Static and dynamic pressure prediction for prosthetic socket fitting assessment utilising an inverse problem approach

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

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

Journal: Artificial Intelligence in Medicine

Volume: 54

Issue: 1

Pages: 29-41

ISSN: 0933-3657

DOI: 10.1016/j.artmed.2011.09.005

Objective: It has been recognised in a review of the developments of lower-limb prosthetic socket fitting processes that the future demands new tools to aid in socket fitting. This paper presents the results of research to design and clinically test an artificial intelligence approach, specifically inverse problem analysis, for the determination of the pressures at the limb/prosthetic socket interface during stance and ambulation.

Methods: Inverse problem analysis is based on accurately calculating the external loads or boundary conditions that can generate a known amount of strain, stresses or displacements at pre-determined locations on a structure. In this study a backpropagation artificial neural network (ANN) is designed and validated to predict the interfacial pressures at the residual limb/socket interface from strain data collected from the socket surface. The subject of this investigation was a 45-year-old male unilateral trans-tibial (below-knee) traumatic amputee who had been using a prosthesis for 22 years.

Results: When comparing the ANN predicted interfacial pressure on 16 patches within the socket with actual pressures applied to the socket there is shown to be 8.7% difference, validating the methodology. Investigation of varying axial load through the subject’s prosthesis, alignment of the subject’s prosthesis, and pressure at the limb/socket interface during walking demonstrates that the validated ANN is able to give an accurate full-field study of the static and dynamic interfacial pressure distribution.

Conclusions: To conclude, a methodology has been developed that enables a prosthetist to quantitatively analyse the distribution of pressures within the prosthetic socket in a clinical environment. This will aid in facilitating the “right first time” approach to socket fitting which will benefit both the patient in terms of comfort and the prosthetist, by reducing the time and associated costs of providing a high level of socket fit.

This data was imported from PubMed:

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

Journal: Artif Intell Med

Volume: 54

Issue: 1

Pages: 29-41

eISSN: 1873-2860

DOI: 10.1016/j.artmed.2011.09.005

OBJECTIVE: It has been recognised in a review of the developments of lower-limb prosthetic socket fitting processes that the future demands new tools to aid in socket fitting. This paper presents the results of research to design and clinically test an artificial intelligence approach, specifically inverse problem analysis, for the determination of the pressures at the limb/prosthetic socket interface during stance and ambulation. METHODS: Inverse problem analysis is based on accurately calculating the external loads or boundary conditions that can generate a known amount of strain, stresses or displacements at pre-determined locations on a structure. In this study a backpropagation artificial neural network (ANN) is designed and validated to predict the interfacial pressures at the residual limb/socket interface from strain data collected from the socket surface. The subject of this investigation was a 45-year-old male unilateral trans-tibial (below-knee) traumatic amputee who had been using a prosthesis for 22 years. RESULTS: When comparing the ANN predicted interfacial pressure on 16 patches within the socket with actual pressures applied to the socket there is shown to be 8.7% difference, validating the methodology. Investigation of varying axial load through the subject's prosthesis, alignment of the subject's prosthesis, and pressure at the limb/socket interface during walking demonstrates that the validated ANN is able to give an accurate full-field study of the static and dynamic interfacial pressure distribution. CONCLUSIONS: To conclude, a methodology has been developed that enables a prosthetist to quantitatively analyse the distribution of pressures within the prosthetic socket in a clinical environment. This will aid in facilitating the "right first time" approach to socket fitting which will benefit both the patient in terms of comfort and the prosthetist, by reducing the time and associated costs of providing a high level of socket fit.

This data was imported from DBLP:

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

Journal: Artificial Intelligence in Medicine

Volume: 54

Pages: 29-41

This data was imported from Scopus:

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

Journal: Artificial Intelligence in Medicine

Volume: 54

Issue: 1

Pages: 29-41

eISSN: 1873-2860

ISSN: 0933-3657

DOI: 10.1016/j.artmed.2011.09.005

Objective: It has been recognised in a review of the developments of lower-limb prosthetic socket fitting processes that the future demands new tools to aid in socket fitting. This paper presents the results of research to design and clinically test an artificial intelligence approach, specifically inverse problem analysis, for the determination of the pressures at the limb/prosthetic socket interface during stance and ambulation. Methods: Inverse problem analysis is based on accurately calculating the external loads or boundary conditions that can generate a known amount of strain, stresses or displacements at pre-determined locations on a structure. In this study a backpropagation artificial neural network (ANN) is designed and validated to predict the interfacial pressures at the residual limb/socket interface from strain data collected from the socket surface. The subject of this investigation was a 45-year-old male unilateral trans-tibial (below-knee) traumatic amputee who had been using a prosthesis for 22 years. Results: When comparing the ANN predicted interfacial pressure on 16 patches within the socket with actual pressures applied to the socket there is shown to be 8.7% difference, validating the methodology. Investigation of varying axial load through the subject's prosthesis, alignment of the subject's prosthesis, and pressure at the limb/socket interface during walking demonstrates that the validated ANN is able to give an accurate full-field study of the static and dynamic interfacial pressure distribution. Conclusions: To conclude, a methodology has been developed that enables a prosthetist to quantitatively analyse the distribution of pressures within the prosthetic socket in a clinical environment. This will aid in facilitating the " right first time" approach to socket fitting which will benefit both the patient in terms of comfort and the prosthetist, by reducing the time and associated costs of providing a high level of socket fit. © 2011 Elsevier B.V.

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

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

Journal: ARTIFICIAL INTELLIGENCE IN MEDICINE

Volume: 54

Issue: 1

Pages: 29-41

ISSN: 0933-3657

DOI: 10.1016/j.artmed.2011.09.005

This data was imported from Europe PubMed Central:

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

Journal: Artificial intelligence in medicine

Volume: 54

Issue: 1

Pages: 29-41

eISSN: 1873-2860

ISSN: 0933-3657

OBJECTIVE: It has been recognised in a review of the developments of lower-limb prosthetic socket fitting processes that the future demands new tools to aid in socket fitting. This paper presents the results of research to design and clinically test an artificial intelligence approach, specifically inverse problem analysis, for the determination of the pressures at the limb/prosthetic socket interface during stance and ambulation. METHODS: Inverse problem analysis is based on accurately calculating the external loads or boundary conditions that can generate a known amount of strain, stresses or displacements at pre-determined locations on a structure. In this study a backpropagation artificial neural network (ANN) is designed and validated to predict the interfacial pressures at the residual limb/socket interface from strain data collected from the socket surface. The subject of this investigation was a 45-year-old male unilateral trans-tibial (below-knee) traumatic amputee who had been using a prosthesis for 22 years. RESULTS: When comparing the ANN predicted interfacial pressure on 16 patches within the socket with actual pressures applied to the socket there is shown to be 8.7% difference, validating the methodology. Investigation of varying axial load through the subject's prosthesis, alignment of the subject's prosthesis, and pressure at the limb/socket interface during walking demonstrates that the validated ANN is able to give an accurate full-field study of the static and dynamic interfacial pressure distribution. CONCLUSIONS: To conclude, a methodology has been developed that enables a prosthetist to quantitatively analyse the distribution of pressures within the prosthetic socket in a clinical environment. This will aid in facilitating the "right first time" approach to socket fitting which will benefit both the patient in terms of comfort and the prosthetist, by reducing the time and associated costs of providing a high level of socket fit.

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