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dc.contributor.authorAyala-Viñas, Inmaculada 
dc.contributor.authorFuentes-Fernández, Lidia 
dc.contributor.authorAmor-Pinilla, María Mercedes 
dc.contributor.authorCaro-Romero, Juan
dc.contributor.authorBallesteros-Gómez, Joaquín 
dc.date.accessioned2019-12-11T11:31:41Z
dc.date.available2019-12-11T11:31:41Z
dc.date.created2019
dc.date.issued2019-12-11
dc.identifier.urihttps://hdl.handle.net/10630/19013
dc.description.abstractNowadays, more than one billion people are in need of one or more assistive technologies, and this number is expected to increase beyond two billion by 2050. The majority of assistive technologies are supported by battery-operated devices like smartphones and wearables. This means that battery weight is an important concern in such assistive devices because it may affect negatively its ergonomics. Saving power in these assistive devices is of utmost importance for its potential twofold benefits: extend the device life and reduce the global warming aggravated by billion of these devices. Dynamic Software Product Lines (DSPLs) are a suitable technology that supports system adaptation, in this case, to reduce energy consumption at runtime, considering contextual information and the current state of the device. However, a reduction in battery consumption could negatively affect other quality of service parameters, like response time. Therefore, it is important to trade-off battery saving and these other concerns. This work illustrates how to approach the self-adaptation of smart assistive devices by means of a DSPL-based strategy that optimizes battery consumption taking into account other QoS parameters at the same time. We illustrate our proposal with a real case study: a Smart Cane that is integrated with a DSPL platform, Tanit. Experimentation shows that it is possible to make a trade-off between different quality concerns (energy consumption and relative error). The results of the experiments allow us to conclude that the Tanit approach elongates battery duration of the Smart Cane in one day (an increase of a 6% with a relative error of 1%), so we improve the user quality of experience and reduce the energy footprint with a reasonable relative error.en_US
dc.description.sponsorshipThis research was funded by the projects Magic P12-TIC1814 and TASOVA MCIU-AEI TIN2017-90644-REDT, by the projects co-financed by FEDER funds HADAS TIN2015-64841-R, MEDEA RTI2018-099213-B-I00 and LEIA UMA18-FEDERJA-157, by the post-doctoral plan of the University of Málaga and the Swedish Knowledge Foundation (KKS) through the research profile Embedded Sensor Systems for Health Plus at Mälardalen University, Sweden. -Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech.en_US
dc.language.isoengen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectSaluden_US
dc.subject.otherM-healthen_US
dc.subject.otherSelf-adaptationen_US
dc.subject.otherE-healthen_US
dc.subject.otherSmart caneen_US
dc.subject.otherGait analysisen_US
dc.subject.otherPhase detectionen_US
dc.titleSelf-Adaptation of mHealth Devices: The Case of the Smart Cane Platformen_US
dc.typeinfo:eu-repo/semantics/conferenceObjecten_US
dc.centroE.T.S.I. Informáticaen_US
dc.relation.eventtitle13th International Conference on Ubiquitous Computing and Ambient Intelligence - UCAmI 2019en_US
dc.relation.eventplaceToledo (Spain)en_US
dc.relation.eventdate2 - 5 diciembre 2019en_US


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