Abstract
Advances in multimedia technologies have led to the emergence of smart home applications. In fact, mobile multimedia technologies provide the infrastructure to adopt smart solutions and track inhabitants' activities. In-home activity recognition significantly enhances the performance of healthcare monitoring and emergency-control applications for elderly and people with special needs. Developing and validating data models for such applications requires training sets that reflect a ground truth in the form of labeled or annotated data. With the accelerated development of Internet-of-Things applications, automated annotation processes have emerged understanding resident behavior in terms of activities. This paper presents a methodology for automatic data annotation by profiling sensing nodes. Our proposed methodology models activities based on spatially recognized actions, with every activity expected to have a direct relationship with a specific set of locations. Furthermore, the proposed technique validates the assignment of labels based on the temporal relations among consecutive actions. We performed experiments to evaluate our proposed methodology on CASAS data sets, which indicated that the proposed methodology achieved better performance, to a statistically significant extent, than the state-of-the-art methodologies presented in the literature.