The University of Southampton
University of Southampton Institutional Repository

Moving approximate entropy and its application to the electromyographic control of an artificial hand

Record type: Thesis (Doctoral)

A multiple-degree-of-freedom artificial hand has been developed at the University of Southampton with the aim of including control philosophies to form a highly functional prosthesis hand. Using electromyographic signals is an established technique for the control of a hand. In it simplest form, the signals allow for opening a hand and subsequent closing to grasp an object.
This thesis describes the work carried out in the development of an electromyographic control system, with the aim to have a simple and robust method. A model of the control system was developed to differentiate grip postures using two surface electromyographic signals. A new method, moving approximate entropy was employed to investigate whether any significant patterns can be observed in the structure of the electromyographic signals. An investigation, using moving approximate entropy, on twenty healthy participants' wrist muscles (flexor carpi ulnaris and extensor carpi radialis) during wrist exion, wrist extension and cocontraction at different speeds has shown repeatable and distinct patterns at three states of contraction: start, middle and end. An analysis of the results also showed differences at different speeds of contraction. There is a low variation of the approximate entropy values between participants. This result, if used in the control of an artificial hand, would eliminate any training requirement. Other methods, mean absolute value, number of zero crossings, sample entropy, standard deviation, skewness and kurtosis have been determined from the signals. Of these features, mean absolute value and kurtosis were selected for information extraction. These three methods: moving approximate entropy, mean absolute value and kurtosis are used in the feature extraction process of the control system. A fuzzy logic system is used to classify the extracted information in discriminating the final grip posture. The results demonstrate the ability of the system to classify the information related to different grip postures.

PDF FinalThesis.pdf - Other
Download (7MB)

Citation

Ahmad, Siti Anom (2009) Moving approximate entropy and its application to the electromyographic control of an artificial hand University of Southampton, School of Electronics and Computer Science, Doctoral Thesis , 180pp.

More information

Published date: June 2009
Organisations: University of Southampton

Identifiers

Local EPrints ID: 66794
URI: http://eprints.soton.ac.uk/id/eprint/66794
PURE UUID: 05face64-1922-41d4-ad3c-1fdacaa11c71

Catalogue record

Date deposited: 22 Jul 2009
Last modified: 19 Jul 2017 00:22

Export record

Contributors

Author: Siti Anom Ahmad
Thesis advisor: Paul Chappell

University divisions


Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×