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Feature selection for prosthetic control using myoelectric signals

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Khong, LMD (2016) Feature selection for prosthetic control using myoelectric signals. Research Master thesis, University of Tasmania.

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Abstract

This thesis investigates selection of time domain (TD) signal features for myoelectric
signal (MES) based control of motorised hand and wrist prostheses. A signal
feature represents a distinguishing property of a MES to be used in pattern recognition
algorithms. In particular, TD features reflect the mathematical functions and
physical expression of the transient signal waveform with respect to time. Extracted
features capture the structural details of a MES, minimise loss of information upon
conversion, and simplify movement classification. The advantage of TD features is
that they produce lower dimensional input vectors while maintaining sufficient accuracy
of various movements if adequate information is provided. Feature sets as a
solution to gather information in MES based control has not been thoroughly studied
in the literature. We aim to develop methods to elevate the use of TD features
and suggest a comprehensive feature set that is helpful in pattern recognition.
Myolectric signals used in this study were from the BioPatRec database, an open
source platform for research and control of artificial limbs via pattern recognition
using bioelectric signals. This database is named as 10mov4chUntargetedForearm
comprising data on10 hand and wrist movements acquired by 4 bipolar sEMG
channels from the left or right forearm.
Based on feature selection (FS) which preserves information of the MES, we propose
three methods, namely a genetic algorithm (GA), class relevant criteria and
a self-organising feature map (SOFM) to assemble feature sets from TD features
of twenty one candidates. To evaluate these feature sets, we implemented three
pattern recognition algorithms, particularly the Multilayer Perceptron (MLP), Linear
Discriminant Analysis (LDA) and Support Vector Machine (SVM) algorithms.
The reported movement accuracy and Wilcoxon p value demonstrated that the
proposed feature sets consistently outperformed a typical feature set found in the
literature; in particular, improved the accuracy of poor quality datasets from 85%
to 93%.
The thesis has made a thorough investigation of TD features contributing in three
categories. Firstly, we developed a variety of independent methods for FS. It was
noticed that FS has been limited in meta-heuristic searches in the literature. We
have demonstrated that there are several solutions that use potential TD features
to assemble a feature set to be used in pattern recognition. Secondly, we have
shown that statistical tests can be successfully applied in FS. Thirdly, we explored an
investigation of data along time series vectors instead of analysing it conventionally
by time segmentation. The success of this method suggests a new way that may
value further inspection.
In brief, this thesis presents possible solutions for TD feature based pre-processing
of the input of pattern recognition algorithms for prosthetic control. It provides immediate accuracy improvement through a replacement of feature sets and further
implementation in methodology for FS.

Item Type: Thesis (Research Master)
Keywords: Myoelectric signal (MES), time domain features, BioPatRec, feature selection, pattern recognition, prostheses
Copyright Information:

Copyright 2016 the Authors

Date Deposited: 01 Nov 2016 01:45
Last Modified: 14 Dec 2016 00:47
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