An artificial intelligence approach for measurement and monitoring of pressure at the residual limb/socket interface - A clinical study

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

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

http://www.atypon-link.com/BINT/doi/abs/10.1784/insi.2008.50.7.374

Journal: Insight - Non-Destructive Testing and Condition Monitoring

Volume: 50

Issue: 7

Pages: 374-383

ISSN: 1354-2575

DOI: 10.1784/insi.2008.50.7.374

A good-fitting prosthetic socket requires the pressure between the stump and socket to be distributed to ensure the load is carried by pressure tolerant regions of the limb. Transducers and Finite Element Analysis have been utilised to measure and monitor these pressures. However, it has been recognised that both techniques have limitations, making them impractical for everyday clinical use to aid the prosthetist in the socket fitting process. This paper details the design of a Hybrid Inverse Problem Engine (HIPE) which combines Artificial Intelligence (AI) and experimental/numerical data to create a less invasive and passive approach to develop a practical clinical tool for predicting the pressure distribution at the limb/socket interface. Testing and validation of the HIPE under laboratory conditions showed that the technique was able to predict the location and magnitude of pressures applied manually to the socket. A comparison of the predicted pressure distribution found using the HIPE, at the limb/socket interface of a patient in a clinical environment with photoelastic data of the actual pressure distribution, further indicated the technique's potential benefits. It is hoped that the HIPE will eventually become a general tool suitable for monitoring the fit of a prosthesis in a clinical environment.

This data was imported from Scopus:

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

Journal: Insight: Non-Destructive Testing and Condition Monitoring

Volume: 50

Issue: 7

Pages: 374-383

ISSN: 1354-2575

DOI: 10.1784/insi.2008.50.7.374

A good-fitting prosthetic socket requires the pressure between the stump and socket to be distributed to ensure the load is carried by pressure tolerant regions of the limb. Transducers and Finite Element Analysis have been utilised to measure and monitor these pressures. However, it has been recognised that both techniques have limitations, making them impractical for everyday clinical use to aid the prosthetist in the socket fitting process. This paper details the design of a Hybrid Inverse Problem Engine (HIPE) which combines Artificial Intelligence (AI) and experimental/numerical data to create a less invasive and passive approach to develop a practical clinical tool for predicting the pressure distribution at the limb/socket interface. Testing and validation of the HIPE under laboratory conditions showed that the technique was able to predict the location and magnitude of pressures applied manually to the socket. A comparison of the predicted pressure distribution found using the HIPE, at the limb/socket interface of a patient in a clinical environment with photoelastic data of the actual pressure distribution, further indicated the technique's potential benefits. It is hoped that the HIPE will eventually become a general tool suitable for monitoring the fit of a prosthesis in a clinical environment.

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

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

Journal: INSIGHT

Volume: 50

Issue: 7

Pages: 374-383

ISSN: 1354-2575

DOI: 10.1784/insi.2008.50.7.374

The data on this page was last updated at 05:18 on July 19, 2019.