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Development of an Automated Minimum Foot Clearance Measurement System: Proof of Principle
Over half of older adult falls are caused by tripping. Many of these trips are likely due to obstacles present on walkways that put older adults or other individuals with low foot clearance at risk. Yet, Minimum Foot Clearance (MFC) values have not been measured in real-world settings and existing m...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7867138/ https://www.ncbi.nlm.nih.gov/pubmed/33540502 http://dx.doi.org/10.3390/s21030976 |
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author | Delfi, Ghazaleh Kamachi, Megan Dutta, Tilak |
author_facet | Delfi, Ghazaleh Kamachi, Megan Dutta, Tilak |
author_sort | Delfi, Ghazaleh |
collection | PubMed |
description | Over half of older adult falls are caused by tripping. Many of these trips are likely due to obstacles present on walkways that put older adults or other individuals with low foot clearance at risk. Yet, Minimum Foot Clearance (MFC) values have not been measured in real-world settings and existing methods make it difficult to do so. In this paper, we present the Minimum Foot Clearance Estimation (MFCE) system that includes a device for collecting calibrated video data from pedestrians on outdoor walkways and a computer vision algorithm for estimating MFC values for these individuals. This system is designed to be positioned at ground level next to a walkway to efficiently collect sagittal plane videos of many pedestrians’ feet, which is then processed offline to obtain MFC estimates. Five-hundred frames of video data collected from 50 different pedestrians was used to train (370 frames) and test (130 frames) a convolutional neural network. Finally, data from 10 pedestrians was analyzed manually by three raters and compared to the results of the network. The footwear detection network had an Intersection over Union of 85% and was able to find the bottom of a segmented shoe with a 3-pixel average error. Root Mean Squared (RMS) errors for the manual and automated methods for estimating MFC values were 2.32 mm, and 3.70 mm, respectively. Future work will compare the accuracy of the MFCE system to a gold standard motion capture system and the system will be used to estimate the distribution of MFC values for the population. |
format | Online Article Text |
id | pubmed-7867138 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-78671382021-02-07 Development of an Automated Minimum Foot Clearance Measurement System: Proof of Principle Delfi, Ghazaleh Kamachi, Megan Dutta, Tilak Sensors (Basel) Communication Over half of older adult falls are caused by tripping. Many of these trips are likely due to obstacles present on walkways that put older adults or other individuals with low foot clearance at risk. Yet, Minimum Foot Clearance (MFC) values have not been measured in real-world settings and existing methods make it difficult to do so. In this paper, we present the Minimum Foot Clearance Estimation (MFCE) system that includes a device for collecting calibrated video data from pedestrians on outdoor walkways and a computer vision algorithm for estimating MFC values for these individuals. This system is designed to be positioned at ground level next to a walkway to efficiently collect sagittal plane videos of many pedestrians’ feet, which is then processed offline to obtain MFC estimates. Five-hundred frames of video data collected from 50 different pedestrians was used to train (370 frames) and test (130 frames) a convolutional neural network. Finally, data from 10 pedestrians was analyzed manually by three raters and compared to the results of the network. The footwear detection network had an Intersection over Union of 85% and was able to find the bottom of a segmented shoe with a 3-pixel average error. Root Mean Squared (RMS) errors for the manual and automated methods for estimating MFC values were 2.32 mm, and 3.70 mm, respectively. Future work will compare the accuracy of the MFCE system to a gold standard motion capture system and the system will be used to estimate the distribution of MFC values for the population. MDPI 2021-02-02 /pmc/articles/PMC7867138/ /pubmed/33540502 http://dx.doi.org/10.3390/s21030976 Text en © 2021 by the authors. 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 (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Communication Delfi, Ghazaleh Kamachi, Megan Dutta, Tilak Development of an Automated Minimum Foot Clearance Measurement System: Proof of Principle |
title | Development of an Automated Minimum Foot Clearance Measurement System: Proof of Principle |
title_full | Development of an Automated Minimum Foot Clearance Measurement System: Proof of Principle |
title_fullStr | Development of an Automated Minimum Foot Clearance Measurement System: Proof of Principle |
title_full_unstemmed | Development of an Automated Minimum Foot Clearance Measurement System: Proof of Principle |
title_short | Development of an Automated Minimum Foot Clearance Measurement System: Proof of Principle |
title_sort | development of an automated minimum foot clearance measurement system: proof of principle |
topic | Communication |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7867138/ https://www.ncbi.nlm.nih.gov/pubmed/33540502 http://dx.doi.org/10.3390/s21030976 |
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