Buciakowski, M.; Witczak, M.; Puig, V.; Rotondo, D.; Nejjari, F.; Korbicz, J. Journal of process control Vol. 52, p. 14-25 DOI: 10.1016/j.jprocont.2017.01.002 Data de publicació: 2017-04-01 Article en revista
This paper proposes an approach for the joint state and fault estimation for a class of uncertain nonlinear systems with simultaneous unknown input and actuator faults. This is achieved by designing an unknown input observer combined with a set-membership estimation in the presence of disturbances and measurement noise. The observer is designed using quadratic boundedness approach that is used to overbound the estimation error. Sufficient conditions for the existence and stability of the proposed state and actuator fault estimator are expressed in the form of linear matrix inequalities (LMIs). Simulation results for a quadruple-tank system show the effectiveness of the proposed approach.
Pérez, J.; Cóbreces, S.; Griño, R.; Rodriguez, F.J. IEEE transactions on power electronics Vol. 32, num. 4, p. 3180-3191 DOI: 10.1109/TPEL.2016.2574560 Data de publicació: 2017-04-01 Article en revista
This paper presents a current controller that shapes, in the frequency domain, the input admittance of voltage-source converters connected to the grid. The controller is obtained by means of a H8 synthesis procedure, which minimizes the difference between the application closed-loop input admittance and a model-reference defined by the designer. This formulation achieves good accuracy in both modulus and phase. The proposed methodology allows the fulfilment of other current control objectives, such as current tracking, by defining frequency regions where each objective is desired. Experimental results show the good response of the proposed controller, both in frequency and time domain
Las pilas de combustible de membrana de intercambio protónico (PEM), las cuales utilizan hidrógeno como combustible, proporcionan altas densidades de potencia operando a bajas temperaturas, reduciéndose el coste de los materiales y el mantenimiento. Las pilas de combustible de tipo PEM son apropiadas para un amplio rango de aplicaciones, tales como estacionarias, de ciclo combinado (CHP), sistemas portátiles y automoción. El rendimiento y la degradación en sistemas basados en pilas de combustible de tipo PEM están importantemente influenciados por las condiciones internas.En la presente tesis doctoral, se realiza un extenso estudio de modelado y estrategias de observación y control en un sistema basado en pila de combustible de tipo PEM. El objetivo es obtener soluciones avanzadas de control que ayuden en la mejora de la durabilidad e incrementar la eficiencia de las pilas de combustible. Dichas soluciones de control tienen que tener en cuenta las condiciones internas de la pila de combustible, y utilizar esta información para operar el sistema bajo condiciones que garanticen que la degradación del sistema no se incrementa. Al mismo tiempo, los controladores deben garantizar que el sistema alcanza altas eficiencias, considerando las pérdidas por consumos parasíticos de los auxiliares del balance de la planta (BoP). Las soluciones de observación y control son evaluadas utilizando el perfil de conducción New European Driving Cycle (NEDC).La primera parte de la tesis introduce la motivación tras el presente trabajo y la estructura del documento. Se estudia en detalle el estado actual de la investigación referente a modelado, observadores de estados y estrategias de control para sistemas basados en pilas de combustible. Tras este estudio del estado del arte, se presentan los objetivos de la tesis.La segunda parte de la tesis está enfocada en el desarrollo, implementación y estudio en un entorno de simulación de un modelo de sistema basado en pila de combustible de tipo PEM. El modelo considera derivadas espaciales para representar el comportamiento de la dinámica interna de la pila. Dicha dinámica interna afecta la eficiencia y el grado de degradación del sistema. En la presente tesis, la degradación de la capa catalizadora se ve reflejada en la pérdida de área de la superficie electroquímicamente activa (ECSA). La ECSA en la capa catalizadora del cátodo (CCL) se modela utilizando un modelo bifásico de agua en el lado catódico de la pila, con el objeto de representar fielmente su efecto en el voltaje de salida.Una vez que las ecuaciones del modelo son presentadas, observadores no-lineales de parámetros distribuidos (NDPO) basados en modelo se desarrollan en la tercera parte del presente trabajo. Primero, las ecuaciones en derivadas parciales (PDE) de la pila de combustible de tipo PEM son discretizadas y reformuladas para obtener el modelo de observación.Utilizando este modelo, se presentan y comparan dos novedosos enfoques de control por modos deslizantes (SMC) para la observación de las condiciones internas de la pila de combustible.La cuarta parte de la presente tesis está dedicada al control predictivo basado en modelos del sistema de pila de combustible de tipo PEM. En particular, se propone una estrategia de controlador predictivo no-lineal basado en modelo (NMPC) para la mejora de la eficiencia y a la vez, la mejora del ciclo de vida de la pila de combustible. El uso de los NDPOs en el esquema de control suministra información crítica acerca de las condiciones internas en la pila de combustible. Este hecho permite el diseño de objetivos de control avanzados que no serían realizables utilizando únicamente las limitadas mediciones que están disponibles en los sistemas basados en pilas de combustible de tipo PEM.La quinta y última parte de la tesis está dedicada a la extracción de conclusiones.
Barban, F.; Annicchiarico, R.; Melideo, M.; Barrue, C.; Cortes, C.; Cortes, A. Brain sciences Vol. 7, num. 2, p. 19-1-19-14 DOI: 10.3390/brainsci7020019 Data de publicació: 2017-02-10 Article en revista
Background. Falling is a major clinical problem in elderly people, demanding effective solutions. At present, the only effective intervention is motor training of balance and strength. Executive function-based training (EFt) might be effective at preventing falls according to evidence showing a relationship between executive functions and gait abnormalities. The aim was to assess the effectiveness of a motor and a cognitive treatment developed within the EU co-funded project I-DONT-FALL. Methods. In a sample of 481 elderly people at risk of falls recruited in this multicenter randomised controlled trial, the effectiveness of a motor treatment (pure motor or mixed with EFt) of 24 one-hour sessions delivered through an i-Walker with a non-motor treatment (pure EFt or control condition) was evaluated. Similarly, a 24 one-hour session cognitive treatment (pure EFt or mixed with motor training), delivered through a touch-screen computer was compared with a non-cognitive treatment (pure motor or control condition). Results. Motor treatment, particularly when mixed with EFt, reduced significantly fear of falling (F(1,478) = 6.786, p = 0.009) although to a limited extent (ES -0.25) restricted to the period after intervention. Conclusions. This study suggests the effectiveness of motor treatment empowered by EFt in reducing fear of falling.
A thorough analysis of continuous adventitious sounds (CAS) can provide distinct and complementary information about bronchodilator response (BDR), beyond that provided by spirometry. Nevertheless, previous approaches to CAS analysis were limited by certain methodology issues. The aim of this study is to propose a new integrated approach to CAS analysis that contributes to improving the assessment of BDR in clinical practice for asthma patients.
Respiratory sounds and flow were recorded in 25 subjects, including 7 asthma patients with positive BDR (BDR+), assessed by spirometry, 13 asthma patients with negative BDR (BDR-), and 5 controls. A total of 5149 acoustic components were characterized using the Hilbert spectrum, and used to train and validate a support vector machine classifier, which distinguished acoustic components corresponding to CAS from those corresponding to other sounds. Once the method was validated, BDR was assessed in all participants by CAS analysis, and compared to BDR assessed by spirometry.
BDR+ patients had a homogenous high change in the number of CAS after bronchodilation, which agreed with the positive BDR by spirometry, indicating high reversibility of airway obstruction. Nevertheless, we also found an appreciable change in the number of CAS in many BDR- patients, revealing alterations in airway obstruction that were not detected by spirometry. We propose a categorization for the change in the number of CAS, which allowed us to stratify BDR- patients into three consistent groups. From the 13 BDR- patients, 6 had a high response, similar to BDR+ patients, 4 had a noteworthy medium response, and 1 had a low response.
In this study, a new non-invasive and integrated approach to CAS analysis is proposed as a high-sensitive tool for assessing BDR in terms of acoustic parameters which, together with spirometry parameters, contribute to improving the stratification of BDR levels in patients with obstructive pulmonary diseases.
Calvo, M.; Gomis, P.; Romero, D.; Le Rolle, V.; Béhar, N.; Mabo, P.; Hernández, A. Physiological measurement Vol. 38, num. 2, p. 387-396 DOI: 10.1088/1361-6579/aa513c Data de publicació: 2017-02-01 Article en revista
Symptoms such as ventricular arrhythmias in Brugada syndrome (BS) typically occur at rest, especially during sleep, suggesting that the autonomic nervous system (ANS) function may be relevant in the arrhythmogenesis of the disease. The aim of this work was to assess the ANS response captured by a non-linear heart rate variability (HRV) measure in 69 patients diagnosed with BS, who underwent a standardized physical stress test. Heart rate complexity (HRC) was evaluated by the power-law scaling analysis (Beta slope) during rest, exercise, recovery and rest post-recovery, in order to discriminate between symptomatic and asymptomatic BS patients. Symptomatic patients showed a significant reduction in HRC in comparison to asymptomatic subjects, after exertion (p=0.015); during the whole recovery period (p=0.023), and, in particular within the passive recovery phase (p=0.025), as well as during rest post-recovery (p=0.022). Based on these results, symptoms could be associated with a lower ANS complexity during the stress test stages where parasympathetic activity is predominant. Therefore, the proposed HRV indicators could be of help in the risk stratification of asymptomatic patients.
