• <tr id="yyy80"></tr>
  • <sup id="yyy80"></sup>
  • <tfoot id="yyy80"><noscript id="yyy80"></noscript></tfoot>
  • 99热精品在线国产_美女午夜性视频免费_国产精品国产高清国产av_av欧美777_自拍偷自拍亚洲精品老妇_亚洲熟女精品中文字幕_www日本黄色视频网_国产精品野战在线观看 ?

    Parkinson’s Detection Using RNN-Graph-LSTM with Optimization Based on Speech Signals

    2022-08-24 12:59:12AhmedAlmasoudTaiseerAbdallaElfadilEisaFahdAlWesabiAbubakarElsafiMesferAlDuhayyimIshfaqYaseenManarAhmedHamzaandAbdelwahedMotwakel
    Computers Materials&Continua 2022年7期

    Ahmed S.Almasoud, Taiseer Abdalla Elfadil Eisa, Fahd N.Al-Wesabi, Abubakar Elsafi,Mesfer Al Duhayyim, Ishfaq Yaseen, Manar Ahmed Hamza,*and Abdelwahed Motwakel

    1Department of Information Systems, College of Computer and Information Sciences, Prince Sultan University, Mahayil,62529, Saudi Arabia

    2Department of Information Systems-Girls Section, King Khalid University, Mahayil, 62529, Saudi Arabia

    3Department of Computer Science, College of Science and Artsat Mahayil, King Khalid University, Mahayil, 62529,Saudi Arabia

    4Faculty of Computer and IT, Sana’a University, Sana’a, Yemen

    5Department of Software Engineering, College of Computer Science and Engineering, University of Jeddah, Saudi Arabia

    6Department of Natural and Applied Sciences, College of Community-Aflaj, Prince Sattam bin Abdulaziz University,Mahayil, 62529, Saudi Arabia

    7Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University,AlKharj, 62529, Saudi Arabia

    Abstract: Early detection of Parkinson’s Disease (PD) using the PD patients’voice changes would avoid the intervention before the identification of physical symptoms.Various machine learning algorithms were developed to detect PD detection.Nevertheless, these ML methods are lack in generalization and reduced classification performance due to subject overlap.To overcome these issues, this proposed work apply graph long short term memory (GLSTM)model to classify the dynamic features of the PD patient speech signal.The proposed classification model has been further improved by implementing the recurrent neural network (RNN) in batch normalization layer of GLSTM and optimized with adaptive moment estimation (ADAM) on network hidden(ADAM)on network hidden layer.To consider the importance of feature engineering, this proposed system use Linear Discriminant analysis (LDA) for dimensionality reduction and Sparse Auto-Encoder(SAE)for extracting the dynamic speech features.Based on the computation of energy content transited from unvoiced to voice(onset) and voice to voiceless (offset), dynamic features are measured.The PD datasets is evaluated under 10 fold cross validation without sample overlap.The proposed smart PD detection method called RNN-GLSTM-ADAM is numerically experimented with persistent phonations in terms of accuracy,sensitivity, and specificity and Matthew correlation coefficient.The evaluated result of RNN-GLSTM-ADAM extremely improves the PD detection accuracy than static feature based conventional ML and DL approaches.

    Keywords: Dimensionality reduction; LDA; feature extraction; auto encoder;classification; recurrent neural network; LSTM and optimization

    1 Introduction

    Parkinson‘s disease (PD) is a neurodegenerative disorder which affects the human brain nerve cells.This is considered to be the second common disease after Alzheimer disease [1] that targets the peoples age more than 60 years.Most of the PD patients are diagnosed in the age of 70 and also 15% of cases are occurred under the age of 50.PD symptoms can be characterized as motor and non motor symptoms.PD motor symptoms includes tremor, stiffness, slowness of movement and postural instability.PD non motor symptoms includes cognitive dysfunction, mood disorders, sensory dysfunction, pain and dysautonomia [2].Among these two motor symptoms are very common in PD patients.PD detection based on phonation is the proven method with 90% effectiveness [3]which shows voice impairments.Hence, Voice impairments through speech signals are considered to be the one of the earlier symptom thatmight not be noticeable to the listeners.PDvocal dysfunction is characterized as such as reduced tongue flexibility, longer pauses, reduced vocal volume, narrowing and variations in pitch range, articulation rate and voice intensity level.Acoustic analysis is considered as non-invasive tool by many researches to diagnose PD.Further more, early diagnosis of PD is done with phonation and speech data by many researchers [4-6].Diagnosing the Parkinson’s disease is difficult.It diagnosed through brain scans and clinical assessments which are very cost, leads to erroneous sometime and need some professional monitoring.

    Speech signal processing is becomes the interdisciplinary research that includes artificial intelligence and signal processing.In order to solve human-computer interaction (HCI) issues, more methods are developed by many researchers [7].Based on this many Machine Learning (ML)algorithms are applied to detect PD patients [8-15].ML algorithms are considered PD detection as a classification task.Machine learning is a scheme that analyze the data, automatically learn the system data and attitudes [16].ML can be divided into traditional ML algorithm and Deep Learning (DL)based algorithms.DLis inspired bybrain signal processingthatused programmed neural network[17]to make accurate decisions by the machine without human interference.

    ML methods such as Artificial Neural Network [18], K-Nearest Neighbors (KNN) [19], Extreme Gradient Boosting (XGBoost) [20], Random Forest (RF) [21], Support Vector Machine (SVM),Decision Tree (DT) [22], Genetic algorithm (GA) [23] were used for PD classification using speech signals.

