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Relieved commissioning and human behavior detection in Ambient Assisted Living Systems

Vereinfachte Kommissionierung von und Erkennung menschlichen Verhaltens mit Ambient Assistend Living-Systemen

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Abstract

The goal of Ambient Assisted Living Systems is to provide automated technological aids for the elderly to allow for longer independent living in one’s own premises without the need for transition to stationary care. Such systems target to overcome problems introduced by particular risks of the target group like falling down, risk of illnesses, risk of dementia, etc. Current systems, however, still impose substantial effort in commissioning the system and they lack accuracy in detecting serious problems of the resident. In this article we present methods for relieved commissioning, i.e. automatic detection of the sensors’ types and topology, for added fault tolerance, and for modeling and evaluating human activity patterns with the goal of launching meaningful alarms.

Zusammenfassung

Das Ziel von Ambient Assisted Living-Systemen ist es, automatisierte technische Hilfestellung für ältere Personen zur Verfügung zu stellen, um längeres unabhängiges Wohnen im eigenen, gewohnten Wohnumfeld zu ermöglichen, ohne dass eine Aufnahme in die stationäre Betreuung nötig wird. Diese Systeme zielen darauf ab, den Problemen durch typische altersbedingte Risken wie Demenz, Stürzen oder Krankheit entgegenzuwirken. Aktuell erhältliche Systeme haben aber die Nachteile, dass sie großen Kommissionierungsaufwand benötigen und dass die Detektionsrate bei kritischen Fällen noch zu niedrig ist. In diesem Artikel zeigen wir daher Methoden für eine vereinfachte, teilautomatische Kommissionierung, in diesem Fall durch automatische Erkennung der Sensoren und deren Typen, durch automatisches Lernen der Topologie des Systems und durch Einführen von Fehlertoleranz. Weiters wird eine Methode zur Modellierung und Evaluierung von Tagesablaufmustern vorgestellt, mit dem Ziel, sinnvollere Alarme zu generieren.

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Notes

  1. capsil.org.

  2. Due to structure learning of the model the exact mapping of the initial sequences to states gets lost (the model generalizes), with the gain of similar sequences achieving similar results. That parity is measured in terms of probability, which decreases with every comparison in the model (i.e. in our model over time, from left to right). Since the probability reaches very little and cumbersome values (10−20), the logarithm thereof is used on the scale.

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Authors and Affiliations

  1. Institute of Computer Technology, Vienna University of Technology, Gußhausstraße 25-29, 1040, Vienna, Austria

    Dietmar Bruckner, Guo Qing Yin & Armin Faltinger

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  1. Dietmar Bruckner
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  2. Guo Qing Yin
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  3. Armin Faltinger
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Correspondence to Dietmar Bruckner.

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Bruckner, D., Yin, G.Q. & Faltinger, A. Relieved commissioning and human behavior detection in Ambient Assisted Living Systems. Elektrotech. Inftech. 129, 293–298 (2012). https://doi.org/10.1007/s00502-012-0015-2

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  • DOI: https://doi.org/10.1007/s00502-012-0015-2

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