Alfredo Goñi

Real-time classification of ECGs on a PDA

The new advances in sensor technology, personal digital assistants (PDAs), and wireless communications favor the development of a new type of monitoring system that can provide patients with assistance anywhere and at any time. Of particular interest are the monitoring systems designed for people that suffer from heart arrhythmias, due to the increasing number of people with cardiovascular diseases. PDAs can play a very important role in these kinds of systems because they are portable devices that can execute more and more complex tasks. The main questions answered in this paper are whether PDAs can perform a complete electrocardiogram beat and rhythm classifier, if the classifier has a good accuracy, and if they can do it in real time. In order to answer these questions, in this paper, we show the steps that we have followed to build the algorithm that classifies beats and rhythms, and the obtained results, which show a competitive accuracy. Moreover, we also show the feasibility of incorporating the built algorithm into the PDA.

Real-Time Detection of Apneas on a PDA

Patients suspected of suffering sleep apnea and hypopnea syndrome (SAHS) have to undergo sleep studies such as expensive polysomnographies to be diagnosed. Healthcare professionals are constantly looking for ways to improve the ease of diagnosis and comfort for this kind of patients as well as reducing both the number of sleep studies they need to undergo and the waiting times. Relating to this scenario, some research proposals and commercial products are appearing, but all of them record the physiological data of patients to portable devices and, in the morning, these data are loaded into hospital computers where physicians analyze them by making use of specialized software. In this paper, we present an alternative proposal that promotes not only a transmission of physiological data but also a real-time analysis of these data locally at a mobile device. For that, we have built a classifier that provides an accuracy of 93% and a receiver operating characteristic-area under the curve (ROC-AUC) of 98.5% on SpO2 signals available in the annotated Apnea-ECG Database. This local analysis allows the detection of anomalous situations as soon as they are generated. The classifier has been implemented taking into consideration the restricted resources of mobile devices.

Relationships between physical education classes and the enhancement of fifth grade pupils' self-concept.

Physical education classes given to a group of 46 pupils (27 boys and 19 girls) over a 6-mo. term differed from the usual program (control group of 29 pupils) in identifying individual goals, promoting participative and noncompetitive games, and praising students every chance possible during the classes. The enhancement of self-concept, particularly the physical self-concept, was significant for the experimental group as measured by the Self-description Questionnaire and the Child and Youth Physical Self-perception Profile.

BUILDING A FEDERATED RELATIONAL DATABASE SYSTEM: AN APPROACH USING A KNOWLEDGE-BASED SYSTEM

Due to the emerging interest in integrating different application environments, there have been many recent proposals for federated systems. In this paper, a federated system that permits the integration of heterogeneous relational databases using a terminological knowledge representation system is presented. In particular, two of the systems components: the translator and the integrator are explained in depth. The translator permits one to obtain a terminology from a relational schema, either semiautomatically, by expressing database properties, or manually, by using a set of predefined operations. In turn, the integrator generates a federated terminology by integrating several terminologies using the semantics expressed as correspondences between the data elements of different terminologies. Unlike many other approaches, the use of a terminological system permits us to obtain a semantically richer federated terminology and, at the same time, define a wider and more consistent integration process.

Intelligent monitoring of elderly people

The aim of the article is to present our proposal for an intelligent monitoring of elderly people based on: agent technology, a decision support system implemented using a description logic based system and personal digital assistants (PDAs). Agent technology allows us to customize the monitoring service and to distribute the global functionality of the system among several agents. We use the DAML+OIL description logic based system to describe an ontology that categorizes different anomalous situations. The reasoning mechanism provided by that system permits the detection of anomalous situations for many different user conditions. Finally, the use of PDAs allows us to offer an anywhere and anytime monitoring.

Autoconcepto físico y estilos de vida en la adolescencia

Healthy lifestyles are associated with both physical self-concept and psychological well-being, at least during the adolescence. In this study the results confirm the relation.

