Discovering frequent user-environment interactions in intelligent environments

Intelligent Environments are expected to act proactively, anticipating the user's needs and preferences. To do that, the environment must somehow obtain knowledge of those need and preferences, but unlike current computing systems, in Intelligent Environments the user ideally should be released from the burden of providing information or programming any device as much as possible. Therefore, automated learning of a user's most common behaviors becomes an important step towards allowing an environment to provide highly personalized services. In this paper we present a system that takes information collected by sensors as a starting point, and then discovers frequent relationships between actions carried out by the user. The algorithm developed to discover such patterns is supported by a language to represent those patterns and a system of interaction which provides the user the option to fine tune their preferences in a natural way, just by speaking to the system

Surveying human habit modeling and mining techniques in smart spaces

A smart space is an environment, mainly equipped with Internet-of-Things (IoT) technologies, able to provide services to humans, helping them to perform daily tasks by monitoring the space and autonomously executing actions, giving suggestions and sending alarms. Approaches suggested in the literature may differ in terms of required facilities, possible applications, amount of human intervention required, ability to support multiple users at the same time adapting to changing needs. In this paper, we propose a Systematic Literature Review (SLR) that classifies most influential approaches in the area of smart spaces according to a set of dimensions identified by answering a set of research questions. These dimensions allow to choose a specific method or approach according to available sensors, amount of labeled data, need for visual analysis, requirements in terms of enactment and decision-making on the environment. Additionally, the paper identifies a set of challenges to be addressed by future research in the field

Visual analysis of sensor logs in smart spaces: Activities vs. situations

Models of human habits in smart spaces can be expressed by using a multitude of representations whose readability influences the possibility of being validated by human experts. Our research is focused on developing a visual analysis pipeline (service) that allows, starting from the sensor log of a smart space, to graphically visualize human habits. The basic assumption is to apply techniques borrowed from the area of business process automation and mining on a version of the sensor log preprocessed in order to translate raw sensor measurements into human actions. The proposed pipeline is employed to automatically extract models to be reused for ambient intelligence. In this paper, we present an user evaluation aimed at demonstrating the effectiveness of the approach, by comparing it wrt. a relevant state-of-the-art visual tool, namely SITUVIS

On the Design of Ambient Intelligent Systems in the Context of Assistive Technologies

The design of Ambient Intelligent Systems (AISs) is discussed in the context of assistive technologies. The main issues include ubiquitous communications, context awareness, natural interactions and heterogeneity, which are analyzed using some examples. A layered architecture is proposed for heterogeneous sub-systems integration with three levels of interactions that may be used as a framework to design assistive AISs.Ministerio de Ciencia y Tecnología TIC2001-1868-C0

Automated user modeling for personalized digital libraries

Digital libraries (DL) have become one of the most typical ways of accessing any kind of digitalized information. Due to this key role, users welcome any improvements on the services they receive from digital libraries. One trend used to improve digital services is through personalization. Up to now, the most common approach for personalization in digital libraries has been user-driven. Nevertheless, the design of efficient personalized services has to be done, at least in part, in an automatic way. In this context, machine learning techniques automate the process of constructing user models. This paper proposes a new approach to construct digital libraries that satisfy user’s necessity for information: Adaptive Digital Libraries, libraries that automatically learn user preferences and goals and personalize their interaction using this information

On the Integration of Adaptive and Interactive Robotic Smart Spaces

© 2015 Mauro Dragone et al.. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License. (CC BY-NC-ND 3.0)Enabling robots to seamlessly operate as part of smart spaces is an important and extended challenge for robotics R&D and a key enabler for a range of advanced robotic applications, such as AmbientAssisted Living (AAL) and home automation. The integration of these technologies is currently being pursued from two largely distinct view-points: On the one hand, people-centred initiatives focus on improving the user’s acceptance by tackling human-robot interaction (HRI) issues, often adopting a social robotic approach, and by giving to the designer and - in a limited degree – to the final user(s), control on personalization and product customisation features. On the other hand, technologically-driven initiatives are building impersonal but intelligent systems that are able to pro-actively and autonomously adapt their operations to fit changing requirements and evolving users’ needs,but which largely ignore and do not leverage human-robot interaction and may thus lead to poor user experience and user acceptance. In order to inform the development of a new generation of smart robotic spaces, this paper analyses and compares different research strands with a view to proposing possible integrated solutions with both advanced HRI and online adaptation capabilities.Peer reviewe

