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A Fall and Near-Fall Assessment and Evaluation System
The FANFARE (Falls And Near Falls Assessment Research and Evaluation) project has developed a system to fulfill the need for a wearable device to collect data for fall and near-falls analysis. The system consists of a computer and a wireless sensor network to measure, display, and store fall related...
Autores principales: | , , , , , , , , |
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Formato: | Texto |
Lenguaje: | English |
Publicado: |
Bentham Open
2009
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2709926/ https://www.ncbi.nlm.nih.gov/pubmed/19662151 http://dx.doi.org/10.2174/1874120700903010001 |
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author | Dinh, Anh Shi, Yang Teng, Daniel Ralhan, Amitoz Chen, Li Dal Bello-Haas, Vanina Basran, Jenny Ko, Seok-Bum McCrowsky, Carl |
author_facet | Dinh, Anh Shi, Yang Teng, Daniel Ralhan, Amitoz Chen, Li Dal Bello-Haas, Vanina Basran, Jenny Ko, Seok-Bum McCrowsky, Carl |
author_sort | Dinh, Anh |
collection | PubMed |
description | The FANFARE (Falls And Near Falls Assessment Research and Evaluation) project has developed a system to fulfill the need for a wearable device to collect data for fall and near-falls analysis. The system consists of a computer and a wireless sensor network to measure, display, and store fall related parameters such as postural activities and heart rate variability. Ease of use and low power are considered in the design. The system was built and tested successfully. Different machine learning algorithms were applied to the stored data for fall and near-fall evaluation. Results indicate that the Naïve Bayes algorithm is the best choice, due to its fast model building and high accuracy in fall detection. |
format | Text |
id | pubmed-2709926 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2009 |
publisher | Bentham Open |
record_format | MEDLINE/PubMed |
spelling | pubmed-27099262009-08-06 A Fall and Near-Fall Assessment and Evaluation System Dinh, Anh Shi, Yang Teng, Daniel Ralhan, Amitoz Chen, Li Dal Bello-Haas, Vanina Basran, Jenny Ko, Seok-Bum McCrowsky, Carl Open Biomed Eng J Article The FANFARE (Falls And Near Falls Assessment Research and Evaluation) project has developed a system to fulfill the need for a wearable device to collect data for fall and near-falls analysis. The system consists of a computer and a wireless sensor network to measure, display, and store fall related parameters such as postural activities and heart rate variability. Ease of use and low power are considered in the design. The system was built and tested successfully. Different machine learning algorithms were applied to the stored data for fall and near-fall evaluation. Results indicate that the Naïve Bayes algorithm is the best choice, due to its fast model building and high accuracy in fall detection. Bentham Open 2009-01-21 /pmc/articles/PMC2709926/ /pubmed/19662151 http://dx.doi.org/10.2174/1874120700903010001 Text en © Dinh et al.; Licensee Bentham Open. http://creativecommons.org/licenses/by-nc/3.0/ This is an open access article licensed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/)which permits unrestricted, non-commercial use, distribution and reproduction in any medium, provided the work is properly cited. |
spellingShingle | Article Dinh, Anh Shi, Yang Teng, Daniel Ralhan, Amitoz Chen, Li Dal Bello-Haas, Vanina Basran, Jenny Ko, Seok-Bum McCrowsky, Carl A Fall and Near-Fall Assessment and Evaluation System |
title | A Fall and Near-Fall Assessment and Evaluation System |
title_full | A Fall and Near-Fall Assessment and Evaluation System |
title_fullStr | A Fall and Near-Fall Assessment and Evaluation System |
title_full_unstemmed | A Fall and Near-Fall Assessment and Evaluation System |
title_short | A Fall and Near-Fall Assessment and Evaluation System |
title_sort | fall and near-fall assessment and evaluation system |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2709926/ https://www.ncbi.nlm.nih.gov/pubmed/19662151 http://dx.doi.org/10.2174/1874120700903010001 |
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