referred to as
Two information, similarly, the second information can also be referred to as the first information.Depend on linguistic context, as in this institute
Use word " if " can be construed to " and ... time " or " when ... time " or " response
In determining ".
The method provided in disclosure embodiment may be used for intelligence wearable device, includes but not limited to intelligence
Energy bracelet, intelligent watch, smart bracelet, intelligence ring, intelligence necklace, Intelligent foot chain, Intelligent leather belt
Deng, as it is shown in figure 1, Fig. 1 is to analyze method according to a kind of sleep state shown in an exemplary embodiment,
Comprise the following steps:
In a step 101, physiological characteristic data value during user's daily routines is gathered.
In disclosure embodiment, alternatively, described physiological characteristic data value at least includes for characterizing described
The body of severe degree during user's daily routines moves data value and during for characterizing described user's daily routines
The heart rate value of changes in heart rate.
In this step, described intelligence wearable device can pass through acceleration transducer, gyro sensor
Or magnetic induction sensor gathers described body according to predeterminated frequency and moves data value.Meanwhile, described intelligence is wearable
Equipment can gather described by EGC sensor or photoelectricity heart rate sensor also according to described predeterminated frequency
Heart rate value.
In a step 102, the sleep period of described user is determined according to described physiological characteristic data value.
Alternatively, step 102 is as in figure 2 it is shown, Fig. 2 illustrates on the basis of embodiment illustrated in fig. 1
Another kind of sleep state analyzes method flow diagram, may include that
In step 102-1, described physiological characteristic data value is carried out pretreatment.
In this step, in order to reduce influence of noise and data fluctuations, described intelligence wearable device can be by
According to correlation technique, the described physiological characteristic data collected carried out medium filtering process or mean filter processes.
If the described physiological characteristic data value after processing after filtering also includes exceptional data point, such as heart rate
Value exceedes the data point of normal cardiac rate value scope, needs too to filter, so that it is guaranteed that described user's sleeps
The accuracy of dormancy state analysis result.
In step 102-2, according to pretreated described physiological characteristic data value, determine described user
Sleep period.
Alternatively, step 102-2 is as it is shown on figure 3, Fig. 3 is to show on the basis of embodiment illustrated in fig. 2
The another kind of sleep state gone out analyzes method flow diagram, may include that
In step 102-21, according to corresponding with each preset time period through pretreated described life
Reason characteristic data value, determines that the state belonging to each described preset time period is interval.
Alternatively, described state interval includes characterizing dormant sleep interval and characterizing waking state
Clear-headed interval.
In this step, can be first to moving data through pretreated described body in described preset time period
Value and described heart rate value are added up, and respectively obtain body and move data statistics value and heart rate statistical value.
Wherein, in order to ensure the sleep state precision of analysis of described user, can be to choosing
Described physiological characteristic data value in described preset time period is overlapped processing, i.e. deletion is positioned at described pre-
If the described physiological characteristic data value of the first object preset time period of foremost in the time period, increase described
The described physiological characteristic data value of described first object preset time period adjacent after preset time period, will
To described physiological characteristic data value be defined as the described physiological feature number that described first preset time period is corresponding
According to value.Wherein, described first object preset time period is for be less than for described overlap-add procedure is set in advance
The time period of described preset time period duration.
Such as, body to a length of during preset time period 10 milliseconds (ms) moves data value and is overlapped every time
Processing, the duration of described first object preset time period is less than the duration of described preset time period, for 2ms.
The described body corresponding in the described preset time period of 1ms to 10ms moves in data value, deletes 1ms
Described body to 2ms moves data value, and the described body of 11ms to 12ms is moved data value increases to
The described body that the described preset time period of described 1ms to 10ms is corresponding moves in data value.Finally,
It is 3ms to that described body corresponding to the described preset time period of 1ms to 10ms moves data value
The described body of 12ms moves data value.
Similarly, described heart rate value can also use same superposition processing method to process.
Through above-mentioned overlap-add procedure, described wearable device can be avoided to gather described physiological characteristic data
The time delay produced during value, it is ensured that the accuracy that physiological characteristic data gathers.Furthermore it is also possible to make adjacent
Between the described physiological characteristic data value that described preset time period is corresponding, relatedness is higher, can obtain more
Sleep state analysis result exactly.
In disclosure embodiment, the described body through overlap-add procedure is moved data value and described heart rate value according to
Correlation technique is added up, and can obtain described body and move data statistics value and described heart rate statistical value.
Wherein, described body move data statistics value can include described body move data value body moving-target average,
First body moving-target average of body moving-target variance, described body moving-target average and target preset time period
First body of the first body moment value and described body moving-target variance and described second target preset time period
At least one in second body moment value of moving-target variance, described second target preset time period is current
First described preset time period before or after described preset time period.
Similarly, described heart rate statistical value can include the heart rate goal average of described heart rate value, heart rate mesh
Mark variance, described heart rate goal average and described target preset time period the first heart rate goal average the
Wholeheartedly the first heart rate goal of rate difference and described heart rate goal variance and described target preset time period
At least one in second heart rate difference of variance.
In disclosure embodiment, if described body moves data statistics value and moves preset value, and institute less than the first body
State heart rate statistical value and be less than the first preset value, then the state interval belonging to described preset time period is defined as
Described sleep interval.If described body moves data statistics value and is not less than described first body and moves preset value, and institute
State heart rate statistical value and be not less than described second preset value, then the state belonging to described preset time period is interval
It is defined as described clear-headed interval.
In step 102-22, if multiple described clear-headed interval and target sleep interval are adjacent, by described
The sleep interval start time point of target sleep interval is defined as the described sleep starting point of described user.
