A hybrid approach of knowledge-driven and data-driven reasoning for activity recognition in smart homes

TLDR

Accurate activity recognition is crucial in smart homes for assisting elderly and cognitively impaired users, yet knowledge‑driven approaches are limited by incomplete models that fail to capture all activity types. This paper proposes a hybrid knowledge‑ and data‑driven reasoning method that allows activity models to evolve and adapt automatically to individual users. The method first infers an initial activity model using knowledge‑driven reasoning, then refines it with data‑driven training to produce a dynamic model that learns users’ varying actions. Evaluation on a public dataset shows the hybrid model achieves significantly higher recognition rates than the initial knowledge‑driven model.

Abstract

Accurate activity recognition plays a major role in smart homes to provide assistance and support for users, especially elderly and cognitively impaired people. To realize this task, knowledge-driven approaches are one of the emerging research areas that have shown interesting advantages and featu res. However, several limitations have been associated with these approaches. The produced models are usually incomplete to capture all types of human activities. This resulted in the limited ability to accurately infer users’ activities. This paper presents an alternative approach by combining knowledge-driven with data-driven reasoning to allow activity models to evolve and adapt automatically based on users’ particularities. Firstly, a knowledge-driven reasoning is presented for inferring an initial activity model. The model is then trained using data-driven techniques to produce a dynamic activity model that learns users’ varying action. This approach has been evaluated using a publicly available dataset and the experimental results show the learned activity model yields significantly higher recognition rates compared to the initial activity model.