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Our research team previously developed an accelerometry-based device, which can be worn on the waist during daily life activities and detects the occurrence of dyskinesia in patients with Parkinson’s disease. The goal of this study was to analyze the magnitude of correlation between the numeric output of the device algorithm and the results of the Unified Dyskinesia Rating Scale (UDysRS), administered by a physician. In this study, 13 Parkinson’s patients, who were symptomatic with dyskinesias, were monitored with the device at home, for an average period of 30 minutes, while performing normal daily life activities. Each patient’s activity was simultaneously video-recorded. A physician was in charge of reviewing the recorded videos and determining the severity of dyskinesia through the UDysRS for every patient. The sensor device yielded only one value for dyskinesia severity, which was calculated by averaging the recorded device readings. Correlation between the results of physician’s assessment and the sensor output was analyzed with the Spearman’s correlation coefficient. The correlation coefficient between the sensor output and UDysRS result was 0.70 (CI 95%: 0.33–0.88; p = 0.01). Since the sensor was located on the waist, the correlation between the sensor output and the results of the trunk and legs scale sub-items was calculated: 0.91 (CI 95% 0.76–0.97: p

Balance impairment is a major mechanism behind falling along with environmental hazards. Under physiological conditions, ageing leads to a progressive decline in balance control per se. Moreover, various neurological disorders further increase the risk of falls by deteriorating specific nervous system functions contributing to balance. Over the last 15 years, significant advancements in technology have provided wearable solutions for balance evaluation and the management of postural instability in patients with neurological disorders. This narrative review aims to address the topic of balance and wireless sensors in several neurological disorders, including Alzheimer’s disease, Parkinson’s disease, multiple sclerosis, stroke, and other neurodegenerative and acute clinical syndromes. The review discusses the physiological and pathophysiological bases of balance in neurological disorders as well as the traditional and innovative instruments currently available for balance assessment. The technical and clinical perspectives of wearable technologies, as well as current challenges in the field of teleneurology, are also examined.

e-offprint for personal use only and shall not be selfarchived in electronic repositories. Posture transitions are one of the most mechanically demanding tasks and are useful to evaluate the motor status of patients with motor impairments, frail individuals or the elderly, among others. So far, wearable inertial systems have been one of the most employed tools in the study of these movements due to their suitable size and weight, being non-invasive systems. These devices are mainly composed of accelerometers and, to a lesser extent, gyroscopes, magnetometers or barometers. Although accelerometers provide the most reliable measurement, detecting activities where a change of altitude is observed, such as some posture transitions, may require additional sensors to reliably detect these activities. In this work, we present an algorithm that combines the information of a barometer and an accelerometer to detect posture transitions and falls. In contrast to other works, we test different activities (where altitude is involved) in order to achieve a reliable classifier against false positives. Furthermore, by means of feature selection methods, we obtain optimal subsets of features for the accelerometer and barometer sensors to contextualise these activities. The selected features are tested through several machine learning classifiers, which are assessed with an evaluation data set. Results show that the inclusion of barometer features in addition to those obtained for an accelerometer clearly enhances the detection accuracy up to a 11%, in terms of geometric mean between sensitivity and specificity, compared to algorithms where only the accelerometer is used. Finally, we have also analysed the computer burden; in this sense, the usage of barometers, in addition to increase the accuracy, also reduces the computational resources required to classify a new pattern, as shown by a reduction in the number of support vectors.

