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. 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 dec. 2019 14:18 |
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