CN112926555A - Small sample passive behavior sensing method based on self-encoder data enhancement - Google Patents

Small sample passive behavior sensing method based on self-encoder data enhancement Download PDF

Info

Publication number
CN112926555A
CN112926555A CN202110465721.XA CN202110465721A CN112926555A CN 112926555 A CN112926555 A CN 112926555A CN 202110465721 A CN202110465721 A CN 202110465721A CN 112926555 A CN112926555 A CN 112926555A
Authority
CN
China
Prior art keywords
encoder
data
self
layers
sample
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN202110465721.XA
Other languages
Chinese (zh)
Other versions
CN112926555B (en
Inventor
盛碧云
关翔宇
肖甫
李群
沙乐天
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Nanjing University of Posts and Telecommunications
Original Assignee
Nanjing University of Posts and Telecommunications
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Nanjing University of Posts and Telecommunications filed Critical Nanjing University of Posts and Telecommunications
Priority to CN202110465721.XA priority Critical patent/CN112926555B/en
Publication of CN112926555A publication Critical patent/CN112926555A/en
Application granted granted Critical
Publication of CN112926555B publication Critical patent/CN112926555B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
    • G06F2218/02—Preprocessing
    • G06F2218/04—Denoising
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/045—Combinations of networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/047—Probabilistic or stochastic networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
    • G06F2218/08—Feature extraction

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Artificial Intelligence (AREA)
  • General Engineering & Computer Science (AREA)
  • General Health & Medical Sciences (AREA)
  • Biomedical Technology (AREA)
  • Data Mining & Analysis (AREA)
  • Evolutionary Computation (AREA)
  • Biophysics (AREA)
  • Molecular Biology (AREA)
  • Computing Systems (AREA)
  • Computational Linguistics (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Mathematical Physics (AREA)
  • Software Systems (AREA)
  • Health & Medical Sciences (AREA)
  • Probability & Statistics with Applications (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Signal Processing (AREA)
  • Image Analysis (AREA)

Abstract

The invention relates to a small sample passive behavior perception method based on self-encoder data enhancement, which comprises the following steps: (1) collecting channel state information containing human behavior information; (2) denoising the channel state information; (3) constructing a neural network model based on a data enhancement self-encoder; (4) a small amount of real sample training data is adopted to enhance an autoencoder, and a large amount of reconstructed samples with different feature vectors are generated; (5) and constructing a human behavior recognition model based on a convolutional neural network, taking the enhanced data sample and the real sample as the input of the model, optimizing the network model, and obtaining a behavior recognition result according to the response values of the classification network to the samples in different classes. The method solves the problem of model overfitting of passive behavior perception in a small sample training data state, enhances the generalization and stability of the model, and ensures the accuracy of human behavior recognition.