Among Parkinson’s disease (PD) symptoms, freezing of gait (FoG) is one of the most debilitating. To assess FoG, current clinical practice mostly employs repeated evaluations over weeks and months based on questionnaires, which may not accurately map the severity of this symptom. The use of a non-invasive system to monitor the activities of daily living (ADL) and the PD symptoms experienced by patients throughout the day could provide a more accurate and objective evaluation of FoG in order to better understand the evolution of the disease and allow for a more informed decision-making process in making adjustments to the patient’s treatment plan. This paper presents a new algorithm to detect FoG with a machine learning approach based on Support Vector Machines (SVM) and a single tri-axial accelerometer worn at the waist. The method is evaluated through the acceleration signals in an outpatient setting gathered from 21 PD patients at their home and evaluated under two different conditions: first, a generic model is tested by using a leave-one-out approach and, second, a personalised model that also uses part of the dataset from each patient. Results show a significant improvement in the accuracy of the personalised model compared to the generic model, showing enhancement in the specificity and sensitivity geometric mean (GM) of 7.2%. Furthermore, the SVM approach adopted has been compared to the most comprehensive FoG detection method currently in use (referred to as MBFA in this paper). Results of our novel generic method provide an enhancement of 11.2% in the GM compared to the MBFA generic model and, in the case of the personalised model, a 10% of improvement with respect to the MBFA personalised model. Thus, our results show that a machine learning approach can be used to monitor FoG during the daily life of PD patients and, furthermore, personalised models for FoG detection can be used to improve monitoring accuracy.
Perez-Sala, X.; De La Torre, F.; Igual, L.; Escalera, S.; Angulo, C. International journal of computer vision Vol. 121, num. 3, p. 327-343 DOI: 10.1007/s11263-016-0938-x Data de publicació: 2017-02 Article en revista
Procrustes analysis (PA) has been a popular technique to align and build 2-D statistical models of shapes. Given a set of 2-D shapes PA is applied to remove rigid transformations. Later, a non-rigid 2-D model is computed by modeling the residual (e.g., PCA). Although PA has been widely used, it has several limitations for modeling 2-D shapes: occluded landmarks and missing data can result in local minima solutions, and there is no guarantee that the 2-D shapes provide a uniform sampling of the 3-D space of rotations for the object. To address previous issues, this paper proposes subspace PA (SPA). Given several instances of a 3-D object, SPA computes the mean and a 2-D subspace that can model rigid and non-rigid deformations of the 3-D object. We propose a discrete (DSPA) and continuous (CSPA) formulation for SPA, assuming that 3-D samples of an object are provided. DSPA extends the traditional PA, and produces unbiased 2-D models by uniformly sampling different views of the 3-D object. CSPA provides a continuous approach to uniformly sample the space of 3-D rotations, being more efficient in space and time. We illustrate the benefits of SPA in two different applications. First, SPA is used to learn 2-D face and body models from 3-D datasets. Experiments on the FaceWarehouse and CMU motion capture (MoCap) datasets show the benefits of our 2-D models against the state-of-the-art PA approaches and conventional 3-D models. Second, SPA learns an unbiased 2-D model from CMU MoCap dataset and it is used to estimate the human pose on the Leeds Sports dataset.
Serra, M.; Ocampo-Martinez, C.A.; Li, M.; Llorca, J. International journal of hydrogen energy Vol. 42, num. 4, p. 1949-1961 DOI: 10.1016/j.ijhydene.2016.10.110 Data de publicació: 2017-01-26 Article en revista
This paper focuses on the dynamic modelling and the predictive control of an ethanol steam reformer (ESR) with Pdsingle bondAg membrane separation stage for the generation of pure hydrogen. Hydrogen purity necessary to feed a proton exchange membrane fuel cell (PEMFC) is required. A non-linear dynamic model of the ESR is developed together with a procedure for adjusting the model parameters in order to fit a bank of experimental data of a real ESR system. Static and dynamic analysis of the non-linear ESR model is presented. From this non-linear model, a linear, reduced order and discretised model is derived and a model predictive controller (LMPC) is designed for the ESR system. Control objectives are pure hydrogen flowrate tracking and ethanol inlet minimization. Comparisons between the non-linear and linear models are carried out to determine the control constraints. Finally, simulation results for the implemented LMPC controller are presented and discussed.
Current approaches do not allow robots to execute a task and simultaneously convey emotions to users using their body motions. This paper explores the capabilities of the Jacobian null space of a humanoid robot to convey emotions. A task priority formulation has been implemented in a Pepper robot which allows the specification of a primary task (waving gesture, transportation of an object, etc.) and exploits the kinematic redundancy of the robot to convey emotions to humans as a lower priority task. The emotions, defined by Mehrabian as points in the pleasure–arousal–dominance space, generate intermediate motion features (jerkiness, activity and gaze) that carry the emotional information. A map from this features to the joints of the robot is presented. A user study has been conducted in which emotional motions have been shown to 30 participants. The results show that happiness and sadness are very well conveyed to the user, calm is moderately well conveyed, and fear is not well conveyed. An analysis on the dependencies between the motion features and the emotions perceived by the participants shows that activity correlates positively with arousal, jerkiness is not perceived by the user, and gaze conveys dominance when activity is low. The results indicate a strong influence of the most energetic motions of the emotional task and point out new directions for further research. Overall, the results show that the null space approach can be regarded as a promising mean to convey emotions as a lower priority task.
