Activity recognition using a hierarchical model

Tırkaz, Çağlar and Bruckner, Dietmar and Yin, GuoQing and Haase, Jan (2012) Activity recognition using a hierarchical model. In: 38th Annual Conference on IEEE Industrial Electronics Society (IECON 2012), Montreal, Canada

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In this paper, we propose a human daily activity recognition method that is used for Ambient Assisted Living. The proposed system is able to learn a user's activities using the data from motion and door sensors. We extract low level features from the sensor data and feed the features to a model that combines support vector machines (SVMs) and conditional random fields (CRFs) to give accurate recognition results. We propose to combine SVM and CRF classifiers in a hierarchical model which results in better accuracies and can also make use of high level features. We conducted experiments and presented the effectiveness and accuracies of the proposed method.
Item Type: Papers in Conference Proceedings
Divisions: Faculty of Engineering and Natural Sciences > Academic programs > Computer Science & Eng.
Faculty of Engineering and Natural Sciences
Depositing User: Çağlar Tırkaz
Date Deposited: 14 Mar 2016 14:44
Last Modified: 26 Apr 2022 09:22

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