Description

Small sample passive behavior sensing method based on self-encoder data enhancement
Technical Field
The invention relates to the technical field of passive behavior perception, in particular to a small sample passive behavior perception method based on self-encoder data enhancement.
Background
The human behavior is an intuitive and natural interaction means between people and machines, and the human behavior recognition plays an important role in practical applications such as intelligent home, medical care, fitness tracking and the like. The passive sensing technology can analyze the influence of human body behaviors on channel state information by utilizing WiFi signals widely existing in the environment, thereby realizing the sensing task and having good universality and expansibility. Compared with the traditional human behavior sensing technology, the WiFi sensing technology has the advantages of non-line-of-sight, passive sensing, no need of carrying a sensor, low cost, easiness in deployment, no limitation of illumination conditions, strong expansibility and the like.
ZL2020109594551 discloses a passive positioning method based on amplitude and phase information of CSI, which comprises the steps of setting test points, enabling a person to stand on each test point to collect fingerprint data, establishing a database, extracting and preprocessing two characteristics of amplitude and phase of data in parallel, fusing the two characteristics into a characteristic matrix, performing model training by using a convolutional neural network, and then forming a positioning model.
ZL2020106663102 discloses a CNN-based Wi-Move behavior sensing method, which fully utilizes information of subcarriers in all receiving antennas to convert CSI information into a two-dimensional image structure, and adopts an image processing technology based on a convolutional neural network to perform feature extraction on the CSI information, but original phase information extracted from the CSI cannot be directly used, errors need to be eliminated through some transformations, such as differences between phases of all subcarriers of adjacent antennas, and as the method extracts characteristic information of amplitudes and phases in all subcarriers of the CSI, network training speed is slow, noise is easily generated due to influences of environmental changes, electromagnetic interference and the like, and denoising effect is poor.
Disclosure of Invention
In order to solve the technical problems, the invention provides a small sample passive behavior sensing method based on self-encoder data enhancement, designs a depth self-encoder based on a convolutional neural network, and aims to solve the problems of overfitting caused by insufficient data samples and single sample in human behavior identification in the prior art.
In order to achieve the purpose, the invention is realized by the following technical scheme:
the invention relates to a small sample passive behavior perception method based on self-encoder data enhancement, which comprises the following steps:
(1) data collection: transmitting a data packet to a WiFi signal receiving end through an indoor WiFi signal transmitting end, and collecting channel state information containing human behavior information;
(2) data processing: the directly collected signal information not only contains human body activity information, but also contains a part of environment, experimental subject information and other noises and interferences, so that a series of processing needs to be carried out on the signals to eliminate the noise interferences; because the internal state of the equipment changes, the signal can have abnormal mutation, and therefore outliers generated by mutation need to be processed, the invention firstly adopts Hampel filtering to remove the outliers and replaces the outliers with the median of the data window;
in addition, the frequency of human body action is concentrated in a low-frequency interval, so that when a signal is processed, a filter is applied to filter a high-frequency component and a direct-current component, the Butterworth filter is adopted to carry out noise removal processing on data, most of noise is removed, and behavior information in a Channel State Information (CSI) data stream is reserved;
further, in order to better adapt to the input of the convolutional neural network, the data needs to be converted into a uniform size, and the data is uniformly converted into a 512 x 90 data matrix by adopting a linear interpolation method.
(3) Constructing a neural network model based on a data enhancement self-encoder;
according to the characteristics, firstly, a deep convolutional neural network is constructed, the deep convolutional neural network comprises a coding part and a decoding part, the number of channels of convolutional layers of the coding part is reasonably designed, and the purposes of compressing data and coding characteristics are achieved. The data enhancement self-encoder adopts a symmetrical convolutional neural network structure, namely the network structure of a decoder part is completely symmetrical to that of an encoder part and comprises the encoder and the decoder part, the encoder encodes high-dimensional input into low-dimensional hidden vectors, and the decoder restores hidden variables into initial dimensions. The encoder part comprises 5 convolution layers and 3 maximum pooling layers, regularization processing is carried out after each convolution layer, and parameters are reduced while characteristics of the pooling layers are reserved; the decoder part comprises 5 deconvolution layers and 3 upsampling layers, wherein the deconvolution layers correspond to the number of channels of the convolution layers and the upsampling layers correspond to the number of channels of the pooling layer.
Let the kth intermediate layer of the self-encoder be denoted hkThen the relationship between the encoder section network layers can be expressed as:
hk＝σ(a*Wk+bk)
wherein a is a previous layer feature, WkAnd bkRespectively representing the weight and deviation of the kth convolution kernel, wherein sigma is a ReLU activation function, the structures of a decoder part and an encoder part are completely symmetrical, the decoding part reconstructs the characteristics into original input data, and the relation between network layers is as follows:
Figure BDA0003043845060000031
wherein h iskIs a feature of the previous layer, WkRepresents the weight of the kth convolution kernel, H represents the number of channels of the network, c represents the variance, and σ is the ReLU activation function.
(4) A small amount of real sample training data is adopted to enhance an autoencoder, and a large amount of reconstructed samples with different feature vectors are generated;
the mean square error loss is calculated using the euclidean distance between the reconstructed and the true samples:
Figure BDA0003043845060000032
wherein xiAs original, yiTo reconstruct the samples, n is the number of samples, and θ is the network parameter of the self-encoder. E (theta) is compared with a preset threshold value T1，T2(T1＜T2) Comparing:
Figure BDA0003043845060000041
wherein, PsIs a training sample set for a behavior recognition model.
(5) And (3) behavior recognition: the method comprises the steps of constructing a human body behavior recognition model based on a convolutional neural network after data are denoised and enhanced, using an enhanced data sample and a real sample as input of the model, optimizing the network model, performing feature extraction and behavior classification, adopting the convolutional neural network to extract features from the data, classifying the features by using a linear network structure, obtaining behavior recognition results according to response values of the classification network to the samples in different classes, and finally selecting the class with the largest response value as the recognition result.
