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

Simple and computationally efficient movement classification approach for EMG-controlled prosthetic hand: ANFIS vs. artificial neural network

The aim of this paper is to propose an exploratory study on simple, accurate and computationally efficient movement classification technique for prosthetic hand application. The surface myoelectric signals were acquired from 2 muscles—Flexor Carpi Ulnaris and Extensor Carpi Radialis of 4 normal-limb subjects. These signals were segmented and the features extracted using a new combined time-domain method of feature extraction. The fuzzy C-mean clustering method and scatter plots were used to evaluate the performance of the proposed multi-feature versus other accurate multi-features. Finally, the movements were classified using the adaptive neuro-fuzzy inference system (ANFIS) and the artificial neural network. Comparison results indicate that ANFIS not only displays higher classification accuracy (88.90%) than the artificial neural network, but it also improves computation time significantl

Latent Space Reinforcement Learning

Often we have to handle high dimensional spaces if we want to learn motor skills for robots. In policy search tasks we have to find several parameters to learn a desired movement. This high dimensionality in parameters can be challenging for reinforcement algorithms, since more samples for finding an optimal solution are needed with every additional dimension. On the other hand, if the robot has a high number of actuators, an inherent correlation between these can be found for a specific motor task, which we can exploit for a faster convergence. One possibility is to use techniques to reduce the dimensionality of the space, which is used as a pre-processing step or as an independent process in most applications. In this thesis we present a novel algorithm which combines the theory of policy search and probabilistic dimensionality reduction to uncover the hidden structure of high dimensional action spaces. Evaluations on an inverse kinematics task indicate that the presented algorithm is able to outperform the reference algorithms PoWER and CMA-ES, especially in high dimensional spaces. Furthermore we evaluate our algorithm on a real-world task. In this task, a NAO robot learns to lift his leg while keeping balance. The issue of collecting samples for learning on a real robot in such a task, which is often very time and cost consuming, is considered in here by using a small number of samples in each iteration

Ferramenta computacional gráfica para estimação de características de sinais de eletromiografia de superfície multicanal

