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Novel Hierarchical Fall Detection Algorithm Using a Multiphase Fall Model
Falls are the primary cause of accidents for the elderly in the living environment. Reducing hazards in the living environment and performing exercises for training balance and muscles are the common strategies for fall prevention. However, falls cannot be avoided completely; fall detection provides...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5335954/ https://www.ncbi.nlm.nih.gov/pubmed/28208694 http://dx.doi.org/10.3390/s17020307 |
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author | Hsieh, Chia-Yeh Liu, Kai-Chun Huang, Chih-Ning Chu, Woei-Chyn Chan, Chia-Tai |
author_facet | Hsieh, Chia-Yeh Liu, Kai-Chun Huang, Chih-Ning Chu, Woei-Chyn Chan, Chia-Tai |
author_sort | Hsieh, Chia-Yeh |
collection | PubMed |
description | Falls are the primary cause of accidents for the elderly in the living environment. Reducing hazards in the living environment and performing exercises for training balance and muscles are the common strategies for fall prevention. However, falls cannot be avoided completely; fall detection provides an alarm that can decrease injuries or death caused by the lack of rescue. The automatic fall detection system has opportunities to provide real-time emergency alarms for improving the safety and quality of home healthcare services. Two common technical challenges are also tackled in order to provide a reliable fall detection algorithm, including variability and ambiguity. We propose a novel hierarchical fall detection algorithm involving threshold-based and knowledge-based approaches to detect a fall event. The threshold-based approach efficiently supports the detection and identification of fall events from continuous sensor data. A multiphase fall model is utilized, including free fall, impact, and rest phases for the knowledge-based approach, which identifies fall events and has the potential to deal with the aforementioned technical challenges of a fall detection system. Seven kinds of falls and seven types of daily activities arranged in an experiment are used to explore the performance of the proposed fall detection algorithm. The overall performances of the sensitivity, specificity, precision, and accuracy using a knowledge-based algorithm are 99.79%, 98.74%, 99.05% and 99.33%, respectively. The results show that the proposed novel hierarchical fall detection algorithm can cope with the variability and ambiguity of the technical challenges and fulfill the reliability, adaptability, and flexibility requirements of an automatic fall detection system with respect to the individual differences. |
format | Online Article Text |
id | pubmed-5335954 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-53359542017-03-16 Novel Hierarchical Fall Detection Algorithm Using a Multiphase Fall Model Hsieh, Chia-Yeh Liu, Kai-Chun Huang, Chih-Ning Chu, Woei-Chyn Chan, Chia-Tai Sensors (Basel) Article Falls are the primary cause of accidents for the elderly in the living environment. Reducing hazards in the living environment and performing exercises for training balance and muscles are the common strategies for fall prevention. However, falls cannot be avoided completely; fall detection provides an alarm that can decrease injuries or death caused by the lack of rescue. The automatic fall detection system has opportunities to provide real-time emergency alarms for improving the safety and quality of home healthcare services. Two common technical challenges are also tackled in order to provide a reliable fall detection algorithm, including variability and ambiguity. We propose a novel hierarchical fall detection algorithm involving threshold-based and knowledge-based approaches to detect a fall event. The threshold-based approach efficiently supports the detection and identification of fall events from continuous sensor data. A multiphase fall model is utilized, including free fall, impact, and rest phases for the knowledge-based approach, which identifies fall events and has the potential to deal with the aforementioned technical challenges of a fall detection system. Seven kinds of falls and seven types of daily activities arranged in an experiment are used to explore the performance of the proposed fall detection algorithm. The overall performances of the sensitivity, specificity, precision, and accuracy using a knowledge-based algorithm are 99.79%, 98.74%, 99.05% and 99.33%, respectively. The results show that the proposed novel hierarchical fall detection algorithm can cope with the variability and ambiguity of the technical challenges and fulfill the reliability, adaptability, and flexibility requirements of an automatic fall detection system with respect to the individual differences. MDPI 2017-02-08 /pmc/articles/PMC5335954/ /pubmed/28208694 http://dx.doi.org/10.3390/s17020307 Text en © 2017 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 | Article Hsieh, Chia-Yeh Liu, Kai-Chun Huang, Chih-Ning Chu, Woei-Chyn Chan, Chia-Tai Novel Hierarchical Fall Detection Algorithm Using a Multiphase Fall Model |
title | Novel Hierarchical Fall Detection Algorithm Using a Multiphase Fall Model |
title_full | Novel Hierarchical Fall Detection Algorithm Using a Multiphase Fall Model |
title_fullStr | Novel Hierarchical Fall Detection Algorithm Using a Multiphase Fall Model |
title_full_unstemmed | Novel Hierarchical Fall Detection Algorithm Using a Multiphase Fall Model |
title_short | Novel Hierarchical Fall Detection Algorithm Using a Multiphase Fall Model |
title_sort | novel hierarchical fall detection algorithm using a multiphase fall model |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5335954/ https://www.ncbi.nlm.nih.gov/pubmed/28208694 http://dx.doi.org/10.3390/s17020307 |
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