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Predicting Interfacial Loads between the Prosthetic Socket and the Residual Limb for Below‐Knee Amputees – A Case Study
Author(s) -
Amali R.,
Noroozi S.,
Vinney J.,
Sewell P.,
Andrews S.
Publication year - 2006
Publication title -
strain
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.477
H-Index - 47
eISSN - 1475-1305
pISSN - 0039-2103
DOI - 10.1111/j.1475-1305.2006.00245.x
Subject(s) - strain gauge , residual , structural engineering , artificial neural network , biomedical engineering , load cell , internal pressure , computer science , engineering , simulation , materials science , artificial intelligence , composite material , algorithm
In this study, an artificial neural network (ANN) was deployed as a tool to determine the internal loads between the residual limb and prosthetic socket for below‐knee amputees. This was achieved by using simulated load data to validate the ANN and captured clinical load data to predict the internal loads at the residual limb–socket interface. Load/pressure was applied to 16 regions of the socket, using loading pads in conjunction with a load applicator, and surface strains were collected using 15 strain gauge rosettes. A super‐position program was utilised to generate training and testing patterns from the original load/strain data collected. Using this data, a back‐propagation ANN, developed at the University of the West of England, was trained. The input to the trained network was the surface strains and the output the internal loads/pressure. The system was validated and the mean square error (MSE) of the system was found to be 8.8% for 1000 training patterns and 8.9% for 50 testing patterns, which was deemed an acceptable error. Finally, the validated system was used to predict pressure‐sensitive/‐tolerant regions at the limb–socket interface with great success.