[u' @article{azkune_ezagutzan_2015, title = {Ezagutzan {Oinarritutako} {Giza}-{Jardueren} {Eredu} {Dinamiko} eta {Pertsonalizatuak} {Ikasten} (2)}, issn = {0214-9001}, url = {http://www.ehu.eus/ojs/index.php/ekaia/article/view/14662}, doi = {10.1387/ekaia.14662}, abstract = {Being able to recognise human activities by means of sensor and computational devices can be a key competence in order to achieve human centred technologies. For that purpose, it is mandatory to build computational models of the activities which have to be recognised. There are two major approaches for activity modelling: the data-driven and the knowledge-driven approaches. Both of them have advantages and drawbacks. The objective of this work is to combine both modelling approaches with the aim of building dynamic and personalised activity models, using generic knowledge-based models. This would allow implementing modelling processes which can adapt themselves to the evolution of specific people.}, number = {1333}, journal = {Ekaia. Euskal Herriko Unibertsitateko Zientzi eta Teknologi Aldizkaria}, author = {Azkune, Gorka and Almeida, Aitor and Lopez de Ipina, Diego and Chen, Liming Luke}, month = oct, year = {2015}, note = {00000}, keywords = {AI for health, Activity Recognition, Activity model, Artificial Intelligence, Data analysis, Survey, intelligent environments, machine learning}, } ']