Mostra el registre d'ítem simple
Transition-aware human activity recognition using smartphones
dc.contributor.author | Reyes Ortiz, Jorge Luis |
dc.contributor.author | Oneto, Luca |
dc.contributor.author | Samà Monsonís, Albert |
dc.contributor.author | Ghio, Alessandro |
dc.contributor.author | Llanas Parra, Xavier |
dc.contributor.author | Anguita, Davide |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament d'Enginyeria Electrònica |
dc.date.accessioned | 2015-11-24T10:07:20Z |
dc.date.available | 2015-11-24T10:07:20Z |
dc.date.issued | 2015-08-08 |
dc.identifier.citation | Reyes, J., Oneto, L., Sama, A., Ghio, A., Parra, X., Anguita, D. Transition-aware human activity recognition using smartphones. "Neurocomputing", 08 Agost 2015. |
dc.identifier.issn | 0925-2312 |
dc.identifier.uri | http://hdl.handle.net/2117/79600 |
dc.description.abstract | This work presents the Transition-Aware Human Activity Recognition (TAHAR) system architecture for the recognition of physical activities using smartphones. It targets real-time classification with a collection of inertial sensors while addressing issues regarding the occurrence of transitions between activities and unknown activities to the learning algorithm. We propose two implementations of the architecture which differ in their prediction technique as they deal with transitions either by directly learning them or by considering them as unknown activities. This is accomplished by combining the probabilistic output of consecutive activity predictions of a Support Vector Machine (SVM) with a heuristic filtering approach. The architecture is validated over three case studies that involve data from people performing a broad spectrum of activities (up to 33), while carrying smartphones or wearable sensors. Results show that TAHAR outperforms state-of-the-art baseline works and reveal the main advantages of the architecture. |
dc.language.iso | eng |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ |
dc.subject | Àrees temàtiques de la UPC::Enginyeria electrònica |
dc.subject.lcsh | Machine learning |
dc.subject.other | Activity Recognition |
dc.subject.other | Smartphones |
dc.subject.other | Transitions |
dc.subject.other | Support Vector Machines |
dc.subject.other | Machine learning |
dc.title | Transition-aware human activity recognition using smartphones |
dc.type | Article |
dc.subject.lemac | Aprenentatge automàtic |
dc.contributor.group | Universitat Politècnica de Catalunya. CETpD -Centre d'Estudis Tecnològics per a l'Atenció a la Dependència i la Vida Autònoma |
dc.identifier.doi | 10.1016/j.neucom.2015.07.085 |
dc.relation.publisherversion | http://www.sciencedirect.com/science/article/pii/S0925231215010930 |
dc.rights.access | Open Access |
local.identifier.drac | 16968249 |
dc.description.version | Postprint (author's final draft) |
local.citation.author | Reyes, J.; Oneto, L.; Sama, A.; Ghio, A.; Parra, X.; Anguita, D. |
local.citation.publicationName | Neurocomputing |
Fitxers d'aquest items
Aquest ítem apareix a les col·leccions següents
-
Articles de revista [31]
-
Articles de revista [1.721]