CN109091150A - Recognition methods, sleep quality appraisal procedure and the intelligent wearable device that body of sleeping moves - Google Patents
Recognition methods, sleep quality appraisal procedure and the intelligent wearable device that body of sleeping moves Download PDFInfo
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- 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
- A61B5/1116—Determining posture transitions
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- 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
- A61B5/4809—Sleep detection, i.e. determining whether a subject is asleep or not
-
- 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
- A61B5/4815—Sleep quality
-
- 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
- A61B5/681—Wristwatch-type devices
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- 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
Abstract
A kind of dynamic recognition methods of sleep body of the invention establishes the multiple standards model under normal speed comprising steps of acquiring the three-dimensional acceleration data in a variety of body actions in user's sleep procedure by acceleration sensor and carrying out data processing to it；The new sleep movement for acquiring user, generates the test template of user's sleep movement；The movement of body in user's sleep procedure is determined by DTW algorithm；And user's body motion frequency is obtained by the body action of user；Analyzing evaluation is carried out to user's sleep quality by being combined the subjective criterion of user's body motion frequency and user.A kind of intelligent wearable device for moving recognition methods or sleep quality appraisal procedure based on sleep body.User is helped to count the mechanics of sleeping time and body, to be better understood by oneself vivo biodistribution clock, urges user to form better sleep habit, understand and hold the turn in sleep procedure or movable rhythm, health is paid close attention to, the sleep result of itself is improved.
Description
Technical field
The present invention relates to fields of communication technology, assess more particularly to a kind of recognition methods that sleep body is dynamic, sleep quality
Method and intelligent wearable device.
Background technique
Intelligent bracelet is a wearable intelligent equipment.User can recorde exercise in daily life, sleep, heart rate,
The data such as blood pressure blood sample, and these data are synchronous with smart phone, tablet computer etc., it plays and instructs healthy living by data
Effect.Since intelligent wearable device is quickly grown, and simple and practical Intelligent bracelet is even more the hot spot that market is pursued.
In life, many people think that body action is few during sleep and just sleep soundly that normal person sleeps to date greatly every night and turn in fact
Body 20 times or so.Though turning over is unintentional motion, sleep can be improved as a result, promoting health.
If body and bedding are always with same face contact, that is, when motionless for a long time in sleep procedure or sleep is static
Between it is too long, the skin of this one side will assemble heat, due to be close to bedding and cause skin not perspire normally, cannot be good
Body temperature is adjusted, skin temperature is caused to increase, influences the depth of sleep, enters you and shallowly sleeps state.Turning over when sleeping can avoid one side
Skin temperature is excessively high, eliminates uncomfortable feeling, you is helped to maintain deep sleep.Always with the sleep of same posture, body
Body is applied with pressure to mattress, and the pressure that is generated by weight and gravity will lead to blood and body fluid circulatory is unsmooth, especially after
Back and buttocks etc. are also easy to produce the position of gravity.Turn over the circulation for then facilitating to improve blood and body fluid, also adjustable blood
Imbalance allows you to enjoy more comfortable sleep.
It is turned over when sleeping or sleep cycle is adjusted in activity, feed them into good sleep state.If could not grasp
Turn or movable rhythm, will lead to sleep cycle disorder, not can guarantee good sleep when sleeping.
And common sleep monitor software/equipment on the market, rely primarily on what acceleration sensor measured.Each company
For the data of acceleration transducer, the mode of processing is different, and the algorithm of each product is also different, from simple threshold value
Method arrives various statistical methods, meanwhile, each wearer is also different, it will be difficult to obtain accurate data.
Therefore, how to grasp and turned over when user sleeps or movable rhythm, it is how more acurrate, more fully provide user and sleep
It sleeps, health analysis report, could suggest and user is guided to form better sleep habit and work and rest rule, guarantee that user is good
Sleep, so that personal physical fitness be gradually increased.
Summary of the invention
The present invention is to overcome the shortcomings of to provide a kind of recognition methods that sleep body is dynamic, sleep matter described in the above-mentioned prior art
Measure appraisal procedure and intelligent wearable device.
