CN106388779B - The mask method and system of sleep state sample data type - Google Patents
The mask method and system of sleep state sample data type Download PDFInfo
- Publication number
- CN106388779B CN106388779B CN201610843519.5A CN201610843519A CN106388779B CN 106388779 B CN106388779 B CN 106388779B CN 201610843519 A CN201610843519 A CN 201610843519A CN 106388779 B CN106388779 B CN 106388779B
- Authority
- CN
- China
- Prior art keywords
- sample data
- type
- atom
- sleep state
- sleep
- 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.)
- Active
Links
Classifications
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
- A61B5/316—Modalities, i.e. specific diagnostic methods
- A61B5/369—Electroencephalography [EEG]
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
- A61B5/316—Modalities, i.e. specific diagnostic methods
-
- 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
-
- 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
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7235—Details of waveform analysis
- A61B5/7264—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
Landscapes
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Biophysics (AREA)
- General Health & Medical Sciences (AREA)
- Veterinary Medicine (AREA)
- Public Health (AREA)
- Animal Behavior & Ethology (AREA)
- Surgery (AREA)
- Molecular Biology (AREA)
- Medical Informatics (AREA)
- Heart & Thoracic Surgery (AREA)
- Pathology (AREA)
- Biomedical Technology (AREA)
- Artificial Intelligence (AREA)
- Psychiatry (AREA)
- Evolutionary Computation (AREA)
- Signal Processing (AREA)
- Physiology (AREA)
- Mathematical Physics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Fuzzy Systems (AREA)
- Anesthesiology (AREA)
- Psychology (AREA)
- Measurement Of The Respiration, Hearing Ability, Form, And Blood Characteristics Of Living Organisms (AREA)
Abstract
The present invention relates to the mask methods and system of a kind of sleep state sample data type, the described method includes: sample data of the acquisition user in sleep state analysis, cluster center made of the feature vector and feature vector aggregation of the sample data of a variety of sleep state types is constructed, objective function is established according to feature vector and its cluster center；Wherein, the sample data of objective function characterization minimum same type is at a distance from dictionary atom, and the distance between different types of atom of maximization；It selects several feature vectors as the initial value of atom from the sample data of a variety of sleep state types respectively, each sample data is distributed to the atom and solves the objective function, obtains classifying dictionary；Sample data is inputted into classifying dictionary, compares the type and distance of the atom nearest with sample data, if distance is less than preset threshold value, the type of the sample data is labeled as consistent with the type of the atom.The present invention can accurately mark the type of sample data.
Description
Technical field
The present invention relates to assisting sleep technical fields, more particularly to a kind of mark side of sleep state sample data type
Method and system.
Background technique
In sleep, human body has carried out the process self loosened and restored, therefore good sleep is to maintain health
A primary condition；But due to operating pressure is big, daily life system is irregular etc., result in the sleep matter of part population
It measures not good enough, shows as that insomnia, midnight wakes up with a start.
There are some equipment that people is helped to fall asleep on the market at present, has improved sleep quality.Such as it specific is slept a certain
By manual interventions such as sound, optical signals under dormancy state, avoid waking user etc. under the state of sleeping soundly.For setting for assisting sleep
For standby, in order to be really achieved the purpose for improving user's sleep quality, it is extremely important for correctly detecting the sleep state of user
's.
At present clinically mainly using polysomnogram identify sleep state, mainly using EEG signals come to sleep into
Row analysis, identifies the sleep state of measured by training sleep state model, such as judges which of sleep user be in
Stage, but since the specificity of EEG signals is stronger, and intensity is very weak easy by external interference.Point of prior art training
There are errors for detection of the class device to many users, it is difficult to be labeled to the type of sleep state sample data.
Summary of the invention
Based on this, it is necessary in view of the above-mentioned problems, providing a kind of mask method of sleep state sample data type and being
System effectively improves the accuracy of sleep state classification device identification.
