CN108510075A - The monitoring inference system of sleep quality and environmental variance correlation - Google Patents
The monitoring inference system of sleep quality and environmental variance correlation Download PDFInfo
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- CN108510075A CN108510075A CN201810351748.4A CN201810351748A CN108510075A CN 108510075 A CN108510075 A CN 108510075A CN 201810351748 A CN201810351748 A CN 201810351748A CN 108510075 A CN108510075 A CN 108510075A
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- sleep
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- causality
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computing arrangements using knowledge-based models
- G06N5/04—Inference or reasoning models
- G06N5/046—Forward inferencing; Production systems
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
Abstract
The invention discloses the monitoring inference systems of a kind of sleep quality and environmental variance correlation, environmental variance acquisition module is for acquiring environmental variance data, sleep quality monitoring modular is used to monitor the dormant data of user, environmental variance acquisition module is by the environmental variance data transmission acquired to sleep and environment causality inference module, sleep quality monitoring modular sends the user's dormant data monitored to sleep and environment causality inference module, sleep and environment causality inference module establish the causality model between sleep quality and sleep environment according to the environmental variance data and dormant data that receive, sleep rational analysis module calculates the sleep environment of user by relational model according to the sleep event of user and from which further follows that sleep environment Improving advice.The system can solve the technical issues of being monitored with environmental variance correlation to the sleep quality of user.
Description
Technical field
The present invention relates to the monitorings of sleep monitor technical field more particularly to a kind of sleep quality and environmental variance correlation
Inference system.
Background technology
Medical research proves that sleep quality has a direct impact health, social activities and working efficiency.Sleep is strong
Health has become an important medical research project, achieves abundant achievement in research.Clinical research shows environmental factor,
Such as light intensity, indoor temperature, air humidity, atmospheric pressure and ambient noise etc., it is to lead to interruptions of sleep or low quality
The major reason of sleep, therefore improve sleep environment, be conducive to obtain better sleep quality.
With the development of mobile technology and wearable device technology, occur on the market more and more for personal health prison
Portable wearable device of survey, such as sleep monitor Intelligent bracelet etc., these equipment can monitor sleeping for people in daily life
Dormancy quality.In addition, also more and more using the mobile APP that mobile phone sensor changes come monitoring of environmental.
Up to the present, there has been no the schemes and system that are combined sleep quality with environmental factor.
Invention content
Technical problem solved by the invention is to provide a kind of monitoring reasoning system of sleep quality and environmental variance correlation
System, the technical issues of being monitored with the correlation of environmental variance with analyzing to the sleep quality of user with solution.
In order to solve the above-mentioned technical problem, the technical solution adopted in the present invention is:A kind of sleep quality and environmental variance
The monitoring inference system of correlation, including environmental variance acquisition module, sleep quality monitoring modular, sleep and environment causality
Inference module and sleep rational analysis module, the environmental variance acquisition module are described to sleep for acquiring environmental variance data
Dormancy quality-monitoring module is used to monitor the dormant data of user, and the environmental variance acquisition module and the sleep quality monitor mould
Block is separately connected the sleep and environment causality inference module, and the environmental variance acquisition module becomes the environment acquired
It measures data transmission to sleep and environment causality inference module to described, the user that the sleep quality monitoring modular will be monitored
Dormant data sends the sleep and environment causality inference module, the sleep and environment causality inference module root to
The causality between sleep quality and sleep environment is established according to the environmental variance data received and the dormant data
Model, the sleep rational analysis module calculate the sleep environment of user according to the sleep event of user by the relational model
And analyze sleep environment Improving advice.
As an improvement mode, the environmental variance acquisition module includes acoustics signal processing unit, the acoustics
Signal processing unit includes sound pick-up, and the ambient acoustic signal that the sound pick-up is acquired includes ambient sound and sleeper itself
The sound sent out, the acoustics signal processing unit decomposite the acoustic feature of time domain and frequency domain, the sound from acoustic signal
It includes short-time energy, loudness, zero-crossing rate, power spectral density and spectrum to learn feature, then these feature unbalanced input graders,
For isolating unexpected and periodic ambient sound.
