Activity recognition is of great importance for a variety of context-aware applications and especially for assistance provided for independent living of the elderly. One of the factors that has impact on the activity recognition accuracy is feature representation. Many approaches have been proposed to select the most discriminative subset of features for activity recognition, but they are data-dependent and related to real numbers gathered from wearable sensors/devices. However, feature representation and transformation in smart environments, in which the data is very often a discrete sensor reading, has not been fully explored. In this paper, we map the activity model in a smart environment to a topic model and leverage the hierarchical Dirichlet process (HDP) to discover hidden structures from the sensor reading sequences. We use a hybrid approach: we combine the discovered latent structures with discriminative classifier such as support vector machines (SVMs), and demonstrate, through comparison studies, its effectiveness in improving activity recognition accuracy and the robustness to sensor noises. The comparison studies are carried out on three publicly available datasets from smart environments.
['Jiahui Wen', 'Jadwiga Indulska', 'Zhiying Wang']
Discovering Latent Structures for Activity Recognition in Smart Environments