Ambient-aware continuous care through semantic context dissemination

Background: The ultimate ambient-intelligent care room contains numerous sensors and devices to monitor the patient, sense and adjust the environment and support the staff. This sensor-based approach results in a large amount of data, which can be processed by current and future applications, e. g., task management and alerting systems. Today, nurses are responsible for coordinating all these applications and supplied information, which reduces the added value and slows down the adoption rate. The aim of the presented research is the design of a pervasive and scalable framework that is able to optimize continuous care processes by intelligently reasoning on the large amount of heterogeneous care data. Methods: The developed Ontology-based Care Platform (OCarePlatform) consists of modular components that perform a specific reasoning task. Consequently, they can easily be replicated and distributed. Complex reasoning is achieved by combining the results of different components. To ensure that the components only receive information, which is of interest to them at that time, they are able to dynamically generate and register filter rules with a Semantic Communication Bus (SCB). This SCB semantically filters all the heterogeneous care data according to the registered rules by using a continuous care ontology. The SCB can be distributed and a cache can be employed to ensure scalability. Results: A prototype implementation is presented consisting of a new-generation nurse call system supported by a localization and a home automation component. The amount of data that is filtered and the performance of the SCB are evaluated by testing the prototype in a living lab. The delay introduced by processing the filter rules is negligible when 10 or fewer rules are registered. Conclusions: The OCarePlatform allows disseminating relevant care data for the different applications and additionally supports composing complex applications from a set of smaller independent components. This way, the platform significantly reduces the amount of information that needs to be processed by the nurses. The delay resulting from processing the filter rules is linear in the amount of rules. Distributed deployment of the SCB and using a cache allows further improvement of these performance results

Interaction Analysis in Smart Work Environments through Fuzzy Temporal Logic

Interaction analysis is defined as the generation of situation descriptions from machine perception. World models created through machine perception are used by a reasoning engine based on fuzzy metric temporal logic and situation graph trees, with optional parameter learning and clustering as preprocessing, to deduce knowledge about the observed scene. The system is evaluated in a case study on automatic behavior report generation for staff training purposes in crisis response control rooms

Conflict resolution when axioms are materialized in semantic-based smart environments.

International audienceIn SemanticWeb applications, reasoning engines that are data intensive commonly materialise inferences to speed up processing at query time. However, in evolving systems, such as smart environments, semantic-based context aware systems (SCAS) [6] or social software with user-generated data, knowledge does not grow monotonically: newer facts may contradict older ones, knowledge may be deprecated, discarded or updated such that knowledge must sometimes be retracted. We are describing a technique to retract explicit and inferred statements, when some information becomes obsolete, as well as retracting any statement that would lead to get back the removed explicit statements. This technique is based on OWL justifications and is triggered whenever a knowledge base becomes inconsistent, such that the system stays in a consistent state all the time, in spite of uncontrolled evolution.We prove termination and correctness of the algorithm, and describe the implementation and evaluation of the proposal

Context-based scene recognition from visual data in smart homes: an Information Fusion approach

Ambient Intelligence (AmI) aims at the development of computational systems that process data acquired by sensors embedded in the environment to support users in everyday tasks. Visual sensors, however, have been scarcely used in this kind of applications, even though they provide very valuable information about scene objects: position, speed, color, texture, etc. In this paper, we propose a cognitive framework for the implementation of AmI applications based on visual sensor networks. The framework, inspired by the Information Fusion paradigm, combines a priori context knowledge represented with ontologies with real time single camera data to support logic-based high-level local interpretation of the current situation. In addition, the system is able to automatically generate feedback recommendations to adjust data acquisition procedures. Information about recognized situations is eventually collected by a central node to obtain an overall description of the scene and consequently trigger AmI services. We show the extensible and adaptable nature of the approach with a prototype system in a smart home scenario.This research activity is supported in part by Projects CICYT TIN2008-06742-C02-02/TSI, CICYT TEC2008- 06732-C02-02/TEC, CAM CONTEXTS (S2009/TIC-1485) and DPS2008-07029-C02-02.Publicad

Interaction Analysis in Smart Work Environments through Fuzzy Temporal Logic

Interaction analysis is defined as the generation of situation descriptions from machine perception. World models created through machine perception are used by a reasoning engine based on fuzzy metric temporal logic and situation graph trees, with optional parameter learning and clustering as preprocessing, to deduce knowledge about the observed scene. The system is evaluated in a case study on automatic behavior report generation for staff training purposes in crisis response control rooms

A general cognitive framework for context-aware systems: extensions and applications for high level information fusion approaches