In this step, if adjacent with a certain described sleep interval in multiple described clear-headed intervals, then this is slept
Dormancy interval is described target sleep interval.May determine that described user is transferred to sleep state by waking state,
The described sleep then the sleep interval start time point of described target sleep interval being defined as described user rises
Initial point.
In step 102-23, if multiple described sleep interval and target are clear-headed interval adjacent, by described
The clear-headed interval clear-headed interval start time point of target is defined as the described sleep termination point of described user.
In this step, if adjacent with a certain described clear-headed interval at multiple described sleep intervals, then this is clear
Awake interval is the clear-headed interval of described target.May determine that described user is transferred to waking state by sleep state,
Then clear-headed for described target interval clear-headed interval start time point is defined as the described sleep of described user eventually
Stop.
In step 102-24, by true for the time period between described sleep starting point and described sleep termination point
It is set to described sleep period.
In this step, the time period between described sleep starting point and described sleep termination point be exactly described in sleep
Sleep the time period.
In disclosure embodiment, based at least including that described body moves the described life of data value and described heart rate value
Reason characteristic data value determines the described sleep period of described user, and user has only to wear intelligence and can wear
Wear equipment, it is possible to determined described sleep state analysis result, in real time by described intelligence wearable device
Property high, it is achieved easy, availability is high, improve intelligence wearable device intelligentized while, improve
Consumer's Experience.
Further, the above-mentioned sleep state analysis method that disclosure embodiment provides, as shown in Figure 4,
Fig. 4 is that the another kind of sleep state illustrated on the basis of embodiment illustrated in fig. 1 analyzes method flow diagram,
Can also include:
In step 103, according to described physiological characteristic data value to user described in described sleep period
Sleep state be analyzed, determine sleep state analysis result.
Wherein, alternatively, described sleep state analysis result includes that described user is at described sleep period
Inside it is in sound sleep time period and the sound sleep duration of deep sleep state, is in the shallow of sleeping state and sleeps the time period
And the shallow duration and being in rapid eye movement time period and the rapid eye movement duration of rapid-eye-movement sleep (REM sleep) state of sleeping
At least one.
Step 103 is as it is shown in figure 5, Fig. 5 is the another kind illustrated on the basis of embodiment illustrated in fig. 4
Sleep state analyzes method flow diagram, may include that
In step 103-1, according to described physiological characteristic data value, determine that described user is in described sleep
The sleep state that each described preset time period in time period is corresponding.
Described sleep state includes deep sleep state, sleeping state or rapid-eye-movement sleep (REM sleep) state.
In disclosure embodiment, can divide for marking described dormant data according to the most existing
Phase labeled data determines described sleep state in different ways.If the described data obtained
The number of labeled data is not less than preset number by stages, and the most described data labeled data by stages is less, then may be used
Described sleep state is determined without monitor mode to use.If the described data obtained mark number by stages
According to described number exceed described preset number, the most described data labeled data by stages is more, then can adopt
Described sleep state is determined with there being monitor mode.It is described below respectively.
First kind of way, without monitor mode.
It is possible, firstly, to determine the first heart rate threshold and the second heart rate threshold according to above-mentioned heart rate statistical value.
It is alternatively possible to terminating point heart rate value corresponding for described sleep termination point is defined as described first heart rate threshold
Value, is defined as described second heart rate threshold by starting point heart rate value corresponding for described sleep starting point.
If the described heart rate value in described preset time period is above described first heart rate threshold, it is determined that
The described user described sleep state in described preset time period is described rapid-eye-movement sleep (REM sleep) state, as
Shown in Fig. 6.
If the described heart rate value in described preset time period is below the second heart rate threshold, and described default
Described body in time period moves data value and moves preset value less than the second body, determines that described user presets described
Described sleep state in time period is described deep sleep state, also shown in FIG. 6.
Described sleep state is i.e. not belonging to described rapid-eye-movement sleep (REM sleep) state and is not belonging to again described deep sleep
The described sleep state of the described preset time period of state is defined as described sleeping state, same such as Fig. 6
Shown in.
The second way, has monitor mode.
Step 101 can be gathered described physiological characteristic data value input preset model, described preset model
Can be hidden Markov model, support vector machine, maximum entropy model, perceptron etc., thus according to
Correlation technique carries out model training.Sleep model is set up according to model training result.It is possible to further
Based on existing described data labeled data by stages, to described user in the institute residing for each preset time period
State sleep state to be labeled.Employing has monitor mode can also synchronize to mark out described user in certain institute
State preset time period and be in waking state.
As a example by hidden Markov model, in the model training stage, move statistical value and institute based on described body
State heart rate statistical value, on the basis of existing described data labeled data by stages, add up each sleep shape
Transition probability between state and emission probability.
Wherein, described transition probability is when Markov chain is made up of m state, from any one shape
State is set out, and through arbitrarily once shifting, necessarily goes out present condition 1,2 ..., and in m is this
Transfer between state is referred to as transition probability.Described hidden Markov model includes hidden state and shows
According to weather { raining, become a fine day }, certain friend b of state, such as user a determines that the activity on the same day is { public every day
Garden take a walk, shopping, clean rooms in one, user a can only see friend every day on social network sites
The information that b sends out for ", taking a walk in my park day before yesterday, yesterday does shopping, today clean rooms！", then
This weather of three days of information inference friend b site that user a can send out according to friend b.Wherein,
Aobvious state is the active state of friend b, and hidden state is state of weather.Described emission probability is described hidden
Aobvious shape probability of state is shown as by hidden state containing in Markov model.
