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SmartFall: A Smartwatch-Based Fall Detection System Using Deep Learning
This paper presents SmartFall, an Android app that uses accelerometer data collected from a commodity-based smartwatch Internet of Things (IoT) device to detect falls. The smartwatch is paired with a smartphone that runs the SmartFall application, which performs the computation necessary for the pre...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6210545/ https://www.ncbi.nlm.nih.gov/pubmed/30304768 http://dx.doi.org/10.3390/s18103363 |
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author | Mauldin, Taylor R. Canby, Marc E. Metsis, Vangelis Ngu, Anne H. H. Rivera, Coralys Cubero |
author_facet | Mauldin, Taylor R. Canby, Marc E. Metsis, Vangelis Ngu, Anne H. H. Rivera, Coralys Cubero |
author_sort | Mauldin, Taylor R. |
collection | PubMed |
description | This paper presents SmartFall, an Android app that uses accelerometer data collected from a commodity-based smartwatch Internet of Things (IoT) device to detect falls. The smartwatch is paired with a smartphone that runs the SmartFall application, which performs the computation necessary for the prediction of falls in real time without incurring latency in communicating with a cloud server, while also preserving data privacy. We experimented with both traditional (Support Vector Machine and Naive Bayes) and non-traditional (Deep Learning) machine learning algorithms for the creation of fall detection models using three different fall datasets (Smartwatch, Notch, Farseeing). Our results show that a Deep Learning model for fall detection generally outperforms more traditional models across the three datasets. This is attributed to the Deep Learning model’s ability to automatically learn subtle features from the raw accelerometer data that are not available to Naive Bayes and Support Vector Machine, which are restricted to learning from a small set of extracted features manually specified. Furthermore, the Deep Learning model exhibits a better ability to generalize to new users when predicting falls, an important quality of any model that is to be successful in the real world. We also present a three-layer open IoT system architecture used in SmartFall, which can be easily adapted for the collection and analysis of other sensor data modalities (e.g., heart rate, skin temperature, walking patterns) that enables remote monitoring of a subject’s wellbeing. |
format | Online Article Text |
id | pubmed-6210545 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-62105452018-11-02 SmartFall: A Smartwatch-Based Fall Detection System Using Deep Learning Mauldin, Taylor R. Canby, Marc E. Metsis, Vangelis Ngu, Anne H. H. Rivera, Coralys Cubero Sensors (Basel) Article This paper presents SmartFall, an Android app that uses accelerometer data collected from a commodity-based smartwatch Internet of Things (IoT) device to detect falls. The smartwatch is paired with a smartphone that runs the SmartFall application, which performs the computation necessary for the prediction of falls in real time without incurring latency in communicating with a cloud server, while also preserving data privacy. We experimented with both traditional (Support Vector Machine and Naive Bayes) and non-traditional (Deep Learning) machine learning algorithms for the creation of fall detection models using three different fall datasets (Smartwatch, Notch, Farseeing). Our results show that a Deep Learning model for fall detection generally outperforms more traditional models across the three datasets. This is attributed to the Deep Learning model’s ability to automatically learn subtle features from the raw accelerometer data that are not available to Naive Bayes and Support Vector Machine, which are restricted to learning from a small set of extracted features manually specified. Furthermore, the Deep Learning model exhibits a better ability to generalize to new users when predicting falls, an important quality of any model that is to be successful in the real world. We also present a three-layer open IoT system architecture used in SmartFall, which can be easily adapted for the collection and analysis of other sensor data modalities (e.g., heart rate, skin temperature, walking patterns) that enables remote monitoring of a subject’s wellbeing. MDPI 2018-10-09 /pmc/articles/PMC6210545/ /pubmed/30304768 http://dx.doi.org/10.3390/s18103363 Text en © 2018 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 Mauldin, Taylor R. Canby, Marc E. Metsis, Vangelis Ngu, Anne H. H. Rivera, Coralys Cubero SmartFall: A Smartwatch-Based Fall Detection System Using Deep Learning |
title | SmartFall: A Smartwatch-Based Fall Detection System Using Deep Learning |
title_full | SmartFall: A Smartwatch-Based Fall Detection System Using Deep Learning |
title_fullStr | SmartFall: A Smartwatch-Based Fall Detection System Using Deep Learning |
title_full_unstemmed | SmartFall: A Smartwatch-Based Fall Detection System Using Deep Learning |
title_short | SmartFall: A Smartwatch-Based Fall Detection System Using Deep Learning |
title_sort | smartfall: a smartwatch-based fall detection system using deep learning |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6210545/ https://www.ncbi.nlm.nih.gov/pubmed/30304768 http://dx.doi.org/10.3390/s18103363 |
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