Mención Internacional en el título de doctorContext-aware systems aims at the development of computational systems that process data acquired from different datasources and adapt their behaviour in order to provide the 'right' information, at the 'right' time, in the 'right' place, in the 'right' way to the 'right' person (Fischer, 2012). Traditionally computational research has tried to answer these needs by means of low-level algorithms. In the last years the combination of numeric and symbolic approaches has offered the opportunity to create systems to deal with these issues. However, although the performance of algorithms and the quality of the data directly provided by computers and devices has quickly improved, symbolic models used to represent the resulting knowledge have not yet been adapted to smart environments. This lack of representation does not allow to take advantage of the semantic quality of the information provided by new sensors. This dissertation proposes a set of extensions and applications focused on a cognitive framework for the implementation of context-aware systems based on a general model inspired by the Information Fusion paradigm. This model is stepped in several abstraction levels from low-level raw data to high level scene interpretation whose structure is determined by a set of ontologies. Each ontology level provides a skeleton that includes general concepts and relations to describe entities and their connections. This structure has been designed to promote extensibility and modularity, and might be refined to apply this model in specific domains. This framework combines a priori context knowledge represented with ontologies with real data coming from sensors to support logic-based high-level interpretation of the current situation and to automatically generate feedback recommendations to adjust data acquisition procedures. This work advocates for the introduction of general purpose cognitive layers in order to obtain a closer representation to the human cognition, generate additional knowledge and improve the high-level interpretation. Extensibility and adaptability of the basic ontology levels is demonstrated with the introduction of these traverse semantic layers which are able to be present and represent information at several granularity levels of knowledge using a common formalism. Context-based system must be able to reason about uncertainty. However the reasoning associated to ontologies has been limited to classical description logic mechanisms. This research also tackle the problem of reasoning under uncertainty circumstances through a logic-based paradigm for abductive reasoning: the Belief-Argumentation System. The main contribution of this dissertation is the adaptation of the general architecture and the theoretical proposals to several context-aware application areas such as Ambient Intelligence, Social Signal Processing and surveillance systems. The implementation of prototypes and examples for these areas are explained along this dissertation to progressively illustrate the improvements and extensions in the framework. To initially depict the general model, its components and the basic reasoning mechanisms a video-based Ambient Intelligence application is presented. The advantages and features of the framework extensions through traverse cognitive layers are demonstrated in a Social Signal Processing case for the elaboration of automatic market researches. Finally, the functioning of the system under uncertainty circumstances is illustrated with several examples to support decision makers in the detection of potential threats in common harbor scenarios.Programa Oficial de Doctorado en Ciencia y Tecnología InformáticaPresidente: José Manuel Molina López.- Secretario: Ángel Arroyo.- Vocal: Nayat Sánchez P

it could rain weather forecasting as a reasoning process

Abstract Meteorological forecasting is the process of providing reliable prediction about the future weathear within a given interval of time. Forecasters adopt a model of reasoning that can be mapped onto an integrated conceptual framework. A forecaster essentially precesses data in advance by using some models of machine learning to extract macroscopic tendencies such as air movements, pressure, temperature, and humidity differentials measured in ways that depend upon the model, but fundamentally, as gradients. Limit values are employed to transform these tendencies in fuzzy values, and then compared to each other in order to extract indicators, and then evaluate these indicators by means of priorities based upon distance in fuzzy values. We formalise the method proposed above in a workflow of evaluation steps, and propose an architecture that implements the reasoning techniques

Ontology-Based Framework for the Automatic Recognition of Activities of Daily Living Using Class Expression Learning Techniques

The miniaturization and price reduction of sensors have encouraged the proliferation of smart environments, in which multitudinous sensors detect and describe the activities carried out by inhabitants. In this context, the recognition of activities of daily living has represented one of the most developed research areas in recent years. Its objective is to determine what daily activity is developed by the inhabitants of a smart environment. In this field, many proposals have been presented in the literature, many of them being based on ad hoc ontologies to formalize logical rules, which hinders their reuse in other contexts. In this work, we propose the use of class expression learning (CEL), an ontology-based data mining technique, for the recognition of ADL. This technique is based on combining the entities in the ontology, trying to find the expressions that best describe those activities. As far as we know, it is the first time that this technique is applied to this problem. To evaluate the performance of CEL for the automatic recognition of activities, we have first developed a framework that is able to convert many of the available datasets to all the ontology models we have found in the literature for dealing with ADL. Two different CEL algorithms have been employed for the recognition of eighteen activities in two different datasets. Although all the available ontologies in the literature are focused on the description of the context of the activities, the results show that the sequence of the events produced by the sensors is more relevant for their automatic recognition, in general terms

Handling Real-World Context Awareness, Uncertainty and Vagueness in Real-Time Human Activity Tracking and Recognition with a Fuzzy Ontology-Based Hybrid Method

Human activity recognition is a key task in ambient intelligence applications to achieve proper ambient assisted living. There has been remarkable progress in this domain, but some challenges still remain to obtain robust methods. Our goal in this work is to provide a system that allows the modeling and recognition of a set of complex activities in real life scenarios involving interaction with the environment. The proposed framework is a hybrid model that comprises two main modules: a low level sub-activity recognizer, based on data-driven methods, and a high-level activity recognizer, implemented with a fuzzy ontology to include the semantic interpretation of actions performed by users. The fuzzy ontology is fed by the sub-activities recognized by the low level data-driven component and provides fuzzy ontological reasoning to recognize both the activities and their influence in the environment with semantics. An additional benefit of the approach is the ability to handle vagueness and uncertainty in the knowledge-based module, which substantially outperforms the treatment of incomplete and/or imprecise data with respect to classic crisp ontologies. We validate these advantages with the public CAD-120 dataset (Cornell Activity Dataset), achieving an accuracy of 90.1% and 91.07% for low-level and high-level activities, respectively. This entails an improvement over fully data-driven or ontology-based approaches.This work was funded by TUCS (Turku Centre for Computer Science), Finnish Cultural Foundation, Nokia Foundation, Google Anita Borg Scholarship, CEI BioTIC Project CEI2013-P-3, Contrato-Programa of Faculty of Education, Economy and Technology of Ceuta and Project TIN2012-30939 from National I+D Research Program (Spain). We also thank Fernando Bobillo for his support with FuzzyOWL and FuzzyDL tools