PSINES: Activity and Availability Prediction for Adaptive Ambient Intelligence

Autonomy and adaptability are essential components of ambient intelligence. For example, in smart homes, proactive acting and occupants advising, adapted to current and future contexts of living, are essential to go beyond limitations of previous domotic services. To reach such autonomy and adaptabi...

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Bibliographic Details
Published in:ACM transactions on autonomous and adaptive systems 2020-12, Vol.15 (1), p.1-12, Article 1
Main Authors: Cumin, Julien, Lefebvre, Grégoire, Ramparany, Fano, Crowley, James L.
Format: Article
Language:English
Subjects:
Ambient intelligence
Artificial Intelligence
Bayesian networks
Computer Science
Human-centered computing
Mathematics of computing
Probabilistic representations
Probability and statistics
Ubiquitous and mobile computing
Ubiquitous and mobile computing theory, concepts and paradigms
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Summary:Autonomy and adaptability are essential components of ambient intelligence. For example, in smart homes, proactive acting and occupants advising, adapted to current and future contexts of living, are essential to go beyond limitations of previous domotic services. To reach such autonomy and adaptability, ambient systems need to automatically grasp their users’ ambient context. In particular, users’ activities and availabilities for communication are valuable pieces of contextual information that can help such systems to adapt to user needs and behaviours. While significant research work exists on activity recognition in homes, less attention has been given to prediction of future activities, as well as to availability recognition and prediction in general. In this article, we investigate several Dynamic Bayesian Network (DBN) architectures for activity and availability prediction of occupants in homes, including our novel model, called Past SItuations to predict the NExt Situation (PSINES). This predictive architecture utilizes context information, sensor event aggregations, and latent user cognitive states to accurately predict future home situations based on previous situations. We experimentally evaluate PSINES, as well as intermediate DBN architectures, on multiple state-of-the-art datasets, with prediction accuracies of up to 89.52% for activity and 82.08% for availability on the Orange4Home dataset.
ISSN:1556-4665
1556-4703
DOI:10.1145/3424344