RT Conference Proceedings
T1 Self-Adaptation of mHealth Devices: The Case of the Smart Cane Platform
A1 Ayala-Viñas, Inmaculada
A1 Fuentes-Fernández, Lidia
A1 Amor-Pinilla, María Mercedes
A1 Caro-Romero, Juan
A1 Ballesteros-Gómez, Joaquín
K1 Salud
AB Nowadays, 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.
YR 2019
FD 2019-12-11
LK https://hdl.handle.net/10630/19013
UL https://hdl.handle.net/10630/19013
LA eng
NO This research was funded by the projects Magic P12-TIC1814 and TASOVA MCIU-AEITIN2017-90644-REDT, by the projects co-financed by FEDER funds HADAS TIN2015-64841-R, MEDEARTI2018-099213-B-I00 and LEIA UMA18-FEDERJA-157, by the post-doctoral plan of the University of Málaga andthe Swedish Knowledge Foundation (KKS) through the research profile Embedded Sensor Systems for HealthPlus at Mälardalen University, Sweden.-Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech.
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 20 ene 2026