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Enhanced Algorithm for the Detection of Preimpact Fall for Wearable Airbags

Fall-related injury is a common cause of mortality among the elderly. Hip fractures are especially dangerous and can even be fatal. In this study, a threshold-based preimpact fall detection algorithm was developed for wearable airbags that minimize the impact of falls on the user’s body. Acceleratio...

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Autores principales: Jung, Haneul, Koo, Bummo, Kim, Jongman, Kim, Taehee, Nam, Yejin, Kim, Youngho
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7085770/
https://www.ncbi.nlm.nih.gov/pubmed/32111090
http://dx.doi.org/10.3390/s20051277
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author Jung, Haneul
Koo, Bummo
Kim, Jongman
Kim, Taehee
Nam, Yejin
Kim, Youngho
author_facet Jung, Haneul
Koo, Bummo
Kim, Jongman
Kim, Taehee
Nam, Yejin
Kim, Youngho
author_sort Jung, Haneul
collection PubMed
description Fall-related injury is a common cause of mortality among the elderly. Hip fractures are especially dangerous and can even be fatal. In this study, a threshold-based preimpact fall detection algorithm was developed for wearable airbags that minimize the impact of falls on the user’s body. Acceleration sum vector magnitude (SVM), angular velocity SVM, and vertical angle, calculated using inertial data captured from an inertial measurement unit were used to develop the algorithm. To calculate the vertical angle accurately, a complementary filter with a proportional integral controller was used to minimize integration errors and the effect of external impacts. In total, 30 healthy young men were recruited to simulate 6 types of falls and 14 activities of daily life. The developed algorithm achieved 100% sensitivity, 97.54% specificity, 98.33% accuracy, and an average lead time (i.e., the time between the fall detection and the collision) of 280.25 ± 10.29 ms with our experimental data, whereas it achieved 96.1% sensitivity, 90.5% specificity, and 92.4% accuracy with the SisFall public dataset. This paper demonstrates that the algorithm achieved a high accuracy using our experimental data, which included some highly dynamic motions that had not been tested previously.
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spelling pubmed-70857702020-03-25 Enhanced Algorithm for the Detection of Preimpact Fall for Wearable Airbags Jung, Haneul Koo, Bummo Kim, Jongman Kim, Taehee Nam, Yejin Kim, Youngho Sensors (Basel) Article Fall-related injury is a common cause of mortality among the elderly. Hip fractures are especially dangerous and can even be fatal. In this study, a threshold-based preimpact fall detection algorithm was developed for wearable airbags that minimize the impact of falls on the user’s body. Acceleration sum vector magnitude (SVM), angular velocity SVM, and vertical angle, calculated using inertial data captured from an inertial measurement unit were used to develop the algorithm. To calculate the vertical angle accurately, a complementary filter with a proportional integral controller was used to minimize integration errors and the effect of external impacts. In total, 30 healthy young men were recruited to simulate 6 types of falls and 14 activities of daily life. The developed algorithm achieved 100% sensitivity, 97.54% specificity, 98.33% accuracy, and an average lead time (i.e., the time between the fall detection and the collision) of 280.25 ± 10.29 ms with our experimental data, whereas it achieved 96.1% sensitivity, 90.5% specificity, and 92.4% accuracy with the SisFall public dataset. This paper demonstrates that the algorithm achieved a high accuracy using our experimental data, which included some highly dynamic motions that had not been tested previously. MDPI 2020-02-26 /pmc/articles/PMC7085770/ /pubmed/32111090 http://dx.doi.org/10.3390/s20051277 Text en © 2020 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
Jung, Haneul
Koo, Bummo
Kim, Jongman
Kim, Taehee
Nam, Yejin
Kim, Youngho
Enhanced Algorithm for the Detection of Preimpact Fall for Wearable Airbags
title Enhanced Algorithm for the Detection of Preimpact Fall for Wearable Airbags
title_full Enhanced Algorithm for the Detection of Preimpact Fall for Wearable Airbags
title_fullStr Enhanced Algorithm for the Detection of Preimpact Fall for Wearable Airbags
title_full_unstemmed Enhanced Algorithm for the Detection of Preimpact Fall for Wearable Airbags
title_short Enhanced Algorithm for the Detection of Preimpact Fall for Wearable Airbags
title_sort enhanced algorithm for the detection of preimpact fall for wearable airbags
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7085770/
https://www.ncbi.nlm.nih.gov/pubmed/32111090
http://dx.doi.org/10.3390/s20051277
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