[u' @article{bilbao_jayo_behavior_2021, title = {Behavior {Modeling} for a {Beacon}-{Based} {Indoor} {Location} {System}}, volume = {21}, issn = {1424-8220}, url = {https://www.mdpi.com/1424-8220/21/14/4839}, doi = {10.3390/s21144839}, abstract = {In this work we performed a comparison between two different approaches to track a person in indoor environments using a locating system based on BLE technology with a smartphone and a smartwatch as monitoring devices. To do so, we provide the system architecture we designed and describe how the different elements of the proposed system interact with each other. Moreover, we have evaluated the system\u2019s performance by computing the mean percentage error in the detection of the indoor position. Finally, we present a novel location prediction system based on neural embeddings, and a soft-attention mechanism, which is able to predict user\u2019s next location with 67\\% accuracy.}, language = {English}, number = {14}, journal = {Sensors}, author = {Bilbao Jayo, Aritz and Almeida, Aitor and Sergi, Ilaria and Montanaro, Teodoro and Fasano, Luca and Emaldi, Mikel and Patrono, Luigi}, month = jul, year = {2021}, keywords = {AI for health, Artificial Intelligence, BD4QoL, Behavior modelling, Behavior prediction, FuturAAL, Intelligent Environments, IoT, JCR3.576, Machine learning, Q1, activity recognition, indoor navigation, location systems}, pages = {4839}, } ']