Document Detail

Static and dynamic pressure prediction for prosthetic socket fitting assessment utilising an inverse problem approach.
MedLine Citation:
PMID:  21963113     Owner:  NLM     Status:  Publisher    
OBJECTIVE: It has been recognised in a review of the developments of lower-limb prosthetic socket fitting processes that the future demands new tools to aid in socket fitting. This paper presents the results of research to design and clinically test an artificial intelligence approach, specifically inverse problem analysis, for the determination of the pressures at the limb/prosthetic socket interface during stance and ambulation. METHODS: Inverse problem analysis is based on accurately calculating the external loads or boundary conditions that can generate a known amount of strain, stresses or displacements at pre-determined locations on a structure. In this study a backpropagation artificial neural network (ANN) is designed and validated to predict the interfacial pressures at the residual limb/socket interface from strain data collected from the socket surface. The subject of this investigation was a 45-year-old male unilateral trans-tibial (below-knee) traumatic amputee who had been using a prosthesis for 22 years. RESULTS: When comparing the ANN predicted interfacial pressure on 16 patches within the socket with actual pressures applied to the socket there is shown to be 8.7% difference, validating the methodology. Investigation of varying axial load through the subject's prosthesis, alignment of the subject's prosthesis, and pressure at the limb/socket interface during walking demonstrates that the validated ANN is able to give an accurate full-field study of the static and dynamic interfacial pressure distribution. CONCLUSIONS: To conclude, a methodology has been developed that enables a prosthetist to quantitatively analyse the distribution of pressures within the prosthetic socket in a clinical environment. This will aid in facilitating the "right first time" approach to socket fitting which will benefit both the patient in terms of comfort and the prosthetist, by reducing the time and associated costs of providing a high level of socket fit.
Philip Sewell; Siamak Noroozi; John Vinney; Ramin Amali; Stephen Andrews
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Publication Detail:
Type:  JOURNAL ARTICLE     Date:  2011-9-29
Journal Detail:
Title:  Artificial intelligence in medicine     Volume:  -     ISSN:  1873-2860     ISO Abbreviation:  -     Publication Date:  2011 Sep 
Date Detail:
Created Date:  2011-10-3     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  8915031     Medline TA:  Artif Intell Med     Country:  -    
Other Details:
Languages:  ENG     Pagination:  -     Citation Subset:  -    
Copyright Information:
Copyright © 2011 Elsevier B.V. All rights reserved.
School of Design, Engineering & Computing, Bournemouth University, Poole, Dorset, BH12 5BB, UK.
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