ProCAVIAR: Hybrid Data-Driven and Probabilistic Knowledge-Based Activity Recognition

The recognition of physical activities using sensors on mobile devices has been mainly addressed with supervised and semi-supervised learning. The state-of-the-art methods are mainly based on the analysis of the user\u2019s movement patterns that emerge from inertial sensors data. While the literature on this topic is quite mature, existing approaches are still not adequate to discriminate activities characterized by similar physical movements. The context that surrounds the user (e.g., semantic location) could be used as additional information to significantly extend the set of recognizable activities. Since collecting a comprehensive training set with activities performed in every possible context condition is too costly, if possible at all, existing works proposed knowledge-based reasoning over ontological representation of context data to refine the predictions obtained from machine learning. A problem with this approach is the rigidity of the underlying logic formalism that cannot capture the intrinsic uncertainty of the relationships between activities and context. In this work, we propose a novel activity recognition method that combines semisupervised learning and probabilistic ontological reasoning. We model the relationships between activities and context as a combination of soft and hard ontological axioms. For each activity, we use a probabilistic ontology to compute its compatibility with the current context conditions. The output of probabilistic semantic reasoning is combined with the output of a machine learning classifier based on inertial sensor data to obtain the most likely activity performed by the user. The evaluation of our system on a dataset with 13 types of activities performed by 26 subjects shows that our probabilistic framework outperforms both a pure machine learning approach and previous hybrid approaches based on classic ontological reasoning

Smart City Ontologies and Their Applications: A Systematic Literature Review

The increasing interconnections of city services, the explosion of available urban data, and the need for multidisciplinary analysis and decision making for city sustainability require new technological solutions to cope with such complexity. Ontologies have become viable and effective tools to practitioners for developing applications requiring data and process interoperability, big data management, and automated reasoning on knowledge. We investigate how and to what extent ontologies have been used to support smart city services and we provide a comprehensive reference on what problems have been addressed and what has been achieved so far with ontology-based applications. To this purpose, we conducted a systematic literature review finalized to presenting the ontologies, and the methods and technological systems where ontologies play a relevant role in shaping current smart cities. Based on the result of the review process, we also propose a classification of the sub-domains of the city addressed by the ontologies we found, and the research issues that have been considered so far by the scientific community. We highlight those for which semantic technologies have been mostly demonstrated to be effective to enhance the smart city concept and, finally, discuss in more details about some open problems

Ontological representation of time-of-flight camera data to support vision-based AmI

Proceedings of: 4th International Workshop on Sensor Networks and Ambient Intelligence, 19-23 March 2012, Lugano ( Switzerland)Recent advances in technologies for capturing video data have opened a vast amount of new application areas. Among them, the incorporation of Time-of-Flight (ToF) cameras on Ambient Intelligence (AmI) environments. Although theperformance of tracking algorithms have quickly improved, symbolic models used to represent the resulting knowledge have not yet been adapted for smart environments. This paper presents an extension of a previous system in the area of videobased AmI to incorporate ToF information to enhance sceneinterpretation. The framework is founded on an ontologybased model of the scene, which is extended to incorporate ToF data. The advantages and new features of the model are demonstrated in a Social Signal Processing (SSP) application.This work was supported in part by Projects CICYT TIN2011-28620-C02-01, CICYT TEC2011-28626-C02-02, CAM CONTEXTS (S2009/TIC-1485) and DPS2008-07029- C02-02.Publicad

Representação da informação incerta por meio de ontologias: um framework para smart homes

Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro de Ciências da Educação, Programa de Pós-Graduação em Ciência da Informação, Florianópolis, 2019.Nas smart homes e outros cenários da Internet das Coisas (IoT), muitas vezes, as informações coletadas estão sujeitas a interferências externas. Além disso, pode ser necessário representar situações nas quais não é possível se obter informações completas ou precisas sobre determinado fenômeno, gerando a necessidade de se lidar com a informação incerta. As ontologias apresentam um formato amplamente utilizado para a representação das informações coletadas nas smart homes. Sendo assim, atualmente existem várias abordagens não padronizadas na literatura baseadas em ontologias para a representação da informação incerta, ou ontologias incertas . Diante desse contexto, o objetivo deste trabalho é propor um framework para ser utilizado como ferramenta de referência no processo de seleção de ontologias incertas para cenários de smart homes. Para isso, foram identificadas ontologias incertas para smart homes por meio de uma Revisão Sistemática da Literatura (RSL) e foram realizadas pesquisas nos anais do International Workshop on Uncertainty Reasoning for the Semantic Web (URSW). O framework proposto é composto por dois artefatos gerados a partir de informações extraídas das ontologias incertas identificadas: a) questionário para auxiliar na identificação das necessidades de representação da informação incerta; e b) quadro de referência para ser consultado durante a seleção de uma ontologia incerta de acordo com as necessidades de representação da informação incerta. Ao todo, foram identificados 16 trabalhos que propõem ontologias incertas. Com base nestes trabalhos, elaborou-se o questionário com seis questões e diferentes opções de respostas que remetem as ontologias incertas. O quadro de referência foi elaborado contendo os 16 trabalhos identificados e as características das ontologias incertas propostas por cada trabalho. O framework foi aplicado em nove cenários de smart homes que utilizam ontologias, mas não representam a informação incerta, de modo a exemplificar o papel do framework como ferramenta de referência. Como resultado de sua aplicação, para cada cenário, exceto um, identificou-se uma ou mais opções de ontologias incertas. Isto indica que as ontologias incertas disponíveis cobrem grande parte das necessidades de representação atualmente, mas não completamente. Espera-se que o framework proposto possa ser utilizado como referência para facilitar o acesso e uso das ontologias incertas pelos profissionais interessados na construção de ontologias. Finalmente, espera-se gerar oportunidades para que sejam desenvolvidas aplicações que elevem a qualidade e capacidade dos cenários de smart homes tendo em vista principalmente as necessidades e bem-estar das pessoas.Abstract : In smart homes and other Internet of Things (IoT) scenarios, often information collected is subject to external interference. Moreover, it may be necessary to represent situations in which it is not possible to obtain complete or accurate information about a specific phenomenon, causing the need to deal with uncertain information. Ontologies provides a widespread format for representing information collected in smart homes. This way, nowadays there are many non-standard ontology-based approaches in literature focused in the task of uncertain information representation, or \"uncertain ontologies\". Given this context, the objective of this work is to propose a framework to be used as a reference tool in the process of selecting uncertain ontologies for smart home scenarios. For this purpose, uncertain ontologies for smart homes and other IoT scenarios are identified by means of a Systematic Review of Literature (RSL) and by research in proceedings from International Workshop on Uncertainty Reasoning for the Semantic Web (URSW). The proposed framework is composed by two artifacts generated from information extracted from identified uncertain ontologies: a) a survey to assist in identifying the needs for representing uncertain information; and b) a reference table which can be used for selection of uncertain ontologies according to the representation needs. Altogether, 16 uncertain ontologies proposals have been identified. Based on these proposals, the questionnaire was elaborated with six questions and different options of answers referring to uncertain ontologies. The reference table was built containing the 16 ontologies proposals and its specific features. The framework was applied in nine scenarios of smart homes which use ontologies, but do not represent the uncertain information, in order to exemplify the role of the framework as a reference tool. As a result of its application one or more uncertain ontologies options were identified for most of the work. This indicates that the available uncertain ontologies cover most of the representation needs currently, but not all. It is expected that the proposed framework will be used as a reference to ease the access and use of uncertain ontologies by professionals interested in the creation of ontologies. Finally, it is expected to generate opportunities to develop applications which raise the quality and capacity of smart home scenarios especially in view of the needs and well-being of people