A kind of mask method of sleep state sample data type, comprising:
The EEG signals that acquisition user generates in sleep state analysis, obtain sample data；
Cluster center made of the feature vector and feature vector aggregation of the sample data of a variety of sleep state types is constructed,
Objective function is established according to described eigenvector and its cluster center；Wherein, the objective function characterization minimizes same type
Sample data is at a distance from dictionary atom, and the distance between different types of atom of maximization；
Select several feature vectors as the initial value of atom from the sample data of a variety of sleep state types respectively,
Each sample data is distributed to the atom and solves the objective function, obtains classifying dictionary；
Sample data is inputted into classifying dictionary, compares the type and distance of the atom nearest with sample data, if apart from small
In preset threshold value, then the type of the sample data is labeled as consistent with the type of the atom.
A kind of labeling system of sleep state sample data type, comprising:
Sample collection module, the EEG signals generated in sleep state analysis for acquiring user, obtains sample data；
Dictionary constructs module, the feature vector and feature vector of the sample data for constructing a variety of sleep state types
Objective function is established according to described eigenvector and its cluster center in cluster center made of aggregation；Wherein, the objective function characterization
The distance between the sample data of same type is minimized at a distance from dictionary atom, and maximizes different types of atom；
Dictionary training module, for selecting several feature vectors from the sample data of a variety of sleep state types respectively
As the initial value of atom, each sample data is distributed to the atom and solves the objective function, obtains classifying dictionary；
Sample labeling module compares the class of the atom nearest with sample data for sample data to be inputted classifying dictionary
The type of the sample data is labeled as consistent with the type of the atom by type and distance if distance is less than preset threshold value.
The mask method and system of above-mentioned sleep state sample data type, based on the clustering algorithm with classification capacity come
Training dictionary, atom respectively corresponds a kind of sleep state in dictionary, using the number of atom as the parameter of algorithm, by most when training
The sample of smallization same type is at a distance from dictionary atom, while the mode for the distance between maximizing different types of atom,
To train corresponding atom for every kind of sleep state, then utilizes and correspond to different types of atom and distance to sample
Type is judged, so as to accurately mark the type of sample data, so that being subsequently used for oneself of sleep state classification device
More accurate sleep state classification device can be trained in learning process, promote the accuracy of sleep state detection.
Detailed description of the invention
Fig. 1 is the flow chart of the mask method of the sleep state sample data type of one embodiment；
Fig. 2 is the labeling system structural schematic diagram of the sleep state sample data type of one embodiment.
Specific embodiment
The mask method of sleep state sample data type of the invention and the embodiment of system are illustrated with reference to the accompanying drawing.
Refering to what is shown in Fig. 1, Fig. 1 is the flow chart of the mask method of the sleep state sample data type of one embodiment,
Include:
Step S101, the EEG signals that acquisition user generates in sleep state analysis, obtains sample data；
In this step, when carrying out assisting sleep analysis to user, related transducer equipment is worn by user, detects user
EEG signals, when acquiring EEG signals, can with 30s be a frame be acquired.
The carrying out sleep state identification as needed of the task, determines feature data types, extracts therewith from EEG signals
Corresponding sample data；For example, to identify 1~N kind sleep state, the sample data for carrying out this N kind state recognition is extracted.
Step S102, the feature vector and feature vector for constructing the sample data of a variety of sleep state types are assembled
Cluster center, objective function is established according to described eigenvector and its cluster center；Wherein, the objective function characterization minimizes phase
The sample data of same type is at a distance from dictionary atom, and the distance between different types of atom of maximization；
In this step, on the basis of KMeans (K mean value) and KNN (K is closest) algorithm, designing has classification capacity
Clustering algorithm carrys out training dictionary, and atom respectively corresponds a kind of sleep state (such as waking state, sleep state etc.), atom in dictionary
Number be algorithm parameter.
When input sample is sufficiently small at a distance from atom (when similarity is sufficiently large), it may be considered that the type of sample and original
The type of son is consistent；Establish objective function, training when by minimize same type sample and dictionary atom away from
From, while the mode for the distance between maximizing different types of atom, all to be trained accordingly for every kind of particular state
Atom.
