Multimodal Daily Activity Recognition in Smart Homes
Mohammed G.H. Al Zamil · 2019
Recent research on modeling human daily activities in smart showed substantive challenges due to the heterogeneity of incoming data and the natural interleaving among human activities. This paper introduces an approach for handling multimodal activity recognition systems in smart homes by modeling atomic actions using multi-layer perceptron neural network; enriched with background knowledge from sensors' profiles. The proposed methodology defines the activity recognition problem as multiclass classification problem, in which an atomic action might belong to more than one activity at a given time. For each sensor, a profile of selected features has been created to identify the spatiotemporal aspect of its triggered actions. Then, a multimodal segmentation technique is applied to assemble actions that formulate a given activity into independent blocks of tuples. Such segmentation considers the interleaving among actions by applying multiclass classification. Experiments on multimodal dataset have been conducted to measure the performance of the proposed methodology. Measures such as F-measure and accuracy have been used to compare among different classifiers. The preliminary results showed a significant enhancement especially on activities that share high number of similar actions (performed concurrently).