    Neural network model with more generalization provides the accurate result on the analysis while the data tested before in the training also used for testing.With recurrent neural network (RNN) with Long Short Term Memory (LSTM), the input data are learned which consists of chunks of memory to retain the input information [24,25].This paper illustrates how the classifiers variation, parameters representation and feature extraction are affect the performance of the model with more accuracy,efficient and robust.The major contribution of the paper is as follows:

    ?Proposed a PD detection model using static and dynamic features using deep learning model for earlier diagnosis.To scale the input features values in a range, preprocessing method called min-max normalization used.

    ?To create most relevant low dimensional space, Linear Discriminant Analysis based dimensionalityreduction method used which will transform the data into low dimensional space execution

    ?Proposed a classification model called RNN-Graph LSTM to use the dynamic features of speech signals for PD detection.For each batch normalization layer, the proposed RNNGLSTM has been applied after hidden layers to standardize the hidden layer output.

    ?In order to improve the classification model performance, ADAM optimizer used to adjust the network weight.Neural network optimization is used to increase the accuracy and also reduces the loss.

    ?The proposed models with speech signal dataset is experimented and quantitative results are compared with conventional PD detection model.The proposed model have generalization abilities that are not identified by previous studies.This model also be used for early diagnosis of PD prediction with lower complexity.

    The rest of the paper is as follows: Section 2 discusses about the review of the literature, Section 3 stated about the dataset used for evaluation, Section 4 proposed an efficient PD classification model,Section 5 discusses about the experimented results and Section 6 concludes the work with future directions.

    2 Related Work

    In order to differentiate PD patients from healthy people, ML and DL techniques would be the best tool.This section reviews various ML and DL based PD prediction system from speech signals.Sharma et al.[26] proposed a method to extract the voice signal features such as MFCC, Jitter,Shimmer, glottal pulse and pitch.Sakar et al., [27] proposed Tunable Q-Factor wavelet transform(TQWT) method to predict PD patients using voice signals.This dataset was experimented using DL methods called convolutional neural networks [28].

    Bouchikhi et al., [29] proposed relief-F feature selection model with SVM classifier.This method chosen 10 features from 22 features.SVM classifier with 10 fold cross validation proves that Relief-F feature selection showed 96.88% accuracy.Experimented dataset consists of 195 voice samples.Relief based algorithms (RBA) are susceptible to noise intrusive with nearest neighbor.Hemmerling et al.[30]proposed nonlinear SVM with PCA based PD detection.This model obtained 93.43% of accuracy on PD classification.The experimented dataset was small and lack in lower precision in prediction.

    Parisi et al.[31] proposed multilayer perceptron with lagrangian SVM based classifier to detect PD patients.The relevant features were assigned by custom cost functions by MLP which consists of both accuracy and AUC score.MLP extract 20 most important and relevant features with the score value.This proposed model obtained 100% accuracy than compared algorithms.Hybrid method for preprocessing and PD classification.Subtractive clustering features weighting (SCFW) was proposed as a preprocessing method to reduce the dataset variance.They proposed kernel based extreme learning machine (KELM) as a classifier and justified the efficiency of KELM in terms accuracy, sensitivity and specificity, Kappa statistic value and Roc curve.

    Caliskan et al., [32] proposed DNN classifier for PD detection that consists of stacked auto encoder to extract the vocal features.This work is compared with traditional ML models and concludes that DNN based PD prediction secure high accuracy.Moreover, DNN needs more data in training phase and also it took more training time for parameter space search.

    In [33] CNN has been used to extract the speech features from short time fourier transform and wavelet transform.Main issue of CNN is to model long distance contextual data while using dilated convolution layers [34].Recurrent neural network (RNN) overcome this issue and able to model long distance contextual data by saving the previous computations.However, RNN based method suffer from gradient problem and it is hard to tune the parameters in the network layer.LSTM [35] has been used to overcome this issue.

    In this paper RNN trained LSTM with graph structure has been proposed as a classification model for PD detection.The classification accuracy is further improved by ADAM optimizer based on various speech features.RNN-GLSTM can hope up with larger dataset without increase the model size.Compared to traditional time series models, GLSTM have more effective as it learn long term dependencies that follow the previous time proceeding and forward to next layers.This proposed model also acquire additional benefit on the use of preprocessing and feature extraction methods.It overcomes the disadvantages of previous methods such as limited dataset size and features that reduce the accuracy for PD prediction due to traditional NN use feed forward layer.RNN-GLSTM used loop network in forward and backward that enhance the accuracy of PD prediction.

    3 Data Set

    The data set used for this analysis is created by Max Little of the University of Oxford which is the collaboration with National centre for voice and speech [36], Denver.The speech signals are recorded by Colorado.The details of the dataset are contained in Tab.1.This data set consists of 31 peoples biomedical voice measurements.Among them 23 patients are with Parkinson’s disease.The column of the table is voice measure of particular person.Each row of the table corresponds to one of the 195 voice recordings of the individuals.Based on the status column in the table with binary value, aim is to classify the unhealthy (PD patients with value 1) from healthy persons (value 0).This data set consists of 24 attributes which includes number of frequencies (low, medium, high),number of variations in terms of frequency called Jitter and its types such as MDVP:Jitter(%),MDVP:Jitter(Abs), MDVP:RAP, MDVP:PPQ, Jitter:DDP [37,38] and number of variations in terms of amplitude called shimmer and its types such as MDVP:Shimmer, MDVP:Shimmer(dB), Shimmer:APQ3, Shimmer:APQ5, MDVP:APQ, Shimmer:DDA.MDVP:Fo(hz)-average vocal fundamental frequency, MDVP:Fhi(Hz)-average vocal fundamental frequency and MDVP:Flo(Hz)-maximum vocal fundamental frequency, Three nonlinear fundamental frequency variations such as Spread1,Spread2 and PPE, two measures of ratio of noise such as NHR and HNR.It is an unbalanced dataset.Sample data from the dataset is shown in Fig.1.

    Table 1: Dataset description

    Figure 1: Sample data

    4 Proposed Model Description

    The model for proposed PD diagnostic system is shown in Fig.2.The voice speech signals are preprocessed with normalization to remove the null values.This model uses training database to construct the model.The generalization of the proposed model has been tested through testing database.The classification system performance is further improved with dimensionality reduction and feature extraction techniques.Dimensionality reduction is the mechanism to reduce the high dimensional data space into low dimensional space.Feature extraction is the procedure to select relevant features from feature set to improve the accuracy of the classifier [39,40].There are numerous dimensionality reduction and feature extraction methods are available.In this work, we use Linear Discriminant Analysis(LDA)for dimensionality reduction and Sparse Auto encoder (SAE) for feature extraction.Each of these methods is explained in upcoming sections.

    4.1 Data Pre-processing

    Raw data are inconsistent due to it contains lots of error and null values.These raw data are transformed into understandable format through pre processing phase that will improve the results.Good preprocessing method yields good classification result.The min-max normalization process is denoted in Eq.(1) for the data set D.After normalization the values are lie in the range [0,1].

    4.2 Training Testing Data Model

    The entire dataset is divided into three parts such as training dataset and testing dataset with the ration of 80:20 respectively.Based on the cross validation, the training set is further divided into training and validation dataset in the ratio 80:20 respectively.Fig.3 shows the cross validation sets which the classification performed.

    4.3 Dimensionality Reduction Using LDA

    Various feature extraction and classification approaches are used original pattern vector with low dimensionality.To meet this objective, this proposed work use Linear Discriminant Analysis (LDA)for dimensionality reduction method at initial stage of classification model for this, LDA uses Fisher ratio which is denoted as in Eq.(2)

    Figure 2: Overview of proposed PD diagnostic model

    Figure 3: Cross validation ratio of dataset

    where,σ1and σ2-variances of first and second classes respectively and μ1-μ2-difference between the mean values of two classes.The LDA maximizes the Fisher ratio by maximizes the scatter between two classes called Sinterand reduces the variance that minimizes the scatter among the class called Sintra.The fisher ration of Eq.(1) is rewritten as,

    The main objective of phase is to transform the data into lower dimensional space by maximize the Eq.(3).To consider this, LDA use transformation matrix called w withSinterandSintraas in Eqs.(4)and (5).

    Hence Eq.(3) becomes

    Transformation matrix w is evaluated by the calculation of eigenvectors of.Thus LDA has been used to transform the p dimensional data into k dimension data where k<=(n-1), n is the number of classes in the dataset.In this proposed work, considered dataset is a binary class dataset.So n=2 and k=1 (healthy or PD patient).With the maximization of class separability of Eq.(2),

    4.4 Feature Extraction Using SAE

    Sparse Autoencoder (SAE) is an unsupervised deep neural network based feature extraction method with single hidden layer [41] to encodes the given data.This also estimates the error and eextracts the relevant features through hidden layer expressions [42].Autoencoder have some functional flaws.AE cannot find features through inputting and copying memory into its implicit layer [43].To overcome this issue, sparsity based auto encoder is proposed [44].SAE or sparsity regularization is denoted in Eq.(7).

    where KL-Kullback-Leibler (KL) divergence,-activation function of jth hidden node,ρ-sparsity parameter.KL is mathematically denoted as in Eq.(8)

    SAE has been trained with the cost function stated in Eq.(9) which consists of three terms such as Mean Squared Error (MSE) denoted in Eq.(10) which reconstruct input X intoover entire training dataset [45], second is lasso regression term denoted in Eq.(11) and third term is sparsity transformation stated in Eq.(7).

    where N-total number of input data points, X-input,-reconstructed output,α-coefficient of lasso regression and β-coefficient of sparsity regularization.Lasso regression is used with SAE to compress the least important features coefficient into zero to denote it as not relevant for further process.This will shrink the parameter space.Mathematical representation of Lasso regression denoted in Eq.(11).

    where l=layer,nl=number of layers,ul-number of units in the layer l and-weight value between ithnode of layer l and jthnode of layer l+1.Lasso regression can add the magnitude absolute value as penalty term.To improve the feature extraction process, this penalty coefficient is set as zero.SAE with three hidden layers is shown in Fig.4 which consists of three parts such as encoder, compression or representation of latent space and decoder.

    Figure 4: SAE with three hidden layers

    Through hidden layers, encoder encodes the input data to latent space and decoder decodes the latent space code to output layer.Sigmoid activation function is used for nonlinear mapping.Rectified Linear Unit (ReLU) is not suitable for auto-encoder due to the capability of dealing negative value as zero.This will reduce network training ability.The considered dataset feature values also consist of negative values.Hence, Sigmoid activation function is used rather than ReLU.Objective of SAE is to learn the speech signal features from the input data.

    4.5 Classification Using RNN-GLSTM

    Recurrent Neural Network (RNN) is the generalized form of feed forward neural network with internal memory.The output of RNN relies with previous computation and sent back to the recurrent network.Internal memory in the RNN is used to operate the input series and make the decision.Long short term memory (LSTM) is based on back propagation for training.LSTM consists of three gates such as input gate, forget and output gate.Input gate used sigmoid activation function which is used to decide the values of input that modify the memory.Forget gate is responsible to decide the details that are to be discarded from previous state and output gate responsible to control the output.Compare to traditional LSTM, in graph LSTM each tree node represents the single LSTM unit, Fig.5.

