Application of Linear Discriminant Analysis in Dimensionality Reduction for Hand Motion Classification

The classification of upper-limb movements based on surface electromyography (EMG) signals is an important issue in the control of assistive devices and rehabilitation systems. Increasing the number of EMG channels and features in order to increase the number of control commands can yield a high dimensional feature vector. To cope with the accuracy and computation problems associated with high dimensionality, it is commonplace to apply a processing step that transforms the data to a space of significantly lower dimensions with only a limited loss of useful information. Linear discriminant analysis (LDA) has been successfully applied as an EMG feature projection method. Recently, a number of extended LDA-based algorithms have been proposed, which are more competitive in terms of both classification accuracy and computational costs/times with classical LDA. This paper presents the findings of a comparative study of classical LDA and five extended LDA methods. From a quantitative comparison based on seven multi-feature sets, three extended LDA-based algorithms, consisting of uncorrelated LDA, orthogonal LDA and orthogonal fuzzy neighborhood discriminant analysis, produce better class separability when compared with a baseline system (without feature projection), principle component analysis (PCA), and classical LDA. Based on a 7-dimension time domain and time-scale feature vectors, these methods achieved respectively 95.2% and 93.2% classification accuracy by using a linear discriminant classifier

Bootstrap Causal Feature Selection for irrelevant feature elimination

Irrelevant features may lead to degradation in accuracy and efficiency of classifier performance. In this paper, Bootstrap Causal Feature Selection (BCFS) algorithm is proposed. BCFS uses bootstrapping with a causal discovery algorithm to remove irrelevant features. The results are evaluated by the number of selected features and classification accuracy. According to the experimental results, BCFS is able to remove irrelevant features and provides slightly higher average accuracy than using the original features and causal feature selection. Moreover, BCFS also reduces complexity in causal graphs which provides more comprehensibility for the casual discovery system. © 2013 IEEE

Bootstrap Causal Feature Selection for irrelevant feature elimination

Irrelevant features may lead to degradation in accuracy and efficiency of classifier performance. In this paper, Bootstrap Causal Feature Selection (BCFS) algorithm is proposed. BCFS uses bootstrapping with a causal discovery algorithm to remove irrelevant features. The results are evaluated by the number of selected features and classification accuracy. According to the experimental results, BCFS is able to remove irrelevant features and provides slightly higher average accuracy than using the original features and causal feature selection. Moreover, BCFS also reduces complexity in causal graphs which provides more comprehensibility for the casual discovery system. © 2013 IEEE

Classifying Breast Cancer Microscopic Images using Fractal Dimension and Ensemble Classifier

To improve the performance of the computer-aided systems for breast cancer diagnosis, the ensemble classifier is proposed for classifying the histological structures in the breast cancer microscopic images into three region types: positive cancer cells, negative cancer cells and non-cancer cell (stromal cells and lymphocyte cells) image. The bagging and boosting ensemble techniques are used with the decision tree (DT) learner. They are also compared with the single classifier, DT. The feature used as an input of classifiers is the fractal dimension (FD) based 12 color channels. It is computed from the image datasets, which are manually prepared in small cropped image with 3 window sizes including 128×128 pixels, 192×192 pixels and 256×256 pixels. The results show that the boosting ensemble classifier gives the best accuracy about 80% from window size of 256, although it is the lowest when using the single DT as classifier. The results indicated that the ensemble method is capable of improving the accuracy in the classification compared to the single classifier. The classification model using FD and the ensemble classifier would be applied to develop the computer- aided systems for breast cancer diagnosis in the future

Critical exponent analysis applied to surface EMG signals for multifunction myoelectric control

Based on recent advances in non-linear analysis, the surface electromyography (sEMG) signal has been studied from the viewpoints of self-affinity and complexity. In this study, we examine usage of critical exponent analysis (CE) method, a fractal dimension (FD) estimator, to study properties of the sEMG signal and to deploy these properties to characterize different movements for gesture recognition. SEMG signals were recorded from thirty subjects with seven hand movements and eight muscle channels. Mean values and coefficient of variations of the CE from all experiments show that there are larger variations between hand movement types but there is small variation within the same type. It also shows that the CE feature related to the self-affine property for the sEMG signal extracted from different activities is in the range of 1.855∼2.754. These results have also been evaluated by analysis-of-variance (p-value). Results show that the CE feature is more suitable to use as a learning parameter for a classifier compared with other representative features including root mean square, median frequency and Higuchi's method. Most p-values of the CE feature were less than 0.0001. Thus the FD that is computed by the CE method can be applied to be used as a feature for a wide variety of sEMG applications

