scholarly journals Surface electromyography feature extraction via convolutional neural network

2019 ◽  
Vol 11 (1) ◽  
pp. 185-196 ◽  
Author(s):  
Hongfeng Chen ◽  
Yue Zhang ◽  
Gongfa Li ◽  
Yinfeng Fang ◽  
Honghai Liu
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Convolutional Neural Network ◽  
Surface Electromyography

Deep convolutional neural networks for human movement detection using wireless signals

2021 ◽  
pp. 1-10
Author(s):  
Chien-Cheng Leea ◽  
Zhongjian Gao ◽  
Xiu-Chi Huanga
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Convolutional Neural Network ◽  
Detection System ◽  
Deep Convolutional Neural Network ◽  
Human Detection ◽  
Two Dimensional ◽  
Dimensional Matrix ◽  
State Classification ◽  
Propagation Paths

This paper proposes a Wi-Fi-based indoor human detection system using a deep convolutional neural network. The system detects different human states in various situations, including different environments and propagation paths. The main improvements proposed by the system is that there is no cameras overhead and no sensors are mounted. This system captures useful amplitude information from the channel state information and converts this information into an image-like two-dimensional matrix. Next, the two-dimensional matrix is used as an input to a deep convolutional neural network (CNN) to distinguish human states. In this work, a deep residual network (ResNet) architecture is used to perform human state classification with hierarchical topological feature extraction. Several combinations of datasets for different environments and propagation paths are used in this study. ResNet’s powerful inference simplifies feature extraction and improves the accuracy of human state classification. The experimental results show that the fine-tuned ResNet-18 model has good performance in indoor human detection, including people not present, people still, and people moving. Compared with traditional machine learning using handcrafted features, this method is simple and effective.

AUTOMATED SCREENING OF DIABETIC RETINOPATHY WITH OPTIMIZED DEEP CONVOLUTIONAL NEURAL NETWORK: ENHANCED MOTH FLAME MODEL

2021 ◽  
Vol 21 (01) ◽  
pp. 2150005
Author(s):  
ARUN T NAIR ◽  
K. MUTHUVEL
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Blood Vessel ◽  
Convolutional Neural Network ◽  
Optimization Algorithm ◽  
Vessel Segmentation ◽  
Deep Convolutional Neural Network ◽  
Gray Level ◽  
Pass Filter ◽  
Blood Vessel Segmentation

Nowadays, analysis on retinal image exists as one of the challenging area for study. Numerous retinal diseases could be recognized by analyzing the variations taking place in retina. However, the main disadvantage among those studies is that, they do not have higher recognition accuracy. The proposed framework includes four phases namely, (i) Blood Vessel Segmentation (ii) Feature Extraction (iii) Optimal Feature Selection and (iv) Classification. Initially, the input fundus image is subjected to blood vessel segmentation from which two binary thresholded images (one from High Pass Filter (HPF) and other from top-hat reconstruction) are acquired. These two images are differentiated and the areas that are common to both are said to be the major vessels and the left over regions are fused to form vessel sub-image. These vessel sub-images are classified with Gaussian Mixture Model (GMM) classifier and the resultant is summed up with the major vessels to form the segmented blood vessels. The segmented images are subjected to feature extraction process, where the features like proposed Local Binary Pattern (LBP), Gray-Level Co-Occurrence Matrix (GLCM) and Gray Level Run Length Matrix (GLRM) are extracted. As the curse of dimensionality seems to be the greatest issue, it is important to select the appropriate features from the extracted one for classification. In this paper, a new improved optimization algorithm Moth Flame with New Distance Formulation (MF-NDF) is introduced for selecting the optimal features. Finally, the selected optimal features are subjected to Deep Convolutional Neural Network (DCNN) model for classification. Further, in order to make the precise diagnosis, the weights of DCNN are optimally tuned by the same optimization algorithm. The performance of the proposed algorithm will be compared against the conventional algorithms in terms of positive and negative measures.

scholarly journals Pattern recognition and bionic manipulator driving by surface electromyography signals using convolutional neural network

2018 ◽  
Vol 15 (5) ◽  
pp. 172988141880213 ◽  
Author(s):  
Yuanfang Wan ◽  
Zishan Han ◽  
Jun Zhong ◽  
Guohua Chen
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Surface Electromyography ◽  
Hot Spot ◽  
Hand Movement ◽  
Ease Of Use ◽  
Electrode Arrays ◽  
Small Surface ◽  
Movement Data ◽  
Electromyography Signals

With the development of robotics, intelligent neuroprosthesis for amputees is more concerned. Research of robot controlling based on electrocardiogram, electromyography, and electroencephalogram is a hot spot. In medical research, electrode arrays are commonly used as sensors for surface electromyograms. Although these sensors collect more accurate data and sampling at higher frequencies, they have no advantage in terms of portability and ease of use. In recent years, there are also some small surface electromyography sensors for research. The portability of the sensor and the calculation speed of the calculation method directly affect the development of the bionic prosthesis. A consumer-grade surface electromyography device is selected as surface electromyography sensor in this study. We first proposed a data structure to convert raw surface electromyography signals from an array structure into a matrix structure (we called it surface electromyography graph). Then, a convolutional neural network was used to classify it. Discrete surface electromyography signals recorded from three persons 14 gestures (widely used in other research to evaluate the performance of classifier) have been applied to train the classifier and we get an accuracy of 97.27%. The impacts of different components used in convolutional neural network were tested with this data, and subsequently, the best results were selected to build the classifier used in this article. The NinaPro database 5 (one of the biggest surface electromyography data sets) was also used to evaluate our method, which comprises of hand movement data of 10 intact subjects with two myo armbands as sensors, and the classification accuracy increased by 13.76% on average when using double myo armbands and increased by 18.92% on average when using single myo armband. In order to driving the robot hand (bionic manipulator), a group of continuous surface electromyography signals was recorded to train the classifier, and an accuracy of 91.72% was acquired. We also used the same method to collect a set of surface electromyography data from a disabled with hand lost, then classified it using the abovementioned network and achieved an accuracy of 89.37%. Finally, the classifier was deployed to the microcontroller to drive the bionic manipulator, and the full video URL is given in the conclusion, with both the healthy man and the disabled tested with the bionic manipulator. The abovementioned results suggest that this method will help to facilitate the development and application of surface electromyography neuroprosthesis.

scholarly journals Deep Learning Assisted Neonatal Cry Classification via Support Vector Machine Models

2021 ◽  
Vol 9 ◽  
Author(s):  
Ashwini K ◽  
P. M. Durai Raj Vincent ◽  
Kathiravan Srinivasan ◽  
Chuan-Yu Chang
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Support Vector Machine ◽  
Feature Extraction ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Support Vector ◽  
Svm Classifier ◽  
Infant Cry ◽  
Learning Techniques

Neonatal infants communicate with us through cries. The infant cry signals have distinct patterns depending on the purpose of the cries. Preprocessing, feature extraction, and feature selection need expert attention and take much effort in audio signals in recent days. In deep learning techniques, it automatically extracts and selects the most important features. For this, it requires an enormous amount of data for effective classification. This work mainly discriminates the neonatal cries into pain, hunger, and sleepiness. The neonatal cry auditory signals are transformed into a spectrogram image by utilizing the short-time Fourier transform (STFT) technique. The deep convolutional neural network (DCNN) technique takes the spectrogram images for input. The features are obtained from the convolutional neural network and are passed to the support vector machine (SVM) classifier. Machine learning technique classifies neonatal cries. This work combines the advantages of machine learning and deep learning techniques to get the best results even with a moderate number of data samples. The experimental result shows that CNN-based feature extraction and SVM classifier provides promising results. While comparing the SVM-based kernel techniques, namely radial basis function (RBF), linear and polynomial, it is found that SVM-RBF provides the highest accuracy of kernel-based infant cry classification system provides 88.89% accuracy.

scholarly journals Persian Handwritten Digit Recognition Using Combination of Convolutional Neural Network and Support Vector Machine Methods

2020 ◽  
Vol 17 (4) ◽  
pp. 572-578
Author(s):  
Mohammad Parseh ◽  
Mohammad Rahmanimanesh ◽  
Parviz Keshavarzi
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Feature Extraction ◽  
Convolutional Neural Network ◽  
Recognition Rate ◽  
Support Vector ◽  
Svm Classifier ◽  
Handwritten Digit Recognition ◽  
Digit Recognition ◽  
Handwritten Digit

Persian handwritten digit recognition is one of the important topics of image processing which significantly considered by researchers due to its many applications. The most important challenges in Persian handwritten digit recognition is the existence of various patterns in Persian digit writing that makes the feature extraction step to be more complicated.Since the handcraft feature extraction methods are complicated processes and their performance level are not stable, most of the recent studies have concentrated on proposing a suitable method for automatic feature extraction. In this paper, an automatic method based on machine learning is proposed for high-level feature extraction from Persian digit images by using Convolutional Neural Network (CNN). After that, a non-linear multi-class Support Vector Machine (SVM) classifier is used for data classification instead of fully connected layer in final layer of CNN. The proposed method has been applied to HODA dataset and obtained 99.56% of recognition rate. Experimental results are comparable with previous state-of-the-art methods

scholarly journals The Role and Utilization of CNN in Automatic Logo Based Document Image Retrieval Methods

2018 ◽  
Vol 7 (3.1) ◽  
pp. 13
Author(s):  
Raveendra K ◽  
R Vinoth Kanna
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Feature Extraction ◽  
Image Retrieval ◽  
Convolutional Neural Network ◽  
Convolutional Neural Networks ◽  
Document Image ◽  
Retrieval Process ◽  
Pictorial Representations

Automatic logo based document image retrieval process is an essential and mostly used method in the feature extraction applications. In this paper the architecture of Convolutional Neural Network (CNN) was elaborately explained with pictorial representations in order to understand the complex Convolutional Neural Networks process in a simplified way. The main objective of this paper is to effectively utilize the CNN in the process of automatic logo based document image retrieval methods.  

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