Skip to main page content
Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2018 Aug 13;18(8):2652.
doi: 10.3390/s18082652.

Green Communication for Tracking Heart Rate With Smartbands

Affiliations
Free PMC article

Green Communication for Tracking Heart Rate With Smartbands

Franks González-Landero et al. Sensors (Basel). .
Free PMC article

Abstract

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.

Keywords: Google fit; body sensor networks; eHealthcare; heart rate; smartband; wearable sensors.

Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Overall experiment.
Figure 2
Figure 2
Algorithm for saving energy.
Figure 3
Figure 3
Diagram for recalculating user’s routines.
Figure 4
Figure 4
Google Fit.
Figure 5
Figure 5
Diagram of class.
Figure 6
Figure 6
Hierarchy of History Application Programming Intreface (API) Class.
Figure 7
Figure 7
Screenshot of Android app.
Figure 8
Figure 8
Heart rate in a normal day.
Figure 9
Figure 9
Heart rate in a physical activity day.
Figure 10
Figure 10
Heart rate of three Thursdays.
Figure 11
Figure 11
Energy consumption from 7 May to 13 May.
Figure 12
Figure 12
Energy consumption from 28 May to 3 June.
Figure 13
Figure 13
Energy consumption on 13 May.
Figure 14
Figure 14
Energy consumption on 11 May.

Similar articles

See all similar articles

Cited by 3 articles

References

    1. Mendis S., Thygesen K., Kuulasmaa K., Giampaoli S., Mähönen M., Ngu Blackett K., Lisheng L. Writing Group on behalf of the Participating Experts of the WHO Consultation for Revision of WHO Definition of Myocardial Infarction. World Health Organization definition of myocardial infarction: 2008–09 revision. Int. J. Epidemiol. 2010;40:139–146. doi: 10.1093/ije/dyq165. - DOI - PubMed
    1. Bax L., Algra A., Willem P.T.M., Edlinger M., Beutler J.J., van der Graaf Y. Renal function as a risk indicator for cardiovascular events in 3216 patients with manifest arterial disease. Atherosclerosis. 2008;200:184–190. doi: 10.1016/j.atherosclerosis.2007.12.006. - DOI - PubMed
    1. Avila K., Sanmartin P., Jabba D., Jimeno M. Applications Based on Service-Oriented Architecture (SOA) in the Field of Home Healthcare. Sensors. 2017;17:1703 doi: 10.3390/s17081703. - DOI - PMC - PubMed
    1. Sendra S., Parra L., Lloret J., Tomás J. Smart system for children’s chronic illness monitoring. Inf. Fusion. 2018;40:76–86. doi: 10.1016/j.inffus.2017.06.002. - DOI
    1. Lloret J., Parra L., Taha M., Tomás J. An architecture and protocol for smart continuous eHealth monitoring using 5G. Comput. Netw. 2017;129:340–351. doi: 10.1016/j.comnet.2017.05.018. - DOI
Feedback