Predicting interfacial loads between the prosthetic socket and the residual limb for below-knee amputees - A case study

This source preferred by John Vinney, Siamak Noroozi and Philip Sewell

Authors: Amali, R., Noroozi, S., Vinney, J., Sewell, P. and Andrews, S.

http://www.blackwell-synergy.com/doi/abs/10.1111/j.1475-1305.2006.00245.x

Journal: Strain

Volume: 42

Pages: 3-10

ISSN: 0039-2103

DOI: 10.1111/j.1475-1305.2006.00245.x

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.

This data was imported from Scopus:

Authors: Amali, R., Noroozi, S., Vinney, J., Sewell, P. and Andrews, S.

Journal: Strain

Volume: 42

Issue: 1

Pages: 3-10

eISSN: 1475-1305

ISSN: 0039-2103

DOI: 10.1111/j.1475-1305.2006.00245.x

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 thesquare 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. © 2006 Blackwell Publishing Ltd.

This data was imported from Web of Science (Lite):

Authors: Amali, R., Noroozi, S., Vinney, J., Sewell, P. and Andrews, S.

Journal: STRAIN

Volume: 42

Issue: 1

Pages: 3-10

ISSN: 0039-2103

DOI: 10.1111/j.1475-1305.2006.00245.x

The data on this page was last updated at 10:28 on April 24, 2019.