Wherein, the feature templates that hidden Markov model uses may include that presently described Preset Time
The described body of front second the described preset time period of section moves statistical value and described heart rate statistical value, presently described
The described body of front first the described preset time period of preset time period moves statistical value and described heart rate statistical value,
The described body of presently described preset time period moves statistical value and described heart rate statistical value, presently described default time
Between the described body of preset time period described in section later move statistical value and described heart rate statistical value, presently described
After preset time period, the described body of second described preset time period moves statistical value and described heart rate statistical value,
And the combination etc. of these statistical values.In the decoding stage, it is possible to use Viterbi decoding algorithm obtains output mark
Note sequence.Thus described user is marked at the described sleep state residing for each described preset time period
Note.
Any one in by the way, the institute of the described user in the described sleep period obtained
State sleep state example as shown in Figure 7.
In step 103-2, calculate the accumulation of described preset time period corresponding to identical described sleep state
Duration, obtains described sleep state analysis result.
In this step, can be by the time progress of described preset time period corresponding for all described deep sleep states
Row is cumulative, obtains described sound sleep duration.Similarly, by corresponding for all described sleeping states described pre-
If the duration of time period adds up, obtain described shallow sleeping duration, by all described rapid-eye-movement sleep (REM sleep) shapes
The duration of the described preset time period that state is corresponding adds up, and obtains described rapid eye movement duration.
In disclosure embodiment, the described sleep state analysis result finally given can include described user
It is in sound sleep time period during described deep sleep state, terminates the time including sound sleep start time point and sound sleep
Point.Similarly, described shallow sleep the time period and include shallow start time point and the shallow termination time point of sleeping of sleeping, described
The rapid eye movement time period includes that rapid eye movement start time point and rapid eye movement terminate time point.Each described
Sleep state can occur repeatedly.
In disclosure embodiment, above-mentioned sleep state analysis result is sent to by described intelligence wearable device
The intelligent electronic device bound in advance, described intelligent electronic device can be smart mobile phone, intelligent computer,
Or any intelligent electronic device etc. in Smart Home.Shown by described intelligent electronic device, with
Toilet is stated user and is learnt described sleep state analysis result.
In above-described embodiment, intelligence wearable device can physiology when collecting user's daily routines special
After levying data, it is analyzed, so that it is determined that go out the sleep period of described user, further,
The sleep state of user described in described sleep period is analyzed, obtains the sleep shape of described user
State analysis result.Wherein, described sleep state analysis result includes that described user is at described sleep period
Inside it is in sound sleep time period and the sound sleep duration of deep sleep state, is in the shallow of sleeping state and sleeps the time period
And the shallow duration and being in rapid eye movement time period and the rapid eye movement duration of rapid-eye-movement sleep (REM sleep) state of sleeping
At least one.By said process, user has only to wear intelligence wearable device, it is possible to by described
Intelligence wearable device determines described sleep state analysis result, and real-time is high, it is achieved easy, available
Property high, improve intelligence wearable device intelligentized while, improve Consumer's Experience.
Corresponding with preceding method embodiment, the disclosure additionally provides the embodiment of device.
As shown in Figure 8, Fig. 8 is that the disclosure is divided according to a kind of sleep state shown in an exemplary embodiment
Analysis apparatus block diagram, including:
Data acquisition module 210, physiological characteristic data value during for gathering user's daily routines, described
The dynamic number of the body of severe degree when physiological characteristic data value at least includes for characterizing described user's daily routines
The heart rate value of changes in heart rate during according to value with for characterizing described user's daily routines；
Sleep period determines module 220, for determining described user according to described physiological characteristic data value
Sleep period.
Alternatively, described data acquisition module includes:
First gathers submodule, for being sensed by acceleration transducer, gyro sensor or magnetic induction
The described body in described physiological characteristic data value when device gathers described user's daily routines moves data value；
Second gathers submodule, for gathering described user by EGC sensor or photoelectricity heart rate sensor
The described heart rate value in described physiological characteristic data value during daily routines.
Alternatively, described sleep period determines that module includes:
Pretreatment submodule, for carrying out pretreatment to described physiological characteristic data value；
Sleep period determines submodule, is used for according to pretreated described physiological characteristic data value, really
The sleep period of fixed described user.
Alternatively, described pretreatment submodule includes:
Filter processing unit, for carrying out medium filtering process or average filter to described physiological characteristic data value
Ripple processes.
Alternatively, described sleep period determines that submodule includes:
State interval determination unit, for according to corresponding with each preset time period through pretreated
Described physiological characteristic data value, determines that the state belonging to each described preset time period is interval, described state
Interval includes characterizing dormant sleep interval and characterizing the clear-headed interval of waking state；
Sleep starting point determines unit, if adjacent for multiple described clear-headed intervals and target sleep interval,
The described sleep that the sleep interval start time point of described target sleep interval is defined as described user is initiateed
Point；
Sleep termination point determines unit, if clear-headed interval adjacent for multiple described sleep intervals and target,
Clear-headed for described target interval clear-headed interval start time point is defined as the described sleep termination of described user
Point；
Sleep period determines unit, for by between described sleep starting point and described sleep termination point
Time period is defined as described sleep period.
Alternatively, described state interval unit includes:
Statistics subelement, for moving data through pretreated described body in described preset time period
Value and described heart rate value are added up, and respectively obtain body and move data statistics value and heart rate statistical value；
First state interval determines subelement, dynamic less than the first body pre-for described body moves data statistics value
If value, and described heart rate statistical value is interval less than the state belonging to described preset time period of the first preset value
It is defined as described sleep interval；
Second state interval determines subelement, is not less than described first for described body is moved data statistics value
Body moves preset value, and described heart rate statistical value is not less than the described preset time period institute of described second preset value
The state interval belonged to is defined as described clear-headed interval.