The human behavior recognition network model comprises 4 convolution layers, 3 pooling layers and 3 full-connection layers, data are regularized after each convolution layer, extracted features are classified through the 3 full-connection layers, and finally a classification result of human behavior is obtained by using a Softmax function.
The middle layers of the neural network, namely the convolution layer, the maximum pooling layer, the deconvolution layer and the up-sampling layer, adopt the ReLU as an activation function.
The invention has the beneficial effects that: the method takes a deep self-encoder and a behavior recognition model as the basis, considers the relation between the sample quality and the stability of the neural network, adopts a data enhancement method, fully integrates the advantages of the self-encoder, and improves the identification power of the characteristics;
the invention expands the training samples, greatly increases the number and diversity of the training samples, improves the stability and the generalization of the neural network, solves the problem of neural network overfitting under the condition of small samples, and improves the accuracy of neural network classification.
Drawings
Fig. 1 is a basic block diagram illustration of the present invention.
FIG. 2 is a flow chart illustration of the present invention.
Fig. 3 is a schematic diagram of a network structure of a data enhancement self-encoder in the invention.
FIG. 4 is a block diagram illustration of a behavior recognition network of the present invention.
Detailed Description
In the following description, for purposes of explanation, numerous implementation details are set forth in order to provide a thorough understanding of the embodiments of the invention. It should be understood, however, that these implementation details are not to be interpreted as limiting the invention. That is, in some embodiments of the invention, such implementation details are not necessary.
As shown in FIG. 1, the invention provides a small sample passive behavior perception method based on self-encoder data enhancement, which comprises CSI data acquisition, data processing and behavior identification.
As shown in fig. 2, the method comprises the following specific steps:
(1) commercial WiFi equipment is deployed indoors to transmit WiFi signals, when the signals meet human bodies in the transmission process, phenomena of reflection, refraction, diffraction, scattering and the like can occur, and disturbance is generated on normal transmission of the signals. Collecting the CSI signals containing human body behavior information by using a device provided with an Intel5300 as a signal receiver;
(2) and completing data acquisition, and performing data denoising and data enhancement on the CSI signal to obtain a clean data sample, wherein the specific implementation process is as follows:
step 1: data denoising
Step 1-1: the Hampel filter is used for removing outliers, the change of the internal state of the equipment, such as the change of transmitting power, transmission rate and the like, causes the sudden change of obvious abnormality of signals, the points are the outliers, all points which are not in the interval U are regarded as the outliers by the Hampel filter,
U＝[μ-γ×σ，μ+γ×σ]
where μ is the mean of the data, σ is the standard deviation, and γ is 3 in this example.
Step 1-2: the Butterworth filter is used for denoising, the frequency of human body motion is concentrated in a low-frequency range, the collected signals are influenced by various noises, and have a plurality of high-frequency components and direct-current components, if the collected signals are not removed, the result is greatly influenced, the frequency response curve of the Butterworth filter in a pass-band is flat to the maximum extent, and the frequency response curve gradually drops to zero in a resistance band, and most of noise of data can be removed by the Butterworth filter.
Step 1-3: in order to better adapt to the input of the convolutional neural network, the data needs to be converted into a uniform size, and the data is uniformly interpolated into a matrix of 512 x 90 by linear interpolation.
Step 2: data enhancement
The traditional data enhancement modes such as rotation, stretching, translation and the like are not suitable for CSI data, when no action occurs in the environment, a CSI signal is stable, when the action occurs, the signal can be influenced by human behaviors to generate fluctuation, as shown in FIG. 3, the invention constructs and constructs a data enhancement self-encoder, uses the denoised data as the input of the self-encoder, and calculates the mean square error loss E (theta) of a reconstructed sample and an original sample:
Figure BDA0003043845060000061
wherein xiAs original, yiTo reconstruct the samples, n is the number of samples, and θ is the network parameter of the self-encoder. E (theta) is compared with a preset threshold value T1，T2(T1＜T2) Comparing:
Figure BDA0003043845060000062
wherein, PsIs a training sample set for a behavior recognition model.
The invention expands the training samples, greatly increases the number and diversity of the training samples, and improves the stability and generalization of the neural network.
The data enhancement self-encoder adopts a symmetrical convolutional neural network structure, namely the network structure of a decoder part is completely symmetrical to that of an encoder part and comprises the encoder part and the decoder part, the encoder encodes high-dimensional input into low-dimensional hidden vectors, the decoder reduces hidden variables into initial dimensions, the encoder part comprises 5 convolutional layers and 3 maximum pooling layers, regularization processing is carried out after convolution of each layer, and parameters are reduced while characteristics of the pooling layers are kept; the decoder part comprises 5 deconvolution layers and 3 upsampling layers, wherein the deconvolution layers correspond to the number of channels of the convolution layers and the upsampling layers correspond to the number of channels of the pooling layer.
(3) As shown in fig. 4, the human behavior recognition network model includes 4 convolutional layers, 3 pooling layers and 3 full-link layers, the human behavior recognition network model takes original data and reconstruction data meeting requirements as input, regularizes the data after convolution of each layer, classifies the extracted features through the 3 full-link layers, and finally obtains a human behavior classification result by using a Softmax function.
The method comprises the steps of denoising Channel State Information (CSI) data containing human body behavior information; designing a network structure of the self-encoder, and training network parameters of the deep self-encoder by adopting the processed wireless data; performing data enhancement by using a trained deep self-encoder to generate a large number of reconstructed samples with different feature vectors; designing a human body behavior perception model, training the model by using original data and a reconstructed sample, extracting sample characteristics, obtaining a final classification result according to response values of the sample in various categories, and finishing human body behavior recognition. The method solves the problem of model overfitting of passive behavior perception in a small sample training data state, enhances the generalization and stability of the model, and ensures the accuracy of human behavior recognition.
The above description is only an embodiment of the present invention, and is not intended to limit the present invention. Various modifications and alterations to this invention will become apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of the claims of the present invention.

Claims (7)

1. A small sample passive behavior perception method based on self-encoder data enhancement is characterized in that: the method comprises the following steps:
(1) transmitting a data packet to a WiFi signal receiving end through an indoor WiFi signal transmitting end, and collecting channel state information containing human behavior information;
(2) denoising the channel state information to obtain a clean data sample;
(3) constructing a neural network model based on a data enhancement self-encoder;
(4) a small amount of real sample training data is adopted to enhance an autoencoder, and a large amount of reconstructed samples with different feature vectors are generated;
(5) and constructing a human behavior recognition model based on a convolutional neural network, taking the enhanced data sample and the real sample as the input of the model, optimizing the network model, and obtaining a behavior recognition result according to the response values of the classification network to the samples in different classes.
2. The small sample passive behavior sensing method based on self-encoder data enhancement as claimed in claim 1, characterized in that: in the step (3), the data enhancement self-encoder adopts a symmetrical convolutional neural network structure and comprises an encoder and a decoder, the encoder encodes high-dimensional input into low-dimensional hidden vectors, and the decoder restores the hidden variables into initial dimensions.
3. The small sample passive behavior sensing method based on self-encoder data enhancement as claimed in claim 2, characterized in that: the encoder part comprises 5 convolution layers and 3 maximum pooling layers, regularization processing is carried out after each convolution layer, and parameters are reduced while characteristics of the pooling layers are reserved; the decoder part comprises 5 deconvolution layers and 3 upsampling layers, wherein the deconvolution layers correspond to the number of channels of the convolution layers and the upsampling layers correspond to the number of channels of the pooling layer.
4. The small sample passive behavior sensing method based on self-encoder data enhancement as claimed in claim 2 or 3, characterized in that: let the kth intermediate layer of the data-enhanced self-encoder be denoted as hkThen the relationship between the network layers of the encoder portion can be expressed as:
hk＝σ(a*Wk+bk)
wherein a is a previous layer feature, WkAnd bkRespectively representing the weight and deviation of the kth convolution kernel, wherein sigma is a ReLU activation function;
the decoder part reconstructs the features into original input data, and the relation between the network layers is as follows:
Figure FDA0003043845050000021
wherein h iskIs a feature of the previous layer, WkRepresenting the weight of the kth convolution kernel, H representing the number of channels of the network, c representing the deviation, and sigma being a ReLU activation function;
the mean square error loss is calculated using the euclidean distance between the reconstructed and the true samples:
Figure FDA0003043845050000022
wherein xiAs original, yiFor reconstructing samples, n is the number of samples, θ is the network parameter from the encoder, E (θ) is compared with a predetermined threshold T1，T2(T1＜T2) Comparing:
Figure FDA0003043845050000023
wherein, PsIs a training sample set for a behavior recognition model.
5. The small sample passive behavior sensing method based on self-encoder data enhancement as claimed in claim 1, characterized in that: in the step (2), the denoising method of the acquired channel state information is as follows:
step 1: replacing outliers of the data with a median of the window of data using a Hampel filter;
step 2: denoising the data by using a Butterworth filter to remove most of noise;
and step 3: the samples are linearly interpolated to a matrix of fixed size 512 x 90 as input to the network model.
6. The small sample passive behavior sensing method based on self-encoder data enhancement as claimed in claim 1, characterized in that: in the step (4), a root mean square error between the training sample and the reconstructed sample is calculated, and when the error is within a preset threshold range, the generated reconstructed sample is used as a training object of the behavior recognition model.
7. The small sample passive behavior sensing method based on self-encoder data enhancement as claimed in claim 1, characterized in that: the human behavior recognition network model in the step (5) comprises 4 convolution layers, 3 pooling layers and 3 full-connection layers, data are regularized after convolution of each convolution layer, extracted features are classified through the 3 full-connection layers, and finally a human behavior classification result is obtained by using a Softmax function.
CN202110465721.XA 2021-04-28 2021-04-28 Small sample passive behavior sensing method based on self-encoder data enhancement Active CN112926555B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202110465721.XA CN112926555B (en) 2021-04-28 2021-04-28 Small sample passive behavior sensing method based on self-encoder data enhancement