Automatic Ontology Construction for a Multiagent-Based Software Gathering Service

Ontologies and agents are two topics that raise a particular attention those days from the theoretical as well as from the application point of view. In this paper we present a software gathering service that is mainly supported by an ontology, SoftOnt, and several agents. The main goal of the paper is to show how the SoftOnt ontology is built from distributed and heterogeneous software repositories. In the particular domain considered, software repositories, we advocate for an automatic creation of a global unique ontology versus a manual creation and the use of multiple ontologies.

Exercise Recognition for Kinect-based Telerehabilitation

BACKGROUND An aging population and peoples higher survival to diseases and traumas that leave physical consequences are challenging aspects in the context of an efficient health management. This is why telerehabilitation systems are being developed, to allow monitoring and support of physiotherapy sessions at home, which could reduce healthcare costs while also improving the quality of life of the users. OBJECTIVES Our goal is the development of a Kinect-based algorithm that provides a very accurate real-time monitoring of physical rehabilitation exercises and that also provides a friendly interface oriented both to users and physiotherapists. METHODS The two main constituents of our algorithm are the posture classification method and the exercises recognition method. The exercises consist of series of movements. Each movement is composed of an initial posture, a final posture and the angular trajectories of the limbs involved in the movement. The algorithm was designed and tested with datasets of real movements performed by volunteers. We also explain in the paper how we obtained the optimal values for the trade-off values for posture and trajectory recognition. RESULTS Two relevant aspects of the algorithm were evaluated in our tests, classification accuracy and real-time data processing. We achieved 91.9% accuracy in posture classification and 93.75% accuracy in trajectory recognition. We also checked whether the algorithm was able to process the data in real-time. We found that our algorithm could process more than 20,000 postures per second and all the required trajectory data-series in real-time, which in practice guarantees no perceptible delays. Later on, we carried out two clinical trials with real patients that suffered shoulder disorders. We obtained an exercise monitoring accuracy of 95.16%. CONCLUSIONS We present an exercise recognition algorithm that handles the data provided by Kinect efficiently. The algorithm has been validated in a real scenario where we have verified its suitability. Moreover, we have received a positive feedback from both users and the physiotherapists who took part in the tests.

KiReS: A Kinect-based telerehabilitation system

The goal of this paper is to show the main features of KiReS, a telerehabilitation system based on Kinect for Windows, that offers, for both, users and physiotherapists some specific elements that make it more friendly to them. From the point of view of users, they can see in two 3D avatars how an exercise must be executed and how they execute it respectively. This feature can help them improve exercises performance. Moreover during the rehabilitation session they will always see an informative list that shows the exercises to be done in the session. From the point of view of physiotherapists the system allows them on the one hand, to define customized rehabilitation therapies. That can be done by defining different exercises that combine pre-defined movements. Moreover, they can add tests oriented to specific illnesses so that users themselves evaluate their physical state. On the other hand, they can create new exercises just performing those exercises in front of the system and recording them. Those features, not fully supported by already existing telerehabilitation systems, provide an added value that is well valued by both groups. Moreover, a prototype of KiReS is in operation, and allowed us to test its suitability from the point of view of real time performance as well as from the point of view of usability.

Real-time detection of transient cardiac ischemic episodes from ECG signals

We propose a new algorithm to detect and classify transient cardiac ischemia episodes, designed with the goal of providing a real-time execution without penalizing the classifier accuracy much. The algorithm is based on a novel mixture of time-domain analysis and machine learning techniques, specifically bagging of decision trees, and it has been developed using a well-recognized and freely distributed database, namely the long-term ST database. The ST episode detection sensitivity/positive predictivity using the annotation protocol A for this database is 68.26%/74.91%. The sensitivity result increases until 93.97% for the most dangerous episodes in terms of duration and magnitude (annotated according to protocol C). The test of the algorithm over the freely distributed part of the European Society of Cardiology database has shown results of sensitivity and positive predictivity of 83.33% and 77.31%, respectively. Those results are close to the results obtained by related works that present approaches to detect ischemia episodes off-line, which is remarkable if we take into account that in our real-time approach, less information is available during the classification process.