DocumentCode :
3605516
Title :
Activity Discovery and Detection of Behavioral Deviations of an Inhabitant From Binary Sensors
Author :
Saives, Jeremie ; Pianon, Clement ; Faraut, Gregory
Author_Institution :
LURPA, ENS Cachan, Cachan, France
Volume :
12
Issue :
4
fYear :
2015
Firstpage :
1211
Lastpage :
1224
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.
Keywords :
data mining; finite automata; home computing; patient monitoring; sensor fusion; activity recognition; behavioral deviation detection; binary sensor; finite automaton; medically monitored patient; pattern mirroring activity; sequence mining method; smart home; Automata; Data mining; Discrete-event systems; Intelligent sensors; Smart homes; Activities of daily living; automata; discrete-event systems; home automation;
fLanguage :
English
Journal_Title :
Automation Science and Engineering, IEEE Transactions on
Publisher :
ieee
ISSN :
1545-5955
Type :
jour
DOI :
10.1109/TASE.2015.2471842
Filename :
7244262
Link To Document :
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