Dissertação (mestrado)— Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Elétrica, 2013.A literatura científica na área da eletromiografia de superfície (S-EMG)vem se aprofundando recentemente e expondo as potencialidades da S-EMG na análise e investigação de problemasneuromotores, doenças e outros aspectos referentes à estrutura neuromuscular, através da análise de característicasobtidas por estimadores aplicados aos sinais de eletrodos multicanais. Apesar do uso cada vez mais difundido da S-EMG, não se verifica um aumento proporcional da quantidade de ferramentas computacionais disponíveis para profissionais que não tenham experiência específica de programação e de métodos matemáticos relacionados aos estimadores. Nesse contexto, esse trabalho propõe o desenvolvimento de uma ferramenta computacional com interface gráfica que possa ser utilizado por qualquer profissional que atue na análise de sinais de S-EMG, a partir de sinais coletados por uma matriz de eletrodos, disponibilizando recursos gráficos para a visualização do sinal no domínio do tempo e da frequência, davelocidade de condução (CV), raiz quadrática média (RMS), valor retificado médio (ARV), frequência mediana (MDF), frequência média (MNF), coeficiente de correlação (CC) e força. Foi aplicado um estudo de caso, no qual foram realizadas 2 etapas: A primeira foi realizada com a contribuição de 40 indivíduos dos quais se extraíram sinais de S-EMG, com uma matriz de 64 canais e tempo total da coleta de 90 segundos. Desses sinais escolhidos foram extraídos, utilizando-se a ferramenta desenvolvida, os estimadores ARV, CV, MDF, MNF e RMS, e em seguida, foiaplicado um teste de análise de variância nos estimadores de tal forma a verificar se os sinais pertencentes a um mesmo indivíduo são estatisticamente iguais. Asegunda etapa constituiu-se de verificar a capacidade de interpretação de gráficos, gerados pela ferramenta desenvolvida, de estimadores junto a 10 voluntários.Observou- se que o programa permitiu a aplicação de todos os estimadores necessários para o caso particular sem necessidade de manipular as equações previamente implementadas. Isso sugere que a ferramenta gráfica desenvolvida pode ser utilizada por profissionais sem treinamento em programação e na matemática por trás das características extraídas do S-EMG. E, ao possuir apenas rotinas de código aberto, a ferramenta desenvolvida torna-se atrativa tanto para se acrescentar novos recursos quanto para a sua ampla utilização em quaisquerestudos acadêmicos e científicos sobre S-EMG. ______________________________________________________________________________ ABSTRACTThe scientific literature in the area of surface electromyography (S-EMG) has recently deepened and exposing the potential of S-EMG analysis and investigation of neuromotor problems, diseases and other aspects of neuromuscular structure, by analyzing characteristics obtained by estimators applied to signals from multichannel electrodes. Despite the increasingly widespread use of S-EMG, there is not a proportional increase in the amount of computational tools available to professionals who have specific experience of programming and mathematical methods related to estimators. In this context, this work proposes the development of a computational tool with graphical user interface that can be used by any professional acting on the analysis of S-EMG signals from signals collected by an array of electrodes, providing graphics capabilities for viewing signal in the time domain and frequency, conduction velocity (CV), root mean square (RMS), average rectified value (ARV), median frequency (MDF), mean frequency (MNF), correlation coefficient (CC) and strength. We applied a case study, in which we performed two steps: The first was carried out with contributions from 40 individuals of which were extracted S-EMG signals, with an array of 64 channels and the total collection time of 90 seconds. Chosen such signals were extracted, using a tool developed, the estimators ARV, CV, MDF, MNF and RMS, and then a test was applied in the analysis of variance estimators such as to check whether the signals belonging to a same individual are statistically equal. The second step was to verify the ability of interpreting graphs generated by the tool developed estimators with 10 volunteers. It was observed that the application program has all the estimators needed for the particular case without manipulating the equations previously implemented. This suggests that the graphical tool developed can be used by professionals without training in programming and mathematics behind the features extracted from the S- EMG. And, just to have routines open source tool developed to become attractive both to add new features and to their extensive use in any scientific and academic studies on S-EMG

Empirical modelling and classification of surface electromyogram

This thesis develops an effective feature extraction technique for sEMG signals. Surface electromyography (sEMG) is the recording of a muscle’s electrical activity from the surface of the skin. The signal contains information that is related to the anatomy and physiology of the muscle. In clinical applications, the signal is used for the diagnosis of neuro-muscular diseases and disorders. Another application of sEMG is for device control application where the signal is used for controlling devices such as prosthetic devices, robots, and human – machine interfaces. Signal classification is used to extract relevant information that represent a particular state (or class) of the sEMG signal. This stater (or class) of the sEMG depicts the information about the underlying pathology or is used as control input for other devices. Therefore it is important that the sEMG is classified in to the relevant class with high accuracy to ensure reliable application in a given field. Many researchers have attempted to improve the classification accuracy of the sEMG signal. Generally the number of electrodes attached to the surface of the skin also needs to be increased in order to increase the classification accuracy. In some cases this number becomes prohibitively high. On the other hand, with a decrease in the number of electrodes the classification accuracy has been reported to decrease. In order to overcome these challenges, in this thesis a new feature extraction technique has been developed. As opposed to the established global time or frequency domain analysis of the sEMG signal, the technique developed in this thesis relies on the well established volume conduction model of sEMG generation. Developed feature extraction technique is then applied to sEMG recorded from low level digital contraction with low signal to noise ratio. A high classification rate of approximately 93% in four classes of low level contraction was achieved by using single channel of sEMG recording. It was further established that the placement of electrode did not have significant effect on the accuracy and reliability of the classification. Further developments that may improve on the methods established in this thesis are presented in the end