Incipient Alzheimer’s Disease (AD) is characterized by a slow onset of clinical symptoms, with pathological brain changes starting several years earlier. Consequently, it is necessary to first understand and differentiate age-related changes in brain regions in the absence of disease, and then to support early and accurate AD diagnosis. However, there is poor understanding of the initial stage of AD; seemingly healthy elderly brains lose matter in regions related to AD, but similar changes can also be found in non-demented subjects having mild cognitive impairment (MCI). By using a Linear Mixed Effects approach, we modelled the change of 166 Magnetic Resonance Imaging (MRI)-based biomarkers available at a 5-year follow up on healthy elderly control (HC, n = 46) subjects. We hypothesized that, by identifying their significant variant (vr) and quasi-variant (qvr) brain regions over time, it would be possible to obtain an age-based null model, which would characterize their normal atrophy and growth patterns as well as the correlation between these two regions. By using the null model on those subjects who had been clinically diagnosed as HC (n = 161), MCI (n = 209) and AD (n = 331), normal age-related changes were estimated and deviation scores (residuals) from the observed MRI-based biomarkers were computed. Subject classification, as well as the early prediction of conversion to MCI and AD, were addressed through residual-based Support Vector Machines (SVM) modelling. We found reductions in most cortical volumes and thicknesses (with evident gender differences) as well as in sub-cortical regions, including greater atrophy in the hippocampus. The average accuracies (ACC) recorded for men and women were: AD-HC: 94.11%, MCI-HC: 83.77% and MCI converted to AD (cAD)-MCI non-converter (sMCI): 76.72%. Likewise, as compared to standard clinical diagnosis methods, SVM classifiers predicted the conversion of cAD to be 1.9 years earlier for females (ACC:72.5%) and 1.4 years earlier for males (ACC:69.0%).
Ochoa, J. F.; Alonso, J.F.; Duque, J.; Tobón, C.; Mañanas, M.A.; Lopera, F.; Hernández, A.M. Journal of alzheimers disease Vol. 55, num. 3, p. 1195-1205 DOI: 10.3233/JAD-160803 Data de publicació: 2017-01-01 Article en revista
Background: Recent studies report increases in neural activity in brain regions critical to episodic memory at preclinical stages of Alzheimer’s disease (AD). Although electroencephalography (EEG) is widely used in AD studies, given its non-invasiveness and low cost, there is a need to translate the findings in other neuroimaging methods to EEG.
Objective: To examine how the previous findings using functional magnetic resonance imaging (fMRI) at preclinical stage in presenilin-1 E280A mutation carriers could be assessed and extended, using EEG and a connectivity approach.
Methods: EEG signals were acquired during resting and encoding in 30 normal cognitive young subjects, from an autosomal dominant early-onset AD kindred from Antioquia, Colombia. Regions of the brain previously reported as hyperactive were used for connectivity analysis.
Results: Mutation carriers exhibited increasing connectivity at analyzed regions. Among them, the right precuneus exhibited the highest changes in connectivity.
Conclusion: Increased connectivity in hyperactive cerebral regions is seen in individuals, genetically-determined to develop AD, at preclinical stage. The use of a connectivity approach and a widely available neuroimaging technique opens the possibility to increase the use of EEG in early detection of preclinical AD.
Efficient management of a drinking water network reduces the economic costs related to water production and transport (pumping). Model predictive control (MPC) is nowadays a quite well-accepted approach for the efficient management of the water networks because it allows formulating the control problem in terms of the optimization of the economic costs. Therefore, short-term forecasts are a key issue in the performance of MPC applied to water distribution networks. However, the short-term horizon demand forecast in a horizon of 24 hours in an hourly based scale presents some challenges as the water consumption can change from one day to another, according to certain patterns of behavior (e.g., holidays and business days). This paper focuses on the problem of forecasting water demand for the next 24 hours. In this work, we propose to use a bank of models instead of a single model. Each model is designed for forecasting one particular hour. Hourly models use artificial neural networks. The architecture design and the training process are performed using genetic algorithms. The proposed approach is assessed using demand data from the Barcelona water network.
García, D.; Creus, R.; Minoves, M.; Pardo, X.; Quevedo, J.; Puig, V. Journal of hydroinformatics Vol. 19, num. 1, p. 123-137 DOI: 10.2166/hydro.2016.048 Data de publicació: 2017-01-01 Article en revista
Water quality management is a key area to guarantee drinking water safety to users. This task is based on disinfection techniques, such as chlorination, applied to the drinking water network to prevent the growth of microorganisms present in the water. The continuous monitoring of water quality parameters is fundamental to assess the sanitary conditions of the drinking water and to detect unexpected events. The whole process is based on the assumption that the information retrieved from quality sensors is totally reliable, but due to the complexity of the calibration and maintenance of these chemical sensors, several factors affect the accuracy of the raw data collected. Consequently, any decision might be based on a non-solid base. Therefore, this work presents a data analytics monitoring methodology based on temporal and spatial models to discover if a sensor is detecting a real change in water quality parameters or is actually providing inconsistent information due to some malfunction. The methodology presented anticipated by 12.4 days, on average, the detection of a sensor problem before the fault was reported by the water utilities expert using knowledge accumulated with visual analysis. The proposed methodology has been satisfactorily tested on the Barcelona drinking water network.