The treatment of Parkinson's disease (PD) with levodopa is very effective. However, over time, motor complications (MCs) appear, restricting the patient from leading a normal life. One of the most disabling MCs is ON-OFF fluctuations. Gathering accurate information about the clinical status of the patient is essential for planning treatment and assessing its effect. Systems such as the REMPARK system, capable of accurately and reliably monitoring ON-OFF fluctuations, are of great interest. Objective To analyze the ability of the REMPARK System to detect ON-OFF fluctuations. Methods Forty-one patients with moderate to severe idiopathic PD were recruited according to the UK Parkinson’s Disease Society Brain Bank criteria. Patients with motor fluctuations, freezing of gait and/or dyskinesia and who were able to walk unassisted in the OFF phase, were included in the study. Patients wore the REMPARK System for 3 days and completed a diary of their motor state once every hour. Results The record obtained by the REMPARK System, compared with patient-completed diaries, demonstrated 97% sensitivity in detecting OFF states and 88% specificity (i.e., accuracy in detecting ON states). Conclusion The REMPARK System detects an accurate evaluation of ON-OFF fluctuations in PD; this technology paves the way for an optimisation of the symptomatic control of PD motor symptoms as well as an accurate assessment of medication efficacy.

Among Parkinson’s disease (PD) motor symptoms, freezing of gait (FOG) may be the most incapacitating. FOG episodes may result in falls and reduce patients’ quality of life. Accurate assessment of FOG would provide objective information to neurologists about the patient’s condition and the symptom’s characteristics, while it could enable non-pharmacologic support based on rhythmic cues. This paper is, to the best of our knowledge, the first study to propose a deep learning method for detecting FOG episodes in PD patients. This model is trained using a novel spectral data representation strategy which considers information from both the previous and current signal windows. Our approach was evaluated using data collected by a waist-placed inertial measurement unit from 21 PD patients who manifested FOG episodes. These data were also employed to reproduce the state-of-the-art methodologies, which served to perform a comparative study to our FOG monitoring system. The results of this study demonstrate that our approach successfully outperforms the state-of-the-art methods for automatic FOG detection. Precisely, the deep learning model achieved 90% for the geometric mean between sensitivity and specificity, whereas the state-of-the-art methods were unable to surpass the 83% for the same metric.

Parkinson’s Disease (PD) is a neurodegenerative disorder that manifests with motor and non-motor symptoms. PD treatment is symptomatic and tries to alleviate the associated symptoms through an adjustment of the medication. As the disease is evolving and this evolution is patient specific, it could be very difficult to properly manage the disease. The current available technology (electronics, communication, computing, etc.), correctly combined with wearables, can be of great use for obtaining and processing useful information for both clinicians and patients allowing them to become actively involved in their condition. Parkinson’s Disease Management through ICT: The REMPARK Approach presents the work done, main results and conclusions of the REMPARK project (2011 – 2015) funded by the European Union under contract FP7-ICT-2011-7-287677. REMPARK system was proposed and developed as a real Personal Health Device for the Remote and Autonomous Management of Parkinson’s Disease, composed of different levels of interaction with the patient, clinician and carers, and integrating a set of interconnected sub-systems: sensor, auditory cueing, Smartphone and server. The sensor subsystem, using embedded algorithmics, is able to detect the motor symptoms associated with PD in real time. This information, sent through the Smartphone to the REMPARK server, is used for an efficient management of the disease. Implementation of REMPARK will increase the independence and Quality of Life of patients; and improve their disease management, treatment and rehabilitation.

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. Part of this project has been performed within the framework of the MASPARK project which is funded by La Fundació La Marató de TV3 20140431. This work also forms part of the framework of the FP7 project REMPARK ICT-287677, which is funded by the European Community. peer-reviewed

Parkinson's Disease (PD) is a neurodegenerative disorder that manifests with motor and non-motor symptoms. PD treatment is symptomatic and tries to alleviate the associated symptoms through an adjustment of the medication. As the disease is evolving and this evolution is patient specific, it could be very difficult to properly manage the disease. The current available technology (electronics, communication, computing, etc.), correctly combined with wearables, can be of great use for obtaining and processing useful information for both clinicians and patients allowing them to become actively involved in their condition. Parkinson's Disease Management through ICT: The REMPARK Approach presents the work done, main results and conclusions of the REMPARK project (2011 - 2015) funded by the European Union under contract FP7-ICT-2011-7-287677. REMPARK system was proposed and developed as a real Personal Health Device for the Remote and Autonomous Management of Parkinson's Disease, composed of different levels of interaction with the patient, clinician and carers, and integrating a set of interconnected sub-systems: sensor, auditory cueing, Smartphone and server. The sensor subsystem, using embedded algorithmics, is able to detect the motor symptoms associated with PD in real time. This information, sent through the Smartphone to the REMPARK server, is used for an efficient management of the disease. Implementation of REMPARK will increase the independence and Quality of Life of patients; and improve their disease management, treatment and rehabilitation.