As it is shown in figure 9, Fig. 9 is that the disclosure is according to the another kind of sleep state shown in an exemplary embodiment
Analytical equipment block diagram, this embodiment is on the basis of aforementioned embodiment illustrated in fig. 8, and described device also includes:
Sleep state analysis result determines module 230, is used for according to described physiological characteristic data value described
In sleep period, the sleep state of described user is analyzed, and determines sleep state analysis result；
Wherein, described sleep state analysis result includes that described user is in described sleep period deeply
Dormant sound sleep time period and sound sleep duration, it is in sleeping state shallow and sleeps the time period and shallow when sleeping
Long and be in rapid eye movement time period of rapid-eye-movement sleep (REM sleep) state and rapid eye movement duration at least one.
Alternatively, described sleep state analysis result determines that module includes:
Sleep state determines submodule, for according to described physiological characteristic data value, determines that described user exists
The sleep state that each described preset time period in described sleep period is corresponding；
Calculating sub module, for calculating the accumulation of described preset time period corresponding to identical described sleep state
Duration, obtains described sleep state analysis result.
Alternatively, described sleep state determines that submodule includes:
First sleep state determines unit, if the described heart rate value in described preset time period is the highest
In the first heart rate threshold, determine that the described user described sleep state in described preset time period is described
Rapid-eye-movement sleep (REM sleep) state；
Second sleep state determines unit, if the described heart rate value in described preset time period is the lowest
It is dynamic less than the second body default that described body in the second heart rate threshold, and described preset time period moves data value
Value, determines that the described user described sleep state in described preset time period is described deep sleep state；
3rd sleep state determines unit, if the described sleep state in described preset time period is not
Belong to described rapid-eye-movement sleep (REM sleep) state and be not belonging to described deep sleep state, determining that described user is described
Described sleep state in preset time period is described sleeping state.
As shown in Figure 10, Figure 10 is that the disclosure is according to the another kind of sleep shape shown in an exemplary embodiment
State analytical equipment block diagram, this embodiment is on the basis of aforementioned embodiment illustrated in fig. 8, and described device also wraps
Include:
Model training module 240, for by after described physiological characteristic data value input preset model, carrying out
Model training；
Sleep model building module 250, for setting up sleep model according to model training result；
Labeling module 260, for marking out described user at each Preset Time by described sleep model
Described sleep state residing for Duan.
For device embodiment, owing to it corresponds essentially to embodiment of the method, so relevant part ginseng
See that the part of embodiment of the method illustrates.Device embodiment described above is only schematically,
The unit wherein illustrated as separating component can be or may not be physically separate, as list
The parts of unit's display can be or may not be physical location, i.e. may be located at a place, or
Can also be distributed on multiple NE.Part therein or complete can be selected according to the actual needs
Portion's module realizes the purpose of disclosure scheme.Those of ordinary skill in the art are not paying creative work
In the case of, i.e. it is appreciated that and implements.
Accordingly, the disclosure also provides for a kind of intelligence wearable device, including:
Processor；
For storing the memorizer of processor executable；
Wherein, described processor is configured to the sleep state analysis method performed described in any of the above-described item.
The disclosure also proposed the intelligence of the exemplary embodiment according to the application shown in Figure 11 and can wear
Wear the schematic configuration diagram of equipment.As shown in figure 11, at hardware view, this intelligence wearable device includes place
Reason device, internal bus, network interface, internal memory and nonvolatile memory, be also possible that it certainly
Hardware required for his business.Processor read from nonvolatile memory correspondence computer program to
Then running in internal memory, this processor can perform above-mentioned sleep state and analyze method.Certainly, except
Outside software realization mode, the application is not precluded from other implementations, such as logical device or soft or hard
Mode that part combines etc., say, that the executive agent of following handling process is not limited to each logic
Unit, it is also possible to be hardware or logical device.
Those skilled in the art, after considering description and putting into practice invention disclosed herein, will readily occur to this
Other embodiment of application.The application is intended to any modification, purposes or the adaptability of the application
Change, these modification, purposes or adaptations are followed the general principle of the application and include this Shen
Please undocumented common knowledge in the art or conventional techniques means.Description and embodiments only by
Being considered as exemplary, the true scope of the application and spirit are pointed out by claim below.
Also, it should be noted term " includes ", " comprising " or its any other variant are intended to non-exclusive
Comprising, so that include that the process of a series of key element, method, commodity or equipment not only include that of property
A little key elements, but also include other key elements being not expressly set out, or also include for this process, side
The key element that method, commodity or equipment are intrinsic.In the case of there is no more restriction, statement " include one
It is individual ... " key element that limits, it is not excluded that in including the process of described key element, method, commodity or equipment
There is also other identical element.
The foregoing is only the preferred embodiment of the disclosure, not in order to limit the disclosure, all at this
Within disclosed spirit and principle, any modification, equivalent substitution and improvement etc. done, should be included in
Within the scope of disclosure protection.
Claims (21)
1. sleep state analyzes a method, for intelligence wearable device, it is characterised in that described
Method includes:
Gathering physiological characteristic data value during user's daily routines, described physiological characteristic data value at least includes
The body of severe degree during for characterizing described user's daily routines moves data value and for characterizing described user
The heart rate value of changes in heart rate during daily routines；
The sleep period of described user is determined according to described physiological characteristic data value.
Method the most according to claim 1, it is characterised in that described collection user's daily routines
Time physiological characteristic data value, including:
The daily work of described user is gathered by acceleration transducer, gyro sensor or magnetic induction sensor
The described body in described physiological characteristic data value time dynamic moves data value；
Described life during described user's daily routines is gathered by EGC sensor or photoelectricity heart rate sensor
Described heart rate value in reason characteristic data value.
