Hand Movement Classification Using Burg Reflection Coefficients

Abstract

Classification of electromyographic signals has a wide range of applications, from clinical diagnosis of different muscular diseases to biomedical engineering, where their use as input for the control of prosthetic devices has become a hot topic of research. The challenge of classifying these signals relies on the accuracy of the proposed algorithm and the possibility of its implementation in hardware. This paper considers the problem of electromyography signal classification, solved with the proposed signal processing and feature extraction stages, with the focus lying on the signal model and time domain characteristics for better classification accuracy. The proposal considers a simple preprocessing technique that produces signals suitable for feature extraction and the Burg reflection coefficients to form learning and classification patterns. These coefficients yield a competitive classification rate compared to the time domain features used. Sometimes, the feature extraction from electromyographic signals has shown that the procedure can omit less useful traits for machine learning models. Using feature selection algorithms provides a higher classification performance with as few traits as possible. The algorithms achieved a high classification rate up to 100% with low pattern dimensionality, with other kinds of uncorrelated attributes for hand movement identification.

Discussion of the Influence of Multiscale PCA Denoising Methods with Three Different Features

“…In the Toeplitz definition, the autoregressive coefficients a p can be obtained using the recursive Levinson-Durbin algorithm [20]. By observing the above matrix, we can see that this matrix fulfils the definition.…”

“…There are many mathematical methods to derive the autoregressive coefficients that define the autoregressive model, the most famous of which is the Yule-Walker model. This model uses the estimated values of the correlation function, which can be calculated as [20]…”

Perspective Chapter: Classification of Grasping Gestures for Robotic Hand Prostheses Using Deep Neural Networks

“…It is a non-invasive device, easier to use compared to conventional electrodes [38,39]. Despite the low sampling frequency, its performance has been shown to be similar to that of full-band EMG recordings using conventional electrodes [22,40], and the technology has been used in many studies [29,35,38] (Figure 2). sEMG recording: Prior to carrying out the tests, the patients will be instructed on the experimental procedure and as a first step.…”

Sensor Technologies for Caring People with Disabilities

“…Ramírez-Martínez et al tackled the problem of electromyography signal classification, solved with the proposed signal processing and feature extraction stages, with the focus lying on the signal model and time domain characteristics for better classification accuracy. The proposal considers a simple preprocessing technique that produces signals suitable for feature extraction and the Burg reflection coefficients to form learning and classification patterns [34]. Torres-Carrión et al focused on the reduction in cognitive abilities caused by the Down syndrome, with visual-motor skills being particularly affected.…”