Deep Learning and IoT to Assist Multimorbidity Home Based Healthcare

The authors present a proposal to develop intelligent assisted living environments for home based healthcare in the presence of multimorbidity chronic patients. These environments unite the chronicle patient clinical history sematic representation ICP (Individual Care Process) with the ability of monitoring the living conditions using IoT technologies and events recurring to a fully managed Semantic Web of Things (SWoT) and Machine Learning Algorithms in order to activate the LDC (Less Differentiated Caregiver) for a specific care need. With these capabilities at hand, home based healthcare providing becomes a viable possibility reducing the institutionalization needs. The resulting integrated healthcare framework will provide significant savings while improving the generality of health and satisfaction indicators

Design-time formal verification for smart environments: an exploratory perspective

Smart environments (SmE) are richly integrated with multiple heterogeneous devices; they perform the operations in intelligent manner by considering the context and actions/behaviors of the users. Their major objective is to enable the environment to provide ease and comfort to the users. The reliance on these systems demands consistent behavior. The versatility of devices, user behavior and intricacy of communication complicate the modeling and verification of SmE's reliable behavior. Of the many available modeling and verification techniques, formal methods appear to be the most promising. Due to a large variety of implementation scenarios and support for conditional behavior/processing, the concept of SmE is applicable to diverse areas which calls for focused research. As a result, a number of modeling and verification techniques have been made available for designers. This paper explores and puts into perspective the modeling and verification techniques based on an extended literature survey. These techniques mainly focus on some specific aspects, with a few overlapping scenarios (such as user interaction, devices interaction and control, context awareness, etc.), which were of the interest to the researchers based on their specialized competencies. The techniques are categorized on the basis of various factors and formalisms considered for the modeling and verification and later analyzed. The results show that no surveyed technique maintains a holistic perspective; each technique is used for the modeling and verification of specific SmE aspects. The results further help the designers select appropriate modeling and verification techniques under given requirements and stress for more R&D effort into SmE modeling and verification researc

Collaborative Ontology Engineering Methodologies for the Development of Decision Support Systems: Case Studies in the Healthcare Domain

New models and technological advances are driving the digital transformation of healthcare systems. Ontologies and Semantic Web have been recognized among the most valuable solutions to manage the massive, various, and complex healthcare data deriving from different sources, thus acting as backbones for ontology-based Decision Support Systems (DSSs). Several contributions in the literature propose Ontology engineering methodologies (OEMs) to assist the formalization and development of ontologies, by providing guidelines on tasks, activities, and stakeholders' participation. Nevertheless, existing OEMs differ widely according to their approach, and often lack of sufficient details to support ontology engineers. This paper performs a meta-review of the main criteria adopted for assessing OEMs, and major issues and shortcomings identified in existing methodologies. The key issues requiring specific attention (i.e., the delivery of a feasibility study, the introduction of project management processes, the support for reuse, and the involvement of stakeholders) are then explored into three use cases of semantic-based DSS in health-related fields. Results contribute to the literature on OEMs by providing insights on specific tools and approaches to be used when tackling these issues in the development of collaborative OEMs supporting DSS

A Telerehabilitation System for the Selection, Evaluation and Remote Management of Therapies

Telerehabilitation systems that support physical therapy sessions anywhere can help save healthcare costs while also improving the quality of life of the users that need rehabilitation. The main contribution of this paper is to present, as a whole, all the features supported by the innovative Kinect-based Telerehabilitation System (KiReS). In addition to the functionalities provided by current systems, it handles two new ones that could be incorporated into them, in order to give a step forward towards a new generation of telerehabilitation systems. The knowledge extraction functionality handles knowledge about the physical therapy record of patients and treatment protocols described in an ontology, named TRHONT, to select the adequate exercises for the rehabilitation of patients. The teleimmersion functionality provides a convenient, effective and user-friendly experience when performing the telerehabilitation, through a two-way real-time multimedia communication. The ontology contains about 2300 classes and 100 properties, and the system allows a reliable transmission of Kinect video depth, audio and skeleton data, being able to adapt to various network conditions. Moreover, the system has been tested with patients who suffered from shoulder disorders or total hip replacement.This research was funded by the Spanish Ministry of Economy and Competitiveness grant number FEDER/TIN2016-78011-C4-2R