NDLI: Activity Discovery and Detection of Behavioral Deviations of an Inhabitant From Binary Sensors

Content Provider	IEEE Xplore Digital Library
Author	Saives, J. Pianon, C. Faraut, G.
Copyright Year	2004
Abstract	The aim of this paper is to improve the autonomy of medically monitored patients in a smart home instrumented only with binary sensors; overwatching the disease evolution, that can be characterized by behavior changes, is helped by detecting the activities the inhabitant performs. Two contributions are presented. On one hand, using sequence mining methods in the flow of sensor events, the most frequent patterns mirroring activities of the inhabitant are discovered; these activities are then modeled by an extended finite automaton, which can then be used for activity recognition and generate activity events. On the other hand, given the set of activities that can be recognized, another automaton is built to model requirements from the medical staff supervising the inhabitant; it accepts activity events, and residuals are defined to detect any behavior deviation. The whole method is applied to the dataset of Domus, an instrumented smart home.
Sponsorship	IEEE Robotics and Automation Society
Starting Page	1211
Ending Page	1224
Page Count	14
File Size	2315454
File Format	PDF
ISSN	15455955
Volume Number	12
Issue Number	4
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-01-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Automata Intelligent sensors Data mining Smart homes Discrete-event systems home automation Activities of daily living automata discrete-event systems
Content Type	Text
Resource Type	Article
Subject	Control and Systems Engineering Electrical and Electronic Engineering

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