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An Approach for Fall Prediction Based on Kinematics of Body Key Points Using LSTM
Many studies have used sensors attached to adults in order to collect signals by which one can carry out analyses to predict falls. In addition, there are research studies in which videos and photographs were used to extract and analyze body posture and body kinematics. The present study proposes an...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9657864/ https://www.ncbi.nlm.nih.gov/pubmed/36360642 http://dx.doi.org/10.3390/ijerph192113762 |
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author | Mobasheri, Bahareh Tabbakh, Seyed Reza Kamel Forghani, Yahya |
author_facet | Mobasheri, Bahareh Tabbakh, Seyed Reza Kamel Forghani, Yahya |
author_sort | Mobasheri, Bahareh |
collection | PubMed |
description | Many studies have used sensors attached to adults in order to collect signals by which one can carry out analyses to predict falls. In addition, there are research studies in which videos and photographs were used to extract and analyze body posture and body kinematics. The present study proposes an integrated approach consisting of body kinematics and machine learning. The model data consist of video recordings collected in the UP-Fall Detection dataset experiment. Three models based on long-short-term memory (LSTM) network—4p-SAFE, 5p-SAFE, and 6p-SAFE for four, five, and six parameters—were developed in this work. The parameters needed for these models consist of some coordinates and angles extracted from videos. These models are easy to apply to the sequential images collected by ordinary cameras, which are installed everywhere, especially on aged-care premises. The accuracy of predictions was as good as 98%. Finally, the authors discuss that, by applying these models, the health and wellness of adults and elderlies will be considerably promoted. |
format | Online Article Text |
id | pubmed-9657864 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-96578642022-11-15 An Approach for Fall Prediction Based on Kinematics of Body Key Points Using LSTM Mobasheri, Bahareh Tabbakh, Seyed Reza Kamel Forghani, Yahya Int J Environ Res Public Health Article Many studies have used sensors attached to adults in order to collect signals by which one can carry out analyses to predict falls. In addition, there are research studies in which videos and photographs were used to extract and analyze body posture and body kinematics. The present study proposes an integrated approach consisting of body kinematics and machine learning. The model data consist of video recordings collected in the UP-Fall Detection dataset experiment. Three models based on long-short-term memory (LSTM) network—4p-SAFE, 5p-SAFE, and 6p-SAFE for four, five, and six parameters—were developed in this work. The parameters needed for these models consist of some coordinates and angles extracted from videos. These models are easy to apply to the sequential images collected by ordinary cameras, which are installed everywhere, especially on aged-care premises. The accuracy of predictions was as good as 98%. Finally, the authors discuss that, by applying these models, the health and wellness of adults and elderlies will be considerably promoted. MDPI 2022-10-22 /pmc/articles/PMC9657864/ /pubmed/36360642 http://dx.doi.org/10.3390/ijerph192113762 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 Mobasheri, Bahareh Tabbakh, Seyed Reza Kamel Forghani, Yahya An Approach for Fall Prediction Based on Kinematics of Body Key Points Using LSTM |
title | An Approach for Fall Prediction Based on Kinematics of Body Key Points Using LSTM |
title_full | An Approach for Fall Prediction Based on Kinematics of Body Key Points Using LSTM |
title_fullStr | An Approach for Fall Prediction Based on Kinematics of Body Key Points Using LSTM |
title_full_unstemmed | An Approach for Fall Prediction Based on Kinematics of Body Key Points Using LSTM |
title_short | An Approach for Fall Prediction Based on Kinematics of Body Key Points Using LSTM |
title_sort | approach for fall prediction based on kinematics of body key points using lstm |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9657864/ https://www.ncbi.nlm.nih.gov/pubmed/36360642 http://dx.doi.org/10.3390/ijerph192113762 |
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