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

Abstract

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.

EMG Pattern Recognition in the Era of Big Data and Deep Learning

“…The development of EMG feature extraction and classification methods that are robust to noise is also important [64,65], as is the reduction of data (or dimensionality) when dealing with large-scale data sets. Determining relevant and meaningful features from a given larger set of features which may contain irrelevant, redundant, or noisy information is commonly accomplished using either feature selection [66][67][68][69] or feature projection methods [70][71][72][73]. When properly executed, these methods not only reduce the impact of noise and irrelevant information, but also the amount of computational time required for classification.…”

Sparse Signal Acquisition via Compressed Sensing and Principal Component Analysis

“…PCA is convenient with signals that would require complicated models or their analytical model is unknown. The PCA is particularly prominent in biomedical applications [21]- [24], and has been utilized for basis extraction in various CS applications [3], [7], [10], [25]. The CS framework with both sensing and decoding side is summarized in Fig.1.…”

A performance comparison of hand motion EMG classification

“…Therefore, one reasonable alternative in the literature is EMG signal pattern recognition [3,4]. Using this technique, discriminating a number of motion classes, such as hand open and hand close, with a few channels of sEMG is a practical approach [5].…”