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Redundancy Reduction for Sensor Deployment in Prosthetic Socket: A Case Study

The irregular pressure exerted by a prosthetic socket over the residual limb is one of the major factors that cause the discomfort of amputees using artificial limbs. By deploying the wearable sensors inside the socket, the interfacial pressure distribution can be studied to find the active regions...

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Autores principales: Zhu, Wenyao, Chen, Yizhi, Ko, Siu-Teing, Lu, Zhonghai
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9105868/
https://www.ncbi.nlm.nih.gov/pubmed/35590792
http://dx.doi.org/10.3390/s22093103
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author Zhu, Wenyao
Chen, Yizhi
Ko, Siu-Teing
Lu, Zhonghai
author_facet Zhu, Wenyao
Chen, Yizhi
Ko, Siu-Teing
Lu, Zhonghai
author_sort Zhu, Wenyao
collection PubMed
description The irregular pressure exerted by a prosthetic socket over the residual limb is one of the major factors that cause the discomfort of amputees using artificial limbs. By deploying the wearable sensors inside the socket, the interfacial pressure distribution can be studied to find the active regions and rectify the socket design. In this case study, a clustering-based analysis method is presented to evaluate the density and layout of these sensors, which aims to reduce the local redundancy of the sensor deployment. In particular, a Self-Organizing Map (SOM) and K-means algorithm are employed to find the clustering results of the sensor data, taking the pressure measurement of a predefined sensor placement as the input. Then, one suitable clustering result is selected to detect the layout redundancy from the input area. After that, the Pearson correlation coefficient (PCC) is used as a similarity metric to guide the removal of redundant sensors and generate a new sparser layout. The Jenson–Shannon Divergence (JSD) and the mean pressure are applied as posterior validation metrics that compare the pressure features before and after sensor removal. A case study of a clinical trial with two sensor strips is used to prove the utility of the clustering-based analysis method. The sensors on the posterior and medial regions are suggested to be reduced, and the main pressure features are kept. The proposed method can help sensor designers optimize sensor configurations for intra-socket measurements and thus assist the prosthetists in improving the socket fitting.
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spelling pubmed-91058682022-05-14 Redundancy Reduction for Sensor Deployment in Prosthetic Socket: A Case Study Zhu, Wenyao Chen, Yizhi Ko, Siu-Teing Lu, Zhonghai Sensors (Basel) Article The irregular pressure exerted by a prosthetic socket over the residual limb is one of the major factors that cause the discomfort of amputees using artificial limbs. By deploying the wearable sensors inside the socket, the interfacial pressure distribution can be studied to find the active regions and rectify the socket design. In this case study, a clustering-based analysis method is presented to evaluate the density and layout of these sensors, which aims to reduce the local redundancy of the sensor deployment. In particular, a Self-Organizing Map (SOM) and K-means algorithm are employed to find the clustering results of the sensor data, taking the pressure measurement of a predefined sensor placement as the input. Then, one suitable clustering result is selected to detect the layout redundancy from the input area. After that, the Pearson correlation coefficient (PCC) is used as a similarity metric to guide the removal of redundant sensors and generate a new sparser layout. The Jenson–Shannon Divergence (JSD) and the mean pressure are applied as posterior validation metrics that compare the pressure features before and after sensor removal. A case study of a clinical trial with two sensor strips is used to prove the utility of the clustering-based analysis method. The sensors on the posterior and medial regions are suggested to be reduced, and the main pressure features are kept. The proposed method can help sensor designers optimize sensor configurations for intra-socket measurements and thus assist the prosthetists in improving the socket fitting. MDPI 2022-04-19 /pmc/articles/PMC9105868/ /pubmed/35590792 http://dx.doi.org/10.3390/s22093103 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Zhu, Wenyao
Chen, Yizhi
Ko, Siu-Teing
Lu, Zhonghai
Redundancy Reduction for Sensor Deployment in Prosthetic Socket: A Case Study
title Redundancy Reduction for Sensor Deployment in Prosthetic Socket: A Case Study
title_full Redundancy Reduction for Sensor Deployment in Prosthetic Socket: A Case Study
title_fullStr Redundancy Reduction for Sensor Deployment in Prosthetic Socket: A Case Study
title_full_unstemmed Redundancy Reduction for Sensor Deployment in Prosthetic Socket: A Case Study
title_short Redundancy Reduction for Sensor Deployment in Prosthetic Socket: A Case Study
title_sort redundancy reduction for sensor deployment in prosthetic socket: a case study
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9105868/
https://www.ncbi.nlm.nih.gov/pubmed/35590792
http://dx.doi.org/10.3390/s22093103
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