Fiche du document
23 mai 2014
- handle: 10670/1.95trw5
- hal: hal-01350955
- doi: 10.1007/978-3-642-55355-4_47
Ce document est lié à :
info:eu-repo/semantics/altIdentifier/doi/10.1007/978-3-642-55355-4_47
http://creativecommons.org/licenses/by/ , info:eu-repo/semantics/OpenAccess
Mots-clés
Multimodal PCA LDA RBM Activity recognition Feature learningSujets proches
Natural science Sciences Science of science Novels Fiction--Plots Dramatic plots Drama--Plot Scenarios Screen plays Filmscripts Photoplays Film scripts Moving-picture plays Scripts (Motion pictures) Film plays Screenplays Motion picture scripts Scenarios Automatic recognition of human activity Activity recognition, Human Recognition of human activity, Automatic Activity inferencing, Human Human activity inferencingCiter ce document
Girija Chetty et al., « A Novel Multimodal Data Analytic Scheme for Human Activity Recognition », HAL-SHS : sciences de l'information, de la communication et des bibliothèques, ID : 10.1007/978-3-642-55355-4_47
Métriques
Partage / Export
Résumé
In this article, we propose a novel multimodal data analytics scheme for human activity recognition. Traditional data analysis schemes for activity recognition using heterogeneous sensor network setups for eHealth application scenarios are usually a heuristic process, involving underlying domain knowledge. Relying on such explicit knowledge is problematic when aiming to create automatic, unsupervised or semi-supervised monitoring and tracking of different activities, and detection of abnormal events. Experiments on a publicly available OPPORTUNITY activity recognition database from UCI machine learning repository demonstrates the potential of our approach to address next generation unsupervised automatic classification and detection approaches for remote activity recognition for novel, eHealth application scenarios, such as monitoring and tracking of elderly, disabled and those with special needs.