IWANN is a biennial conference that seeks to provide a discussion forum for scientists, engineers, educators and students about the latest ideas and realizations in the foundations, theory, models and applications of hybrid systems inspired on nature (neural networks, fuzzy logic and evolutionary systems) as well as in emerging areas related to the above items. As in previous editions of IWANN, it also aims to create a friendly environment that could lead to the establishment of scientific collaborations and exchanges among attendees. Since the first edition in Granada (LNCS 540, 1991), the conference has evolved and matured, and most of the topics involved have achieved a maturity and reinforced consolidation. The twelveth edition of the IWANN conference “International Work-Conference on Artificial Neural Networks” was held in Puerto de la Cruz, Tenerife, (Spain) during June 12–14, 2013. The list of topics in the successive Call for Papers has also evolved, resulting in the following list for the present edition

This paper describes a Fault Tolerance System (FTS) implemented in a new self-adaptive hardware architecture. This architecture is based on an array of cells that implements in a distributed way self-adaptive capabilities. The cell includes a configurable multiprocessor, so it can have between one and four processors working in parallel, with a programmable configuration mode that allows selecting the size of program and data memories. The self-elimination and self-replication capabilities of cell(s) are performed when the FTS detects a failure in any of the processors that include it, so that this cell(s) will be self-discarded for future implementations. Other adaptive capabilities of the system are self-routing, self-placement and runtime selfconfiguration. Additionally, it is described as an example application and a software tool that has been implemented to facilitate the development of applications to test the system., Este artículo describe un sistema de tolerancia a fallos (FTS) implementado en una nueva arquitectura de hardware autoadaptativa. Esta arquitectura se basa en una matriz de células que implementa de forma distribuida capacidades autoadaptativas. La célula incluye un multiprocesador configurable, por lo que puede tener entre uno y cuatro procesadores trabajando en paralelo, con un modo de configuración programable que permite seleccionar el tamaño de las memorias de programa y datos. Las capacidades de autoeliminación y autorreplicación de la(s) célula(s) se llevan a cabo cuando el FTS detecta un fallo en alguno de los procesadores que la(s) incluye, de forma que esta(s) célula(s) se autodescarta(n) para futuras implementaciones. Otras capacidades adaptativas del sistema son el autoenrutamiento, la autocolocación y la autoconfiguración en tiempo de ejecución. Además, se describe una aplicación de ejemplo y una herramienta de software que se ha implementado para facilitar el desarrollo de aplicaciones para probar el sistema.

Analysis of human body movement is an important research area, specially for health applications. In order to assess the quality of life of people with mobility problems like Parkinson’s disease o stroke patients, it is crucial to monitor and assess their daily life activities. The main goal of this work is the characterization of basic activities using a single triaxial accelerometer located at the waist. This paper presents a novel postural detection algorithm based in SVM methods which is able to detect and identify Walking, Stand, Sit, Lying, Sit to Stand, Stand to sit, Bending up/down, Lying from Sit and Sit from Lying transitions with a sensitivity of 97% and specificity of 84% with 2884 postures analyzed from 31 healthy volunteers. Parameters and models found have been tested in another dataset from Parkinson’s disease patients, achieving results of 98% of sensitivity and 78% of specificity in postural transitions. The proposed algorithm has been optimized to be easily implemented in real-time system for on-line monitoring applications.

