A Smart Home Agent for Plan Recognition of Cognitively-impaired Patients.

Bouchard, Bruno; Giroux, Sylvain; Bouzouane, Abdenour

doi:10.4304/jcp.1.5.53-62

Cited by 72 publications

(26 citation statements)

References 16 publications

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“…Oro har, kafe bat presta- tzeko bost minutu inguru behar izaten dira. Modu horretako ezagutzaz baliatuz, ikertzaile batzuek eredu logikoak sortzen dituzte [9,10]. Beste ikertzaile batzuek, berriz, Web Semantikaren oinarri den OWL (Web Ontology Language) lengoaia darabilte jarduerak deskribatzeko [11,12].…”

Section: Arloko Egoeraunclassified

Ezagutzan Oinarritutako Giza Jardueren Eredu Dinamiko eta Pertsonalizatuak Ikasten

Azkune¹,

Almeida

López‐de‐Ipiña

et al. 2016

EKAIA

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Laburpena: Gizakietara egokitutako teknologiak garatzeko, ezinbestekoa da makinek giza jarduerak antzemateko gaitasuna izatea, sentsoreak eta konputazio-gailuak erabiliz. Horretarako, antzeman nahi diren jarduera horien eredu konputazionalak sortu behar dira. Gaur egun, jarduera-ereduak sortzeko garaian, bi joera nagusi aurki daitezke: datuetan oinarritutako ereduak eta ezagutzan oinarritutakoak. Biek ere abantaila eta desabantailak dituzte. Lan honen helburua da bi joerak elkartzea eredu dinamiko eta pertsonalizatuak lortzeko, ezagutzan oinarritutako eredu orokor batzuetatik hasita. Modu horretan, pertsona bakoitzaren bilakaerara egokitutako modelatze-prozesuak lor daitezke.Hitz gakoak: Giza jarduera ereduak, ezagutzan oinarritutako ereduak, ikasketa automatikoa.Abstract: Being able to recognise human activities by means of sensor and computational devices can be a key competence in order to achieve human centred technologies. For that purpose, it is mandatory to build computational models of the activities which have to be recognised. There are two major approaches for activity modelling: the data-driven and the knowledge-driven approaches. Both of them have advantages and drawbacks. The objective of this work is to combine both modelling approaches with the aim of building dynamic and personalised activity models, using generic knowledge-based models. This would allow implementing modelling processes which can adapt themselves to the evolution of specific people.

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Section: Arloko Egoeraunclassified

Ezagutzan Oinarritutako Giza Jardueren Eredu Dinamiko eta Pertsonalizatuak Ikasten

Azkune¹,

Almeida

López‐de‐Ipiña

et al. 2016

EKAIA

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“…Wobke (Wobke, 2002) extended Kauz's work using situation theory to address the different probabilities of inferred plans. Bouchard (Bouchard, 2006) used action Description Logic (DL) and lattice theory for plan recognition with particular emphasis on the modelling and reasoning of plan intra-dependencies. Chen (0) exploited the event theory -a logical formalism, for explicit specification, manipulation and reasoning of events, to formalise an activity domain for activity recognition and assistance.…”

Section: Activity Recognition Algorithmsmentioning

confidence: 99%

Ontology‐based activity recognition in intelligent pervasive environments

Chen

Nugent

2009

International Journal of Web Information Systems

225

150

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Purpose -This paper aims to serve two main purposes. In the first instance it aims to it provide an overview addressing the state-of-the-art in the area of activity recognition, in particular, in the area of object-based activity recognition. This will provide the necessary material to inform relevant research communities of the latest developments in this area in addition to providing a reference for researchers and system developers who ware working towards the design and development of activity-based context aware applications. In the second instance this paper introduces a novel approach to activity recognition based on the use of ontological modeling, representation and reasoning, aiming to consolidate and improve existing approaches in terms of scalability, applicability and easy-of-use.Design/methodology/approach -The paper initially reviews the existing approaches and algorithms which have been used for activity recognition in a number of related areas. Form each of these their strengths and weaknesses are discussed with particular emphasis being placed on the application domain of sensor enabled intelligent pervasive environments. Based on an analysis of existing solutions, the paper then proposes an integrated ontology-based approach to activity recognition. The proposed approach adopts ontologies for modeling sensors, objects and activities, and exploits logical semantic reasoning for the purposes of activity recognition. This enables incremental progressive activity recognition at both coarse-grained and fine-grained levels. The approach has been considered within the realms of a real world activity recognition scenario in the context of assisted living within Smart Home environments.Findings -Existing activity recognition methods are mainly based on probabilistic reasoning, which inherently suffer from a number of limitations such as ad-hoc static models, data scarcity and scalability. Analysis of the state-of-the-art has helped to identify a major gap between existing approaches and the need for novel recognition approaches posed by the emerging multimodal sensor technologies and context-aware personalised activity-based applications in intelligent pervasive environments. The proposed ontology based approach to activity recognition is believed to be the first of its kind which provides an integrated framework based on the 2 Liming Chen, Chris Nugent unified conceptual backbone, i.e., activity ontologies, addressing the lifecycle of activity recognition. The approach allows easy incorporation of domain knowledge and machine understandability which facilitates interoperability, reusability and intelligent processing at a higher level of automation.Research limitations/implications -There are a number of challenges which have not been addressed within this paper. These mainly relate to the explicit modelling of the temporal dimension of the problem and managing uncertainty in both the sensor and decision making process. These will be key areas for consideration within our future work.Practical...

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“…It is also difficult to apply model and learning results from one person to another. The third approach is based on logic formulas such as event calculus [10] and lattice theory [11]. Although these logic models have more semantic information which is directly related to high recognition precision, it is more harder to build logic model.…”

Section: Related Workmentioning

confidence: 99%