Arianna+: Scalable Human Activity Recognition by Reasoning with a Network of Ontologies

EasyChair Preprint 484

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 features, internal working, and computational performance. Results indicate scalability and robustness for human activity recognition processes.

Keyphrases: Activities of Daily Living, Computational time, Distributed sensor, Human Activity Recognition, In-home healthcare, Smart Home, activity model, activity recognition, daily living, elderly individual, ontology network, owl dl reasoner, reasoner computational time, reasoning layer, reasoning process, scalable activity recognition, system complexity

@booklet{EasyChair:484,
  author    = {Syed Yusha Kareem and Luca Buoncompagni and Fulvio Mastrogiovanni},
  title     = {Arianna+: Scalable Human Activity Recognition by Reasoning with a Network of Ontologies},
  doi       = {10.29007/hldz},
  howpublished = {EasyChair Preprint 484},
  year      = {EasyChair, 2018}}