As an improvement mode, the environmental variance acquisition module includes non-acoustic signal processing unit, described non-
Acoustics signal processing unit includes the light sensor for acquiring intensity of illumination, and the temperature sensor for acquiring room temperature is used
In the humidity sensor of acquisition indoor humidity, baroceptor for acquiring atmospheric pressure and one are for acquiring ambient sound
The sound pick-up of sound carrys out continuous monitoring of environmental signal.
As an improvement mode, the non-acoustic signal processing unit for eliminate human body it is not noticeable arrive it is quick
The non-acoustic signal of of short duration variation, the non-acoustic signal processing unit extract amplitude from non-acoustic signal againDurationAnd frequencyFeature, pass through the threshold value TH of predefined environmental variance variation rangeAWith the threshold value TH of variation durationD
Judge, if the feature of non-acoustic signal is less than corresponding threshold value, just gives up the signal value.
As an improvement mode, the sleep quality monitoring modular include Polysomnography and it is wearable just
The dormant data of portable device, the Polysomnography acquisition includes total sleep time, always awakening number, each sleep stage
Duration, periodic limb motion and respiration case；The heart rate of the wearable portable device acquisition user, breathing
And the data of body kinematics.
As an improvement mode, it is described sleep with environment causality inference module utilize Granger causality,
Build a multivariable constrained control to identify the causal relation between sleep quality variable X and environmental variance Y, specially：
The illumination for including described in environmental variance, room epidemic disaster, air pressure, ambient noise is enabled to use X respectively1,…,X5It indicates, sleeps
Dormancy event indicates that the multivariable constrained control for building a Granger Causality is as follows with Y：
WhereinIt is environmental factor j from t-LaggedjWhen
It is carved into the value at t-1 moment, LaggedjIt is the maximum delay time of environmental factor j, ai,j=[ai,j,1,…,ai,j,Lagged] be back
Return coefficient vector, element ai,j,k(k=1..Lagged) Y is representedi(t) to delay time sequenceDegree of dependence, Yi
(t) it is the sleep event for being happened at t moment, ε is residual vector.
As an improvement mode, the sleep rational analysis module passes through the relationship according to the sleep event of user
Model analysis goes out sleep environment Improving advice.
Acquired technique effect is due to the adoption of the above technical scheme,
The present invention is for analyzing the relationship between sleep quality and environmental variance and proposing Improving advice, with following spy
Point：
1) different processing methods is respectively adopted to acoustic signal and non-acoustic signal, can remove influences not sleep quality
Big factor；
2) inferred by Granger Causality and multivariable autoregression establishes one between sleep quality and sleep environment
Causality model；
3) causality model can provide the bad reason of sleep quality, and be capable of providing building for improvement sleep environment
View helps user to improve sleep quality.
The present invention in environmental signal acoustic signal and non-acoustic signal, using different data processing solutions, extraction
Go out the ingredient being had an impact to sleep quality；Environment and the sleep quality monitoring system of the present invention can be inferred that environmental variance with
Relationship between sleep quality；The present invention inference system by probability inference can provide the bad reason of sleep quality and to
Go out to change sleep environment to improve the suggestion of sleep quality.
Description of the drawings
Fig. 1 is the link schematic diagram of each module of the present invention；
Fig. 2 is the internal structure chart of each module of the present invention；
Specific implementation mode
General layout Plan such as Fig. 1 and Fig. 2 of the present invention jointly shown in, a kind of sleep quality and environmental variance correlation
Monitor inference system, including environmental variance acquisition module, sleep quality monitoring modular, sleep and environment causality inference module
And sleep rational analysis module.