In recent years, many researchers have addressed the issue of making Unmanned Aerial Vehicles (UAVs) more and more autonomous. In this context, the state estimation of the vehicle position is a fundamental necessity for any application involving autonomy. However, the problem of position estimation could not be solved in some scenarios, even when a GPS signal is available, for instance, an application requiring performing precision manoeuvres in a complex environment. Therefore, some additional sensory information should be integrated into the system in order to improve accuracy and robustness. In this work, a novel vision-based simultaneous localization and mapping (SLAM) method with application to unmanned aerial vehicles is proposed. One of the contributions of this work is to design and develop a novel technique for estimating features depth which is based on a stochastic technique of triangulation. In the proposed method the camera is mounted over a servo-controlled gimbal that counteracts the changes in attitude of the quadcopter. Due to the above assumption, the overall problem is simplified and it is focused on the position estimation of the aerial vehicle. Also, the tracking process of visual features is made easier due to the stabilized video. Another contribution of this work is to demonstrate that the integration of very noisy GPS measurements into the system for an initial short period of time is enough to initialize the metric scale. The performance of this proposed method is validated by means of experiments with real data carried out in unstructured outdoor environments. A comparative study shows that, when compared with related methods, the proposed approach performs better in terms of accuracy and computational time.
Brunel, H.; Massanet, R.; Martinez, A.; Ziyatdinov, A.; Martin-Fernandez, L.; Souto, J.; Perera, A.; Soria, J. PLoS one Vol. 11, num. 12, p. 1-14 DOI: 10.1371/journal.pone.0167187 Data de publicació: 2016-12-22 Article en revista
Traditional genetic studies of single traits may be unable to detect the pleiotropic effects involved in complex diseases. To detect the correlation that exists between several phenotypes involved in the same biological process, we introduce an original methodology to analyze sets of correlated phenotypes involved in the coagulation cascade in genome-wide association studies. The methodology consists of a two-stage process. First, we define new phenotypic meta-variables (linear combinations of the original phenotypes), named metaphenotypes, by applying Independent Component Analysis for the multivariate analysis of correlated phenotypes (i.e. the levels of coagulation pathway–related proteins). The resulting
metaphenotypes integrate the information regarding the underlying biological process (i.e. thrombus/clot formation). Secondly, we take advantage of a family based Genome Wide Association Study to identify genetic elements influencing these metaphenotypes and consequently thrombosis risk. Our study utilized data from the GAIT Project (Genetic Analysis of Idiopathic Thrombophilia). We obtained 15 metaphenotypes, which showed significant heritabilities, ranging from 0.2 to 0.7. These results indicate the importance of genetic factors in the variability of these traits. We found 4 metaphenotypes that showed significant associations with SNPs. The most relevant were those mapped in a region near the HRG, FETUB and KNG1 genes. Our results are provocative since they show that the KNG1 locus plays a central role as a genetic determinant of the entire coagulation pathway and thrombus/clot formation. Integrating data from multiple correlated measurements through metaphenotypes is a promising approach to elucidate the hidden genetic mechanisms underlying complex diseases.
Knowledge of the location of muscle Innervation Zones (IZs) is important in many applications, e.g. for minimizing the quantity of injected botulinum toxin for the treatment of spasticity or for deciding on the type of episiotomy during child delivery. Surface EMG (sEMG) can be noninvasively recorded to assess physiological and morphological characteristics of contracting muscles. However, it is not often possible to record signals of high quality. Moreover, muscles could have multiple IZs, which should all be identified. We designed a fully-automatic algorithm based on the enhanced image Graph-Cut segmentation and morphological image processing methods to identify up to five IZs in 60-ms intervals of very-low to moderate quality sEMG signal detected with multi-channel electrodes (20 bipolar channels with Inter Electrode Distance (IED) of 5 mm). An anisotropic multilayered cylinder model was used to simulate 750 sEMG signals with signal-to-noise ratio ranging from -5 to 15 dB (using Gaussian noise) and in each 60-ms signal frame, 1 to 5 IZs were included. The micro- and macro- averaged performance indices were then reported for the proposed IZ detection algorithm. In the micro-averaging procedure, the number of True Positives, False Positives and False Negatives in each frame were summed up to generate cumulative measures. In the macro-averaging, on the other hand, precision and recall were calculated for each frame and their averages are used to determine F1-score. Overall, the micro (macro)-averaged sensitivity, precision and F1-score of the algorithm for IZ channel identification were 82.7% (87.5%), 92.9% (94.0%) and 87.5% (90.6%), respectively. For the correctly identified IZ locations, the average bias error was of 0.02±0.10 IED ratio. Also, the average absolute conduction velocity estimation error was 0.41±0.40 m/s for such frames. The sensitivity analysis including increasing IED and reducing interpolation coefficient for time samples was performed. Meanwhile, the effect of adding power-line interference and using other image interpolation methods on the deterioration of the performance of the proposed algorithm was investigated. The average running time of the proposed algorithm on each 60-ms sEMG frame was 25.5±8.9 (s) on an Intel dual-core 1.83 GHz CPU with 2 GB of RAM. The proposed algorithm correctly and precisely identified multiple IZs in each signal epoch in a wide range of signal quality and is thus a promising new offline tool for electrophysiological studies.
