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A Context Driven Indoor Localization Framework for Assisted Living in Smart Homes

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Part of the Lecture Notes in Computer Science book series (LNISA,volume 12426)

Abstract

The proposed Context Driven Indoor Localization Framework aims to implement a standard for indoor localization to address the multiple needs in different indoor environments with a specific focus to contribute towards Ambient Assisted Living (AAL) in the Future of Smart Homes for healthy aging of the rapidly increasing elderly population. This framework has multiple functionalities. First, it presents the methodology to analyze any given IoT-based environment, in terms of spatial information, to classify it into specific zones or functional areas based on the activities performed by users with different environment parameters in those respective zones. Second, it discusses the approach to analyze the macro and micro level user interactions with context parameters for any user in the given IoT environment. Finally, it possesses the methodology to track user interactions, analyze and access the Big Data associated with these interactions and map the user to a specific zone or area in the given IoT-based environment using a learning model. To evaluate the efficacy of this framework, it has been implemented on a dataset related to performing different activities in IoT-based settings. The results presented and discussed uphold the relevance and potential for real-time implementation of this framework for addressing multiple needs associated with aging in smart homes as well as for various other applications of indoor localization in different environments and contexts.

Keywords

  • Indoor localization
  • Big Data
  • Assisted Living
  • Smart Homes

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  • DOI: 10.1007/978-3-030-60149-2_30
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Correspondence to Nirmalya Thakur .

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Thakur, N., Han, C.Y. (2020). A Context Driven Indoor Localization Framework for Assisted Living in Smart Homes. In: Stephanidis, C., Antona, M., Gao, Q., Zhou, J. (eds) HCI International 2020 – Late Breaking Papers: Universal Access and Inclusive Design. HCII 2020. Lecture Notes in Computer Science(), vol 12426. Springer, Cham. https://doi.org/10.1007/978-3-030-60149-2_30

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  • DOI: https://doi.org/10.1007/978-3-030-60149-2_30

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  • Print ISBN: 978-3-030-60148-5

  • Online ISBN: 978-3-030-60149-2

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