Inertial measurement units (IMUs) are devices used, among other fields, in health applications, since they are light, small and effective. More concretely, IMUs have been demonstrated to be useful in the monitoring of motor symptoms of Parkinson's disease (PD). In this sense, most of previous works have attempted to assess PD symptoms in controlled environments or short tests. This paper presents the design of an IMU, called 9 × 3, that aims to assess PD symptoms, enabling the possibility to perform a map of patients' symptoms at their homes during long periods. The device is able to acquire and store raw inertial data for artificial intelligence algorithmic training purposes. Furthermore, the presented IMU enables the real-time execution of the developed and embedded learning models. Results show the great flexibility of the 9 × 3, storing inertial information and algorithm outputs, sending messages to external devices and being able to detect freezing of gait and bradykinetic gait. Results obtained in 12 patients exhibit a sensitivity and specificity over 80%. Additionally, the system enables working 23 days (at waking hours) with a 1200 mAh battery and a sampling rate of 50 Hz, opening up the possibility to be used for other applications like wellbeing and sports.

HighlightsThe necessary algorithms to evaluate the occurrence of dopaminergic-induced dyskinesias in the activities of daily life are developed.Sensor placement at the waist provides a good resolution for almost any choreic dyskinesias and provides a good usability and comfort to the patient.A new frequency based approach is proposed to evaluate the occurrence of dyskinesias.The algorithm presented has been evaluated on a database of signals of 92 PD patients and provides specificities and sensitivities above 90%. BackgroundAfter several years of treatment, patients with Parkinson's disease (PD) tend to have, as a side effect of the medication, dyskinesias. Close monitoring may benefit patients by enabling doctors to tailor a personalised medication regimen. Moreover, dyskinesia monitoring can help neurologists make more informed decisions in patient's care. ObjectiveTo design and validate an algorithm able to be embedded into a system that PD patients could wear during their activities of daily living with the purpose of registering the occurrence of dyskinesia in real conditions. Materials and methodsData from an accelerometer positioned in the waist are collected at the patient's home and are annotated by experienced clinicians. Data collection is divided into two parts: a main database gathered from 92 patients used to partially train and to evaluate the algorithms based on a leave-one-out approach and, on the other hand, a second database from 10 patients which have been used to also train a part of the detection algorithm. ResultsResults show that, depending on the severity and location of dyskinesia, specificities and sensitivities higher than 90% are achieved using a leave-one-out methodology. Although mild dyskinesias presented on the limbs are detected with 95% specificity and 39% sensitivity, the most important types of dyskinesia (any strong dyskinesia and trunk mild dyskinesia) are assessed with 95% specificity and 93% sensitivity. ConclusionThe presented algorithmic method and wearable device have been successfully validated in monitoring the occurrence of strong dyskinesias and mild trunk dyskinesias during activities of daily living.

Identification of activities of daily living is essential in order to evaluate the quality of life both in the elderly and patients with mobility problems. Posture transitions (PT) are one of the most mechanically demanding activities in daily life and, thus, they can lead to falls in patients with mobility problems. This paper deals with PT recognition in Parkinson's disease (PD) patients by means of a triaxial accelerometer situated between the anterior and the left lateral part of the waist. Since sensor's orientation is susceptible to change during long monitoring periods, a hierarchical structure of classifiers is proposed in order to identify PT while allowing such orientation changes. Results are presented based on signals obtained from 20 PD patients and 67 healthy people who wore an inertial sensor on different positions among the anterior and the left lateral part of the waist. The algorithm has been compared to a previous approach in which only the anterior-lateral location was analyzed improving the sensitivity while preserving specificity. Moreover, different supervised machine learning techniques have been evaluated in distinguishing PT. Results show that the location of the sensor slightly affects method's performance and, furthermore, PD motor state does not alter its accuracy. Posture transition identification is performed by means of a tri-axial accelerometer located in the waist.A hierarchical structure of classifiers allows to determine the human posture.SVM techniques have been used to set parameters of the algorithm.The algorithm allows different locations along waist's left side.The algorithm is focused on Parkinson's disease patients.

