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Anomaly Detection in Elderly Daily Behavior in Ambient Sensing Environments

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Human Behavior Understanding (HBU 2016)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9997))

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Abstract

Current ubiquitous computing applications for smart homes aim to enhance people’s daily living respecting age span. Among the target groups of people, elderly are a population eager for “choices for living arrangements”, which would allow them to continue living in their homes but at the same time provide the health care they need. Given the growing elderly population, there is a need for statistical models able to capture the recurring patterns of daily activity life and reason based on this information. We present an analysis of real-life sensor data collected from 40 different households of elderly people, using motion, door and pressure sensors. Our objective is to automatically observe and model the daily behavior of the elderly and detect anomalies that could occur in the sensor data. For this purpose, we first introduce an abstraction layer to create a common ground for home sensor configurations. Next, we build a probabilistic spatio-temporal model to summarize daily behavior. Anomalies are then defined as significant changes from the learned behavioral model and detected using a cross-entropy measure. We have compared the detected anomalies with manually collected annotations and the results show that the presented approach is able to detect significant behavioral changes of the elderly.

Do is currently affiliated with Gameloft, Ho Chi Minh City, Vietnam.

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Acknowledgments

This work has been funded by the Swiss Commission for Technology and Innovation (CTI) through the Domocare and Swisko projects. The data used in this study was collected in the context of the Domocare project. The data collection was led by DomoSafety, Switzerland (Guillaume DuPasquier, Edouard Goupy, and Hieu Pham) and La Source, School of Nursing Sciences, University of Applied Sciences of Western Switzerland (Henk Verloo and Christine Cohen.) We would also like to thank Hieu Pham (DomoSafety) and Florent Monay (Idiap) for technical discussions.

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Authors and Affiliations

  1. Idiap Research Institute, Martigny, Switzerland

    Oya Aran, Dairazalia Sanchez-Cortes, Minh-Tri Do & Daniel Gatica-Perez

  2. Ecole Polytechnique Federale de Lausanne (EPFL), Lausanne, Switzerland

    Daniel Gatica-Perez

Authors
  1. Oya Aran
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  2. Dairazalia Sanchez-Cortes
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  3. Minh-Tri Do
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  4. Daniel Gatica-Perez
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Correspondence to Oya Aran .

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Editors and Affiliations

  1. Université Pierre et Marie Curie , Paris, France

    Mohamed Chetouani

  2. University of Pittsburgh , Pittsburgh, Pennsylvania, USA

    Jeffrey Cohn

  3. Bogazici University , Bebek, Istanbul, Turkey

    Albert Ali Salah

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Aran, O., Sanchez-Cortes, D., Do, MT., Gatica-Perez, D. (2016). Anomaly Detection in Elderly Daily Behavior in Ambient Sensing Environments. In: Chetouani, M., Cohn, J., Salah, A. (eds) Human Behavior Understanding. HBU 2016. Lecture Notes in Computer Science(), vol 9997. Springer, Cham. https://doi.org/10.1007/978-3-319-46843-3_4

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  • DOI: https://doi.org/10.1007/978-3-319-46843-3_4

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  • Online ISBN: 978-3-319-46843-3

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