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Online Guest Detection in a Smart Home using Pervasive Sensors and Probabilistic Reasoning
Örebro University, School of Science and Technology. (Machine Perception and Interaction, Center for Applied Autonomous Sensor Systems (AASS))ORCID iD: 0000-0002-2385-9470
Örebro University, School of Science and Technology. (Center for Applied Autonomous Sensor Systems (AASS))
Örebro University, School of Science and Technology. (Machine Perception and Interaction, Center for Applied Autonomous Sensor Systems (AASS))ORCID iD: 0000-0002-3122-693X
2018 (English)In: Ambient Intelligence / [ed] Achilles Kameas, Kostas Stathis, Springer, 2018, Vol. 11249, p. 74-89Conference paper, Published paper (Refereed)
Abstract [en]

Smart home environments equipped with distributed sensor networks are capable of helping people by providing services related to health, emergency detection or daily routine management. A backbone to these systems relies often on the system’s ability to track and detect activities performed by the users in their home. Despite the continuous progress in the area of activity recognition in smart homes, many systems make a strong underlying assumption that the number of occupants in the home at any given moment of time is always known. Estimating the number of persons in a Smart Home at each time step remains a challenge nowadays. Indeed, unlike most (crowd) counting solution which are based on computer vision techniques, the sensors considered in a Smart Home are often very simple and do not offer individually a good overview of the situation. The data gathered needs therefore to be fused in order to infer useful information. This paper aims at addressing this challenge and presents a probabilistic approach able to estimate the number of persons in the environment at each time step. This approach works in two steps: first, an estimate of the number of persons present in the environment is done using a Constraint Satisfaction Problem solver, based on the topology of the sensor network and the sensor activation pattern at this time point. Then, a Hidden Markov Model refines this estimate by considering the uncertainty related to the sensors. Using both simulated and real data, our method has been tested and validated on two smart homes of different sizes and configuration and demonstrates the ability to accurately estimate the number of inhabitants.

Place, publisher, year, edition, pages
Springer, 2018. Vol. 11249, p. 74-89
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 11249
Keywords [en]
probabilistic reasoning, smart home
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:oru:diva-71113DOI: 10.1007/978-3-030-03062-9_6Scopus ID: 2-s2.0-85056486514OAI: oai:DiVA.org:oru-71113DiVA, id: diva2:1275633
Conference
14th European Conference on Ambient Intelligence, Larnaca, Cyprus, November 12-14
Funder
Knowledge FoundationEU, Horizon 2020, 732158Available from: 2019-01-07 Created: 2019-01-07 Last updated: 2019-01-18Bibliographically approved

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Renoux, JenniferKöckemann, UweLoutfi, Amy

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