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Identification of the cause of fall during the pre-impact fall period
[Purpose] This study aimed to develop and validate a method for identifying factors that may cause a fall during the pre-impact fall period using wearable sensors. [Participants and Methods] The participants were 23 young people from the public data set (mean age, 23.4 years). Acceleration and angul...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Society of Physical Therapy Science
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8989476/ https://www.ncbi.nlm.nih.gov/pubmed/35400837 http://dx.doi.org/10.1589/jpts.34.320 |
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author | Sasaki, Sho Yamamoto, Hiroaki Kitagawa, Kodai Wada, Chikamune |
author_facet | Sasaki, Sho Yamamoto, Hiroaki Kitagawa, Kodai Wada, Chikamune |
author_sort | Sasaki, Sho |
collection | PubMed |
description | [Purpose] This study aimed to develop and validate a method for identifying factors that may cause a fall during the pre-impact fall period using wearable sensors. [Participants and Methods] The participants were 23 young people from the public data set (mean age, 23.4 years). Acceleration and angular velocity information obtained from sensors attached to the participant’s waist was used to generate the pre-impact fall. The cause of the fall (slip, trip, fainting, get up, sit down) was then classified with and without the addition of activity of daily living data using three different support vector machine. In addition, we investigated the influence of lead time (0–2.0s) on accuracy. [Results] The quadratic and cubic support vector machine identified the activity of daily living and fall patterns more accurately than the linear support vector machine, and the cubic support vector machine was better for classification, although the difference was slight. The greatest accuracy for predicting the cause of the fall (87.9%) was obtained when the cubic support vector machine was used, activity of daily living was factored into the analysis, and the lead time was 0.25 sec. [Conclusion] Support vector machine can identify the cause of the fall during the pre-impact fall period. Appropriate individualized interventions may be designed based on the most likely cause of fall as identified by this analysis method. |
format | Online Article Text |
id | pubmed-8989476 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | The Society of Physical Therapy Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-89894762022-04-08 Identification of the cause of fall during the pre-impact fall period Sasaki, Sho Yamamoto, Hiroaki Kitagawa, Kodai Wada, Chikamune J Phys Ther Sci Original Article [Purpose] This study aimed to develop and validate a method for identifying factors that may cause a fall during the pre-impact fall period using wearable sensors. [Participants and Methods] The participants were 23 young people from the public data set (mean age, 23.4 years). Acceleration and angular velocity information obtained from sensors attached to the participant’s waist was used to generate the pre-impact fall. The cause of the fall (slip, trip, fainting, get up, sit down) was then classified with and without the addition of activity of daily living data using three different support vector machine. In addition, we investigated the influence of lead time (0–2.0s) on accuracy. [Results] The quadratic and cubic support vector machine identified the activity of daily living and fall patterns more accurately than the linear support vector machine, and the cubic support vector machine was better for classification, although the difference was slight. The greatest accuracy for predicting the cause of the fall (87.9%) was obtained when the cubic support vector machine was used, activity of daily living was factored into the analysis, and the lead time was 0.25 sec. [Conclusion] Support vector machine can identify the cause of the fall during the pre-impact fall period. Appropriate individualized interventions may be designed based on the most likely cause of fall as identified by this analysis method. The Society of Physical Therapy Science 2022-04-08 2022-04 /pmc/articles/PMC8989476/ /pubmed/35400837 http://dx.doi.org/10.1589/jpts.34.320 Text en 2022©by the Society of Physical Therapy Science. Published by IPEC Inc. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution Non-Commercial No Derivatives (by-nc-nd) License. (CC-BY-NC-ND 4.0: https://creativecommons.org/licenses/by-nc-nd/4.0/) |
spellingShingle | Original Article Sasaki, Sho Yamamoto, Hiroaki Kitagawa, Kodai Wada, Chikamune Identification of the cause of fall during the pre-impact fall period |
title | Identification of the cause of fall during the pre-impact fall period |
title_full | Identification of the cause of fall during the pre-impact fall period |
title_fullStr | Identification of the cause of fall during the pre-impact fall period |
title_full_unstemmed | Identification of the cause of fall during the pre-impact fall period |
title_short | Identification of the cause of fall during the pre-impact fall period |
title_sort | identification of the cause of fall during the pre-impact fall period |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8989476/ https://www.ncbi.nlm.nih.gov/pubmed/35400837 http://dx.doi.org/10.1589/jpts.34.320 |
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