Perez, C.; Sama, A.; Rodriguez-Martin, D.; Catala, A.; Cabestany, J.; Moreno, J.; De Mingo, E.; Rodríguez, A. Sensors Vol. 16, num. 12, p. 1-25 DOI: 10.3390/s16122132 Data de publicació: 2016-12-15 Article en revista
Altered movement control is typically the first noticeable symptom manifested by
Parkinson’s disease (PD) patients. Once under treatment, the effect of the medication is very patent
and patients often recover correct movement control over several hours. Nonetheless, as the disease
advances, patients present motor complications. Obtaining precise information on the long-term
evolution of these motor complications and their short-term fluctuations is crucial to provide
optimal therapy to PD patients and to properly measure the outcome of clinical trials. This paper
presents an algorithm based on the accelerometer signals provided by a waist sensor that has been
validated in the automatic assessment of patient’s motor fluctuations (ON and OFF motor states)
during their activities of daily living. A total of 15 patients have participated in the experiments in
ambulatory conditions during 1 to 3 days. The state recognised by the algorithm and the motor state
annotated by patients in standard diaries are contrasted. Results show that the average specificity
and sensitivity are higher than 90%, while their values are higher than 80% of all patients, thereby
showing that PD motor status is able to be monitored through a single sensor during daily life of
patients in a precise and objective way.
Larriba, F.; Raya, C.; Angulo, C.; Albo-Canals, J.; Diaz, M.; Boldú, R. Biomedical engineering online Vol. 16, num. Supl. 1:S72, p. 187-196 DOI: 10.1186/s12938-016-0180-3 Data de publicació: 2016-12-01 Article en revista
Background:This PATRICIA research project is about using pet robots to reduce pain
and anxiety in hospitalized children. The study began 2 years ago and it is believed that
the advances made in this project are significant. Patients, parents, nurses, psycholo-
gists, and engineers have adopted the Pleo robot, a baby dinosaur robotic pet, which
works in different ways to assist children during hospitalization.
Methods: Focus is spent on creating a wireless communication system with the Pleo
in order to help the coordinator, who conducts therapy with the child, monitor, under-
stand, and control Pleo’s behavior at any moment. This article reports how this techno-
logical function is being developed and tested.
Results: Wireless communication between the Pleo and an Android device is
achieved. The developed Android app allows the user to obtain any state of the robot
without stopping its interaction with the patient. Moreover, information is sent to a
cloud, so that robot moods, states and interactions can be shared among different
Conclusions: Pleo attachment was successful for more than 1 month, working with
children in therapy, which makes the investment capable of positive therapeutic
possibilities. This technical improvement in the Pleo addresses two key issues in social
robotics: needing an enhanced response to maintain the attention and engagement of
the child, and using the system as a platform to collect the states of the child’s progress
for clinical purposes.
This paper proposes a new approach to control the grid-side current of LCL-grid connected voltage source converters using hysteretic relay feedback controllers. The closed loop system is stabilized by designing a local feedback around the relay element. The compensator allows the use of relay feedback controllers by making the controlled plant almost strictly positive real. The article proposes the use of the locus of the perturbed relay system as analysis and design tool and studies orbital stability for several plant and controller conditions. The approach is validated by means of simulation testing.