Method the most according to claim 1, it is characterised in that described according to described physiological feature
Data value determines the sleep period of described user, including:
Described physiological characteristic data value is carried out pretreatment；
According to pretreated described physiological characteristic data value, determine the sleep period of described user.
Method the most according to claim 3, it is characterised in that described to described physiological feature number
Pretreatment is carried out according to value, including:
Described physiological characteristic data value is carried out medium filtering process or mean filter processes.
Method the most according to claim 3, it is characterised in that described according to pretreated institute
State physiological characteristic data value, determine the sleep period of described user, including:
According to corresponding with each preset time period through pretreated described physiological characteristic data value, really
Fixed state belonging to each described preset time period is interval, and described state interval includes characterizing dormant
Sleep interval and the clear-headed interval of sign waking state；
If multiple described clear-headed intervals and target sleep interval are adjacent, by sleeping of described target sleep interval
Interval start time point of sleeping is defined as the described sleep starting point of described user；
If multiple described sleep intervals and target are clear-headed interval adjacent, the clear of interval that described target is regained consciousness
Interval start time point of waking up is defined as the described sleep termination point of described user；
When time period between described sleep starting point and described sleep termination point is defined as described sleep
Between section.
Method the most according to claim 5, it is characterised in that described basis with each default time
Between section corresponding through pretreated described physiological characteristic data value, determine each described preset time period
Affiliated state is interval, including:
To moving data value and described heart rate value through pretreated described body in described preset time period
Add up, respectively obtain body and move data statistics value and heart rate statistical value；
Described body is moved data statistics value and moves preset value less than the first body, and described heart rate statistical value is less than the
The state interval belonging to described preset time period of one preset value is defined as described sleep interval；
Described body moves data statistics value be not less than described first body and move preset value, and described heart rate statistical value
The state interval belonging to described preset time period being not less than described second preset value is defined as described clear-headed district
Between.
Method the most according to claim 1, it is characterised in that described according to described physiological feature
After data value determines the sleep period of described user, described method also includes:
According to described physiological characteristic data value, the sleep state of user described in described sleep period is entered
Row is analyzed, and determines sleep state analysis result；
Wherein, described sleep state analysis result includes that described user is in described sleep period deeply
Dormant sound sleep time period and sound sleep duration, it is in sleeping state shallow and sleeps the time period and shallow when sleeping
Long and be in rapid eye movement time period of rapid-eye-movement sleep (REM sleep) state and rapid eye movement duration at least one.
Method the most according to claim 7, it is characterised in that described according to described physiological feature
The sleep state of user described in described sleep period is analyzed by data value, determines that sleep state divides
Analysis result, including:
According to described physiological characteristic data value, determine the described user each institute in described sleep period
State the sleep state that preset time period is corresponding；
Calculate the accumulation duration of described preset time period corresponding to identical described sleep state, obtain described in sleep
Dormancy state analysis result.
Method the most according to claim 8, it is characterised in that described according to described physiological feature
Data value, determines the described user corresponding the sleeping of each described preset time period in described sleep period
Dormancy state, including:
If the described heart rate value in described preset time period is above the first heart rate threshold, determine described use
The family described sleep state in described preset time period is described rapid-eye-movement sleep (REM sleep) state；
If the described heart rate value in described preset time period is below the second heart rate threshold, and described default
Described body in time period moves data value and moves preset value less than the second body, determines that described user presets described
Described sleep state in time period is described deep sleep state；
If the described sleep state in described preset time period is not belonging to described rapid-eye-movement sleep (REM sleep) state
And it is not belonging to described deep sleep state, determine the described user described sleep shape in described preset time period
State is described sleeping state.
Method the most according to claim 1, it is characterised in that in the described collection daily work of user
After physiological characteristic data value time dynamic, described method also includes:
After described physiological characteristic data value input preset model, carry out model training；
Sleep model is set up according to model training result；
Described user is marked out in the described sleep residing for each preset time period by described sleep model
State.
11. 1 kinds of sleep state analytical equipments, for intelligence wearable device, it is characterised in that described
Device includes:
Data acquisition module, physiological characteristic data value during for gathering user's daily routines, described physiology
The body of severe degree when characteristic data value at least includes for characterizing described user's daily routines moves data value
Heart rate value with changes in heart rate during for characterizing described user's daily routines；
Sleep period determines module, for determining sleeping of described user according to described physiological characteristic data value
Sleep the time period.
12. devices according to claim 11, it is characterised in that described data acquisition module bag
Include:
First gathers submodule, for being sensed by acceleration transducer, gyro sensor or magnetic induction
The described body in described physiological characteristic data value when device gathers described user's daily routines moves data value；
Second gathers submodule, for gathering described user by EGC sensor or photoelectricity heart rate sensor
The described heart rate value in described physiological characteristic data value during daily routines.
13. devices according to claim 11, it is characterised in that described sleep period determines
Module includes:
Pretreatment submodule, for carrying out pretreatment to described physiological characteristic data value；
Sleep period determines submodule, is used for according to pretreated described physiological characteristic data value, really
The sleep period of fixed described user.
14. devices according to claim 13, it is characterised in that described pretreatment submodule bag
Include:
Filter processing unit, for carrying out medium filtering process or average filter to described physiological characteristic data value
Ripple processes.
15. devices according to claim 13, it is characterised in that described sleep period determines
Submodule includes:
State interval determination unit, for according to corresponding with each preset time period through pretreated
Described physiological characteristic data value, determines that the state belonging to each described preset time period is interval, described state
Interval includes characterizing dormant sleep interval and characterizing the clear-headed interval of waking state；
Sleep starting point determines unit, if adjacent for multiple described clear-headed intervals and target sleep interval,
The described sleep that the sleep interval start time point of described target sleep interval is defined as described user is initiateed
Point；
Sleep termination point determines unit, if clear-headed interval adjacent for multiple described sleep intervals and target,
Clear-headed for described target interval clear-headed interval start time point is defined as the described sleep termination of described user
Point；
Sleep period determines unit, for by between described sleep starting point and described sleep termination point
Time period is defined as described sleep period.
