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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...
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/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. |
format | Online Article Text |
id | pubmed-6111836 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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