Title
Probabilistic Ontology Reasoning in Ambient Assistance: Predicting Human Actions
Abstract
Providing reminders to elderly people in their home environment, while they perform their daily activities, is considered as a user support activity, and thus a relevant topic in Active and Assisted Living (AAL) research and development. Determining such reminders implies decision-making, since the actions' flow (behavior) usually involves probabilistic branches. An automated system needs to decide which of the next actions is the best one for the user in a given situation. Problems of this nature involve uncertainty levels that have to be dealt with. Many approaches to this problem exploit statistical data only, thus ignoring important semantic data as, for instance, are provided by Ontologies. However, ontologies do not support reasoning over uncertainty natively. In this paper, we present a probabilistic semantic model that enables reasoning over uncertainty without losing semantic information. This model will be exemplified by an extension of the Human Behavior Monitoring and Support [HBMS] approach that provides a conceptual model for representing the user's behavior and its context in her/his living environment. The performance of this approach was evaluated using real data collected from a smart home prototype equipped with sensors. The experiments provided promising results which we will discuss regarding limits and challenges to overcome.
Year
DOI
Venue
2018
10.1109/AINA.2018.00092
2018 IEEE 32nd International Conference on Advanced Information Networking and Applications (AINA)
Keywords
Field
DocType
Probabilistic ontologies,Uncertainty reasoning,Ambient assistance,Smart home,Context awareness
Ontology (information science),Conceptual model,Computer science,Home automation,Exploit,Context model,Human–computer interaction,Probabilistic logic,Semantics,Semantic data model,Distributed computing
Conference
ISSN
ISBN
Citations 
1550-445X
978-1-5386-2196-7
1
PageRank 
References 
Authors
0.35
22
8