The increasing interest among manufacturers in monitoring and analyzing industrial systems is generating a problem related to the considerable costs associated with the storage of captured data. This paper presents a three-level hierarchical architecture for time-series data storage on cloud environments that helps to decrease those costs. In the first level, new raw time-series data is stored for a short-period of time (e.g., one day) on electronic non-volatile storage such as solid-state drives (SSDs) that provide fast access for real time visualization of the latest data. In the second level, recent time series are stored for a medium-period of time (e.g., one week) on magnetic hard disk drives (HDDs) that are lower-cost devices with slower data transfer speed. In the third level, a reduced representation of the time series obtained by applying time-series reduction techniques are stored in HDDs, for a longer period of time (e.g., one year). Dealing with those reduced representations, data storage and transmission costs can be decreased, without limiting the future use of the data in different processes.The architecture has been implemented by using the top Database Management System from three different categories: Wide column store, Time series DBMS and Graph DBMS. It has been tested using industrial time series coming from a real manufacturing environment, and with three different types of queries proposed by domain experts. The performance results regarding storage space, storage costs and query time processing are shown on the paper.

The massive captured data from industrial sensors (time-series data) that could serve as relevant indicators for predictive maintenance of equipment, fault diagnosis, etc. is generating a problem related to the considerable costs associated with their storage.

BACKGROUND: has also been used to build these kinds of systems, but contradictory results have been reported: some works conclude that Kinect does not provide an accurate method of measuring gait kinematics variables, but others, on the contrary, report good accuracy results. METHODS: In this work, we have built a Kinect-based system that can distinguish between different PD stages, and have performed a clinical study with 30 patients suffering from PD belonging to three groups: early PD patients without axial impairment, more evolved PD patients with higher gait impairment but without Freezing of Gait (FoG), and patients with advanced PD and FoG. Those patients were recorded by two Kinect devices when they were walking in a hospital corridor. The datasets obtained from the Kinect were preprocessed, 115 features identified, some methods were applied to select the relevant features (correlation based feature selection, information gain, and consistency subset evaluation), and different classification methods (decision trees, Bayesian networks, neural networks and K-nearest neighbours classifiers) were evaluated with the goal of finding the most accurate method for PD stage classification. RESULTS: The classifier that provided the best results is a particular case of a Bayesian Network classifier (similar to a Naïve Bayesian classifier) built from a set of 7 relevant features selected by the correlation-based on feature selection method. The accuracy obtained for that classifier using 10-fold cross validation is 93.40%. The relevant features are related to left shin angles, left humerus angles, frontal and lateral bents, left forearm angles and the number of steps during spin. CONCLUSIONS: In this paper, it is shown that using Kinect is adequate to build a inexpensive and comfortable system that classifies PD into three different stages related to FoG. Compared to the results of previous works, the obtained accuracy (93.40%) can be considered high. The relevant features for the classifier are: a) movement and position of the left arm, b) trunk position for slightly displaced walking sequences, and c) left shin angle, for straight walking sequences. However, we have obtained a better accuracy (96.23%) for a classifier that only uses features extracted from slightly displaced walking steps and spin walking steps. Finally, the obtained set of relevant features may lead to new rehabilitation therapies for PD patients with gait problems.

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.

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 SpO(2) 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.

Continuous pervasive monitoring has the potential to improve the quality of life of many persons (for example those who suffer from heart arrhythmias or elderly people who suffer from chronic diseases). We show the three main steps related to the monitoring process and supported by our system. First, how data are captured by sensors which send those data using wireless communications to a PDA (personal digital assistant). Second, how data are analyzed, locally on the PDA, using techniques proposed in the areas of the semantic Web and machine learning. Finally, how data stored at the PDA can be queried remotely using Web services. Those three steps are illustrated in two different scenarios that the system can deal with: tele-assistance and monitoring of arrhythmias. Moreover, through the paper we highlight the main advantages provided by the system: active monitoring which consists in reacting to anomalous situations without direct intervention of the user; universal assistance, i.e. irrespective of time or place through the use of wireless communications and PDAs; vital signs monitoring which consists in using sensors that capture the value of those vital signs, and subsequently feed them to a decision support system that analyses them and generates an alarm if necessary; and remote monitoring which allows authorized personal to consult data on monitored patients, using the Internet. Finally we present some performance results which demonstrate the feasibility of the system

In this paper we investigate if techniques based on decision trees are also useful to classify ischemia. In order to do that, we have based on a previous own algorithm that is able of detecting ST-segment episodes, can be executed in real time and is lightweight enough to be implemented on mobile devices such as PDAs. Three main steps are performed by that algorithm: 1) signal preprocessing that extracts important features from the ECG signal 2) detection of suspect ST segment events and 3) rejection of irrelevant ST segment events. We have found that techniques based on decision trees are interesting for the last step in order to obtain a set of rules that reject some of the suspect ST segment events detected in the second step. The freely available part of the LTST database has been used to develop the detector and the rest of the same database has been used for validation purposes. The sensitivity of the detector over all the records of the LTST database is 89.89% for episodes annotated according to C protocol and the positive predictivity is 70.03% for episodes annotated according to A protocol. Those results improve our previously obtained ones and we think that they are comparable to other results that appear in the literature. However, it has to be noticed that we have not found works that detect ischemia in real time and show validation results over the LTST database

The ability to retrieve software in an easy and efficient way confers competitive advantage on computer users in general and, even more especially, on users of wireless devices (like some laptops, PDAs, etc.). In this article, we present a software retrieval service that allows users to select and retrieve software in an easy and efficient way, anywhere and anytime. Two relevant components of this service are: 1) a software ontology (software catalog) which provides users with a semantic description of software elements, hiding the location and access method of various software repositories, and 2) a set of specialist agents that allow browsing of the software catalog (automatically customized for each user), and an efficient retrieval method for the selected software. These agents automatically adapt their behavior to different users and situations by considering the profile and preferences of the users and the network status.In summary, our software-obtaining process based on an ontology and autonomous and adaptive agents presents a qualitative advance with respect to existing solutions: our approach adapts to the features of users, relieving them from knowing the technical features of their devices and the location and access method of various remote software repositories.

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.

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 system's 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.

The processing cost of queries with an empty answer, both in the database and knowledge base context, is usually high. One purpose of semantic query optimization methods in the database context is to use semantic knowledge to detect such types of queries. Although semantic query optimization is well known in the database context, this is not the case for knowledge base systems (KBSs). This paper presents a method that allows the detection of queries with an empty answer using only semantic information expressed in the knowledge base definition. The method can be applied in the context of KBSs that provide some of the following features: structuring mechanisms, assertional knowledge, temporal information, and handling of inequality expressions.>