Learning frequent behaviours of the users in intelligent environments

Intelligent Environments (IEs) are expected to support people in their daily lives. One of the hidden assumptions in IEs is that they propose a change ofu perspective in the relationships between humans and technology, shifting from a techno-centered perspective to a human-centered one. Unlike current computing systems where the user has to learn how to use the technology, an IE adapts its behaviour to the user, even anticipating his/her needs, preferences or habits. For that, the environment should learn how to react to the actions and needs of the users, and this should be achieved in an unobtrusive and transparent way. In order to provide personalized and adapted services, it is clear the need of knowing preferences and frequent habits of users. Thus, the ability to learn patterns of behaviour becomes an essential aspect for the successful implementation of IEs. In that sense, a perfect learning system would gain knowledge about everything related to users that would help the environment act intelligently and proactively. The efforts in this research work are focused on discovering frequent behaviours of the users. For that, it has been designed and developed the Learning Frequent Patterns of User Behaviour System (LFPUBS) that, taking into account all the particularities of IEs, learns frequent behaviours of the users. The core of the LFPUBS is the Learning Layer that unlike some other components is independent of the particular environment in which the system is being applied. On the one hand, it includes a language that allows the representation of discovered behaviours in a clear and unambiguous way. On the other hand, coupled with the language, an algorithm that discovers frequent behaviours has been designed and implemented. Finally, LFPUBS was validated using data collected from two real environments. Results obtained in such validation tests showed that LFPUBS was able to discover frequent behaviours of the users. Moreover, some improvements were identified for future versions of the system.Ingurune Adimentsuek bertan bizi diren pertsonei beren egunerokotasunean laguntzea dute helburu. Kontzeptu berri honek pertsona eta teknologiaren arteko erlazioan aldaketa bat dakarki berarekin, teknologiari garrantzia ematetik pertsonengan oinarritzera. Horrela, orain arteko sistemekin alderatuz, non pertsonek teknologia nola erabili ikasi behar duten, orain teknologia (ingurunea) bera da pertsonengana egokitu behar dena. Horretarako, inguruneak bertan dauden pertsonen behar, ohitura, etab. ezagutu behar ditu. Jakintza guzti hori ordea, pertsonak inondik inora gogaitu gabe lortu behar du inguruneak. Pertsona bakoitzari dagozkion edo nahi dituen zerbitzuak emateko inguruneak pertsona horren ohiko jokabideak jakitea behar du. Orduan, ingurune horrek ikasketa prozesu bat jarraitu behar du, non, pertsona horien ohiko jokabideak era automatiko eta garden batean lortuko dituen. Ikerketa lan honen helburua horixe izan da, ingurune adimentsu bateko pertsonen ohiko jokabideak era automatiko eta garden batean deskubrituko dituen sistema bat dise˜natu eta garatzea. Gainera, garatutako sistema horrek, Learning Frequent Patterns of User Behaviour System (LFPUBS), ingurune adimentsuen berezitasun guztiak hartzen ditu kontutan. LFPUBS-en barruan berebiziko garrantzia du Ikasketako geruzak, zein ez dagoen sistema aplikatzen ari den ingurunearen menpe. Geruza horretan bi osagaik merezi dute aparteko aipamena, alde batetik, ikasitako jakintza era garbi batean adieraztea ahalbidetzen duen hizkuntzak, eta beste alde batetik, jakintza bera deskubritzen duen algoritmoak. Azkenik, esan beharra dago LFPUBS balioztatua izan dela benetako bi ingurune adimentsuetan jasotako datuekin. Lortutako emaitzek garbi adierazten dute LFPUBS-ren gaitasuna ohiko jokabideak deskubritzeko garaian. Gainera, emaitzen analisi sakon batek LFPUBS-ri buruzko hobekuntzak argitaratzeko balio izan du.Los Entornos Inteligentes (EIs) tratan de facilitar las actividades diarias a las personas que se encuentran en él. El concepto de Entornos Inteligentes supone un cambio radical en las relaciones entre los usuarios y la tecnología. El cambio consiste en que se pasa desde una perspectiva centrada en la tecnología a una perspectiva centrada en el usuario. A diferencia de los sistemas actuales donde el usuario se tiene que adaptar a la tecnología, ahora, esta es la que se adapta a las preferencias, costumbres o gustos del usuario. Los entornos inteligentes deberán aprender cuáles son los comportamientos frecuentes de los usuarios para así adaptarse a los usuarios y proveer servicios personalizados. De este modo, la capacidad de aprendizaje se convierte en un requisito indispensable para dichos entornos. El objetivo de este trabajo de investigación es desarrollar un sistema que aprenda de forma automática, los comportamientos frecuentes de los usuarios de entornos inteligentes. Para ello, se ha diseñado y desarrollado el Learning Frequent Patterns of User Behaviour System (LFPUBS), que teniendo en cuenta todas las particularidades de dichos entornos, descubre tales comportamientos. El núcleo del LFPUBS es la capa de Aprendizaje que a diferencia de otros componentes es independiente del entorno donde está siendo aplicado el sistema. Dicha capa, por un lado incluye un lenguaje que permite representar los patrones de una forma clara y no ambigua y por otro, en concordancia con el lenguaje, incluye el algoritmo que descubre dichos patrones. Finalmente, LFPUBS ha sido validado utilizando los datos recogidos en dos entornos inteligentes reales. Los resultados obtenidos durante esos experimentos permitieron comprobar que LFPUBS es capaz de descubrir los comportamientos frecuentes de los usuarios. Además, mejoras para futuras versiones del sistema fueron identificadas

Ontology-based data semantic management and application in IoT- and cloud-enabled smart homes

The application of emerging technologies of Internet of Things (IoT) and cloud computing have increasing the popularity of smart homes, along with which, large volumes of heterogeneous data have been generating by home entities. The representation, management and application of the continuously increasing amounts of heterogeneous data in the smart home data space have been critical challenges to the further development of smart home industry. To this end, a scheme for ontology-based data semantic management and application is proposed in this paper. Based on a smart home system model abstracted from the perspective of implementing users’ household operations, a general domain ontology model is designed by defining the correlative concepts, and a logical data semantic fusion model is designed accordingly. Subsequently, to achieve high-efficiency ontology data query and update in the implementation of the data semantic fusion model, a relational-database-based ontology data decomposition storage method is developed by thoroughly investigating existing storage modes, and the performance is demonstrated using a group of elaborated ontology data query and update operations. Comprehensively utilizing the stated achievements, ontology-based semantic reasoning with a specially designed semantic matching rule is studied as well in this work in an attempt to provide accurate and personalized home services, and the efficiency is demonstrated through experiments conducted on the developed testing system for user behavior reasoning