Aggressive and agitated behavior recognition from accelerometer data using non-negative matrix factorization [r-libre/1185]

Chikhaoui, Belkacem; Bing, Ye et Mihailidis, Alex (2018). Aggressive and agitated behavior recognition from accelerometer data using non-negative matrix factorization. Journal of Ambient Intelligence and Humanized Computing, 9 (5), 1375–1389. https://doi.org/10.1007/s12652-017-0537-x

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Catégorie de document :	Articles de revues
Évaluation par un comité de lecture :	Oui
Étape de publication :	Publié
Résumé :	This paper presents a novel approach for aggressive and agitated behavior recognition using accelerometer data. Our approach applies first a noise reduction technique using the moving average filter method. Then, multiple features such as mean, variance, entropy, correlation and covariance are extracted from the filtered acceleration data using a sliding window. Non-negative matrix factorization is then used in order to project the data into a new reduced space that captures the significant structure of the data. The recognition is performed using the rotation forest ensemble method. The proposed approach is validated using extensive experiments on a real dataset collected at Toronto Rehabilitation Institute. We empirically demonstrate that our proposed approach accurately discriminates between behaviors and performs better than several state-of-the-art approaches.
Adresse de la version officielle :	https://link.springer.com/article/10.1007/s12652-0...
Déposant:	Chikhaoui, Belkacem
Responsable :	Belkacem Chikhaoui
Dépôt :	14 sept. 2017 15:43
Dernière modification :	12 déc. 2019 14:18

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