Evaluation of feature projection techniques in object grasp classification using electromyogram signals from different limb positions.

ABSTRACT: A myoelectric prosthesis is manipulated using electromyogram (EMG) signals from the existing muscles for performing the activities of daily living. A feature vector that is formed by concatenating data from many EMG channels may result in a high dimensional space, which may cause prolonged computation time, redundancy, and irrelevant information. We evaluated feature projection techniques, namely principal component analysis (PCA), linear discriminant analysis (LDA), t-Distributed Stochastic Neighbor Embedding (t-SNE), and spectral regression extreme learning machine (SRELM), applied to object grasp classification. These represent feature projections that are combinations of either linear or nonlinear, and supervised or unsupervised types. All pairs of the four types of feature projection

SUBMITTER: Thiamchoo N 

PROVIDER: S-EPMC9138131 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature