Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.640996
Title: Self organising map machine learning approach to pattern recognition for protein secondary structures and robotic limb control
Author: Hall, Vincent Austin
ISNI:       0000 0004 5349 864X
Awarding Body: University of Warwick
Current Institution: University of Warwick
Date of Award: 2014
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Abstract:
With every corner of science, engineering and business generating vast amounts of data, it is becoming increasingly important to be able to understand what these data mean, and make sensible decisions based on the findings. One tool that can assist with this aim is the type of program called a self-organising map (SOM). SOMs are unsupervised Artificial Neural Networks (ANNs) that are used for pattern recognition, dimensionality-reduction of datasets, and can give a visual representation of the data using topology. For this project, SOMs were used to do pattern recognition on circular dichroism (CD) and myoelectric signal (MES) data, among other applications. To the first of these SOMs, we gave the name SSNN for Secondary Structure Neural Network, as it analyses CD spectra to find structures of proteins. CD is a polarised UV light spectroscopy, it is a useful for estimating structures (conformations) of chiral molecules in solution. In this work we report on its use with proteins and lipoproteins. The problem with using CD spectra is that they can be difficult to interpret, especially if quantitative results are required. We have improved the structure estimations compared with similar methodologies. The overall error across all structures for SELCON3 was 0.2, for CDSSTR: 0.3, for K2d: 0.2, but for our methodology, SSNN, it was 0.1. Another difficult problem the world faces is that thousands more people every year have limb amputations or are born with non-fully-functioning limbs. Robotic limbs can help people with these afflictions, and while many are available, none give much dexterity or natural movements, or are easy to use. To help rectify the situation we adapted the SOM tool we developed, SSNN, to work as part of a software platform that is used to control robotic prostheses, calling it HASSANN, Hand Activation Signals, SOM Artificial Neural Network. The system works by performing pattern recognition on myoelectric signals, which are electrical signals from muscles. The software platform is called BioPatRec, and was developed by Max Ortiz-Catalan and his other collaborators. The SOM HASSANN was written by the author, who also tested how well the software works at predicting which robotic limb movements are needed.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.640996  DOI: Not available
Keywords: QA76 Electronic computers. Computer science. Computer software ; QH301 Biology
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