    Figure 5: Proposed RNN-LSTM classification model

    This model consists of seven layers such as input layer, five hidden layers and an output layer.Recurrent Neural network is comprised with the input layer of LSTM cell.Each input layer of LSTM layer represents the Phonation Features (PF) of the speech signals.23 features are represented by 23 neurons in the input layer of LSTM cell which is shown in Fig.6.

    where, b-bias vector, w-weight matrices and H-hidden layer function of each feature.

    Figure 6: GLSTM cell

    For the forward GLSTM, the parameters in Fig.6 such as input gate it, forget gate ft, output gate otand cell state ctfor particular iteration t with activation function σ are updated as in following Eqs.(15) from to (19)

    For backward GLSTM, the parameters are updated as follows in Eqs.(20) to (24).

    4.6 Optimization of RNN-GLSTM with ADAM

    The main objective of machine and deep learning is to reduce the difference between actual and predicted output which is called as cost or loss function.In order to choose the optimal value for the cost function, weight in cost function is updated using optimization algorithm while training the neural network.This will leads to enhance the result of prediction model.This proposed work uses ADAM optimizer [46,47] to update the weight.It is the most suggested optimization method for deep learning networks [48,49].It takes advantages of stochastic gradient descent (SGD) algorithm and root mean square (RMS).This proposed PD classification model chose ADAM optimization due to the advantages such as it does not consume large memory, it uses second moment called uncentred variance of the gradients and first moment called mean.

    Optimization algorithm

    Step 1: Initialize 1stmoment m1=0 and second moment m2=0 and iteration t=0

    Step2: bias of 1stand 2ndmoment are updated according to Eqs.(20) and (21)

    Step 3: bias corrected of 1stand 2ndmoment are calculated according to Eqs.(22) and (23)

    Step 4: parameters of RNN-GLSTM is updated as in Eqs.(24) to (27)

    where β1and β2-hyper parameters (values are 0.8 and 0.88 respectively),ε-learning rate (10-2).It is implemented in python scikit-learn library.Thus the way the training data of proposed model has been optimized.Next, testing data are applied on this trained optimized RNN-GLSTM for classification.

    5 Proposed System Evaluation and Discussions

    This section discusses about the validation and evaluation metrics used and evaluated results.Performance of the proposed system is compared with existing ML algorithms to prove the efficiency of the proposed PD diagnostic model.For experiment, dataset used in Section 3 was used and compared with conventional ML algorithms such as Multi layer perceptron (MLP), K-Nearest Neighbor (KNN), Random forest (RF) and Principal component analysis (PCA) with Support Vector Machine (SVM) are exhibited.

    5.1 Evaluation Metrics

    The output for the proposed RNN-GLSTMmodel has been evaluated with the evaluation metrics such as accuracy, precision, recall, F1 score and Mathew’s correlation Coefficient (MCC) based on confusion matrix shown in Tab.2, Fit time and score time .

    Table 2: Confusion matrix-binary classification

    From Tab.2, accuracy, precision, recall, F1 score and MCC are denoted in Eq.from (28) to (32).MCC can be used with imbalanced dataset due to its robust evaluation characteristics.

    Fit time:It is the time to fit the estimator on training data set for each cross validation split.

    Score time:It is the time for scoring the estimator on testing dataset for each cross validation split.

    5.2 Experimental Results

    The proposed PD diagnostic model RNN-GLSTM optimized by ADAM has been implemented in python Scikit-learn 0.22.1.The parameter settings of traditional ML models are shown in Tab.3.

    Table 3: ML algorithms parameter settings

    The simulation result of the proposed system with dimensionality reduction and optimization are shown in Tab.3.For evaluation, various number of hyper parameters (H1, H2 and H3) are used for each layer.In our proposed work, 3 hidden layers are used.H1 is the number of neurons in first hidden layer, H2 is the number of neurons in second hidden layer and H3 is the number of neurons in third hidden layer of RNN-LSTM-ADAM in Tab.4.

    Table 4: Evaluation of proposed system with hyper parameters

    5.3 Comparative Analysis

    Experimental result in terms of different input speech signal on ML algorithms with proposed is discussed.

    The MLP, RF and PCA-SVM were implemented in order to evaluate the performance of the proposed model.With the same set of training and testing data, PD prediction is made for all the models.From Fig.7,it can be observed that random forest obtained least efficiency than other models and proposed RNN-GLSTM obtained high efficiency than other models in terms of the evaluation metrics.The proposed model obtained high accuracy of 91.21%, high F1 score of 85.02%, high precision of 86.72%, high recall value of 85.9% and high MCC value of 0.352.

    Figure 7: Performance evaluation of proposed vs. conventional methods

    Comparison in terms of standard dimensionality reduction and feature extraction methods and optimization.Compared to standard PCA with proposed classification model, proposed LDA with classification model obtained 93.5% of accuracy.Compared to standard ICA based feature extraction with proposed model shown in Tab.5.

    Table 5: Different evaluation of proposed system

    6 Conclusion

    This paper presented an efficient PD diagnose model called Recurrent Neural Network based graph LSTM optimized with Adam optimizer.This proposed model used min-max normalization to scale the data values in the range [0,1], LDA based dimensionality reduction to transform the dataset into low dimensional space, SAE based feature extraction to consider the most relevant features for classification.This work used PD speech signal dataset and considered the dynamic features.Multiple evaluation in terms of dimensionality reduction, feature extraction methods were computed and the models is evaluated with various evaluation metrics.This classification model obtained 95.4% of accuracy, 93.4% of F1 score and 0.865 of MCC.In future, more features such as handwriting features are also considered for classification and explore this model to applicable on multi label classification with more deep learning based architectures.

    Acknowledgement:The authors would like to acknowledge the support of Prince Sultan University for paying the Article Processing Charges (APC) of this publication.

    Funding Statement:The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work under Grant Number (RGP 1/282/42).www.kku.edu.sa.

    Conflicts of Interest:The authors declare that they have no conflicts of interest to report regarding the present study.

    日日摸夜夜添夜夜添小说| 琪琪午夜伦伦电影理论片6080| 大型av网站在线播放| 亚洲人成伊人成综合网2020| 免费观看精品视频网站| 麻豆成人av在线观看| 少妇人妻一区二区三区视频| 国产一区二区三区在线臀色熟女| 免费在线观看亚洲国产| 国产亚洲精品综合一区在线观看 | 午夜福利在线观看吧| 久久草成人影院| 精品久久久久久久人妻蜜臀av| 久久精品91蜜桃| 日韩精品免费视频一区二区三区| 51午夜福利影视在线观看| 非洲黑人性xxxx精品又粗又长| 国内精品久久久久精免费| 国产精品亚洲一级av第二区| 亚洲成人国产一区在线观看| 两性夫妻黄色片| 伦理电影免费视频| 一区二区三区激情视频| www.999成人在线观看| 一边摸一边抽搐一进一小说| 观看免费一级毛片| 亚洲中文字幕一区二区三区有码在线看 | 亚洲美女黄片视频| 51午夜福利影视在线观看| 国产97色在线日韩免费| 两性午夜刺激爽爽歪歪视频在线观看 | 久久精品成人免费网站| 观看免费一级毛片| 一区二区三区高清视频在线| 亚洲无线在线观看| 亚洲成人久久爱视频| 怎么达到女性高潮| 成人国产一区最新在线观看| 久久中文字幕人妻熟女| 老司机午夜福利在线观看视频| 欧美日本视频| 亚洲国产精品sss在线观看| 久久久精品欧美日韩精品| 国产成人aa在线观看| 高潮久久久久久久久久久不卡| 在线观看免费视频日本深夜| 少妇裸体淫交视频免费看高清 | 三级国产精品欧美在线观看 | 91国产中文字幕| 国产av一区在线观看免费| 国产精品一区二区三区四区免费观看 | 国产熟女xx| 又粗又爽又猛毛片免费看| 国产亚洲欧美在线一区二区| 美女 人体艺术 gogo| 一级毛片精品| av天堂在线播放| 国产一区二区三区在线臀色熟女| 亚洲第一欧美日韩一区二区三区| 亚洲一区二区三区不卡视频| 精华霜和精华液先用哪个| 国产伦人伦偷精品视频| 亚洲精品国产一区二区精华液| 熟妇人妻久久中文字幕3abv| 亚洲精品一区av在线观看| 亚洲人成77777在线视频| 国产成人av教育| 亚洲av成人一区二区三| 成人国产一区最新在线观看| 两个人视频免费观看高清| 中出人妻视频一区二区| 国产精品一区二区精品视频观看| 男女做爰动态图高潮gif福利片| 中亚洲国语对白在线视频| 欧洲精品卡2卡3卡4卡5卡区| 亚洲 欧美 日韩 在线 免费| 变态另类丝袜制服| 久99久视频精品免费| 成人欧美大片| 亚洲乱码一区二区免费版| 欧美日韩亚洲综合一区二区三区_| 亚洲人成伊人成综合网2020| 曰老女人黄片| 国产主播在线观看一区二区| 成人手机av| 中文字幕久久专区| ponron亚洲| 亚洲中文av在线| 亚洲人与动物交配视频| 国产午夜精品论理片| 国产三级在线视频| 国产精品久久久久久人妻精品电影| 欧美另类亚洲清纯唯美| 日韩中文字幕欧美一区二区| 欧美性猛交黑人性爽| 精品一区二区三区四区五区乱码| 亚洲最大成人中文| 欧美黄色片欧美黄色片| 精品欧美国产一区二区三| 日日摸夜夜添夜夜添小说| 久久欧美精品欧美久久欧美| 亚洲无线在线观看| 精品久久久久久成人av| 精品国产超薄肉色丝袜足j| 中文字幕人妻丝袜一区二区| 99在线视频只有这里精品首页| 日日夜夜操网爽| 久久精品人妻少妇| 在线观看美女被高潮喷水网站 | 男人舔奶头视频| 亚洲九九香蕉| 久久久久国产一级毛片高清牌| 老熟妇乱子伦视频在线观看| 亚洲国产欧洲综合997久久,| 97超级碰碰碰精品色视频在线观看| 国产伦在线观看视频一区| 亚洲国产精品sss在线观看| 一个人观看的视频www高清免费观看 | 色综合欧美亚洲国产小说| 亚洲一区中文字幕在线| 日本精品一区二区三区蜜桃| 在线观看免费午夜福利视频| 欧美绝顶高潮抽搐喷水| 亚洲精品色激情综合| 亚洲成人久久爱视频| 国产伦一二天堂av在线观看| 婷婷六月久久综合丁香| 午夜精品在线福利| 国产伦一二天堂av在线观看| 丰满人妻一区二区三区视频av | 久久久久久久午夜电影| 狂野欧美激情性xxxx| 国产精品98久久久久久宅男小说| 黄色视频不卡| 夜夜夜夜夜久久久久| 757午夜福利合集在线观看| 男女做爰动态图高潮gif福利片| 久久性视频一级片| 好看av亚洲va欧美ⅴa在| 巨乳人妻的诱惑在线观看| 国产精品 国内视频| 99热这里只有是精品50| 日本免费一区二区三区高清不卡| 久久精品国产亚洲av高清一级| 级片在线观看| 久久人人精品亚洲av| 大型av网站在线播放| 最新美女视频免费是黄的| 三级国产精品欧美在线观看 | 夜夜夜夜夜久久久久| 国产乱人伦免费视频| 波多野结衣高清作品| 无人区码免费观看不卡| 亚洲一区中文字幕在线| 国产亚洲欧美在线一区二区| 国产精品永久免费网站| 嫩草影视91久久| 国产精品一区二区三区四区久久| 国产精品 国内视频| 美女扒开内裤让男人捅视频| 欧美日韩精品网址| 成人特级黄色片久久久久久久| 久久久国产欧美日韩av| a级毛片在线看网站| 男女那种视频在线观看| 亚洲自拍偷在线| 久久天躁狠狠躁夜夜2o2o| 亚洲欧美日韩高清在线视频| 婷婷精品国产亚洲av在线| 国产高清有码在线观看视频 | 无遮挡黄片免费观看| 老鸭窝网址在线观看| 91老司机精品| 美女大奶头视频| 在线观看午夜福利视频| 久久久久免费精品人妻一区二区| 在线观看免费午夜福利视频| 婷婷亚洲欧美| 亚洲熟妇熟女久久| 国产成人啪精品午夜网站| 每晚都被弄得嗷嗷叫到高潮| 男女之事视频高清在线观看| 亚洲在线自拍视频| 久久精品夜夜夜夜夜久久蜜豆 | 一二三四在线观看免费中文在| 亚洲国产欧洲综合997久久,| 日本撒尿小便嘘嘘汇集6| 成年版毛片免费区| 99国产综合亚洲精品| 日韩欧美在线二视频| 国产99白浆流出| 我要搜黄色片| 精品国内亚洲2022精品成人| 一区福利在线观看| 久久香蕉激情| 国产精品亚洲一级av第二区| 午夜亚洲福利在线播放| 看片在线看免费视频| 国内揄拍国产精品人妻在线| 两人在一起打扑克的视频| 欧美精品亚洲一区二区| 国产精品自产拍在线观看55亚洲| 久久人妻av系列| 99国产极品粉嫩在线观看| 久久久久国内视频| 99国产精品一区二区三区| 亚洲,欧美精品.| 99久久精品国产亚洲精品| 国产精品免费视频内射| 极品教师在线免费播放| 天堂影院成人在线观看| 婷婷精品国产亚洲av在线| 在线观看午夜福利视频| 特级一级黄色大片| 国产精品一区二区三区四区久久| 国产精品 欧美亚洲| 伊人久久大香线蕉亚洲五| a级毛片在线看网站| 中文字幕高清在线视频| 亚洲片人在线观看| 两人在一起打扑克的视频| 亚洲中文字幕日韩| 大型av网站在线播放| 久久 成人 亚洲| 亚洲国产看品久久| bbb黄色大片| 午夜激情av网站| 一个人免费在线观看的高清视频| 久久精品国产99精品国产亚洲性色| 特级一级黄色大片| 黄色视频不卡| 国产在线精品亚洲第一网站| 国产成人系列免费观看| 99久久国产精品久久久| 久久精品91蜜桃| 久久精品国产亚洲av高清一级| 国产探花在线观看一区二区| 两个人免费观看高清视频| 国产精品美女特级片免费视频播放器 | 亚洲色图av天堂| 九色成人免费人妻av| 欧洲精品卡2卡3卡4卡5卡区| 97人妻精品一区二区三区麻豆| 两性夫妻黄色片| 色老头精品视频在线观看| 亚洲精品国产精品久久久不卡| svipshipincom国产片| 伊人久久大香线蕉亚洲五| 一边摸一边抽搐一进一小说| 天天一区二区日本电影三级| 叶爱在线成人免费视频播放| 啦啦啦观看免费观看视频高清| av国产免费在线观看| 国产熟女午夜一区二区三区| 亚洲 欧美 日韩 在线 免费| 中国美女看黄片| 欧美乱码精品一区二区三区| 国产黄a三级三级三级人| 精品久久久久久成人av| 美女免费视频网站| 97超级碰碰碰精品色视频在线观看| 国产一区二区三区视频了| 亚洲黑人精品在线| 久久久久免费精品人妻一区二区| 欧美性猛交黑人性爽| 黑人欧美特级aaaaaa片| 他把我摸到了高潮在线观看| 亚洲男人的天堂狠狠| 国产男靠女视频免费网站| 两性夫妻黄色片| 午夜激情av网站| 免费看美女性在线毛片视频| 中文字幕高清在线视频| 国产精品九九99| 精品第一国产精品| 一a级毛片在线观看| 热99re8久久精品国产| 999久久久精品免费观看国产| 日本a在线网址| 天堂av国产一区二区熟女人妻 | 欧美中文综合在线视频| 美女午夜性视频免费| 黄色片一级片一级黄色片| 精品国产亚洲在线| 桃红色精品国产亚洲av| 一边摸一边做爽爽视频免费| 两个人视频免费观看高清| 欧美高清成人免费视频www| 精品乱码久久久久久99久播| 国产精品久久电影中文字幕| 美女大奶头视频| 99热6这里只有精品| 久久精品91蜜桃| 国产精品 国内视频| 日韩国内少妇激情av| 露出奶头的视频| 亚洲av成人精品一区久久| 久久婷婷人人爽人人干人人爱| 国产视频内射| 97碰自拍视频| 日韩有码中文字幕| 久久婷婷成人综合色麻豆| 最近最新免费中文字幕在线| 婷婷亚洲欧美| cao死你这个sao货| 白带黄色成豆腐渣| 亚洲av熟女| 欧美乱色亚洲激情| 亚洲人成77777在线视频| 国产探花在线观看一区二区| 老汉色av国产亚洲站长工具| www.熟女人妻精品国产| 在线永久观看黄色视频| 日本三级黄在线观看| 亚洲成人精品中文字幕电影| 国产精品亚洲av一区麻豆| 国产三级中文精品| 一进一出抽搐动态| 12—13女人毛片做爰片一| 在线观看午夜福利视频| 国产亚洲av高清不卡| 一个人观看的视频www高清免费观看 | 成人一区二区视频在线观看| 九色成人免费人妻av| 亚洲人成网站高清观看| 中文字幕人妻丝袜一区二区| 人妻丰满熟妇av一区二区三区| 亚洲片人在线观看| svipshipincom国产片| 国产又色又爽无遮挡免费看| 午夜精品久久久久久毛片777| 好看av亚洲va欧美ⅴa在| 麻豆久久精品国产亚洲av| 亚洲精品在线美女| 国产黄a三级三级三级人| 伊人久久大香线蕉亚洲五| 欧美一区二区国产精品久久精品 | 日韩欧美在线二视频| 国产精品亚洲一级av第二区| 在线观看美女被高潮喷水网站 | 制服人妻中文乱码| 99久久久亚洲精品蜜臀av| 亚洲七黄色美女视频| 男女那种视频在线观看| 亚洲成人精品中文字幕电影| 精品久久久久久久末码| 香蕉久久夜色| 美女高潮喷水抽搐中文字幕| 国产97色在线日韩免费| 久久久久久免费高清国产稀缺| 精品免费久久久久久久清纯| 中文字幕av在线有码专区| 国产一区二区在线观看日韩 | 中文亚洲av片在线观看爽| cao死你这个sao货| 亚洲一区二区三区不卡视频| 天天躁狠狠躁夜夜躁狠狠躁| 日本一区二区免费在线视频| 国产午夜精品论理片| 久久香蕉国产精品| 级片在线观看| 男人舔女人下体高潮全视频| 国产成人一区二区三区免费视频网站| 成人高潮视频无遮挡免费网站| 国产成年人精品一区二区| 日韩欧美在线二视频| 99国产极品粉嫩在线观看| 久久久久久久精品吃奶| 亚洲成av人片在线播放无| 亚洲18禁久久av| 成人三级做爰电影| 成人特级黄色片久久久久久久| 亚洲aⅴ乱码一区二区在线播放 | 视频区欧美日本亚洲| 观看免费一级毛片| 国产蜜桃级精品一区二区三区| bbb黄色大片| 亚洲专区国产一区二区| av视频在线观看入口| 亚洲午夜理论影院| 午夜免费观看网址| 欧美一级毛片孕妇| 欧美日本亚洲视频在线播放| 最近在线观看免费完整版| 日本成人三级电影网站| 国产一区二区在线av高清观看| 高清在线国产一区| 成人三级黄色视频| 久久久久国内视频| 亚洲一区二区三区不卡视频| 亚洲精品美女久久av网站| 久久国产精品人妻蜜桃| 在线永久观看黄色视频| 精品久久蜜臀av无| 成人特级黄色片久久久久久久| а√天堂www在线а√下载| 黄色毛片三级朝国网站| 免费在线观看完整版高清| 国产久久久一区二区三区| 欧美在线黄色| 成人av在线播放网站| 久久国产精品影院| av视频在线观看入口| 免费在线观看视频国产中文字幕亚洲| 国产午夜精品久久久久久| 亚洲免费av在线视频| 此物有八面人人有两片| 一个人观看的视频www高清免费观看 | 母亲3免费完整高清在线观看| 精品高清国产在线一区| 88av欧美| 日韩欧美国产在线观看| 亚洲精品在线观看二区| 色精品久久人妻99蜜桃| 老司机深夜福利视频在线观看| 九色成人免费人妻av| 啦啦啦观看免费观看视频高清| 久久精品国产清高在天天线| 国产熟女午夜一区二区三区| 身体一侧抽搐| 一个人免费在线观看电影 | 黄色成人免费大全| 亚洲人成77777在线视频| 久久久久久久久免费视频了| 国产高清激情床上av| 亚洲国产中文字幕在线视频| 国产一区二区在线观看日韩 | 亚洲一区二区三区色噜噜| 日韩高清综合在线| 久热爱精品视频在线9| 一个人免费在线观看电影 | 中国美女看黄片| 国产精品野战在线观看| 久久精品aⅴ一区二区三区四区| 老司机午夜十八禁免费视频| 动漫黄色视频在线观看| 人成视频在线观看免费观看| 色综合婷婷激情| 久久久水蜜桃国产精品网| www.