A DES Simulator for Location Tracking of Inhabitants in Smart Home

Danancher, Mickaël; Faraut, Grégory; Lesage, Jean-Jacques; Litz, Lothar

doi:10.1109/eurosim.2013.66

Cited by 2 publications

(4 citation statements)

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“…A simulation-based approach has been previously introduced in [15] for the case of a fixed and known number of inhabitants. In the following we propose the generalization of the results for an a priori unknown number of inhabitants.…”

Section: B Simulation-based Evaluationmentioning

confidence: 99%

“…These results are interesting and complementary to the analytical ones, however they are obviously strongly relying on the simulated scenario. Moreover, note that alternative timebased definitions of this confusion matrix and of the related criteria have been given in [15].…”

Section: B Simulation-based Evaluationmentioning

confidence: 99%

“…For these reasons, we proposed in previous work [12] an approach aiming at the systematic construction of a Finite Automaton describing the detectable motion of a single inhabitant [13] or of a fixed and predefined number N of inhabitants [14]. Based on these models, we also defined algorithms for LT [13], [14] and we developed two procedures (one analytical approach [13] and one simulation-based method [15]) to evaluate the relevance of an instrumentation and of the designed model for the LT purpose.…”

Section: Introductionmentioning

confidence: 99%

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Model-Based Location Tracking of an a priori Unknown Number of Inhabitants in Smart Homes

Danancher

Lesage

Litz

2016

IEEE Trans. Automat. Sci. Eng.

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International audienceIn this paper we propose an approach for online model-based location tracking of inhabitants in Smart Homes. Based on our previous solution for a fixed and predefined number of inhabitants using finite automata we generalize the approach to handle an a priori unknown number of inhabitants. The online-algorithms to detect the number of inhabitants and to track their location are given and a comprehensive evaluation method is defined. It consists of an analytical and a simulation-based procedure as well and is able to predict the tracking performance for real Smart Homes by a normalized measure. Throughout the paper, previous and new results are illustrated by a realistic case study and illustrative scenarios are given. Note to Practitioners—Tracking the location of several inhabitants in a Smart Home using only ambient, non-invasive, low-cost sensors is a challenge. In this paper, we propose specific evaluation approaches as well as an expert-in-the-loop improvement procedure to help the expert setting up a satisfying instrumentation using only such sensors. We also detail the algorithm for online Location Tracking of any inhabitants in a Smart Home and explain this algorithm with an illustrative scenario

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Section: B Simulation-based Evaluationmentioning

confidence: 99%

Section: B Simulation-based Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Model-Based Location Tracking of an a priori Unknown Number of Inhabitants in Smart Homes

Danancher

Lesage

Litz

2016

IEEE Trans. Automat. Sci. Eng.

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“…For instance, some studies based on cameras [4] can recognize activities, but cameras are intrusive and often refused by the inhabitant. Some other devices like wearable sensors [5] require the inhabitant to be equipped and move, whereas others have very low level information like binary sensors [6], [7], [8], [9], [10], but are the less intrusive. The choice of technology directly impacts the methods used to discover or recognize activities, and binary sensors are chosen in this work.…”

Section: Introductionmentioning

confidence: 99%

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

Saives

Pianon

Faraut

2015

IEEE Trans. Automat. Sci. Eng.

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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 behaviour 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 behaviour deviation. The whole method is applied to the dataset of Domus, an instrumented smart home. Note to Practitioners-This paper was motivated by the will of providing a model of the behaviour of the inhabitant at home, from which formal methods can be applied to verify some properties, namely detect behaviour deviations. The input of the framework is a log file of binary sensor events (rising and falling edges); a pre-filtering of the log might be required to get rid of repetitive, noisy events (namely spurious events generated by motion sensors, for which only the first rising edge and last falling edges are relevant). This recorded log is assumed to be recorded without sensor faults, and to be characteristic of a typical routine of the inhabitant. In order to build this model, based on the Extended Finite Automata (EFA) formalism, sequence mining techniques (first contribution of the article) are adapted: the more frequent the sequence is, the more important for the inhabitant the habit is. The second contribution of the paper is the ability to detect behaviour deviations, which is criticial for healthcare at home and disease management. The technique of "residuals", mainly used in manufacturing, has been adapted to EFAs. It requires medical requirements, defined by the medical staff, based on the activities it wants to keep an eye on. A suiting log of sensors events and medical requirements are the only prerequisite to the application of the method.

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A DES Simulator for Location Tracking of Inhabitants in Smart Home

Cited by 2 publications

References 10 publications

Model-Based Location Tracking of an a priori Unknown Number of Inhabitants in Smart Homes

Model-Based Location Tracking of an a priori Unknown Number of Inhabitants in Smart Homes

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

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