La isquemia de miocardio, consecuencia de la enfermedad de las arterias coronarias, es el principal problema de salud que conduce a infarto de miocardio, arritmias y muerte. El sistema nervioso autónomo (SNA) regula la mayoría de funciones del organismo, entre éstas la frecuencia cardiaca. La caracterización de la complejidad de la respuesta del SNA con técnicas multifractales sobre la serie de intervalos RR, y su análisis antes y durante isquemia de miocardio inducida es un aporte valioso para la comprensión de los mecanismos autónomos de control cardiaco. Sin embargo, hasta el momento, el uso de tales técnicas en el diagnóstico clínico de enfermedades como la isquemia de miocardio y otras ha sido prácticamente nulo. Así mismo, el uso de índices derivados de los intervalos de despolarización y repolarización ventricular en la señal de electrocardiograma (ECG) es una de las vías habituales de detección de enfermedades isquémicas cardiacas.El trabajo desarrollado en esta tesis tiene tres objetivos fundamentales. El primero es caracterizar la respuesta no lineal del SNA durante isquemia de miocardio inducida a través del análisis de la dinámica no lineal presente en las fluctuaciones de la serie de intevalos RR de corta duración, utilizando métodos de análisis fractal (mono y multifractal) y técnicas de surrogate data. El segundo objetivo es analizar índices de repolarización y despolarización ventricular a partir de la señal de ECG de alta resolución para identificar cuáles de éstos describen mejor los episodios de isquemia de miocardio en términos de la potencia estadística discriminatoria de cada uno por separado, del tamaño del efecto, así como su grado de interacción. Por último, costruir un modelo altamente robusto de predicción de isquemia e infarto de miocardio (MI) con métodos de aprendizaje automático basados en las medidas anteriormente identificadas.La base de datos empleada en esta tesis contiene señales de ECG de pacientes admitidos a angioplastia coronaria electiva para aumentar la luz de las arterias coronarias obstruidas. Los síntomas observados durante la oclusión coronaria completa producida por el inflado del balón de angioplastia son similares a los encontrados en pacientes que sufren MI, de ahí que la técnica es un modelo excelente para estudiar la isquemia y el MI. El procedimiento practicado aquí es único pues la duración de la oclusión ha sido más larga que lo habitual, lo cual permite estudiar episodios de isquemia transitoria de miocardio y la fase temprana de un MI.En general, los resultados obtenidos muestran un aumento en la complejidad multifractal y la no linealidad del SNA durante isquemia, lo cual ha sido interpretado como un mecanismo de adaptación beneficioso para incrementar el flujo de sangre hacia las zonas afectadas del miocardio. El análisis desde la perspectiva de la evaluación de los cambios autónomos a través de la serie de intervalos RR de corta duración representa un enfoque novedoso en el estudio de la base de datos aquí empleada. Ello, unido a la particularidad de la angioplastia practicada, brinda una relevancia especial a la investigación. Por otra parte, se ha observado que muchos de los índices de repolarización y despolarización están muy relacionados entre sí y por tanto aportan poco valor diferente al de otros que han resultado ser más significativos para identificar pacientes con isquemia en riesgo de sufrir MI. Finalmente, la cuidadosa metodología seguida para crear los diferentes modelos de predicción ha permitido que el modelo construido con las medidas más importantes tenga una mejor capacidad de generalización que la de otros modelos desarrollados previamente. El modelo puede servir como complemento a otros métodos estándar de diagnóstico de este tipo enfermedades.
High-intensity physical activity for an extended period of time could result in unhealthy disruptions to physiological systems. The main goal of the SUMMIT LabÂ® is to offer to ultra-endurance athletes an assessment of the impact of training with biochemical comparison of pre- and after-race measurements through biochemical (markers of muscle damage, immunity status and cardiovascular system) and physiological responses [localized muscle bioimpedance (LBIA)], cardiac function and differential gene expression. Samples were obtained from 300 participants who completed ultra-distance runs in Europe from 2012 to 2016. We found: (1) CK levels provide a gross indication of muscle-fiber damage but are not specific, they canâ€™t identify the extent of the injury or the type of fibers affected; (2) LBIA serves to track the effects of training or a race on muscle damage, and enables longitudinal assessment of muscle injury through recovery and return to training. (3) Some individuals showed worse right ventricle adaptation during exercise independent from the training volume done or the elite profile. (4) Elite performance is mainly related with training. We have evidence that we can measure quantitatively the effect of training through a specific gene expression profile. Possible additional applications include runners, soccer, army physical condition, among others.
Gas chromatography–mass spectrometry (GC–MS) produces large and complex datasets characterized by co-eluted compounds and at trace levels, and with a distinct compound ion-redundancy as a result of the high fragmentation by the electron impact ionization. Compounds in GC–MS can be resolved by taking advantage of the multivariate nature of GC–MS data by applying multivariate resolution methods. However, multivariate methods have to be applied in small regions of the chromatogram, and therefore chromatograms are segmented prior to the application of the algorithms. The automation of this segmentation process is a challenging task as it implies separating between informative data and noise from the chromatogram. This study demonstrates the capabilities of independent component analysis–orthogonal signal deconvolution (ICA–OSD) and multivariate curve resolution–alternating least squares (MCR–ALS) with an overlapping moving window implementation to avoid the typical hard chromatographic segmentation. Also, after being resolved, compounds are aligned across samples by an automated alignment algorithm. We evaluated the proposed methods through a quantitative analysis of GC–qTOF MS data from 25 serum samples. The quantitative performance of both moving window ICA–OSD and MCR–ALS-based implementations was compared with the quantification of 33 compounds by the XCMS package. Results shown that most of the R2 coefficients of determination exhibited a high correlation (R2 > 0.90) in both ICA–OSD and MCR–ALS moving window-based approaches.
