DocumentCode :
1748932
Title :
A novel approach for assessing interfacial pressure between the prosthetic socket and the residual limb for below knee amputees using artificial neural networks
Author :
Amali, R. ; Noroozi, S. ; Vinney, J. ; Sewell, P. ; Andrews, S.
Author_Institution :
Fac. of Eng., Univ. of the West of England, Bristol, UK
Volume :
4
fYear :
2001
fDate :
2001
Firstpage :
2689
Abstract :
So far the study of interfacial pressures between residual limb and the prosthetic socket has not led to the design of any useful tool that can assist the prosthetist to fit a prosthesis. Researchers at the UWE Bristol have found a novel methodology that can revolutionise this process. It is based on the combined application of a hybrid numerical method and experimental finite element analysis for stress analysis. This paper represents part of the development process of this tool and discusses the step forward from a two-dimensional analysis, discussed previously (2000), into a 3D symmetrical shell analysis which is intended to simulate a structured and ideal socket. The authors feel this is a logical step, which is necessary for understanding the relationship between surface stress/strain, and internal load, which causes those surface stresses. This paper emphasises the significance of this statement because it requires no knowledge of tissue properties
Keywords :
backpropagation; feedforward neural nets; finite element analysis; medical computing; neural nets; prosthetics; stress analysis; backpropagation; finite element analysis; interfacial pressure; knee amputees; multilayer neural networks; prosthetic socket; residual limb; surface stress analysis; Artificial neural networks; Backpropagation; Finite element methods; Internal stresses; Neural networks; Neural prosthesis; Neurons; Prosthetics; Sockets; Transducers;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 2001. Proceedings. IJCNN '01. International Joint Conference on
Conference_Location :
Washington, DC
ISSN :
1098-7576
Print_ISBN :
0-7803-7044-9
Type :
conf
DOI :
10.1109/IJCNN.2001.938795
Filename :
938795
Link To Document :
بازگشت