Evaluating Feature Extraction Methods of Electrooculography (EOG) Signal for Human-Computer Interface

AbstractElectrooculography (EOG) signal is a widely and successfully used to detect activities of human eye. Use of the EOG signals as a control signal for human-computer interface (HCI) plays a central role in the understanding, characterization and classification of eye movements which can be applied to a wide variety of applications consisting virtual mouse and keyboard control, electric power wheelchairs and industrial assistive robots. The advantages of the EOG-based interface over other conventional interfaces have been presented in the last two decades; however, due to a lot of information in EOG signals, the extraction of useful features should be done before the classification task. In this study, fourteen useful features extracted from two directional EOG signals: vertical (V) and horizontal (H) signals have been presented and evaluated. There are the maximum peak and valley amplitude values (PAV and VAV), the maximum peak and valley position values (PAP and VAP), the area under curve value (AUC), the number of threshold crossing value (TCV), and EOG variance (VAR), which are derived from both V and H signals. In the experiments, EOG signals obtained from three healthy subjects with eight directional eye movements were employed: up, down, right, left, up-right, up-left, down-right and down-left. The mean feature values and their standard deviations have been reported. Most features show the difference between the mean feature values. Using the analysis-of-variation test, the differences in mean features between the movements are statistically significant for ten features (p < 0.0001), particularly for the VAV, VAP, AUC, TCV and VAR of V signal, and the PAV, VAV, AUC, TCV and VAR of H signal. The combination of these features may be useful for the classification of EOG signals in both class separability and robustness point of views. Using multiple features with sufficient classifiers or threshold techniques is recommended to be evaluated in further analysis. These features can be useful for various advanced HCI applications in future researches, notably eye-exercise and eye-writing recognitions

Evaluation of feature extraction techniques and classifiers for finger movement recognition using surface electromyography signal

© 2018, International Federation for Medical and Biological Engineering. Electromyography (EMG) in a bio-driven system is used as a control signal, for driving a hand prosthesis or other wearable assistive devices. Processing to get informative drive signals involves three main modules: preprocessing, dimensionality reduction, and classification. This paper proposes a system for classifying a six-channel EMG signal from 14 finger movements. A feature vector of 66 elements was determined from the six-channel EMG signal for each finger movement. Subsequently, various feature extraction techniques and classifiers were tested and evaluated. We compared the performance of six feature extraction techniques, namely principal component analysis (PCA), linear discriminant analysis (LDA), uncorrelated linear discriminant analysis (ULDA), orthogonal fuzzy neighborhood discriminant analysis (OFNDA), spectral regression linear discriminant analysis (SRLDA), and spectral regression extreme learning machine (SRELM). In addition, we also evaluated the performance of seven classifiers consisting of support vector machine (SVM), linear classifier (LC), naive Bayes (NB), k-nearest neighbors (KNN), radial basis function extreme learning machine (RBF-ELM), adaptive wavelet extreme learning machine (AW-ELM), and neural network (NN). The results showed that the combination of SRELM as the feature extraction technique and NN as the classifier yielded the best classification accuracy of 99%, which was significantly higher than those from the other combinations tested. [Figure not available: see fulltext.]

An optical counting technique with vertical hydrodynamic focusing for biological cells

A barrier in scaling laboratory processes into automated microfluidic devices has been the transfer of lab based assays: where engineering meets biological protocol. One basic requirement is to reliably and accurately know the distribution and number of biological cells being dispensed. In this study, a novel optical counting technique to efficiently quantify the number of cells flowing into a microtube is presented. REH, B-lymphoid precursor leukaemia, are stained with a fluorescent dye and frames of moving cells are recorded using a CCD camera. The basic principle is to calculate the total fluorescence intensity of the image and to divide it by the average intensity of a single cell. This method allows counting the number of cells with an uncertainty +/- 5%, which compares favourably to the standard biological methodology, based on the manual Trypan Blue assay, which is destructive to the cells and presents an uncertainty in the order of 20%. The use of a microdevice for vertical hydrodynamic focusing, which can reduce the background noise of out of focus cells by concentrating the cells in a thin layer, has further improved the technique. CFD simulation and Confocal Laser Scanning Microscopy images have shown an 82% reduction in the vertical displacement of the cells. For the flow rates imposed during this study, a throughput of 100-200 cells/sec is achieved