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An AMI System for User Daily Routine Recognition and Prediction

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Advances onto the Internet of Things

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 260))

Abstract

Ambient Intelligence (AmI) defines a scenario involving people living in a smart environment enriched by pervasive sensory devices with the goal of assisting them in a proactive way to satisfy their needs. In a home scenario, an AmI system controls the environment according to a user’s lifestyle and daily routine. To achieve this goal, one fundamental task is to recognize the user’s activities in order to generate his daily activities profile. In this chapter, we present a simple AMI system for a home scenario to recognize and predict users’ activities. With this predictive capability, it is possible to anticipate their actions and improve their quality of life. Our approach uses a Hidden Markov Model (HMM) to recognize activities and deal with the intrinsic uncertainty of sensory information. The concepts of this domain have been formally defined to allow a higher-level system to enrich its knowledge base.

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Acknowledgments

This work has been partially supported by the PON R&C grant MI01_00091 funding the SeNSori project.

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Authors and Affiliations

  1. DICGIM, University of Palermo, Viale delle Scienze, Edificio 6, 90128, Palermo, Italy

    Salvatore Gaglio & Gloria Martorella

Authors
  1. Salvatore Gaglio
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  2. Gloria Martorella
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Correspondence to Salvatore Gaglio .

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Editors and Affiliations

  1. Dipartimento di Ingegneria Chimica, Gestionale, Informatica, Meccanica, Università di Palermo, Palermo, Italy

    Salvatore Gaglio

  2. Dipartimento di Ingegneria Chimica, Gestionale, Informatica, Meccanica, Università di Palermo, Palermo, Italy

    Giuseppe Lo Re

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Gaglio, S., Martorella, G. (2014). An AMI System for User Daily Routine Recognition and Prediction. In: Gaglio, S., Lo Re, G. (eds) Advances onto the Internet of Things. Advances in Intelligent Systems and Computing, vol 260. Springer, Cham. https://doi.org/10.1007/978-3-319-03992-3_3

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  • DOI: https://doi.org/10.1007/978-3-319-03992-3_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-03991-6

  • Online ISBN: 978-3-319-03992-3

  • eBook Packages: EngineeringEngineering (R0)

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