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202110465721.XA CN112926555B (en) 2021-04-28 2021-04-28 Small sample passive behavior sensing method based on self-encoder data enhancement

Publications (2)

Publication Number Publication Date
CN112926555A true CN112926555A (en) 2021-06-08
CN112926555B CN112926555B (en) 2022-07-26

Family

ID=76174762

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202110465721.XA Active CN112926555B (en) 2021-04-28 2021-04-28 Small sample passive behavior sensing method based on self-encoder data enhancement

Country Status (1)

Country Link
CN (1) CN112926555B (en)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113794536A (en) * 2021-09-15 2021-12-14 苏州米特希赛尔人工智能有限公司 Artificial intelligence channel coding and decoding method and device
CN114066089A (en) * 2021-11-25 2022-02-18 中国工商银行股份有限公司 Batch job operation time-consuming interval determining method and device

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111028163A (en) * 2019-11-28 2020-04-17 湖北工业大学 Convolution neural network-based combined image denoising and weak light enhancement method
CN112036433A (en) * 2020-07-10 2020-12-04 天津城建大学 CNN-based Wi-Move behavior sensing method

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111028163A (en) * 2019-11-28 2020-04-17 湖北工业大学 Convolution neural network-based combined image denoising and weak light enhancement method
CN112036433A (en) * 2020-07-10 2020-12-04 天津城建大学 CNN-based Wi-Move behavior sensing method

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113794536A (en) * 2021-09-15 2021-12-14 苏州米特希赛尔人工智能有限公司 Artificial intelligence channel coding and decoding method and device
CN113794536B (en) * 2021-09-15 2024-02-23 苏州米特希赛尔人工智能有限公司 Artificial intelligent channel coding and decoding method and device
CN114066089A (en) * 2021-11-25 2022-02-18 中国工商银行股份有限公司 Batch job operation time-consuming interval determining method and device

Also Published As

Publication number Publication date
CN112926555B (en) 2022-07-26

Similar Documents

Publication Publication Date Title
CN112926555B (en) Small sample passive behavior sensing method based on self-encoder data enhancement
CN110705525A (en) Method and device for diagnosing rolling bearing fault
CN113176092B (en) Motor bearing fault diagnosis method based on data fusion and improved experience wavelet transform
CN114190944B (en) Robust emotion recognition method based on electroencephalogram signals
CN109730818A (en) A kind of prosthetic hand control method based on deep learning
CN109375776A (en) EEG signals based on multitask RNN model act intension recognizing method
CN111832452B (en) Characteristic optimization and recognition method of special gesture instruction based on brain electricity
CN111803028A (en) Individualized epileptic seizure detection device based on network parameter migration
CN114648048B (en) Electrocardiosignal noise reduction method based on variational self-coding and PixelCNN model
CN116881762A (en) Emotion recognition method based on dynamic brain network characteristics
CN110742581A (en) BCG signal processing method and device
CN114036977A (en) Fan power quality disturbance monitoring method based on two-dimensional image coding features
CN112272066B (en) Frequency spectrum data cleaning method used in airport terminal area very high frequency communication
CN111481203B (en) Indoor static passive human body detection method based on channel state information
CN117093857A (en) Rapid processing method for intelligent ring test data
CN116992286A (en) Cavitation fault diagnosis method based on deep learning
CN114757236B (en) Electroencephalogram signal denoising optimization method and system based on TQWT and SVMD
CN115017960A (en) Electroencephalogram signal classification method based on space-time combined MLP network and application
CN116350233A (en) Electrocardiosignal quality assessment method based on self-encoder
CN115708687A (en) Stimulated image reconstruction method and system based on artificial neural network
CN110032968A (en) Denoising method based on dual-tree complex wavelet and adaptive semi-soft threshold model
CN115293210A (en) Instruction prediction output control method based on brain waves
CN111789592B (en) Electroencephalogram recognition method based on topological feature fusion
CN116343324A (en) sEMG processing method and system for fusing MEMD and Hilbert space filling curves
CN112450933B (en) Driving fatigue monitoring method based on multiple types of characteristics of human body

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant