Integration of EMG and Machine Learning for Real-Time Control of a 3D-Printed Prosthetic Arm

Complexity and spectral analysis of the heart rate variability dynamics for distant prediction of paroxysmal atrial fibrillation with artificial intelligence methods.

Accurate diagnosis of cancer plays an importance role in order to save human life. The results of the diagnosis indicate by the medical experts are mostly differentiated based on the experience of different medical experts. This problem could risk the life of the cancer patients. From the literature, it has been found that Artificial Intelligence (AI) machine learning classifiers such as an Artificial Neural Network (ANN) and Support Vector Machine (SVM) can help doctors in diagnosing cancer more precisely. Both of them have been proven to produce good performance of cancer classification accuracy. The aim of this study is to compare the performance of the ANN and SVM classifiers on four different cancer datasets. For breast cancer and liver cancer dataset, the features of the data are based on the condition of the organs which is also called as standard data while for prostate cancer and ovarian cancer; both of these datasets are in the form of gene expression data. The datasets including benign and malignant tumours is specified to classify with proposed methods. The performance of both classifiers is evaluated using four different measuring tools which are accuracy, sensitivity, specificity and Area under Curve (AUC). This research has shown that the SVM classifier can obtain good performance in classifying cancer data compare to ANN classifier.

Fault diagnosis, centered on pattern recognition techniques employing online measurements of process data, has been studied during the past decades. Amongst those techniques, artificial neural networks classifiers received an enormous attention due to some of their remarkable features. Recently, a new machine learning method based on statistical learning theory known as the Support Vector Machine (SVM) classifier is offered in the pattern recognition field. Support vector machine classifiers were originally used to solve binary classification problems. Subsequently, methods were proposed to apply support vector machine classifier to multiclass problems. Two of these mostly used methods are known as one versus one and one versus all. This paper deals with the application of the above mentioned classifiers for fault diagnosis of a chemical process containing a continuous stirred tank reactor and a heat exchanger. The results show a superior classification performance of the support vector machine versus the selected artificial neural network. In addition, the support vector machine classifier is very sensitive to the proper selection of the training parameters. It is shown that the utilization of genetic algorithm for optimal selection of these parameters is feasible and can help to improve the support vector machine classifier performance.

Signal of humming sound is the input which is important for the Query-by-Humming system. This input signal which has variable dimension depend on humming time interval will always affect the feature vector. It cannot be used with some classifiers, which require non-variable dimension of feature vector, such as Artificial Neural Network (ANN) or Support Vector Machine (SVM). Especially, SVM is good classifier and it might be appropriate for our work. Because of each signal of humming sound has variable dimension and length, this is the main problem which we would like to come up with the idea to solve it. We have an idea to create a new feature space that has the same dimension in order to use with SVM classifier. In this paper, we propose indirect feature, it is used distance between template and observation sequence for creating new feature vector. This technique can be briefly described: Firstly, templates are distributed in original feature space. When the observation sequence gets into this space, Dynamic Time Warping (DTW) will measure the distance between observation sequence and existing templates. These distance are used to get the new feature vector in new space, called distance space. In this way, all feature vectors are non-variable dimension therefore we used SVM and ANN classifier. The experimental results show that the new feature vector which is used by SVM classifier gives better results than ANN.

Accurate land use/cover (LUC) classifications from satellite imagery are very important for eco-environment monitoring, land use planning and climatic change detection. Traditional statistical classifiers such as minimum distance (MD) have been used to extract LUC classifications in urban areas, but these classifiers rely on assumptions that may limit their utilities for many datasets. On the contrary, artificial neural network (ANN) and support vector machine (SVM) provide nonlinear and accurate ways to classify LUC from remote sensing images without having to rely on statistical assumptions. This article compared the results and accuracies of the ANN and SMV classifiers with the statistical MD classifier as a reference in extracting urban LUC from ETM+ images in Guangzhou, China. Results show that the overall accuracies of urban LUC classifications are approximately 96.03%, 94.71% and 77.79%, and the kappa coefficients reach 0.94, 0.92 and 0.67 for the ANN, SVM and MD classifiers, respectively, indicating that the ANN and SVM classifiers have greatly better accuracies than the traditional MD algorithm. It is concluded that while the ANN performs slightly better than the SVM, both ANN and SVM are effective algorithms in urban LUC extraction from ETM+ images because of their high accuracy and good performance.

Hemiparesis is one of the most frequent post-stroke conditions, which causes muscle weakness and/or inability to move one side of the body. Physical rehabilitation is the main treatment for hemiparesis recovery, and physiotherapists agree that using the impaired arm in the activities of daily living (ADLs) is crucial for a complete recovery. Currently, rehabilitation is assessed through diaries and self-questionnaires, which are subjective and do not tell the real condition of the patients throughout the day. Assistive devices can objectively evaluate the functional improvement of the impaired arm monitoring its activity. This work aimed to identify the purposeful arm movements during patient's ADLs. We consider arm's swing while walking as a non-purposeful movement. Firstly, the event-based approach was applied to separate movement and non-movement segments. Secondly, movement segments were used to detect the change-point (events) and their locations in time series signal. Two machine learning classifiers, Support Vector Machine (SVM) and Artificial Neural Network (ANN), were trained using 10-fold cross validation for the classification of purposeful and non-purposeful movements. Data from 10 healthy and 12 post-strokes volunteers from Institute Guttmann (Barcelona) were collected using the SensHand device. The volunteers, wearing one SensHand on each wrist, performed the following activities: resting, eating, pouring water, drinking, brushing, folding towel, grasp towel, grasp brush, grasp glass, continuous and walking. Developing a model based on the healthy subjects, the overall classification accuracy obtained from SVM classifier and ANN was 81.21% and 97.06% respectively. Similarly, with post-stroke subjects obtained accuracy with the SVM and ANN was 84.18% and 99.74% respectively. Considering the whole dataset, SVM and ANN obtained maximum accuracy equal to 86.21% and 99.91% respectively. In conclusion, our work showed promising results for the classification of purposeful and non-purposeful movements.

A classification technique using Support Vector Machine (SVM) classifier for detection of rolling element bearing fault is presented here. The SVM was fed from features that were extracted from of vibration signals obtained from experimental setup consisting of rotating driveline that was mounted on rolling element bearings which were run in normal and with artificially faults induced conditions. The time-domain vibration signals were divided into 40 segments and simple features such as peaks in time domain and spectrum along with statistical features such as standard deviation, skewness, kurtosis etc. were extracted. Effectiveness of SVM classifier was compared with the performance of Artificial Neural Network (ANN) classifier and it was found that the performance of SVM classifier is superior to that of ANN. The effect of pre-processing of the vibration signal by Discreet Wavelet Transform (DWT) prior to feature extraction is also studied and it is shown that pre-processing of vibration signal with DWT enhances the effectiveness of both ANN and SVM classifiers. It has been demonstrated from experiment results that performance of SVM classifier is better than ANN in detection of bearing condition and pre-processing the vibration signal with DWT improves the performance of SVM classifier.

PurposeSurveillance of patients with high-grade glioma (HGG) and identification of disease progression remain a major challenge in neurooncology. This study aimed to develop a support vector machine (SVM) classifier, employing combined longitudinal structural and perfusion MRI studies, to classify between stable disease, pseudoprogression and progressive disease (3-class problem).MethodsStudy participants were separated into two groups: group I (total cohort: 64 patients) with a single DSC time point and group II (19 patients) with longitudinal DSC time points (2-3). We retrospectively analysed 269 structural MRI and 92 dynamic susceptibility contrast perfusion (DSC) MRI scans. The SVM classifier was trained using all available MRI studies for each group. Classification accuracy was assessed for different feature dataset and time point combinations and compared to radiologists’ classifications.ResultsSVM classification based on combined perfusion and structural features outperformed radiologists’ classification across all groups. For the identification of progressive disease, use of combined features and longitudinal DSC time points improved classification performance (lowest error rate 1.6%). Optimal performance was observed in group II (multiple time points) with SVM sensitivity/specificity/accuracy of 100/91.67/94.7% (first time point analysis) and 85.71/100/94.7% (longitudinal analysis), compared to 60/78/68% and 70/90/84.2% for the respective radiologist classifications. In group I (single time point), the SVM classifier also outperformed radiologists’ classifications with sensitivity/specificity/accuracy of 86.49/75.00/81.53% (SVM) compared to 75.7/68.9/73.84% (radiologists).ConclusionOur results indicate that utilisation of a machine learning (SVM) classifier based on analysis of longitudinal perfusion time points and combined structural and perfusion features significantly enhances classification outcome (p value= 0.0001).

Aim: The study aims to extract features from EEG signals and classify emotion using Support Vector Machine (SVM) and Hidden Markov Model (HMM) classifier. Materials and methods: The study was conducted using the Support Vector Machine (SVM) and Hidden Markov Model (HMM) programs to analyze and compare the recognition of emotions classified under EEG signals. The results were computed using the MATLAB algorithm. For each group, ten samples were used to compare the efficiency of SVM and HMM classifiers. Result: The study’s performance exhibits the HMM classifier’s accuracy over the SVM classifier and the emotion detection from EEG signals computed. The mean value of the HMM classifier is 52.2, and the SVM classifier is 22.4. The accuracy rate of 35% with the data features is found in HMM classifier. Conclusion: This study shows a higher accuracy level of 35% for the HMM classifier when compared with the SVM classifier. In the detection of emotions using the EEG signal. This result shows that the HMM classifier has a higher significant value of P=.001 < P=.005 than the SVM classifier.

The present work gives a comparative analysis of two different classifiers, namely, Support Vector Machine (SVM) and Artificial Neural Network (ANN) to classify defective and non-defective fabric images. The image dataset is prepared by considering all the varieties of fabric materials. The morphological operations, namely, erosion and dilation are used. A total of twelve morphological features are considered for fabric image analysis. Significant morphological features are selected by adopting Feed Backward Selection Technique (FBST) that is applied in the feature reduction process. The overall classification accuracies of 94% and 86.5% are obtained using SVM and ANN classifiers respectively. The SVM classifier is found to give better classification rate than ANN classifier. The work finds applications in apparel industry, quality analysis, cost estimation, online purchase of fabric etc.

Evaluation of ANN and SVM classifiers as predictors to the diagnosis of students with learning disabilities

This article presents a sufficient comparison of two types of advanced non-parametric classifiers implemented in remote sensing for land cover classification. A SPOT-5 HRG image of Yanqing County, Beijing, China, was used, in which agriculture and forest dominate land use. Artificial neural networks (ANNs), including the adaptive backpropagation (ABP) algorithm, Levenberg–Marquardt (LM) algorithm, Quasi-Newton (QN) algorithm and radial basis function (RBF) were carefully tested. The LM–ANN and RBF–ANN, which outperform the other two, were selected to make a detailed comparison with support vector machines (SVMs). The experiments show that those well-trained ANNs and SVMs have no significant difference in classification accuracy, but the SVM usually performs slightly better. Analysis of the effect of the training set size highlights that the SVM classifier has great tolerance on a small training set and avoids the problem of insufficient training of ANN classifiers. The testing also illustrates that the ANNs and SVMs can vary greatly with regard to training time. The LM–ANN can converge very quickly but not in a stable manner. By contrast, the training of RBF–ANN and SVM classifiers is fast and can be repeatable.

The knowledge of Arctic Sea ice coverage is of particular importance in studies of climate change. This study develops a new sea ice classification approach based on machine learning (ML) classifiers through analyzing spaceborne GNSS-R features derived from the TechDemoSat-1 (TDS-1) data collected over open water (OW), first-year ice (FYI), and multi-year ice (MYI). A total of eight features extracted from GNSS-R observables collected in five months are applied to classify OW, FYI, and MYI using the ML classifiers of random forest (RF) and support vector machine (SVM) in a two-step strategy. Firstly, randomly selected 30% of samples of the whole dataset are used as a training set to build classifiers for discriminating OW from sea ice. The performance is evaluated using the remaining 70% of samples through validating with the sea ice type from the Special Sensor Microwave Imager Sounder (SSMIS) data provided by the Ocean and Sea Ice Satellite Application Facility (OSISAF). The overall accuracy of RF and SVM classifiers are 98.83% and 98.60% respectively for distinguishing OW from sea ice. Then, samples of sea ice, including FYI and MYI, are randomly split into training and test dataset. The features of the training set are used as input variables to train the FYI-MYI classifiers, which achieve an overall accuracy of 84.82% and 71.71% respectively by RF and SVM classifiers. Finally, the features in every month are used as training and testing set in turn to cross-validate the performance of the proposed classifier. The results indicate the strong sensitivity of GNSS signals to sea ice types and the great potential of ML classifiers for GNSS-R applications.

Support Vector Machines (SVMs) are an emerging machine learning technique that has found widespread application in various areas during the past four years. The success of SVMs is mainly due to a number of attractive features, including a) applicability to the processing of high dimensional data, b) ability to achieve a global optimum, and c) the ability to deal with nonlinear data. One potential application for SVMs is High Range Resolution (HRR) radar signatures, typically used for HRR-based Automatic Target Recognition (ATR). HRR signatures are problematic for many traditional ATR algorithms because of the unique characteristics of HRR signatures. For example, HRR signatures are generally high dimensional, linearly inseparable, and extremely sensitive to aspect changes. In this paper we demonstrate that SVMs are a promising alternative in dealing with the challenges of HRR signatures. The studies presented in this paper represent an initial attempt at applying SVMs to HRR data. The most straightforward application of SVMs to HRR-based ATR is to use SVMs as classifiers. We experimentally compare the performance of SVM-based classifiers with several conventional classifiers, such as k-Nearest-Neighbor (kNN) classifiers and Artificial Neural Network (ANN) Classifiers. Experimental results suggest that SVM classifiers possess a number of advantages. For example, a) applying SVM classifiers to HRR data requires little prior knowledge of the target data, b) SVM classifiers require much less computation than kNN classifiers during testing, and c) the structure of a trained SVM classifier can reveal a number of important properties of the target data.

This paper proposes a real-time myoelectric control method based on pattern recognition for an anthropomorphic robotic hand platform. The work presented consists of (i)electromyogram (EMG)data acquisition; (ii)hand gesture discrimination including muscle activity detection, data segmentation, feature extraction, classification, and postprocessing; (iii)the development of an anthropomorphic robotic hand platform. Support Vector Machine (SVM)and Linear Discriminant Analysis (LDA)classifiers along with five EMG signal features are examined and compared for constructing a feasible real-time control system. Offline and real-time testing were conducted in two separate experiments involved both able-bodied and disable subjects. The SVM classifier obtained better performance with single feature sets whereas the LDA classifier achieved slightly higher accuracy for the combined multiple features. The experiment and testing results showed that the presented method has the potential to drive robotic system based on real time human hand gestures.