16. devices according to claim 15, it is characterised in that described state interval unit bag
Include:
Statistics subelement, for moving data through pretreated described body in described preset time period
Value and described heart rate value are added up, and respectively obtain body and move data statistics value and heart rate statistical value；
First state interval determines subelement, dynamic less than the first body pre-for described body moves data statistics value
If value, and described heart rate statistical value is interval less than the state belonging to described preset time period of the first preset value
It is defined as described sleep interval；
Second state interval determines subelement, is not less than described first for described body is moved data statistics value
Body moves preset value, and described heart rate statistical value is not less than the described preset time period institute of described second preset value
The state interval belonged to is defined as described clear-headed interval.
17. devices according to claim 11, it is characterised in that described device also includes:
Sleep state analysis result determines module, is used for according to described physiological characteristic data value described sleep
In time period, the sleep state of described user is analyzed, and determines sleep state analysis result；
Wherein, described sleep state analysis result includes that described user is in described sleep period deeply
Dormant sound sleep time period and sound sleep duration, it is in sleeping state shallow and sleeps the time period and shallow when sleeping
Long and be in rapid eye movement time period of rapid-eye-movement sleep (REM sleep) state and rapid eye movement duration at least one.
18. devices according to claim 17, it is characterised in that described sleep state analysis is tied
Fruit determines that module includes:
Sleep state determines submodule, for according to described physiological characteristic data value, determines that described user exists
The sleep state that each described preset time period in described sleep period is corresponding；
Calculating sub module, for calculating the accumulation of described preset time period corresponding to identical described sleep state
Duration, obtains described sleep state analysis result.
19. devices according to claim 18, it is characterised in that described sleep state determines son
Module includes:
First sleep state determines unit, if the described heart rate value in described preset time period is the highest
In the first heart rate threshold, determine that the described user described sleep state in described preset time period is described
Rapid-eye-movement sleep (REM sleep) state；
Second sleep state determines unit, if the described heart rate value in described preset time period is the lowest
It is dynamic less than the second body default that described body in the second heart rate threshold, and described preset time period moves data value
Value, determines that the described user described sleep state in described preset time period is described deep sleep state；
3rd sleep state determines unit, if the described sleep state in described preset time period is not
Belong to described rapid-eye-movement sleep (REM sleep) state and be not belonging to described deep sleep state, determining that described user is described
Described sleep state in preset time period is described sleeping state.
20. devices according to claim 11, it is characterised in that described device also includes:
Model training module, for by after described physiological characteristic data value input preset model, carrying out model
Training；
Sleep model building module, for setting up sleep model according to model training result；
Labeling module, for marking out described user in each preset time period institute by described sleep model
The described sleep state at place.
21. 1 kinds of intelligent wearable devices, it is characterised in that including:
Processor；
For storing the memorizer of processor executable；
Wherein, described processor is configured to perform the sleep shape described in any one of the claims 1-10
State analyzes method.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610495356.6A CN105902257B (en) | 2016-06-27 | 2016-06-27 | Sleep state analysis method and device, intelligent wearable device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610495356.6A CN105902257B (en) | 2016-06-27 | 2016-06-27 | Sleep state analysis method and device, intelligent wearable device |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN105902257A true CN105902257A (en) | 2016-08-31 |
| CN105902257B CN105902257B (en) | 2019-06-04 |
Family
ID=56758972
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201610495356.6A Active CN105902257B (en) | 2016-06-27 | 2016-06-27 | Sleep state analysis method and device, intelligent wearable device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN105902257B (en) |
Cited By (22)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106725325A (en) * | 2016-12-27 | 2017-05-31 | 广州碧德电子科技有限公司 | Analysis of sleeping quality method and analytical equipment |
| CN107837072A (en) * | 2017-10-25 | 2018-03-27 | 公安部物证鉴定中心 | A kind of sleep quality characteristic analysis method based on intelligent watch data |
| CN107861605A (en) * | 2017-11-06 | 2018-03-30 | 北京小米移动软件有限公司 | Data processing method and device |
| CN107890339A (en) * | 2017-11-09 | 2018-04-10 | 常熟理工学院 | A kind of sleep stage detection method and wearable sleep stage detection means |
