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On the people counting problem in smart homes: undirected graphs and theoretical lower-bounds
Örebro University, School of Science and Technology. (Machine Perception and Interaction)ORCID iD: 0000-0001-9293-7711
Örebro University, School of Science and Technology. (Machine Perception and Interaction)ORCID iD: 0000-0002-3122-693X
2023 (English)In: Journal of Ambient Intelligence and Humanized Computing, ISSN 1868-5137, E-ISSN 1868-5145, Vol. 14, no 4, p. 3839-3851Article in journal (Refereed) Published
Abstract [en]

Smart homes of the future will have to deal with multi-occupancy scenarios. Multi-occupancy systems entail a preliminary and critical feature: the capability of counting people. This can be fulfilled by means of simple binary sensors, cheaper and more privacy preserving than other sensors, such as cameras. However, it is currently unclear how many people can be counted in a smart home, given the set of available sensors. In this paper, we propose a graph-based technique that allows to map a smart home to an undirected graph G and discover the lower-bound of certainly countable people, also defined as certain count. We prove that every independent set of n vertices of an undirected graph G represents a minimum count of n people. We also prove that the maximum number of certainly countable people corresponds to the maximum independent sets of G, and that the maximal independent sets of G provide every combination of active sensors that ensure different minimum count. Last, we show how to use this technique to identify and optimise suboptimal deployment of sensors, so that the assumptions can be tightened and the theoretical lower-bound improved.

Place, publisher, year, edition, pages
Springer, 2023. Vol. 14, no 4, p. 3839-3851
Keywords [en]
Counting, Smart Home, Multi-occupancy, Graph Theory, Independent Set
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:oru:diva-94963DOI: 10.1007/s12652-021-03514-0ISI: 000701599000001Scopus ID: 2-s2.0-85115885370OAI: oai:DiVA.org:oru-94963DiVA, id: diva2:1602313
Note

Funding agency:

Örebro University

Available from: 2021-10-12 Created: 2021-10-12 Last updated: 2023-06-12Bibliographically approved

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Giaretta, AlbertoLoutfi, Amy

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