For the environmental variance acquisition module of the present invention for acquiring environmental variance data, environmental variance acquisition module includes light
Sensor, temperature sensor, humidity sensor, baroceptor and a sound pick-up, collected environmental signal include light
According to intensity, room temperature, indoor humidity, atmospheric pressure and ambient sound, distinguish whether environment becomes by the threshold value of these signals
Change.
Sleep quality monitoring modular is used to monitor the dormant data of user, and it is real that sleep quality monitoring modular may be used at sleep
It tests in room and detects obtained dormant data using Polysomnography (PSG), it is collected with wearable portable device
The data of heart rate, breathing and body kinematics.Polysomnography (PSG) acquisition dormant data include：1) when total sleep
Between, it turns off the light from lying down to the time turned on light of getting up；2) always awaken number, the number awakened in sleep；3) total sleep stage
Duration, including sleep (N1), shallow sleep (N2), sound sleep (N3) and rapid-eye-movement sleep (REM) and respectively account for total sleep time in this 4 stages
Ratio；4) periodic limb motion, limbs unconscious movement when sleep；5) respiration case, apnea, hypopnea
Deng.These sleep quality data can be converted into the time series of sleep event.
Environmental variance acquisition module and sleep quality monitoring modular are separately connected sleep and environment causality inference module,
Environmental variance acquisition module is by the environmental variance data transmission acquired to sleep and environment causality inference module, sleep matter
Amount monitoring modular sends the user's dormant data monitored to sleep and environment causality inference module, sleep and environment because
Fruit relationship inference module is established according to the environmental variance data and dormant data that receive between sleep quality and sleep environment
Causality model,
Sleep is to close environmental change and sleep event with environment causality inference module, is inferred with Granger Causality
(Granger Causality) excavates the causality between them, builds an interpretable Analysis of sleeping quality system.
The causality of subsidiary confidence factor can intuitively be shown to user, and at the same time, sleep rational analysis module is according to user's
Sleep event calculates the sleep environment of user by relational model and analyzes sleep environment Improving advice, to help user to change
Kind sleep quality.
Since each environmental factor has respective characteristic (such as acoustic signal and non-acoustic signal), so environmental monitoring mould
Block detects the change events in environmental signal with different algorithms.Sleep event depends not only upon the width of environmental change
Value, it is also related with its duration and frequency.
The concrete operating principle of the sleep quality and the monitoring inference system of environmental variance correlation is：
(1) processing of non-acoustic signal
The not noticeable variation quick and of short duration to non-acoustic signal of usual human body, so we need to eliminate those and not weigh
The influence for the environmental change wanted.We extract amplitude from non-acoustic signalDurationAnd frequencyEtc. features, pass through
The threshold value TH of predefined environmental variance variation rangeAWith the threshold value TH of variation durationDJudge, if non-acoustic signal
Feature be less than corresponding threshold value, we just give up the signal value.Algorithm is as follows：
Algorithm 1：Non-acoustic data processing
Input：Non-acoustic environmental sensory data, threshold value THA, threshold value THD
Output：Non-acoustic environmental sensory data after processing
(2) acoustics signal processing
When acquiring ambient acoustic signal with sound pick-up, it will usually include ambient sound and the sound that sleeper itself sends out
Sound, we devise a kind of method to identify ambient sound.We decomposite the acoustics of time domain and frequency domain from acoustic signal
Feature, including short-time energy (STE), loudness (LD), zero-crossing rate (ZCR), power spectral density (PSD) and spectrum (entropy PE).Then
These feature unbalanced input graders, for detecting unexpected and periodic ambient sound.Algorithm is as follows：
Algorithm 2：Acoustic data processing
Input：Acoustic enviroment sensing data
Output：Acoustic enviroment sensing data after processing
(3) sleep is excavated with environment causality
We are theoretical (Granger Causality) according to Granger causality：When one time series X is another
Between sequence Y " granger cause ", the effect of following Y value is predicted and if only if the result merged with the past value of X, than list
The pure effect predicted using the past information of Y is more preferable.Using Granger causality, we construct a multivariable and return certainly
Return (MVAR) model to identify the causal relation between sleep quality variable X and environmental variance Y.
Environmental variance (illumination, room epidemic disaster, air pressure, ambient noise) is enabled to use X respectively1,…,X5It indicates, sleep event Y
It indicates, the multivariable constrained control for building a Granger Causality is as follows：
WhereinIt is environmental factor j from t-LaggedjWhen
It is carved into the value at t-1 moment, LaggedjIt is the maximum delay time of environmental factor j.ai,j=[ai,j,1,…,ai,j,Lagged] be back
Return coefficient vector, element ai,j,k(k=1..Lagged) Y is representedi(t) to delay time sequenceDegree of dependence.Yi
(t) it is the sleep event for being happened at t moment.ε is residual vector, is also prediction error.
Algorithm is as follows：
Algorithm 3：
Input：Sleep event SE, environmental data ED
Output：Cause the environmental factor GC, confidence factor CF of sleep event
(4) high-quality sleep environment suggestion
Granger causality is found out by scheme (3), when a sleep event has occurred, system can utilize model
To infer the reason of outgoing event occurs, i.e., most possible environmental factor GC.System can open up our inferred results (GC)
Show to user, and improves sleep environment according to inferred results come suggestion user.For specific environmental factor, can export such as
Lower Improving advice：
| Influence the environmental factor (GC) of sleep quality | Improving advice |
| Light is suddenly bright | Keep room dark or dark |
| Temperature is increased or is reduced | Pay attention to cooling or warming |
| Humidity is increased or is reduced | Pay attention to divulging information or humidify |
| Air pressure is increased or is reduced | |
| Burst or sustained sound | Pay attention to room sound insulation |
Realize target proposed by the present invention, present invention mainly solves technological difficulties below：
1) sound that sound transducer can collect ambient sound simultaneously and sleeper sends out, extracts from acoustic signal
The ambient sound that we need, eliminates the influence of sleeper's sound；
2) environmental sensor susceptibility is higher, the situation of change complete documentation of environmental variance can be got off, but human body pair
The quick or of short duration variation of environment is insensitive, and the present invention can handle the smaller signal intensity of this effect；
3) mathematical model is established to derive the relating attribute between sleep environment and sleep quality, research sleep ring
Border is to the quantitative relationship of sleep quality, and provides Improving advice.
To sum up, the present invention proposes the monitoring and inference system of a kind of sleep quality with environmental variance correlation, have with
Lower feature：
(1) system is for acoustic signal, using time domain and frequency domain characteristic as feature, using Nonlinear Classifier algorithm point
Ambient sound and sleeper's sound are separated out, it is unknown that human feeling is filtered out using the method for threshold decision for non-acoustic signal
Aobvious environmental change.Environmental sensor data processing scheme is first core element of the present invention.
(2) system devises a sleep environment and sleep quality causal reasoning scheme, and the program, which uses, is based on Glan
Outstanding causal multivariable autoregression algorithm, builds the causality model between sleep environment and sleep quality, then uses
Model exports to explain the influence factor of sleep quality.Sleep environment is the second of the present invention with sleep quality causal reasoning scheme
A core element.
(3) present invention devises the Improving advice scheme of sleep environment.The program using sleep environment and sleep quality it
Between imply causality, most probable environmental factor reason is speculated according to the sleep event monitored, then propose improve
The suggestion of environmental factor.
Claims (7)
1. the monitoring inference system of a kind of sleep quality and environmental variance correlation, which is characterized in that acquired including environmental variance
Module, sleep quality monitoring modular, sleep and environment causality inference module and sleep rational analysis module, the environment
Variable acquisition module is used to monitor the dormant data of user for acquiring environmental variance data, the sleep quality monitoring modular,
The environmental variance acquisition module and the sleep quality monitoring modular are separately connected the sleep and infer with environment causality
Module, the environmental variance acquisition module push away the environmental variance data transmission acquired to the sleep with environment causality
Disconnected module, the sleep quality monitoring modular send the user's dormant data monitored to the sleep and environment causality
Inference module, the sleep is with environment causality inference module according to the environmental variance data received and the sleep
Data establish the causality model between sleep quality and sleep environment, sleep rational analysis module the sleeping according to user
Dormancy event calculates the sleep environment of user by the relational model.
2. the monitoring inference system of sleep quality as described in claim 1 and environmental variance correlation, which is characterized in that described
Environmental variance acquisition module includes acoustics signal processing unit, and the acoustics signal processing unit includes sound pick-up, the pickup
The ambient acoustic signal that device is acquired includes ambient sound and the sound that sleeper itself sends out, the acoustics signal processing unit
Decomposite the acoustic feature of time domain and frequency domain from acoustic signal, the acoustic feature include short-time energy, loudness, zero-crossing rate,
Power spectral density and spectrum, then these feature unbalanced input graders, for isolating unexpected and periodic ambient sound
Sound.
3. the monitoring inference system of sleep quality as claimed in claim 2 and environmental variance correlation, which is characterized in that described
Environmental variance acquisition module includes non-acoustic signal processing unit, and the non-acoustic signal processing unit includes for acquiring illumination
The light sensor of intensity, the temperature sensor for acquiring room temperature, the humidity sensor for acquiring indoor humidity, for adopting
The baroceptor and a sound pick-up for acquiring ambient sound for collecting atmospheric pressure carry out continuous monitoring of environmental signal.
4. the monitoring inference system of sleep quality as claimed in claim 3 and environmental variance correlation, which is characterized in that described
Non-acoustic signal processing unit is used to eliminate the non-acoustic signal of the not noticeable quick and of short duration variation arrived of human body, the non-sound
It learns signal processing unit and extracts amplitude from non-acoustic signalDurationAnd frequencyFeature, by predefined
The threshold value TH of environmental variance variation rangeAWith the threshold value TH of variation durationDJudge, if the feature of non-acoustic signal is low
In corresponding threshold value, then just give up the signal value.
5. the monitoring inference system of sleep quality as described in claim 1 and environmental variance correlation, which is characterized in that described
Sleep quality monitoring modular includes Polysomnography and wearable portable device, the Polysomnography acquisition
Dormant data include total sleep time, always awaken number, the duration of each sleep stage, periodic limb motion and breathing
Event；The data of the heart rate of the wearable portable device acquisition user, breathing and body kinematics.
6. the monitoring inference system of sleep quality described in claim 1 and environmental variance correlation, which is characterized in that described to sleep
It sleeps and utilizes Granger causality, one multivariable constrained control of structure to sleep to identify with environment causality inference module
Causal relation between quality variable X and environmental variance Y, specially：
The illumination for including described in environmental variance, room epidemic disaster, air pressure, ambient noise is enabled to use X respectively1,…,X5It indicates, thing of sleeping
Part indicates that the multivariable constrained control for building a Granger Causality is as follows with Y：
WhereinIt is environmental factor j from t-LaggedjMoment arrives
The value at t-1 moment, LaggedjIt is the maximum delay time of environmental factor j, ai,j=[ai,j,1,…,ai,j,Lagged] it is to return system
Number vector, element ai,j,k(k=1..Lagged) Y is representedi(t) to delay time sequenceDegree of dependence, Yi(t) it is
It is happened at the sleep event of t moment, ε is residual vector.
7. the monitoring inference system of sleep quality described in claim 1 and environmental variance correlation, which is characterized in that described to sleep
Dormancy rational analysis module analyzes sleep environment Improving advice according to the sleep event of user by the relational model.
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