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Extracting spatiotemporal human activity patterns in assisted living using a home sensor network

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Abstract

This paper presents an automated methodology for extracting the spatiotemporal activity model of a person using a wireless sensor network deployed inside a home. The sensor network is modeled as a source of spatiotemporal symbols whose output is triggered by the monitored person’s motion over space and time. Using this stream of symbols, the problem of human activity modeling is formulated as a spatiotemporal pattern-matching problem on top of the sequence of symbolic information the sensor network produces, and is solved using an exhaustive search algorithm. The effectiveness of the proposed methodology is demonstrated on a real 30-day dataset extracted from an ongoing deployment of a sensor network inside a home monitoring an elder. The developed algorithm examines the person’s data over these 30 days and automatically extracts the person’s daily pattern.

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Notes

  1. The start time and duration condition parameters can be either entered directly by the user based on prior information or they can be automatically extracted from the input phoneme triplets using the methodology we presented in [15].

  2. In practice any model building method in the literature can be used

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Authors and Affiliations

  1. Microsoft Research, One Microsoft Way, Redmond, WA, USA

    Dimitrios Lymberopoulos

  2. Electrical Engineering, Yale University, New Haven, CT, USA

    Athanasios Bamis & Andreas Savvides

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  1. Dimitrios Lymberopoulos
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  2. Athanasios Bamis
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  3. Andreas Savvides
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Correspondence to Dimitrios Lymberopoulos.

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Lymberopoulos, D., Bamis, A. & Savvides, A. Extracting spatiotemporal human activity patterns in assisted living using a home sensor network. Univ Access Inf Soc 10, 125–138 (2011). https://doi.org/10.1007/s10209-010-0197-5

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