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
Full text not available from this repository.
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.
Repository Staff Only: item control page