Electrochemical sensors, like ion-selective field transistors (ISFET), are electronic devices that merge solid-state electronic technology with chemical sensors so as to be sensitive to the concentration of a particular ion in a solution. However, as it has been previously reported, their response does not only depend on a single ion but also is affected by several interfering ions found in the solution to be measured. These interfering ions can be considered as noise and consequently, a post-processing stage that increases the SNR is obligatory. Our work shows how ensemble learning methods could be used in an array of chemical sensors in order to deal with this problem. In particular, we introduce a novel neural learning architecture for ISFET arrays, which employ ISFET models as prior knowledge. The proposed ensemble learning systems are RBF-like solutions based on bagging and optimal linear combination. Several experimental results are included, which demonstrate the interest and viability of the proposed solution.

Ensemble learning is a well-established method for improving the generalization performance of learning machines. The idea is to combine a number of learning systems that have been trained in the same task. However, since all the members of the ensemble are operating at the same time, large amounts of memory and long execution times are needed, limiting its practical application. This paper presents a new method (called local averaging) in the context of nearest neighbor (NN) classifiers that generates a classifier from the ensemble with the same complexity as the individual members. Once a collection of prototypes is generated from different learning sessions using a Kohonen's LVQ algorithm, a single set of prototypes is computed by applying a cluster algorithm (such as K-means) to this collection. Local averaging can be viewed either as a technique to reduce the variance of the prototypes or as the result of averaging a series of particular bootstrap replicates. Experimental results using several classification problems confirm the utility of the method and show that local averaging can compute a single classifier that achieves a similar (or even better) accuracy than ensembles generated with voting.

In the present issue of Neurocomputing, it is apleasure to present you a collection of 12 extended versions of selected papers from the 10 the dition of the International Work Conference on Artificial Neural Networks (IWANN2009). This is a conference held every two year in Spain, and focus in gon the foundations, theory, models and applications of systems, which are inspired by nature (e.g.neural networks, fuzzy logic and evolutionary systems).

This letter addresses the asymptotic convergence of Kohonen's LVQ1 algorithm when the number of training samples are finite with an analysis that uses the dynamical systems and optimisation theories. It establishes the sufficient conditions to ensure the convergence of LVQ1 near a minimum of its cost function for constant step sizes and cyclic sampling. It also proposes a batch version of LVQ1 based on the very fast Newton optimisation method that cancels the dependence of the on-line version on the order of supplied training samples.

This paper introduces a learning strategy for designing a set of prototypes for a 1-nearest-neighbour (NN) classifier. In learning phase, we transform the 1-NN classifier into a maximum classifier whose discriminant functions use the nearest models of a mixture. Then the computation of the set of prototypes is viewed as a problem of estimating the centres of a mixture model. However, instead of computing these centres using standard procedures like the EM algorithm, we derive to compute a learning algorithm based on minimising the misclassification accuracy of the 1-NN classifier on the training set. One possible implementation of the learning algorithm is presented. It is based on the online gradient descent method and the use of radial gaussian kernels for the models of the mixture. Experimental results using hand-written NIST databases show the superiority of the proposed method over Kohonen's LVQ algorithms.

We introduce a batch learning algorithm to design the set of prototypes of 1 nearest-neighbour classifiers. Like Kohonen's LVQ algorithms, this procedure tends to perform vector quantization over a probability density function that has zero points at Bayes borders. Although it differs significantly from their online counterparts since: (1) its statistical goal is clearer and better defined; and (2) it converges superlinearly due to its use of the very fast Newton's optimization method. Experiments results using artificial data confirm faster training time and better classification performance than Kohonen's LVQ algorithms.

