Support Vector Machine-Based Classification Scheme for Myoelectric Control Applied to Upper Limb

This paper proposes and evaluates the application of support vector machine (SVM) to classify upper limb motions using myoelectric signals. It explores the optimum configuration of SVM-based myoelectric control, by suggesting an advantageous data segmentation technique, feature set, model selection approach for SVM, and postprocessing methods. This work presents a method to adjust SVM parameters before classification, and examines overlapped segmentation and majority voting as two techniques to improve controller performance. A SVM, as the core of classification in myoelectric control, is compared with two commonly used classifiers: linear discriminant analysis (LDA) and multilayer perceptron (MLP) neural networks. It demonstrates exceptional accuracy, robust performance, and low computational load. The entropy of the output of the classifier is also examined as an online index to evaluate the correctness of classification; this can be used by online training for long-term myoelectric control operations. © 2006 IEEE

Adaptive schemes applied to online SVM for BCI data classification

This paper evaluates supervised and unsupervised adaptive schemes applied to online support vector machine (SVM) that classifies BCI data. Online SVM processes fresh samples as they come and update existing support vectors without referring to pervious samples. It is shown that the performance of online SVM is similar to that of the standard SVM, and both supervised and unsupervised schemes improve the classification hit rate. ©2009 IEEE

Feature-channel subset selection for optimising myoelectric human-machine interface design

This paper proposes a feature-channel subset selection method for obtaining an optimal subset of features and channels of myoelectric human-machine interface applied to classify upper limb?s motions using multi-channel myoelectric signals. It employs a multi-objective genetic algorithm as a search strategy and either data separability index or classification rate as an objective function. A wide range of features in time, frequency, and time-scale domains are examined, and a modification that reduces the bias of cardinality in the separability index is evaluated. The proposed method produces a compact subset of features and channels, which results in high accuracy by linear classifiers without a need of preliminary tailor-made adjustments

EMG signal based knee joint angle estimation of flexion and extension with extreme learning machine (ELM) for enhancement of patient-robotic exoskeleton interaction

© Springer International Publishing Switzerland 2015. To capture the intended action of the patient and provide assistance as needed, the robotic rehabilitation device controller needs the intended posture, intended joint angle, intended torque and intended desired impedance of the patient. These parameters can be extracted from sEMG signal that are associated with knee joint. Thus an exoskeleton device requires a multilayer control mechanism to achieve a smooth Human Machine Interaction force. This paper proposes a method to estimate the required knee joint angles and associate parameters. The paper has investigated the feasibility of Extreme Learning Machine (ELM) as a estimator of the operation range of extension and The performance is compared with Generalized Regression Neural Network (GRNN) and Neural Network (NN). ELM has performed relatively better than GRNN and NN

Humic Acid Degradation by ZnO Photocatalyst

Humic acid (HA) is universally present in soils and natural water resources in a yellow-brown form. HA can react with chlorine during drinking water treatment and produce disinfection byproducts (DBPs), such as trihalomethanes (THMs) and haloacetic acids (HAAs), which are harmful for health. Therefore, HA has to be eliminated from water environment. The photocatalysis is an effective alternative solution for the degradation of HA in a water environment. This research aims to degrade HA from water environment. The rapid degradation of HA, using zinc oxide nanoparticles, irradiated by ultraviolet light (ZnO/UV), is investigated. The optimum conditions of pertinent factors, which include the light wavelength (UV-A and UV-C), and light intensity, HA concentration, ZnO dose, and contact time are investigated at neutral pH conditions, considered for drinking water treatment. HA degradation efficiency reached more than 80% after 60 min for both types of irradiation in optimum conditions of 0.3 g/L ZnO dose in 180 min of contact time. Comparisons for degradation efficiency under UV-A and UV-C irradiation indicate that UV-C has higher efficiency, up to 150 min of contact time. The reusability of catalyst is performed for three reuses and still revealed effective for beneficial commercial applications