| CN107943266A (en) * | 2017-11-20 | 2018-04-20 | 北京小米移动软件有限公司 | power consumption control method, device and equipment |
| CN107951466A (en) * | 2016-10-17 | 2018-04-24 | 珠海格力电器股份有限公司 | A kind of quarter-bell control method, apparatus and system |
| CN108030467A (en) * | 2017-11-22 | 2018-05-15 | 广东思派康电子科技有限公司 | Electronics bracelet and computer-readable recording medium |
| CN108601559A (en) * | 2016-12-23 | 2018-09-28 | 华为技术有限公司 | A kind of method of data synchronization, device and terminal device |
| CN109394174A (en) * | 2017-08-18 | 2019-03-01 | 美的集团股份有限公司 | Sleep state method of discrimination, device, computer equipment and readable storage medium storing program for executing |
| CN109528214A (en) * | 2018-11-07 | 2019-03-29 | 深圳市新元素医疗技术开发有限公司 | A kind of Multifunctional wrist BOLD contrast |
| CN109620158A (en) * | 2018-12-28 | 2019-04-16 | 惠州Tcl移动通信有限公司 | Method, intelligent terminal and the storage device of Sleep-Monitoring |
| CN109674459A (en) * | 2018-12-24 | 2019-04-26 | 深圳和而泰数据资源与云技术有限公司 | A kind of method and device of data processing |
| CN109710160A (en) * | 2018-12-28 | 2019-05-03 | 联想(北京)有限公司 | Information processing method and electronic equipment |
| CN109841284A (en) * | 2017-11-24 | 2019-06-04 | 广东乐心医疗电子股份有限公司 | Sleep state discrimination model construction method and device and portable intelligent equipment |
| CN110710962A (en) * | 2019-11-08 | 2020-01-21 | 北京卡路里信息技术有限公司 | Sleep state detection method and device |
| CN111102643A (en) * | 2018-10-26 | 2020-05-05 | 芜湖美的厨卫电器制造有限公司 | Wall-mounted furnace and control method and system thereof |
| CN112244773A (en) * | 2020-10-15 | 2021-01-22 | 上海我乐科技有限公司 | Sleep quality monitoring device and method and mattress |
| CN112826454A (en) * | 2019-11-22 | 2021-05-25 | Oppo广东移动通信有限公司 | Information pushing method and related product |
| CN112955063A (en) * | 2018-12-29 | 2021-06-11 | 深圳迈瑞生物医疗电子股份有限公司 | Sleep state judgment method and device |
| CN115153251A (en) * | 2021-04-02 | 2022-10-11 | 江苏里高智能家居有限公司 | Electric bed control box and electric bed |
| CN117643455A (en) * | 2024-01-29 | 2024-03-05 | 深圳市百泰实业股份有限公司 | Sleep detection method of intelligent wearable device |
| CN117643455B (en) * | 2024-01-29 | 2024-05-03 | 深圳市百泰实业股份有限公司 | Sleep detection method of intelligent wearable device |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20080033304A1 (en) * | 2006-07-19 | 2008-02-07 | Yousufali Dalal | Sleep state detection |
| CN103717125A (en) * | 2011-05-18 | 2014-04-09 | V视股份有限公司 | System and method for determining sleep and sleep stages of a person |
| CN104095615A (en) * | 2014-07-17 | 2014-10-15 | 上海翰临电子科技有限公司 | Human sleep monitoring method and system |
| CN104104983A (en) * | 2014-07-29 | 2014-10-15 | 惠州Tcl移动通信有限公司 | Method and system for automatically turning off television when detecting sleep state of user |
| CN105030199A (en) * | 2015-06-24 | 2015-11-11 | 深圳市元征软件开发有限公司 | Sleep monitoring method and device |
| CN205163032U (en) * | 2014-12-18 | 2016-04-20 | 湖南泰达讯科技有限公司 | Sleep monitoring device |
-
2016
- 2016-06-27 CN CN201610495356.6A patent/CN105902257B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20080033304A1 (en) * | 2006-07-19 | 2008-02-07 | Yousufali Dalal | Sleep state detection |
| CN103717125A (en) * | 2011-05-18 | 2014-04-09 | V视股份有限公司 | System and method for determining sleep and sleep stages of a person |
| CN104095615A (en) * | 2014-07-17 | 2014-10-15 | 上海翰临电子科技有限公司 | Human sleep monitoring method and system |
| CN104104983A (en) * | 2014-07-29 | 2014-10-15 | 惠州Tcl移动通信有限公司 | Method and system for automatically turning off television when detecting sleep state of user |
| CN205163032U (en) * | 2014-12-18 | 2016-04-20 | 湖南泰达讯科技有限公司 | Sleep monitoring device |
| CN105030199A (en) * | 2015-06-24 | 2015-11-11 | 深圳市元征软件开发有限公司 | Sleep monitoring method and device |
Cited By (27)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107951466A (en) * | 2016-10-17 | 2018-04-24 | 珠海格力电器股份有限公司 | A kind of quarter-bell control method, apparatus and system |
| US10791517B2 (en) | 2016-12-23 | 2020-09-29 | Huawei Technologies Co., Ltd. | Data synchronization method and apparatus, and terminal device |
| CN108601559A (en) * | 2016-12-23 | 2018-09-28 | 华为技术有限公司 | A kind of method of data synchronization, device and terminal device |
| CN106725325A (en) * | 2016-12-27 | 2017-05-31 | 广州碧德电子科技有限公司 | Analysis of sleeping quality method and analytical equipment |
| CN109394174A (en) * | 2017-08-18 | 2019-03-01 | 美的集团股份有限公司 | Sleep state method of discrimination, device, computer equipment and readable storage medium storing program for executing |
| CN107837072A (en) * | 2017-10-25 | 2018-03-27 | 公安部物证鉴定中心 | A kind of sleep quality characteristic analysis method based on intelligent watch data |
| CN107861605A (en) * | 2017-11-06 | 2018-03-30 | 北京小米移动软件有限公司 | Data processing method and device |
| CN107890339A (en) * | 2017-11-09 | 2018-04-10 | 常熟理工学院 | A kind of sleep stage detection method and wearable sleep stage detection means |
| CN107890339B (en) * | 2017-11-09 | 2020-09-08 | 常熟理工学院 | Sleep staging detection method and wearable sleep staging detection device |
| CN107943266A (en) * | 2017-11-20 | 2018-04-20 | 北京小米移动软件有限公司 | power consumption control method, device and equipment |
| CN108030467A (en) * | 2017-11-22 | 2018-05-15 | 广东思派康电子科技有限公司 | Electronics bracelet and computer-readable recording medium |
| CN108030467B (en) * | 2017-11-22 | 2021-07-02 | 广东思派康电子科技有限公司 | Electronic bracelet and computer readable storage medium |
