Pairwise Linear Discriminant Analysis of Electromyographic signals
نویسندگان
چکیده
Electromyographic (EMG) signals are used as rich information sources for control of intelligent prosthetics. For efficient classification the machine learning algorithms used should allow the nonlinear nature of the multi class problem. For generalized application they should have the analytical ability to systematically tackle the problem in hand. To meet these requirements a pair-wise Linear Discriminant Analysis(LDA) is performed in a systematic manner on EMG signals captured from forehand muscles. A 6 class classification performance from 4 EMG channels are reported along with the ability of the algorithm to scale and visualize complex multidimensional cases.
منابع مشابه
Optimized Seizure Detection Algorithm: A Fast Approach for Onset of Epileptic in EEG Signals Using GT Discriminant Analysis and K-NN Classifier
Background: Epilepsy is a severe disorder of the central nervous system that predisposes the person to recurrent seizures. Fifty million people worldwide suffer from epilepsy; after Alzheimer’s and stroke, it is the third widespread nervous disorder.Objective: In this paper, an algorithm to detect the onset of epileptic seizures based on the analysis of brain electrical signals (EEG) has b...
متن کاملAnalysis of Extracted Forearm sEMG Signal Using LDA, QDA, K-NN Classification Algorithms
A surface electromyographic (sEMG) signal includes important information on muscular activity and was recently widely used as an input signal in a myoelectric control system. In this manuscript, eight hand motions were classified using different extracted features from sEMG signals. The results of the experiment show that the combination of sample entropy (SampEnt), root mean square (RMS), myop...
متن کاملMulticlass Linear Dimension Reduction by Weighted Pairwise Fisher Criteria
ÐWe derive a class of computationally inexpensive linear dimension reduction criteria by introducing a weighted variant of the well-known K-class Fisher criterion associated with linear discriminant analysis (LDA). It can be seen that LDA weights contributions of individual class pairs according to the Euclidian distance of the respective class means. We generalize upon LDA by introducing a dif...
متن کاملEvaluation of Morphometric Differences among Indigenous Chicken Populations in Bale Zone, Oromia Regional State, Ethiopia
The study was conducted in five selected districts in Bale zone South East, Ethiopia to evaluate the morphometric difference among indigenous chicken populations. Simple random sampling method was used to select 400 households who owned indigenous chicken population. From these households, a total of 840 adult (more than 6 months of age) indigenous chickens (225 males and 615 females) were used...
متن کاملPairwise-Covariance Linear Discriminant Analysis
In machine learning, linear discriminant analysis (LDA) is a popular dimension reduction method. In this paper, we first provide a new perspective of LDA from an information theory perspective. From this new perspective, we propose a new formulation of LDA, which uses the pairwise averaged class covariance instead of the globally averaged class covariance used in standard LDA. This pairwise (av...
متن کامل