2026-08-26T22:06:09Zhttps://gredos.usal.es/oai/request

oai:gredos.usal.es:10366/1348802025-05-28T11:23:35Zcom_10366_122575com_10366_4512com_10366_3823col_10366_134811

Prieto Tejedor, Javier De Paz, Juan F. Villarrubia González, Gabriel Bajo Pérez, Javier Corchado Rodríguez, Juan Manuel 2017-09-06T09:14:28Z 2017-09-06T09:14:28Z 2015-06 Ambient Intelligence- Software and Applications – 6th International Symposium on Ambient Intelligence (ISAmI 2015). Advances in Intelligent Systems and Computing. pp. 223-231. 978-3-319-19694-7(Print) / 978-3-319-19695-4(Online) 2194-5357(Print) / 2194-5365(Online) http://dx.doi.org/10.1007/978-3-319-19695-4_23 http://hdl.handle.net/10366/134880 Indoor localization constitutes one of the main pillars for the provision of context-aware services in e-Healthcare systems. Fingerprinting and ranging have traditionally been placed facing each other to meet the localization requirements. However, accurate fingerprinting may worth the exhaustive calibration effort in some critical areas while easy-to-deploy ranging can provide adequate accuracy for certain non-critical spaces. In this paper, we propose a framework and algorithm for seamless integration of both systems from the Bayesian perspective. We assessed the proposed framework with conventional WiFi devices in comparison to conventional implementations. The presented techniques exhibit a remarkable accuracy improvement while they avoid computationally exhaustive algorithms that impede real-time operation. en https://creativecommons.org/licenses/by-nc-nd/3.0/ info:eu-repo/semantics/openAccess Attribution-NonCommercial-NoDerivs 3.0 Unported Computer Science Unified Fingerprinting/Ranging Localization for e-Healthcare Systems info:eu-repo/semantics/conferenceObject