| CN109841284A (en) * | 2017-11-24 | 2019-06-04 | 广东乐心医疗电子股份有限公司 | Sleep state discrimination model construction method and device and portable intelligent equipment |
| CN111102643A (en) * | 2018-10-26 | 2020-05-05 | 芜湖美的厨卫电器制造有限公司 | Wall-mounted furnace and control method and system thereof |
| CN109528214A (en) * | 2018-11-07 | 2019-03-29 | 深圳市新元素医疗技术开发有限公司 | A kind of Multifunctional wrist BOLD contrast |
| CN109674459A (en) * | 2018-12-24 | 2019-04-26 | 深圳和而泰数据资源与云技术有限公司 | A kind of method and device of data processing |
| CN109710160A (en) * | 2018-12-28 | 2019-05-03 | 联想(北京)有限公司 | Information processing method and electronic equipment |
| CN109620158A (en) * | 2018-12-28 | 2019-04-16 | 惠州Tcl移动通信有限公司 | Method, intelligent terminal and the storage device of Sleep-Monitoring |
| CN112955063A (en) * | 2018-12-29 | 2021-06-11 | 深圳迈瑞生物医疗电子股份有限公司 | Sleep state judgment method and device |
| CN110710962A (en) * | 2019-11-08 | 2020-01-21 | 北京卡路里信息技术有限公司 | Sleep state detection method and device |
| CN112826454A (en) * | 2019-11-22 | 2021-05-25 | Oppo广东移动通信有限公司 | Information pushing method and related product |
| CN112826454B (en) * | 2019-11-22 | 2022-12-13 | Oppo广东移动通信有限公司 | Information pushing method and related product |
| CN112244773A (en) * | 2020-10-15 | 2021-01-22 | 上海我乐科技有限公司 | Sleep quality monitoring device and method and mattress |
| CN112244773B (en) * | 2020-10-15 | 2023-01-24 | 上海我乐科技有限公司 | Sleep quality monitoring device and method and mattress |
| CN115153251A (en) * | 2021-04-02 | 2022-10-11 | 江苏里高智能家居有限公司 | Electric bed control box and electric bed |
| CN117643455A (en) * | 2024-01-29 | 2024-03-05 | 深圳市百泰实业股份有限公司 | Sleep detection method of intelligent wearable device |
| CN117643455B (en) * | 2024-01-29 | 2024-05-03 | 深圳市百泰实业股份有限公司 | Sleep detection method of intelligent wearable device |
Also Published As
| Publication number | Publication date |
|---|---|
| CN105902257B (en) | 2019-06-04 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN105902257A (en) | Sleep state analysis method and device and intelligent wearable equipment | |
| Ghasemzadeh et al. | Physical movement monitoring using body sensor networks: A phonological approach to construct spatial decision trees | |
| Uddin et al. | Human activity recognition from wearable sensors using extremely randomized trees | |
| CN105844101A (en) | Emotion data processing method and system based smart watch and the smart watch | |
| Liu et al. | Research on human activity recognition based on active learning | |
| WO2017222877A1 (en) | Tracking activity data between wearable devices paired with a companion device | |
| KR102089002B1 (en) | Method and wearable device for providing feedback on action | |
| CN105877701A (en) | Method and device for analyzing sleep period in real time and intelligent wearable equipment | |
| CN104049752A (en) | Interaction method based on human body and interaction device based on human body | |
| CN104182041A (en) | Wink type determining method and wink type determining device | |
| CN105492868A (en) | Half step frequency feature for reliable motion classification | |
| CN110443145A (en) | The Human bodys' response of sensor-based separate type deployment is health management system arranged | |
| CN104157168A (en) | Intelligent database application system applied to artificial intelligent robot and interaction method | |
| CN106264545B (en) | Step recognition method and device | |
| CN108095694A (en) | State monitoring method, device and the wearable device of wearable device | |
| CN106776073A (en) | Wearable device and its task execution control method and device | |
| CN103458051A (en) | Somatosensory network and household behavior perception method based on same | |
| CN110111815A (en) | Animal anomaly sound monitoring method and device, storage medium, electronic equipment | |
| CN105031905B (en) | A kind of movable information display methods and device | |
| CN108492829A (en) | A kind of baby cry based reminding method, apparatus and system | |
| CN105232063B (en) | Detection method for mental health of user and intelligent terminal | |
| CN114073493B (en) | Physiological data acquisition method and device and wearable equipment | |
| CN108089693A (en) | Gesture identification method and device, intelligence wearing terminal and server | |
| CN109199325B (en) | Sleep monitoring method and device | |
| WO2024041053A1 (en) | Indoor passive human behavior recognition method and apparatus |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| TR01 | Transfer of patent right | ||
| TR01 | Transfer of patent right |
Effective date of registration: 20191223 Address after: Room 189, building H2, phase II, innovation industrial park, 2800 innovation Avenue, high tech Zone, Hefei City, Anhui Province Patentee after: Anhui China Intelligent Technology Co Ltd Address before: Anhui city of Hefei province innovation road 230088 No. 2800 high tech Zone Innovation Industrial Park two building H8 Patentee before: ANHUI HUAMI INFORMATION TECHNOLOGY CO., LTD. |
|
| CP03 | Change of name, title or address | ||
| CP03 | Change of name, title or address |
Address after: 230088 No. 01, 5th floor, building B2, Zhongan chuanggu Science Park, No. 900, Wangjiang West Road, high tech Zone, Hefei City, Anhui Province Patentee after: Anhui huami Health Technology Co.,Ltd. Address before: Room 189, building H2, phase II, innovation industrial park, 2800 innovation Avenue, high tech Zone, Hefei City, Anhui Province Patentee before: Anhui Huami Information Technology Co.,Ltd. |