If it is more classification problems, the sample equipped with a shared t seed type,Be characterized to
Amount,For cluster center made of feature vector aggregation, the common version of objective function can be expressed as
Following form:
In formula, the sample data equipped with t kind sleep state type,For feature vector,For cluster center made of feature vector aggregation.
By taking waking state and dormant two classes problem as an example, ifFor waking state type (wake)
Feature vector,Cluster center made of feature vector aggregation for waking state type,For
The feature vector of sleep state type (sleep),Cluster made of feature vector aggregation for sleep state type
Center, objective function can be expressed as form:
The objective function are as follows:
In formula,For regain consciousness type feature vector,For the spy for type of regaining consciousness
Cluster center made of vector aggregation is levied,For the feature vector of sleep pattern,To sleep
Cluster center made of the feature vector aggregation of dormancy type, wake indicate awake type, and sleep indicates sleep pattern.
Step S103 selects several feature vectors as atom from the sample data of a variety of sleep state types respectively
Initial value, each sample data is distributed to the atom and solves the objective function, obtains classifying dictionary；
In this step, it is based on the objective function, training classifying dictionary, in training, this programme is in classical KMeans
It is improved on the basis of algorithm, by taking waking state and dormant two classification problem as an example, training process be can be such that
(1) when initializing, if being set at random from the sample data of awake type and the sample data of sleep pattern respectively
Dry feature vector is as atom；Each sample data is distributed to away from nearest atom；
(2) atom is updated, if it (is waking state that all sample datas for belonging to this atom are consistent with the type of atom
Type is sleep state type), then the mean value for belonging to all sample datas of the atom is calculated, and in this, as new
Atom；
The sample data inconsistent with atomic type if it exists then calculates separately the sample data and sleep class of awake type
The mean value of the sample data of type, calculating process may include following formula:
In formula, c'wakeFor the mean value of the sample data for type of regaining consciousness, c'sleepFor the mean value of the sample data of sleep pattern；
According to the quantity of the sample data (negative sample) inconsistent with atomic type and its position of position correction atom,
By the location updating of atom in the farther position apart from negative sample data, calculating process may include following formula:
In formula, c is the position of atom after amendment, and g is discriminant function, and w is weighted value；
Further, the calculation formula of the weighted value w can be such that
In formula, wwakeFor the weighted value for type of regaining consciousness, wsleepFor the weighted value of sleep pattern.
As another embodiment, the calculation formula of the weighted value w also be can be such that
(3) if belong to this atom all sample datas and atom type it is inconsistent, change the class of the atom
Type, and the mean value for belonging to all sample datas of the atom is calculated, and using the mean value as new atom；
(4) it repeats step (2) and (3) is iterated, the difference of the atom before and after iteration is less than setting range (foot
It is enough small), or be assigned without sample data and store current classifying dictionary to the new atomic time and exit training.
Sample data is inputted classifying dictionary by step S104, compares the type and distance of the atom nearest with sample data,
If distance is less than preset threshold value, the type of the sample data is labeled as consistent with the type of the atom.
In this step, classifying dictionary is tested using sample data, by comparing the atom nearest with sample data
Type and distance carry out the types of judgement sample data, if distance is less than threshold value, then it is assumed that the type of sample data and the atom
Type it is consistent, export the judgement of "true", the type of the sample data is labeled as it is consistent with the type of the atom, it is on the contrary then refuse
Judgement absolutely.
The type that sample data is marked using above scheme.It can be used for training classifier, for example, svm is selected (to support
Vector machine) classifier, neural metwork training disaggregated model.
Refering to what is shown in Fig. 2, Fig. 2 is the labeling system structural representation of the sleep state sample data type of one embodiment
Figure, comprising:
Sample collection module 101, the EEG signals generated in sleep state analysis for acquiring user, obtains sample number
According to；
Dictionary constructs module 102, the feature vector and feature of the sample data for constructing a variety of sleep state types
Cluster center made of vector aggregation, establishes objective function according to described eigenvector and its cluster center；Wherein, the objective function
Characterization minimizes the sample data of same type with dictionary atom at a distance from, and between the different types of atom of maximization away from
From；
Dictionary training module 103, for selecting several features from the sample data of a variety of sleep state types respectively
Each sample data is distributed to the atom and solves the objective function, classified by initial value of the vector as atom
Dictionary；
Sample labeling module 104 compares the atom nearest with sample data for sample data to be inputted classifying dictionary
The type of the sample data is labeled as consistent with the type of the atom by type and distance if distance is less than preset threshold value.
The labeling system of sleep state sample data type of the invention and sleep state sample data type of the invention
Mask method correspond, above-mentioned sleep state sample data type mask method embodiment illustrate technical characteristic
And its advantages are suitable for the embodiment of the labeling system of sleep state sample data type, hereby give notice that.
Each technical characteristic of embodiment described above can be combined arbitrarily, for simplicity of description, not to above-mentioned reality
It applies all possible combination of each technical characteristic in example to be all described, as long as however, the combination of these technical characteristics is not deposited
In contradiction, all should be considered as described in this specification.
The embodiments described above only express several embodiments of the present invention, and the description thereof is more specific and detailed, but simultaneously
It cannot therefore be construed as limiting the scope of the patent.It should be pointed out that coming for those of ordinary skill in the art
It says, without departing from the inventive concept of the premise, various modifications and improvements can be made, these belong to protection of the invention
Range.Therefore, the scope of protection of the patent of the invention shall be subject to the appended claims.
Claims (10)
1. a kind of mask method of sleep state sample data type characterized by comprising
The EEG signals that acquisition user generates in sleep state analysis, obtain sample data；
Cluster center made of the feature vector and feature vector aggregation of the sample data of a variety of sleep state types is constructed, according to
Objective function is established at described eigenvector and its cluster center；Wherein, the objective function is used to minimize the sample of same type
Data are at a distance from dictionary atom and the distance between maximize different types of atom；
Select several feature vectors as the initial value of atom from the sample data of a variety of sleep state types respectively, it will be each
A sample data distributes to the atom and solves the objective function, obtains classifying dictionary；
Sample data is inputted into classifying dictionary, compares the type and distance of the atom nearest with sample data, if distance is less than in advance
If threshold value, then the type of the sample data is labeled as consistent with the type of the atom.
2. the mask method of sleep state sample data type according to claim 1, which is characterized in that the target letter
Number are as follows:
In formula, the sample data equipped with t kind sleep state type,For feature vector,For cluster made of feature vector aggregation
Center, wherein i=1 ..., np, j=1 ... kp, p=1 ..., t.
3. the mask method of sleep state sample data type according to claim 2, which is characterized in that the sleep shape
State type includes awake type and sleep pattern；
The objective function are as follows:
In formula,For the feature vector for type of regaining consciousness, i=1 ..., nwake,For regain consciousness type feature vector aggregation and
At cluster center, j=1 ..., kwake,For the feature vector of sleep pattern, i=1 ..., nsleep,For sleep pattern
Feature vector aggregation made of cluster center, j=1 ..., ksleep, the awake type of wake expression, sleep expression sleep pattern.
4. the mask method of sleep state sample data type according to claim 2, which is characterized in that it is described respectively from
Select several feature vectors as the initial value of atom in the sample data of a variety of sleep state types, by each sample data
It distributes to the atom and the step of solving the objective function, obtain classifying dictionary and includes:
(1) several feature vectors work is set at random from the sample data of awake type and the sample data of sleep pattern respectively
For atom；Each sample data is distributed to away from nearest atom；
(2) if all sample datas for belonging to this atom are consistent with the type of atom, all samples for belonging to the atom are calculated
The mean value of notebook data, and in this, as new atom；
The sample data inconsistent with atomic type if it exists then calculates separately the sample data and sleep pattern of awake type
The mean value of sample data；And according to the quantity of the sample data inconsistent with atomic type and its position of position correction atom,
By the location updating of atom in the farther position apart from negative sample data；
(3) if belong to this atom all sample datas and atom type it is inconsistent, change the type of the atom, and
The mean value for belonging to all sample datas of the atom is calculated, and using the mean value as new atom；
(4) it repeats step (2) and (3) is iterated, the difference of the atom before and after iteration is less than setting range, or does not have
There is sample data to be assigned to store current classifying dictionary to the new atomic time and exit training.
