Manuel Pegalajar Cuéllar

A survey on ontologies for human behavior recognition

Describing user activity plays an essential role in ambient intelligence. In this work, we review different methods for human activity recognition, classified as data-driven and knowledge-based techniques. We focus on context ontologies whose ultimate goal is the tracking of human behavior. After studying upper and domain ontologies, both useful for human activity representation and inference, we establish an evaluation criterion to assess the suitability of the different candidate ontologies for this purpose. As a result, any missing features, which are relevant for modeling daily human behaviors, are identified as future challenges.

A fuzzy ontology for semantic modelling and recognition of human behaviour

We propose a fuzzy ontology for human activity representation, which allows us to model and reason about vague, incomplete, and uncertain knowledge. Some relevant subdomains found to be missing in previous proposed ontologies for this domain were modelled as well. The resulting fuzzy OWL 2 ontology is able to model uncertain knowledge and represent temporal relationships between activities using an underlying fuzzy state machine representation. We provide a proof of concept of the approach in work scenarios such as the office domain, and also make experiments to emphasize the benefits of our approach with respect to crisp ontologies. As a result, we demonstrate that the inclusion of fuzzy concepts and relations in the ontology provide benefits during the recognition process with respect to crisp approaches.

Multiobjective Hybrid Optimization and Training of Recurrent Neural Networks

The application of neural networks to solve a problem involves tasks with a high computational cost until a suitable network is found, and these tasks mainly involve the selection of the network topology and the training step. We usually select the network structure by means of a trial-and-error procedure, and we then train the network. In the case of recurrent neural networks (RNNs), the lack of suitable training algorithms sometimes hampers these procedures due to vanishing gradient problems. This paper addresses the simultaneous training and topology optimization of RNNs using multiobjective hybrid procedures. The proposal is based on the SPEA2 and NSGA2 algorithms for making hybrid methods using the Baldwinian hybridization strategy. We also study the effects of the selection of the objectives, crossover, and mutation in the diversity during evolution. The proposals are tested in the experimental section to train and optimize the networks in the competition on artificial time-series (CATS) benchmark.

Full-range optical pH sensor based on imaging techniques.

A new colour-based disposable sensor array for a full pH range (0-14) is described. The pH sensing elements are a set of different pH indicators immobilized in plasticized polymeric membranes working by ion-exchange or co-extraction. The colour changes of the 11 elements of the optical array are obtained from a commercial scanner using the hue or H component of the hue, saturation, value (HSV) colour space, which provides a robust and precise parameter, as the analytical parameter. Three different approaches for pH prediction from the hue H of the array of sensing elements previously equilibrated with an unknown solution were studied: Linear model, Sigmoid competition model and Sigmoid surface model providing mean square errors (MSE) of 0.1115, 0.0751 and 0.2663, respectively, in the full-range studied (0-14). The performance of the optical disposable sensor was tested for pH measurement, validating the results against a potentiometric reference procedure. The proposed method is quick, inexpensive, selective and sensitive and produces results similar to other more complex optical approaches for broad pH sensing.

Online recognition of human activities and adaptation to habit changes by means of learning automata and fuzzy temporal windows

Smart Homes are intelligent spaces that contain resources to collect information about users activities and ease the assisted living. Abnormal behavior detection has been remarked as one of the most challenging application fields in this research area, due to its possibilities for assisting elders or people with special needs. These systems help to maintain peoples independence, enhancing their personal comfort and safety and delaying the process of moving to a nursing home. In this paper, we describe a new approach for the behavior recognition problem based on Learning Automata and fuzzy temporal windows. Our proposal learns the normal behaviors, and uses that knowledge to recognise normal and abnormal human activities in real time. In addition, our proposal is able to adapt online to environmental variations, changes in human habits, and temporal information, defined as an interval of time when the behavior should be performed.

Design and implementation of intelligent systems with LEGO Mindstorms for undergraduate computer engineers

We provide a set of projects to put in practice artificial intelligence techniques using LEGO Mindstorms in an undergraduate computer degree, covering reactive and deliberative agents, rule‐based systems, graph search algorithms, and planning methods. The projects have been applied for teaching in a third‐year undergraduate subject of a computer engineering degree at the University of Granada (Spain). After the contextualization and development of the projects, we discuss the results, advantages, and drawbacks of our experience.

Improving learning management through semantic web and social networks in e-learning environments

Internet social networks have arisen in the last years as powerful tools where people exchange knowledge and multimedia content. They help to share interests between groups of people with common features. Undoubtedly, there is an inherent social network in any e-learning system, where the main actors are teachers, learners and learning resources. Most e-learning software are mainly focused in content dissemination and group work, but the possibilities that Internet LMSs could offer go further. Recently, there has been research work focused on Web Communities for learning and their formulation as Social Networks. Thus, social network analysis may be applied to infer group structures and to make intelligent recommendation systems and data mining. This paper proposes a method for the formulation and interpretation of learning management platforms as social networks. In order to achieve a major generalization, we develop an ontology to integrate the information from different Learning Management Systems. After that, a personalized social network is extracted from the ontology. This change in the point of view of a LMS could be a challenge to make further studies about learners, teachers and learning resources to obtain a better understanding of their social structure, and therefore to make or improve decisions about the learning process.

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.

Feasibility of the use of disposable optical tongue based on neural networks for heavy metal identification and determination.

This study presents the development and characterization of a disposable optical tongue for the simultaneous identification and determination of the heavy metals Zn(II), Cu(II) and Ni(II). The immobilization of two chromogenic reagents, 1-(2-pyridylazo)-2-naphthol and Zincon, and their arrangement forms an array of membranes that work by complexation through a co-extraction equilibrium, producing distinct changes in color in the presence of heavy metals. The color is measured from the image of the tongue acquired by a scanner working in transmission mode using the H parameter (hue) of the HSV color space, which affords robust and precise measurements. The use of artificial neural networks (ANNs) in a two-stage approach based on color parameters, the H feature of the array, makes it possible to identify and determine the analytes. In the first stage, the metals present above a threshold of 10(-7) M are identified with 96% success, regardless of the number of metals present, using the H feature of the two membranes. The second stage reuses the H features in combination with the results of the classification procedure to estimate the concentration of each analyte in the solution with acceptable error. Statistical tests were applied to validate the model over real data, showing a high correlation between the reference and predicted heavy metal ion concentration.

Memetic evolutionary training for recurrent neural networks: an application to time‐series prediction

: Artificial neural networks are bio-inspired mathematical models that have been widely used to solve complex problems. The training of a neural network is an important issue to deal with, since traditional gradient-based algorithms become easily trapped in local optimal solutions, therefore increasing the time taken in the experimental step. This problem is greater in recurrent neural networks, where the gradient propagation across the recurrence makes the training difficult for long-term dependences. On the other hand, evolutionary algorithms are search and optimization techniques which have been proved to solve many problems effectively. In the case of recurrent neural networks, the training using evolutionary algorithms has provided promising results. In this work, we propose two hybrid evolutionary algorithms as an alternative to improve the training of dynamic recurrent neural networks. The experimental section makes a comparative study of the algorithms proposed, to train Elman recurrent neural networks in time-series prediction problems.