Ontology Based Approach to the Detection of Domestics Problems for Independent Senior People

Botía, Juan A.; Palma, José; Villa, Ana; Pérez, David; Iborra, Emilio

doi:10.1007/978-3-642-02267-8_7

Cited by 8 publications

(3 citation statements)

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“…A first prototype scenario has been tackled, in which a user has obtained an alert system (DIA) at home that is able to monitor his or her activity to detect incidents and uncommon activities (Fernández-Luque et al, 2009;Botía-Blaya et al, 2009). The prototype house or scenario has a bedroom, a hall, a corridor, a toilet, a kitchen and a living room.…”

Section: Application Scenariomentioning

confidence: 99%

“…(Ami2) (AmI2 S.L.L., 2012), a technology company that has recruited for the project researchers from both Technical University of Cartagena and University of Murcia. The team with Technical University of Cartagena is in charge of the design and implementation of the sensory layer of the system (Fernández-Luque et al, 2009), whereas the one with University of Murcia is responsible for developing reasoning and context extraction layers (Botía-Blaya et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

“…Figure 2. It is divided into three main layers: open context platform (OCP) layer, management of the dynamics of user model layer and management of inconsistencies layer(Botía-Blaya et al, 2009). The OCP layer is responsible for capturing raw data from sensors through adapters that include sensor drivers, so that new sensors can be easily added by providing their Reliab.…”

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confidence: 99%

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Ambient assisted living system with capacitive occupancy sensor

et al. 2013

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Alert intelligent device is an ambient assisted living (AAL) system that allows the evaluation of potentially dangerous situations for elderly people living alone at home. This evaluation is obtained by an ad hoc network of sensor nodes, working in conjunction with an ambient intelligence layer embedded in a personal computer that learns from user behaviour patterns and warns when a detected pattern differs significantly from previously acquired normal patterns. Each new datum read from sensors is processed in the ambient intelligence layer through three processing levels: shallow, intermediate and deep. The shallow processing level focuses on physical data and sensory features. The intermediate level covers information interpretation and its translation into the form required by the third level: the reasoning processing or deep level. In alert intelligent device, energy is a critical issue, so that sensor devices need to be properly designed and managed to achieve significant energy saving. The use of bed/chair occupancy sensors is mandatory for this kind of ubiquitous computing system. A first way to approach this problem relied on the use of pressure mats, but several environmental drawbacks showed them inappropriate as an efficient and reliable solution for large volume deployments. Moreover, solutions based on force‐to‐resistor transducers entail power consumption budgets that keep them from being integrated on wireless sensor nodes. In this paper, a force‐capacitive transducer based sensor has been proposed, implemented and tested. This sensor is based on electromechanical film (EMFi) transducers, which are able to detect force variations in a quasi‐passive way. This kind of transducer behaves as capacitors with variable capacitance depending on the force exerted on its surface. We have developed a new technique to carry out the characterization of these transducers, where the detection of a force change is used to trigger an active mechanism that allows us to measure the weight by means of a novel modelling approach. A low‐power wireless sensor node prototype that includes this new transducer has been assembled and tested on a wide range of weights. Occupancy detection achieved by this technology has proven to be successful, increasing the total power consumption of the node by less than 15%, which makes it suitable for implementation.

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Section: Application Scenariomentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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