熟女人妻精品国产| 色噜噜av男人的天堂激情| 国产区一区二久久| 亚洲一区二区三区色噜噜| 麻豆av在线久日| 色老头精品视频在线观看| 黄色视频,在线免费观看| 一级毛片女人18水好多| 在线永久观看黄色视频| 亚洲avbb在线观看| 97人妻精品一区二区三区麻豆| aaaaa片日本免费| 日本五十路高清| xxxwww97欧美| 两个人的视频大全免费| 久久久久九九精品影院| 一区二区三区高清视频在线| 久久这里只有精品19| 精品久久久久久,| 91麻豆av在线| 宅男免费午夜| 国产精品电影一区二区三区| 久久中文字幕人妻熟女| 又黄又爽又免费观看的视频| 一本久久中文字幕| 欧美一区二区精品小视频在线| 黄色片一级片一级黄色片| 天天躁狠狠躁夜夜躁狠狠躁| 中文在线观看免费www的网站 | 一本综合久久免费| 麻豆av在线久日| 两个人的视频大全免费| 一级片免费观看大全| 国产一级毛片七仙女欲春2| x7x7x7水蜜桃| 在线观看一区二区三区| 怎么达到女性高潮| 一个人免费在线观看电影 | 色综合亚洲欧美另类图片| 看免费av毛片| 精品免费久久久久久久清纯| 日本黄色视频三级网站网址| 日本撒尿小便嘘嘘汇集6| 日韩精品免费视频一区二区三区| 午夜精品一区二区三区免费看| 舔av片在线| 色老头精品视频在线观看| 毛片女人毛片| 午夜福利在线在线| 国内久久婷婷六月综合欲色啪| 五月玫瑰六月丁香| 在线观看舔阴道视频| 日韩av在线大香蕉| 国产99白浆流出| 夜夜夜夜夜久久久久| 少妇被粗大的猛进出69影院| 久久 成人 亚洲| 欧美日韩亚洲综合一区二区三区_| 欧美精品亚洲一区二区| 免费在线观看黄色视频的| 欧美在线黄色| 国产探花在线观看一区二区| 日本a在线网址| 精品国产超薄肉色丝袜足j| 99久久精品国产亚洲精品| 久久99热这里只有精品18| 久久 成人 亚洲| 国产成人精品久久二区二区91| 精品一区二区三区视频在线观看免费| 三级毛片av免费| 国内揄拍国产精品人妻在线| 可以在线观看毛片的网站| 狠狠狠狠99中文字幕| 国产精品久久久久久久电影 | 最近在线观看免费完整版| 久久国产乱子伦精品免费另类| 色尼玛亚洲综合影院| 久久久久久久午夜电影| 国产黄片美女视频| 亚洲人与动物交配视频| 国产精品香港三级国产av潘金莲| 国产激情欧美一区二区| 亚洲国产精品成人综合色| 99久久久亚洲精品蜜臀av| 99久久综合精品五月天人人| 天堂√8在线中文| 熟妇人妻久久中文字幕3abv| 亚洲成av人片免费观看| 少妇人妻一区二区三区视频| 久久久精品大字幕| 日日干狠狠操夜夜爽| 国产午夜福利久久久久久| 两人在一起打扑克的视频| 国产精品一及| 9191精品国产免费久久| 18禁裸乳无遮挡免费网站照片| 人人妻,人人澡人人爽秒播| 黄色丝袜av网址大全| 一个人免费在线观看的高清视频| 久久国产精品影院| 97超级碰碰碰精品色视频在线观看| 一进一出好大好爽视频| 搡老熟女国产l中国老女人| 国模一区二区三区四区视频 | 一个人免费在线观看的高清视频| 又紧又爽又黄一区二区| 97超级碰碰碰精品色视频在线观看| 欧美午夜高清在线| 好看av亚洲va欧美ⅴa在| 日本一二三区视频观看| 亚洲真实伦在线观看| 正在播放国产对白刺激| 久久中文字幕一级| 妹子高潮喷水视频| 成熟少妇高潮喷水视频| 九色成人免费人妻av| 免费无遮挡裸体视频| 国产成人av教育| 欧美午夜高清在线| 特级一级黄色大片| 最近在线观看免费完整版| 在线观看一区二区三区| 成人精品一区二区免费| 最近最新免费中文字幕在线| 精品人妻1区二区| 天堂影院成人在线观看| 免费观看人在逋| 黄色 视频免费看| 国产精品久久电影中文字幕| 国产精品爽爽va在线观看网站| 亚洲精品一卡2卡三卡4卡5卡| 亚洲欧美激情综合另类| 宅男免费午夜| 日本熟妇午夜| 国产麻豆成人av免费视频| 国产欧美日韩一区二区三| 黑人操中国人逼视频| 久久性视频一级片| 五月伊人婷婷丁香| 99精品在免费线老司机午夜| 日本 欧美在线| 此物有八面人人有两片| 观看免费一级毛片| 欧美成人性av电影在线观看| 欧美一区二区精品小视频在线| 成年女人毛片免费观看观看9| 国产精品av视频在线免费观看| 国产又黄又爽又无遮挡在线| 九色成人免费人妻av| 不卡av一区二区三区| www.www免费av| 欧美+亚洲+日韩+国产| 精品久久蜜臀av无| 岛国视频午夜一区免费看| 黑人操中国人逼视频| av超薄肉色丝袜交足视频| 在线观看免费视频日本深夜| 久久香蕉激情| 丁香欧美五月| 观看免费一级毛片| 欧美一区二区国产精品久久精品 | 中文字幕熟女人妻在线| 国产午夜福利久久久久久| 美女扒开内裤让男人捅视频| 久久久精品欧美日韩精品| 999久久久精品免费观看国产| 亚洲精品国产一区二区精华液| 久久久精品欧美日韩精品| 日本黄大片高清| 欧美国产日韩亚洲一区| 制服丝袜大香蕉在线| 12—13女人毛片做爰片一| 伦理电影免费视频| 亚洲av第一区精品v没综合| 97碰自拍视频| 国产成人av激情在线播放|