In order to solve the above technical problems, technical scheme is as follows:
A kind of recognition methods that sleep body is dynamic, includes the following steps:
S11. master pattern is established: the three-dimensional acquired in a variety of body actions in user's sleep procedure by acceleration sensor adds
Speed data simultaneously carries out data processing to it, establishes the multiple standards model under normal speed, and using multiple standards model as
Reference template sequence is stored in sample database；
S12. user's sleep movement obtains: acquiring the three-dimensional acceleration number in the new sleep movement of user by acceleration sensor
Data processing is carried out according to and to it, generates the test template of user's sleep movement；
S13. action recognition: intercepting test sample sequence from test template, by DTW algorithm by test sample sequence and in advance
Reference template sequence in the sample database of foundation is compared matching one by one, calculates test sample sequence and each reference template sequence
Between consolidation path distance, and most short regular path distance is determined, to identify the movement of body in user's sleep procedure.
Further, as optimal technical scheme, the data processing includes: by three-dimensional acceleration data synthesizing one-dimensional
Resultant acceleration data are simultaneously converted into acceleration signal；Low-pass filtering is carried out to acceleration signal by sliding mean filter method
Processing.
Further, as optimal technical scheme, test sample sequence and reference template sequence are compared in step S13
Include: compared with matching
Test sample sequence truncation: the initial time of effective action is obtained from test template by endpoint algorithm at the end of
Between, obtain the movable test sample sequence of user's body；
Movement matching: carrying out time adjustment for the time shaft of test sample sequence by DTW algorithm, make test sample sequence when
Between axis it is corresponding with the time shaft of reference template sequence.
Further, as optimal technical scheme, the calculating of consolidation path distance includes: in step S13
A. it constructs a matrix grid: choosing reference template sequence, by the time shaft Nonlinear Mapping of test sample sequence to working as
The time shaft of preceding reference template sequence forms two-dimentional rectangular co-ordinate；By the point marked on the horizontal axis in two-dimensional Cartesian coordinate system and
The rounded coordinate of the point marked on the longitudinal axis draws several co-ordinations and forms a matrix grid；
B. route searching: searching route is set out from coordinate (1,1), searches for the seat of all previous lattice points that can reach current lattice point
Mark；
C. regular path distance calculates: calculating the corresponding regular distance of coordinate for the previous lattice point that can reach current lattice point, and takes
Minimum value is obtained along with the Euclidean distance between the corresponding test sample sequence of current lattice point coordinate and reference template sequence
The regular path distance of current lattice point；When the terminal that the coordinate of current lattice point is test sample sequence and current reference template sequence
When, obtain the regular path distance between test sample sequence and current reference template sequence.
Further, as optimal technical scheme, before test sample sequence truncation, pass through acceleration signal and angle speed
The variation of degree signal determines that the effective action in user's sleep procedure starts.
Further, as optimal technical scheme, it includes by the time that the time shaft of test sample sequence, which carries out time adjustment,
Axis is extended and is shortened.
A kind of sleep quality appraisal procedure for being moved recognition methods based on sleep body, is included the following steps:
S21. user's body motion frequency obtains: obtaining user's sleep by the movement of body in user's sleep procedure for identifying
In the process the number of body movement, twice between body movement maximum time interval and single activation maximum duration；
S22. the analysis slept: by the subjective criterion of this objective standard of user's body motion frequency and user be combined to
Family sleep quality is analyzed；
S23. the evaluation slept: user's sleep result grade is evaluated based on the analysis results.
Further, as optimal technical scheme, the objective standard for carrying out analyzing evaluation to user's sleep quality further includes
The rule for turning over number, the total duration and sleep wakefulness of sleeping in user's Sleep-Monitoring.
Further, as optimal technical scheme, the determination of the subjective criterion of user includes the following steps: in step S22
Subjective feeling of sleep list is provided；
The selection instruction of user is received, the current subjective feeling of user is obtained, so that it is determined that supervisor's standard；The subjectivity feeling of sleep column
Table includes tired, alliteration, dizziness.
A kind of intelligent wearable device for moving recognition methods or sleep quality appraisal procedure based on sleep body, the wearable device
For bracelet or wrist-watch.
Compared with prior art, the beneficial effect of technical solution of the present invention is:
1. moving data by the body that acceleration transducer acquires user's sleep procedure body, and data are moved to body by DTW algorithm
It carries out calculation processing and determines that the sleep body of user is dynamic, obtain more accurate action recognition.
2. judging user's sleep procedure by the dynamic body movement frequency counted in user's sleep procedure of user's sleep body
In body activity condition: whether have horrible nightmares, if there is sleep-walking, if turn over it is excessively frequent, if body is chronically at
Motionless state.
3. the total degree for turning over the body movement in number counting user sleep procedure by the user monitored, passes through
Motion frequency judges the time interval combination body movement frequency, sleep total duration and sleep wakefulness of body movement in sleep procedure
Rule obtain the objective standard of user's sleep procedure.
3. the subjective criterion of objective standard combination user can the sleep quality to user more accurately assessed, so as to
User is better understood by the vivo biodistribution clock of oneself, and user is urged to form better sleep habit, understands and hold sleep procedure
In turn or movable rhythm, pay close attention to health, improve the sleep result of itself.
4. appropriately intermittently turned over when simultaneously, sleeping or the not only adjustable skin temperature of activity, promote blood circulation,
Alleviate muscular fatigue, the distortion of correction backbone, adjust sleep cycle, while being also that healthy sleep is indispensable.
Detailed description of the invention
Fig. 1 is the dynamic recognition methods flow chart of steps of the sleep body of the embodiment of the present invention 1.
Fig. 2 is the action recognition flow chart of steps in the dynamic recognition methods of the sleep body of the embodiment of the present invention 1.
Fig. 3 is the matrix grid figure for the action recognition that the present invention applies example 1.
Fig. 4 is the sleep quality appraisal procedure flow chart of steps that the present invention applies example 2.
Fig. 5 is the structural block diagram for the intelligent wearable device that the present invention applies example 3.
The attached figures are only used for illustrative purposes and cannot be understood as limitating the patent；In order to better illustrate this embodiment, attached
Scheme certain components to have omission, zoom in or out, does not represent the size of actual product；To those skilled in the art,
The omitting of some known structures and their instructions in the attached drawings are understandable；The same or similar label corresponds to same or similar
Component；The terms describing the positional relationship in the drawings are only for illustration, should not be understood as the limitation to this patent.
Specific embodiment
The preferred embodiments of the present invention will be described in detail with reference to the accompanying drawing, so that advantages and features of the invention are more
It is easily readily appreciated by one skilled in the art, to make apparent define to protection scope of the present invention.
Embodiment 1
A kind of dynamic recognition methods of sleep body of the invention, as depicted in figs. 1 and 2:
Include the following steps:
S11. master pattern is established: acquiring a variety of body actions in user's sleep procedure by acceleration sensor, including multiple
The movements such as the left side of friction speed is turned over, right side is turned over and left and right is turned over, acquire the three-dimensional acceleration in a variety of body actions
Data ax、ay、az, and data processing is carried out to it, one-dimensional resultant acceleration data a is obtained, the feature of resultant acceleration data a is extracted
Value establishes the multiple standards model that normal speed lower body acts in user's sleep procedure, and using multiple standards model as ginseng
Template sequence C1, C2 ... Cx is examined, sample database is stored in；
Wherein, data processing includes the resultant acceleration data by three-dimensional acceleration data synthesizing one-dimensional and is converted into acceleration
Signal；Low-pass filtering treatment is carried out to acceleration signal by sliding mean filter method；
Resultant acceleration data are calculated by the following formula:
(1)
S12. user's sleep movement obtains: acquiring the three-dimensional acceleration number in the new sleep movement of user by acceleration sensor
According to ax1、ay1、az1Its resultant acceleration data a is calculated by formula (1)1And it is converted into acceleration signal；It is equal by sliding
Value filtering method carries out low-pass filtering treatment to acceleration signal, generates the test template of user's sleep movement.
S13. action recognition intercepts test sample sequence Q from test template, by DTW algorithm by test sample sequence Q
It is compared matches one by one with the reference template sequence C 1 in the sample database pre-established, C2 ... Cx, calculate separately test specimens
Consolidation path distance between this sequence Q and each reference template sequence C x, and determine most short regular path distance, to identify
The movement of body in user's sleep procedure.
In the present invention, DTW algorithmic notation dynamic time adjustment algorithm (i.e. dynamic time warping, DTW calculate
Method).DTW algorithm is a kind of algorithm that Nonlinear Time adjustment can be carried out to the movement of identification, can be by the template sequence of identification
The time shaft of column carries out time adjustment automatically, is allowed to minimum with the time shaft of reference template sequence distance, thus to calculate two
Similitude between sequence.DTW algorithm is able to solve the inconsistency of the movement of user in time, and calculates simple.
Specifically comprise the following steps:
A. test sample sequence Q is intercepted: being determined in user's sleep procedure by the variation of acceleration signal and angular velocity signal
Effective action starts；It initial time and the end time for obtaining effective action from test template by endpoint algorithm, is used
The test sample sequence Q of family body movement；
Wherein, the variation of acceleration signal and angular velocity signal includes, in user's sleep procedure when body attonity, acceleration letter
Number and angular velocity signal it is relatively steady, when movement starts, acceleration signal and violent two kinds of angular velocity signal variation are sentenced with this
Effective action in disconnected user's sleep procedure starts.
B. movement matching: carrying out time adjustment for the time shaft of test sample sequence Q by DTW algorithm, including, by the time
Axis is extended and is shortened, and keeps the time shaft of test sample sequence Q corresponding with the time shaft of current reference template sequence Cx.
C. it constructs a matrix grid: choosing current reference template sequence Cx, the length is m, the test specimens for being n by length
The time shaft Nonlinear Mapping of this sequence Q forms two-dimentional rectangular co-ordinate to the time shaft of current reference template sequence Cx；It will be two-dimentional
The rounded coordinate of the point marked on the point and the longitudinal axis marked on horizontal axis in rectangular coordinate system draws several co-ordinations and forms one
Matrix grid；It is as shown in Figure 3:
Wherein, the point marked on the horizontal axis in two-dimensional Cartesian coordinate system is 1 ~ n of each point of test sample sequence Q, longitudinal axis subscript
Out be current reference template sequence Cx 1 ~ m of each point, each of grid crosspoint indicate test sample sequence Q in certain
Certain point j's crosses in one point i and current reference template sequence Cx；Defining each crosspoint is lattice point, the coordinate of the lattice point
For (i, j).
D. route searching: searching route is set out from coordinate (1,1), and searching for all can reach the current lattice that coordinates are (i, j)
The coordinate of the previous lattice point of point；The coordinate of its previous lattice point has (i-1, j), (i-1, j-1) or (i, j-1), determines previous
The coordinate (i-1, j), (i-1, j-1) or (i, j-1) and current lattice point of lattice point are apart from min coordinates as its front and continued lattice point.
E. regular path distance calculates: calculating the coordinate (i- that can reach the previous lattice point for the current lattice point that coordinate is (i, j)
1, j), (i-1, j-1) or (i, j-1) corresponding regular distance, and be minimized, along with current lattice point coordinate (i, j) is corresponding
Test sample sequence Q and current reference template sequence between Cx Euclidean distance Dist(i, j), obtain the regular of current lattice point
Path distance D(i, j)；Its calculation formula is as follows:
D (i, j)=Dist(i, j)+min D(i-1, j), (i-1, j-1), (i, j-1) } (2)
Regular path distance calculates: when the coordinate (i, j) of current lattice point is test sample sequence Q and current reference template sequence Cx
Terminal (n, m) when, from (1,1), point sets out and (enables Dx (1,1)=0) search one by one in this way, until reaching home after (n, m), obtains
To the regular path distance Dx (n, m) between test sample sequence Q and current reference template sequence Cx；That is: Dx (n, m)=Dx
(i, j)；Wherein, i=n, j=m.
Test sample sequence Q is matched one by one with reference template sequence C 1, C2 ... Cx, determines minimum regular path
Distance Dmin (n, m).That is: Dmin (n, m)=min { D1 (n, m), D2 (n, m) ... ..., DX (n, m) }.
So that it is determined that its similarity, apart from smaller, similarity is bigger, the obtained regular path distance Dmin (n, m) of minimum
Corresponding reference template sequence is recognition result.
The body that the dynamic recognition methods of sleep body of the invention acquires user's sleep procedure body by acceleration transducer is dynamic
Data, and data progress calculation processing is moved to body by DTW algorithm and determines that the sleep body of user is dynamic, obtain more accurate movement
Identification.Embodiment 2
A kind of sleep quality appraisal procedure of the invention, as shown in Figure 4:
Include the following steps:
S21. user's body motion frequency obtains: in the user's sleep procedure identified by using the dynamic recognition methods of sleep body
The movement of body obtains maximum time interval and list between the number of body movement in user's sleep procedure, twice body movement
Secondary movable maximum duration；It is obtained by monitoring and turns over number, sleep total duration and sleep wakefulness in user's sleep procedure
Rule；
Body movement frequency in user's sleep procedure, the rule for turning over number, sleep total duration and sleep wakefulness are regarded as using
The objective standard of family sleep evaluation.
Subjective feeling of sleep list is provided；
The selection instruction of user is received, the current subjective feeling of user is obtained, so that it is determined that supervisor's standard.Wherein, subjective feeling of sleep
List includes the mental situation such as tired, alliteration, dizziness and situation of being short of physical strength.
S22. the analysis slept: the subjective criterion of objective standard and user's selection in user's sleep procedure is combined
User's sleep quality is analyzed；
S23. the evaluation slept: user's sleep result grade is evaluated based on the analysis results.
Sleep result grade is as follows:
| Grade | Score | Evaluation result |
| Level-one | 0-5 | Result of sleeping is fine |
| Second level | 6-10 | Sleep result can manage it |
| Three-level | 11-15 | Result of sleeping is general |
| Level Four | 16-21 | Result of sleeping is very poor |
Sleep quality appraisal procedure of the invention sleeps the dynamic body movement counted in user's sleep procedure of body frequently by user
Whether rate judges the activity condition of the body in user's sleep procedure: having horrible nightmares, if there is sleep-walking, if turns over excessively frequency
It is numerous, if body is chronically at motionless state.
The total degree for turning over the body movement in number counting user sleep procedure by the user monitored, passes through work
Dynamic frequency judges the time interval combination body movement frequency, sleep total duration and sleep wakefulness of body movement in sleep procedure
Rule obtains the objective standard of user's sleep procedure.
The subjective criterion of objective standard combination user can the sleep quality to user more accurately assessed, so as to
Family is better understood by the vivo biodistribution clock of oneself, and user is urged to form better sleep habit, understands and holds in sleep procedure
Turn or movable rhythm, pay close attention to health, improve the sleep result of itself.
Meanwhile it appropriately intermittently being turned over when sleeping or the not only adjustable skin temperature of activity, promotes blood circulation, is slow
It solves muscular fatigue, the distortion of correction backbone, adjust sleep cycle, while being also that healthy sleep is indispensable.
Embodiment 3
A kind of intelligent wearable device of the invention, as shown in Figure 5: the wearable device is bracelet or wrist-watch.
It is as shown in Figure 4: including acquisition module, filter module, processing module and identification module；Wherein, acquisition module is used for
The body acquired in user's sleep procedure moves data；Acquisition module includes acceleration sensor；Filter module is used for the data to acquisition
Carry out low-pass filtering treatment；Processing module is used to carry out analytical calculation to filtered data；Processing module includes MCU；Identification
Module completes the identification dynamic to the body in user's sleep procedure.
Obviously, the above embodiment of the present invention be only to clearly illustrate example of the present invention, and not be pair
The restriction of embodiments of the present invention.For those of ordinary skill in the art, may be used also on the basis of the above description
To make other variations or changes in different ways.There is no necessity and possibility to exhaust all the enbodiments.It is all this
Made any modifications, equivalent replacements, and improvements etc., should be included in the claims in the present invention within the spirit and principle of invention
Protection scope within.
Claims (10)
1. a kind of recognition methods that sleep body is dynamic, which comprises the steps of:
S11. master pattern is established: the three-dimensional acquired in a variety of body actions in user's sleep procedure by acceleration sensor adds
Speed data simultaneously carries out data processing to it, establishes the multiple standards model under normal speed, and using multiple standards model as
Reference template sequence is stored in sample database；
S12. user's sleep movement obtains: acquiring the three-dimensional acceleration number in the new sleep movement of user by acceleration sensor
Data processing is carried out according to and to it, generates the test template of user's sleep movement；
S13. action recognition: intercepting test sample sequence from test template, by DTW algorithm by test sample sequence and in advance
Reference template sequence in the sample database of foundation is compared matching one by one, calculates test sample sequence and each reference template sequence
Between consolidation path distance, and most short regular path distance is determined, to identify the movement of body in user's sleep procedure.
