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Green Communication for Tracking Heart Rate with Smartbands

The trend of using wearables for healthcare is steeply increasing nowadays, and, consequently, in the market, there are several gadgets that measure several body features. In addition, the mixed use between smartphones and wearables has motivated research like the current one. The main goal of this...

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Autores principales: González-Landero, Franks, García-Magariño, Iván, Lacuesta, Raquel, Lloret, Jaime
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6111836/
https://www.ncbi.nlm.nih.gov/pubmed/30104499
http://dx.doi.org/10.3390/s18082652
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author González-Landero, Franks
García-Magariño, Iván
Lacuesta, Raquel
Lloret, Jaime
author_facet González-Landero, Franks
García-Magariño, Iván
Lacuesta, Raquel
Lloret, Jaime
author_sort González-Landero, Franks
collection PubMed
description The trend of using wearables for healthcare is steeply increasing nowadays, and, consequently, in the market, there are several gadgets that measure several body features. In addition, the mixed use between smartphones and wearables has motivated research like the current one. The main goal of this work is to reduce the amount of times that a certain smartband (SB) measures the heart rate (HR) in order to save energy in communications without significantly reducing the utility of the application. This work has used an SB Sony 2 for measuring heart rate, Fit API for storing data and Android for managing data. The current approach has been assessed with data from HR sensors collected for more than three months. Once all HR measures were collected, then the current approach detected hourly ranges whose heart rate were higher than normal. The hourly ranges allowed for estimating the time periods of weeks that the user could be at potential risk for measuring frequently in these (60 times per hour) ranges. Out of these ranges, the measurement frequency was lower (six times per hour). If SB measures an unusual heart rate, the app warns the user so they are aware of the risk and can act accordingly. We analyzed two cases and we conclude that energy consumption was reduced in 83.57% in communications when using training of several weeks. In addition, a prediction per day was made using data of 20 users. On average, tests obtained 63.04% of accuracy in this experimentation using the training over the data of one day for each user.
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spelling pubmed-61118362018-08-30 Green Communication for Tracking Heart Rate with Smartbands González-Landero, Franks García-Magariño, Iván Lacuesta, Raquel Lloret, Jaime Sensors (Basel) Article The trend of using wearables for healthcare is steeply increasing nowadays, and, consequently, in the market, there are several gadgets that measure several body features. In addition, the mixed use between smartphones and wearables has motivated research like the current one. The main goal of this work is to reduce the amount of times that a certain smartband (SB) measures the heart rate (HR) in order to save energy in communications without significantly reducing the utility of the application. This work has used an SB Sony 2 for measuring heart rate, Fit API for storing data and Android for managing data. The current approach has been assessed with data from HR sensors collected for more than three months. Once all HR measures were collected, then the current approach detected hourly ranges whose heart rate were higher than normal. The hourly ranges allowed for estimating the time periods of weeks that the user could be at potential risk for measuring frequently in these (60 times per hour) ranges. Out of these ranges, the measurement frequency was lower (six times per hour). If SB measures an unusual heart rate, the app warns the user so they are aware of the risk and can act accordingly. We analyzed two cases and we conclude that energy consumption was reduced in 83.57% in communications when using training of several weeks. In addition, a prediction per day was made using data of 20 users. On average, tests obtained 63.04% of accuracy in this experimentation using the training over the data of one day for each user. MDPI 2018-08-13 /pmc/articles/PMC6111836/ /pubmed/30104499 http://dx.doi.org/10.3390/s18082652 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
González-Landero, Franks
García-Magariño, Iván
Lacuesta, Raquel
Lloret, Jaime
Green Communication for Tracking Heart Rate with Smartbands
title Green Communication for Tracking Heart Rate with Smartbands
title_full Green Communication for Tracking Heart Rate with Smartbands
title_fullStr Green Communication for Tracking Heart Rate with Smartbands
title_full_unstemmed Green Communication for Tracking Heart Rate with Smartbands
title_short Green Communication for Tracking Heart Rate with Smartbands
title_sort green communication for tracking heart rate with smartbands
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6111836/
https://www.ncbi.nlm.nih.gov/pubmed/30104499
http://dx.doi.org/10.3390/s18082652
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