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

Chikhaoui, Belkacem; Bing, Ye, & 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

File(s) available for this item:

PDF - JAIHC.pdf
Content : Published Version
Restricted access

Request a copy

Item Type:	Journal Articles
Refereed:	Yes
Status:	Published
Abstract:	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.
Official URL:	https://link.springer.com/article/10.1007/s12652-0...
Depositor:	Chikhaoui, Belkacem
Owner / Manager:	Belkacem Chikhaoui
Deposited:	14 Sep 2017 15:43
Last Modified:	12 Dec 2019 14:18

Actions (login required)

RÉVISER