2. the dynamic recognition methods of sleep body according to claim 1, which is characterized in that the data processing includes: by three
It ties up the resultant acceleration data of acceleration information synthesizing one-dimensional and is converted into acceleration signal；By sliding mean filter method pair
Acceleration signal carries out low-pass filtering treatment.
3. the dynamic recognition methods of sleep body according to claim 1, which is characterized in that by test sample sequence in step S13
Column are compared matching with reference template sequence and include:
Test sample sequence truncation: the initial time of effective action is obtained from test template by endpoint algorithm at the end of
Between, obtain the movable test sample sequence of user's body；
Movement matching: carrying out time adjustment for the time shaft of test sample sequence by DTW algorithm, make test sample sequence when
Between axis it is corresponding with the time shaft of reference template sequence.
4. the dynamic recognition methods of sleep body according to claim 3, which is characterized in that consolidation path distance in step S13
Calculating include:
A. it constructs a matrix grid: choosing reference template sequence, by the time shaft Nonlinear Mapping of test sample sequence to working as
The time shaft of preceding reference template sequence forms two-dimentional rectangular co-ordinate；By the point marked on the horizontal axis in two-dimensional Cartesian coordinate system and
The rounded coordinate of the point marked on the longitudinal axis draws several co-ordinations and forms a matrix grid；
B. route searching: searching route is set out from coordinate (1,1), searches for the seat of all previous lattice points that can reach current lattice point
Mark；
C. regular path distance calculates: calculating the corresponding regular distance of coordinate for the previous lattice point that can reach current lattice point, and takes
Minimum value is obtained along with the Euclidean distance between the corresponding test sample sequence of current lattice point coordinate and reference template sequence
The regular path distance of current lattice point；When the terminal that the coordinate of current lattice point is test sample sequence and current reference template sequence
When, obtain the regular path distance between test sample sequence and current reference template sequence.
5. the dynamic recognition methods of sleep body according to claim 3, which is characterized in that test sample sequence truncation it
Before, determine that the effective action in user's sleep procedure starts by the variation of acceleration signal and angular velocity signal.
6. the dynamic recognition methods of sleep body according to claim 3, which is characterized in that the time shaft of test sample sequence into
The adjustment of row time includes that time shaft is extended and shortened.
7. a kind of sleep quality appraisal procedure for moving recognition methods based on any one of claim 1-6 sleep body, which is characterized in that
Include the following steps:
S21. user's body motion frequency obtains: obtaining user's sleep by the movement of body in user's sleep procedure for identifying
In the process the number of body movement, twice between body movement maximum time interval and single activation maximum duration；
S22. the analysis slept: by the subjective criterion of this objective standard of user's body motion frequency and user be combined to
Family sleep quality is analyzed；
S23. the evaluation slept: user's sleep result grade is evaluated based on the analysis results.
8. the dynamic recognition methods of sleep body according to claim 7, which is characterized in that analyze user's sleep quality
The objective standard of evaluation further includes the rule for turning over number, the total duration and sleep wakefulness of sleeping in user's Sleep-Monitoring.
9. the dynamic recognition methods of sleep body according to claim 7, which is characterized in that the subjective mark of user in step S22
Quasi- determination includes the following steps:
Subjective feeling of sleep list is provided；
The selection instruction of user is received, the current subjective feeling of user is obtained, so that it is determined that supervisor's standard；
The subjectivity feeling of sleep list includes tired, alliteration, dizziness.
It is slept described in recognition methods or any one of claim 7 ~ 9 10. one kind is moved based on any one of the claim 1 ~ 6 sleep body
The intelligent wearable device of dormancy method for evaluating quality, which is characterized in that the wearable device is bracelet or wrist-watch.
