Arianna$^{+}$: Scalable Human Activity Recognition by Reasoning with a Network of Ontologies

Abstract

Aging population ratios are rising significantly. Meanwhile, smart home based health monitoring services are evolving rapidly to become a viable alternative to traditional healthcare solutions. Such services can augment qualitative analyses done by gerontologists with quantitative data. Hence, the recognition of Activities of Daily Living (ADL) has become an active domain of research in recent times. For a system to perform human activity recognition in a real-world environment, multiple requirements exist, such as scalability, robustness, ability to deal with uncertainty (e.g., missing sensor data), to operate with multi-occupants and to take into account their privacy and security. This paper attempts to address the requirements of scalability and robustness, by describing a reasoning mechanism based on modular spatial and/or temporal context models as a network of ontologies. The reasoning mechanism has been implemented in a smart home system referred to as Arianna$^{+}$. The paper presents and discusses a use case, and experiments are performed on a simulated dataset, to showcase Arianna$^{+}$’s modularity feature, internal working, and computational performance. Results indicate scalability and robustness for human activity recognition processes.