En este artículo se describe el desarrollo de una aplicación web mediante R que permite acceder fácilmente a la información almacenada en una base de datos SQL compleja construida a partir de datos fisiológicos de rendimiento y genéticos de cinco carreras diferentes de ultra-trail, constituyendo una población total de estudio de 170 participantes. Esta aplicación ofrece el acceso interactivo a las diferentes tablas de la base de datos, muestra información estadística y descriptiva de los datos almacenados y permite la visualización de gráficas de evolución y modelos de tendencia construidos a partir de los mismos.
El objetivo de esta aplicación es extender el uso de esta información y su análisis a todos los implicados en la investigación en este ámbito, y a largo plazo, desarrollar un repositorio de datos fisiológicos en intervenciones deportivas y permitir la investigación multi-prueba en deporte de élite.
Arjona, M.; Giner, A.; Diaz-Douton, F.; Aldaba , M.; García, C.; Sanabria, F.; Pujol, J. Iberoamerican Optics Meeting and Latin American Meeting on Optics, Lasers, and Applications p. 246-247 Data de presentació: 2016-11-21 Presentació treball a congrés
In order to optimize the trade-off between components life and energy consumption, the integration of a system health management and control modules is required. This paper proposes the integration of model predictive control (MPC) with a fatigue estimation approach that minimizes the damage of the components of a pasteurization plant. The fatigue estimation is assessed with the rainflow counting algorithm. Using data from this algorithm, a simplified model that characterizes the health of the system is developed and integrated with MPC. The MPC controller objective is modified by adding an extra criterion that takes into account the accumulated damage. But, a steady-state offset is created by adding this extra criterion. Finally, by including an integral action in the MPC controller, the steady-state error for regulation purpose is eliminated. The proposed control scheme is validated in simulation using a simulator of a utility-scale pasteurization plant.
Rovira, A.; Auger, C.; Alberich, M.; Pareto, D.; Sastre-Garriga, J.; Tintoré, M.; Montalban, X.; Aymerich, F.X. Reunión Anual Sociedad Española Neurología p. 77-78 Data de presentació: 2016-11-18 Presentació treball a congrés
This paper presents a nonlinear model predictive control (NMPC) strategy combined with constraint satisfactions for a quadcopter. The full dynamics of the quadcopter describing the attitude and position are nonlinear, which are quite sensitive to changes of inputs and disturbances. By means of constraint satisfactions, partial nonlinearities and modeling errors of the control-oriented model of full dynamics can be transformed into the inequality constraints. Subsequently, the quadcopter can be controlled by an NMPC controller with the updated constraints generated by constraint satisfactions. Finally, the simulation results applied to a quadcopter simulator are provided to show the e ectiveness of he proposed strategy.
In this study we discuss the application of robust optimization to the problem of economic energy dispatch in smart grids. Robust optimization based MPC strategies for tackling uncertain load demands are developed. Unexpected additive disturbances are modelled by defining an affine dependence between the control inputs and the uncertain load demands.
The developed strategies were applied to a hybrid power system connected to an electrical power grid. Furthermore, to demonstrate the superiority of the standard Economic MPC over the MPC tracking, a comparison (e.g average daily cost) between the standard MPC tracking, the standard Economic MPC, and the integration of both in one-layer and two-layer approaches was carried out. The goal of this research is to design a controller based on Economic MPC
strategies, that tackles uncertainties, in order to minimise economic costs and guarantee service reliability of the system.
Segovia, P.; Rajaoarisoa, L.H.; Nejjari, F.; Blesa, J.; Puig, V.; Duviella, E. European Workshop on Advanced Control and Diagnosis p. 012018- DOI: 10.1088/1742-6596/783/1/012018 Data de presentació: 2016-11-17 Presentació treball a congrés
Inland waterways are large-scale networks used principally for navigation. Even if the transport planning is an important issue, the water resource management is a crucial point. Indeed, navigation is
not possible when there is too little or too much water inside the waterways. Hence, the water resource management of waterways has to be particularly efficient in a context of climate change and increase of water demand. This management has to be done by considering different time and space scales and still requires the development of new methodologies and tools in the topics of the Control and Informatics communities. This work addresses the problem of waterways management in terms of modeling, control, diagnosis and fault-tolerant control by focusing in the inland waterways of the north of France. A review of proposed tools and the ongoing research topics are provided in this paper.
This paper proposes the use of interval observers and viability theory in fault detection and isolation (FDI). Viability theory develops mathematical and algorithmic methods for investigating the
adaptation to viability constraints of evolutions governed by complex systems under uncertainty. These methods can be used for checking the consistency between observed and predicted behavior by using simple sets that approximate the exact set of possible behavior (in the parameter or state space). In this paper, fault detection is based on checking for an inconsistency between the measured and predicted behaviors using viability theory concepts and sets. Finally, an example is provided in order to show the usefulness of the proposed approach.