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| CN112773997A (en) * | 2019-11-11 | 2021-05-11 | 深圳射弧科技有限公司 | Intelligent sleep awakening method and intelligent wearable device |
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| CN113952587A (en) * | 2021-11-16 | 2022-01-21 | 湖北工业大学 | System for controlling playing device based on sleep state |
| CN114451900A (en) * | 2022-04-11 | 2022-05-10 | 深圳市心流科技有限公司 | Sleep quality determination method and system based on electroencephalogram and storage medium |
| CN115137308A (en) * | 2022-06-29 | 2022-10-04 | 东莞市猎声电子科技有限公司 | Method for improving accuracy of in-out sleep detection in sleep algorithm of intelligent wearable device |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104200234A (en) * | 2014-07-11 | 2014-12-10 | 杭州微纳科技有限公司 | Human body action modeling and recognizing method |
| CN106388771A (en) * | 2016-08-16 | 2017-02-15 | 惠州市德赛工业研究院有限公司 | Method for automatically detecting human physiological states and movement bracelet |
| CN106682594A (en) * | 2016-12-13 | 2017-05-17 | 中国科学院软件研究所 | Posture and motion identification method based on dynamic grid coding |
| US20170258407A1 (en) * | 2016-03-14 | 2017-09-14 | Seiko Epson Corporation | Biological information processing system, server, biological information estimation method, and wearable device |
| CN107180223A (en) * | 2017-04-10 | 2017-09-19 | 南京苗米科技有限公司 | Action identification method and system based on WIFI wireless signals |
-
2017
- 2017-11-29 CN CN201711228753.8A patent/CN109091150A/en not_active Withdrawn
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104200234A (en) * | 2014-07-11 | 2014-12-10 | 杭州微纳科技有限公司 | Human body action modeling and recognizing method |
| US20170258407A1 (en) * | 2016-03-14 | 2017-09-14 | Seiko Epson Corporation | Biological information processing system, server, biological information estimation method, and wearable device |
| CN106388771A (en) * | 2016-08-16 | 2017-02-15 | 惠州市德赛工业研究院有限公司 | Method for automatically detecting human physiological states and movement bracelet |
| CN106682594A (en) * | 2016-12-13 | 2017-05-17 | 中国科学院软件研究所 | Posture and motion identification method based on dynamic grid coding |
| CN107180223A (en) * | 2017-04-10 | 2017-09-19 | 南京苗米科技有限公司 | Action identification method and system based on WIFI wireless signals |
Cited By (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110477887A (en) * | 2019-09-18 | 2019-11-22 | 浙江理工大学 | A kind of monitoring device of non-invasive long-range apnea syndrome |
| CN112773997A (en) * | 2019-11-11 | 2021-05-11 | 深圳射弧科技有限公司 | Intelligent sleep awakening method and intelligent wearable device |
| CN112773997B (en) * | 2019-11-11 | 2023-03-10 | 深圳射弧科技有限公司 | Intelligent sleep awakening method and intelligent wearable device |
| CN111166286A (en) * | 2020-01-03 | 2020-05-19 | 珠海格力电器股份有限公司 | Dreamy travel detection method, storage medium and dreamy travel detection device |
| CN111588630A (en) * | 2020-05-27 | 2020-08-28 | 深圳市安瑞国医科技有限公司 | Percutaneous nerve regulation and control strategy generation method and device |
| CN111588630B (en) * | 2020-05-27 | 2022-07-12 | 深圳市安瑞国医科技有限公司 | Percutaneous nerve regulation and control strategy generation method and device |
| CN112527118B (en) * | 2020-12-16 | 2022-11-25 | 郑州轻工业大学 | Head posture recognition method based on dynamic time warping |
| CN112527118A (en) * | 2020-12-16 | 2021-03-19 | 郑州轻工业大学 | Head posture recognition method based on dynamic time warping |
| CN112806962A (en) * | 2021-01-18 | 2021-05-18 | 珠海格力电器股份有限公司 | Child sleep state monitoring method and device based on TOF and infrared module |
| CN113952587A (en) * | 2021-11-16 | 2022-01-21 | 湖北工业大学 | System for controlling playing device based on sleep state |
| CN113952587B (en) * | 2021-11-16 | 2024-01-19 | 杨金刚 | System for controlling playing device based on sleep state |
| CN114451900B (en) * | 2022-04-11 | 2022-09-27 | 深圳市心流科技有限公司 | Sleep quality determination method and system based on electroencephalogram and storage medium |
| CN114451900A (en) * | 2022-04-11 | 2022-05-10 | 深圳市心流科技有限公司 | Sleep quality determination method and system based on electroencephalogram and storage medium |
| CN115137308A (en) * | 2022-06-29 | 2022-10-04 | 东莞市猎声电子科技有限公司 | Method for improving accuracy of in-out sleep detection in sleep algorithm of intelligent wearable device |
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