Predicting interfacial loads between the prosthetic socket and the residual limb for below-knee amputees - A case study
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
Abstract: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.
Source: Scopus
Predicting interfacial loads between the prosthetic socket and the residual limb for below-knee amputees - A case study
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
Source: Web of Science (Lite)
Predicting interfacial loads between the prosthetic socket and the residual limb for below-knee amputees - a case study
Authors: Amali, R., Noroozi, S., Vinney, J., Sewell, P. and Andrews, S.
Journal: Strain
Volume: 42
Pages: 3-10
ISSN: 0039-2103
DOI: 10.1111/j.1475-1305.2006.00245.x
Abstract: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.
http://www.blackwell-synergy.com/doi/abs/10.1111/j.1475-1305.2006.00245.x
Source: Manual
Preferred by: John Vinney, Philip Sewell and Siamak Noroozi