5. the mask method of sleep state sample data type according to claim 4, which is characterized in that the step
(2) falling into a trap, to settle the sample data of awake type and the mean value process of sample data of sleep pattern include following formula:
In formula, c'wakeFor the mean value of the sample data for type of regaining consciousness, c'sleepFor the mean value of the sample data of sleep pattern.
6. the mask method of sleep state sample data type according to claim 4, which is characterized in that the step
(2) position of amendment atom includes following formula in:
In formula, c is the position of atom after amendment, and g is discriminant function, and w is weighted value.
7. the mask method of sleep state sample data type according to claim 6, which is characterized in that the weighted value
The calculation formula of w is as follows:
In formula, wwakeFor the weighted value for type of regaining consciousness, wsleepFor the weighted value of sleep pattern.
8. the mask method of sleep state sample data type according to claim 6, which is characterized in that the weighted value
The calculation formula of w is as follows:
9. the mask method of sleep state sample data type according to claim 3, which is characterized in that described by the sample
The type of notebook data is labeled as the step consistent with the type of the atom
According to the atomic type of classifying dictionary, the sample data of awake type and the sample data of sleep pattern are identified, then
Sample data is marked into corresponding data type.
10. a kind of labeling system of sleep state sample data type characterized by comprising
Sample collection module, the EEG signals generated in sleep state analysis for acquiring user, obtains sample data；
Dictionary constructs module, for constructing the feature vector and feature vector aggregation of the sample data of a variety of sleep state types
Made of cluster center, objective function is established according to described eigenvector and its cluster center；Wherein, the objective function is for minimum
The distance between change the sample data of same type at a distance from dictionary atom and maximize different types of atom；
Dictionary training module, for selected from the sample data of a variety of sleep state types respectively several feature vectors as
Each sample data is distributed to the atom and solves the objective function, obtains classifying dictionary by the initial value of atom；
Sample labeling module, for sample data to be inputted classifying dictionary, compare the type of the atom nearest with sample data with
The type of the sample data is labeled as consistent with the type of the atom by distance if distance is less than preset threshold value.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610843519.5A CN106388779B (en) | 2016-09-21 | 2016-09-21 | The mask method and system of sleep state sample data type |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610843519.5A CN106388779B (en) | 2016-09-21 | 2016-09-21 | The mask method and system of sleep state sample data type |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN106388779A CN106388779A (en) | 2017-02-15 |
| CN106388779B true CN106388779B (en) | 2019-06-11 |
Family
ID=57997362
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201610843519.5A Active CN106388779B (en) | 2016-09-21 | 2016-09-21 | The mask method and system of sleep state sample data type |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN106388779B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107582028B (en) * | 2017-09-25 | 2021-04-13 | 北京小米移动软件有限公司 | Sleep monitoring method and device |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102133100A (en) * | 2011-03-04 | 2011-07-27 | 上海交通大学 | Sparse representation-based electroencephalogram signal detection method |
| CN104780831A (en) * | 2012-11-02 | 2015-07-15 | 皇家飞利浦有限公司 | Electronic switch for controlling a device in dependency on a sleep stage |
| CN104970773A (en) * | 2015-07-21 | 2015-10-14 | 西安交通大学 | Automatic sleep stage classification method based on dual character filtering |
| CN105496363A (en) * | 2015-12-15 | 2016-04-20 | 浙江神灯生物科技有限公司 | Method for classifying sleep stages on basis of sleep EGG (electroencephalogram) signal detection |
| WO2016092433A1 (en) * | 2014-12-11 | 2016-06-16 | Koninklijke Philips N.V. | System and method for determining spectral boundaries for sleep stage classification |
-
2016
- 2016-09-21 CN CN201610843519.5A patent/CN106388779B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102133100A (en) * | 2011-03-04 | 2011-07-27 | 上海交通大学 | Sparse representation-based electroencephalogram signal detection method |
| CN104780831A (en) * | 2012-11-02 | 2015-07-15 | 皇家飞利浦有限公司 | Electronic switch for controlling a device in dependency on a sleep stage |
| WO2016092433A1 (en) * | 2014-12-11 | 2016-06-16 | Koninklijke Philips N.V. | System and method for determining spectral boundaries for sleep stage classification |
| CN104970773A (en) * | 2015-07-21 | 2015-10-14 | 西安交通大学 | Automatic sleep stage classification method based on dual character filtering |
| CN105496363A (en) * | 2015-12-15 | 2016-04-20 | 浙江神灯生物科技有限公司 | Method for classifying sleep stages on basis of sleep EGG (electroencephalogram) signal detection |
Non-Patent Citations (2)
| Title |
|---|
| LEARNING SPARSE DICTIONARY FOR LONG-TERM BIOMEDICAL SIGNAL CLASSIFICATION AND CLUSTERING;Shengkun Xie;《The 11th International Conference on Information Sciences, Signal Processing and their Applications: Special Sessions》;20121231;1118-1123 |
| 基于协作表示与核熵成分分析的脑电睡眠状态分期研究;赵攀博;《生物医学工程学杂志》;20150831;第32卷(第4期);730-734 |
Also Published As
| Publication number | Publication date |
|---|---|
| CN106388779A (en) | 2017-02-15 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Garofalo et al. | Evaluation of the performance of information theory-based methods and cross-correlation to estimate the functional connectivity in cortical networks | |
| CN111161879B (en) | Disease prediction system based on big data | |
| CN110148405B (en) | Voice instruction processing method and device, electronic equipment and storage medium | |
| CN111553127A (en) | Multi-label text data feature selection method and device | |
| CN107092861B (en) | Lower limb action recognition method based on pressure and acceleration sensor | |
| CN106473703B (en) | The training method and system of sleep state classification device | |
| de Arruda et al. | A complex networks approach for data clustering | |
| CN109815987B (en) | Crowd classification method and system | |
| Zaki et al. | Using automated walking gait analysis for the identification of pedestrian attributes | |
| CN112597921B (en) | Human behavior recognition method based on attention mechanism GRU deep learning | |
| Galicki et al. | Common optimization of adaptive preprocessing units and a neural network during the learning period. Application in EEG pattern recognition | |
| Korats et al. | Applying ICA in EEG: Choice of the Window Length and of the Decorrelation Method | |
| CN106388779B (en) | The mask method and system of sleep state sample data type | |
| CN109086794A (en) | A kind of driving behavior mode knowledge method based on T-LDA topic model | |
| CN113627236A (en) | Sitting posture identification method, device, equipment and storage medium | |
| CN111743513B (en) | S1 and REM sleep state detection method and system based on mixed channel EEG signal | |
| El Moudden et al. | Mining human activity using dimensionality reduction and pattern recognition | |
| Thiemjarus et al. | A spatio-temporal architecture for context aware sensing | |
| CN112861679B (en) | Transfer learning method and system for behavior recognition | |
| CN114417969A (en) | Cross-scene cognitive ability assessment method and system based on fine-grained migration | |
| CN109426827B (en) | Multi-mode sensor collaborative sensing method and system | |
| Falcão et al. | Flexible kernels for RBF networks | |
| Karanjgaokar et al. | Comparison of classification methodologies for predicting the stages of diabetic retinopathy | |
| CN106485286A (en) | A kind of Matrix Classification model differentiated based on local sensitivity | |
| Tian et al. | A novel selective ensemble learning method for smartphone sensor-based human activity recognition based on hybrid diversity enhancement and improved binary glowworm swarm optimization |
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 |