2019 16th International Conference on Ubiquitous Robots (UR), Page. 804-805
Abstract
The paper presents methods to expand data acquired from a multi-channel sEMG fabric sensor for the
dexterous control of robotic prosthesis. It is able to improve a
variety of pattern recognition performance in spite of fewer data
and less computational time. A multilayer perceptron (MLP)
is utilized for the classification of eight postures in order to
compare several methods regarding the data expansion such as
data expanded with normal distribution (N-dist), data expanded
with median operations, and data expansion with median plus
normal distribution. Of the methods, an accuracy achieved
using the data expanded with median plus normal distribution
arrives at 99.32% as the highest, followed by the expansion
using median, the expansion using normal distribution.