Developed for the VLSI implementation of neural network models, our novel analog architecture adds flexibility and adaptability by incorporating digital processing capabilities. Its systolic-based architecture avoids static storage of analog values by transferring the activation values through the chip's processing units. This proposed combination of analog and digital technologies produces a densely packed, high-speed, scalable architecture, designed to easily accommodate learning capabilities. >

This paper evaluates a set of computational algorithms for the automatic estimation of human postures and gait properties from signals provided by an inertial body sensor. The use of a single sensor device imposes limitations for the automatic estimation of relevant properties, like step length and gait velocity, as well as for the detection of standard postures like sitting or standing. Moreover, the exact location and orientation of the sensor is also a common restriction that is relaxed in this study. Based on accelerations provided by a sensor, known as the `9 2', three approaches are presented extracting kinematic information from the user motion and posture. Firstly, a two-phases procedure implementing feature extraction and Support Vector Machine based classi cation for daily living activity monitoring is presented. Secondly, Support Vector Regression is applied on heuristically extracted features for the automatic computation of spatiotemporal properties during gait. Finally, sensor information is interpreted as an observation of a particular trajectory of the human gait dynamical system, from which a reconstruction space is obtained, and then transformed using standard principal components analysis, nally Support Vector Regression is used for prediction. Daily living Activities are detected and spatiotemporal parameters of human gait are estimated using methods sharing a common structure based on feature extraction and kernel methods. The approaches presented are susceptible to be used for medical purposes.

This paper describes a Fault Tolerance System (FTS) implemented in a new self-adaptive hardware architecture. This architecture is based on an array of cells that implements in a distributed way self-adaptive capabilities. The cell includes a configurable multiprocessor, so it can have between one and four processors working in parallel, with a programmable configuration mode that allows selecting the size of program and data memories. The self-elimination and self-replication capabilities of cell(s) are performed when the FTS detects a failure in any of the processors that include it, so that this cell(s) will be self-discarded for future implementations. Other self-adaptive capabilities of the system are self-routing, self-placement and runtime self-configuration.

Towards establishing electrical interfaces with patterned in vitro neurons, we have previously described the fabrication of hybrid elastomer-glass devices polymer-on-multielectrode array technology and obtained single-electrode recordings of extracellular potentials from confined neurons (Claverol-Tinturé et al., 2005). Here, we demonstrate the feasibility of spatially localized multisite recordings from individual microchannel-guided neurites extending from microwell-confined somas with good signal-to-noise ratios (20 dB) and spike magnitudes of up to 300 microV. Single-cell current source density (scCSD) analysis of the spatio-temporal patterns of membrane currents along individual processes is illustrated.

10 pages, 7 figures, 4 tables, The present study reviewed some of the critical pre-processing steps required for otolith shape characterisation for automatic classification with heterogeneous distributed data. A common procedure for optimising automatic classification is to apply data pre-processing in order to reduce the dimension of vector inputs. One of the key aspects of these pre-processing methods is the type of codification method used for describing the otolith contour. Two types of codification methods (Cartesian and Polar) were evaluated, and the limitations (loss of information) and the benefits (invariance to affine transformations) associated with each method were pointed out. The comparative study was developed using four types of shape descriptors (morphological, statistical, spectral and multiscale), and focused on data codification techniques and their effects on extracting shape features for automatic classification. A new method derived from the Karhunen–Loève transformation was proposed as the main procedure for standardising the codification of the otolith contours., The present study was funded by the IBACS project (European Union Project QLRT-2001-01610), by the Spanish project AVG-ION (McyT-TIC2000-0376-p4-04) from the Spanish Ministry of Science and Technology and by the IBACS European project.

Background Assessment of pain and physical function is complex in patients with knee osteoarthritis (OA), as standard criteria are lacking.A previous study examining correlations between functional capacity and pain (WOMAC) and anthropometric characteristics and periarticular knee structure (quantified by ultrasound imaging) in females with knee OA found increased quadriceps muscle density was associated with higher functional disability and pain scores, suggesting that not only joint wear and symptom severity are involved and more objective measures are necessary. Objectives To determine and compare the periarticular knee structure in obese patients with knee OA and a healthy control group. Methods Analytical case-control study. Study group. Patients diagnosed with knee OA. Control group . Adults with no history of knee involvement, able to walk normally, with no pain or functional difficulties on examination and no history of surgery in other lower limb joints. Controls were matched for age, sex and body mass index (BMI). Sociodemographic, clinical, functional (Timed Up and Go test [TUG]) and anthropometric (weight, height, BMI, waist circumference, and lower limb [suprapatellar and infrapatellar indices]) data were collected. Periarticular knee structure was assessed by ultrasound (thickness of subcutaneous fat [distance from skin to fascia, in mm] and quadriceps/rectus femoris [distance between fascia and femur, in mm]) and appearance [density on digital image analysis according to Maurits et al]). Statistical Analysis . Groups were compared using the t test for continuous variables and χ 2 test for categorical variables. Results 66 lower limbs from 14 patients (mean age 62.7 [SD 8.6]) years, BMI 30.4 (SD 5.9) and 19 matched controls (mean age 62.6 [SD 8.1] years, BMI 30.1 [SD 4.7]) were evaluated. Comparison between groups: no significant differences in anthropometric measures were found. TUG took a mean 13.7s (6.7) and 9.9s (2.4) in patients and controls, respectively, p=0.002. Mean subcutaneous fat was 18.7 (SD 9.8) mm and 15.2 (4.41) in patients and controls, respectively, p=0.028. Mean quadriceps muscle density was 61.1 (25.9) and 41.7 (13.7), respectively, p=0.001. Conclusions Between-group differences were found in the periarticular knee structure. Patients with knee OA had increased subcutaneous fat thickness and quadriceps muscle density was observed compared with controls. These findings suggest that the assessment of periarticular structures in these patients analyzed by digital image derived from ultrasound could add a variable to determine more objectively uniform methods in the classification of patients and evaluation of results. References Maurits NM et al. Muscle ultrasound analysis: normal values and differentiation between myopathies and neuropathies. Ultrasound Med Biol 2003;29:215-25. Disclosure of Interest None declared DOI 10.1136/annrheumdis-2014-eular.3136

In this paper we shall review the common problems associated with Piecewise Linear Separation incremental algorithms. This kind of neural models yield poor performances when dealing with some classification problems, due to the evolving schemes used to construct the resulting networks. So as to avoid this undesirable behavior we shall propose a modification criterion. It is based upon the definition of a function which will provide information about the quality of the network growth process during the learning phase. This function is evaluated periodically as the network structure evolves, and will permit, as we shall show through exhaustive benchmarks, to considerably improve the performance (measured in terms of network complexity and generalization capabilities) offered by the networks generated by these incremental models.

10 pages, 9 figures, 3 tables, Fish otolith morphology has been closely related to landmark selection in order to establish the most discriminating points that can help to differentiate or find common characteristics in sets of otolith images. Fourier analysis has traditionally been used to represent otolith images, since it can reconstruct a version of the contour that is close to the original by choosing a reduced set of harmonic terms. However, it is difficult to locate the contour’s singularities from this spectrum. As an alternative, wavelet transform and curvature scale space representation allow us to quantify the irregularities of the contour and determine its precise position. These properties make these techniques suitable for pattern recognition purposes, ageing, stock determination and species identification studies. In the present study both techniques are applied and used in an otolith classification system that shows robustness against affine image transformations, shears and the presence of noise. The results are interpreted and discussed in relation to traditional morphology studies., The current work was supported by the Spanish project MYCYT TIC2000-0376-P4-04.

A microprocessor-based solar cell standard characteristics measurement system is described. Data aquisition and digital conversion of the current-to-voltage characteristics and the spectral response allows the performance of several operations as averaging, storage, minicomputer connection, and parameter determination of the theoretical models introduced. Several results are shown concerning the accuracy of the method on the determination of series resistance, reverse saturation current, and the minority carriers lifetime on the base region of solar cells.

2014 A new and simple technique to evaluate the series resistance of a solar cell is described. This procedure only needs dark I(V) measurements and a simple experimental-arrangement including a calibrated standard resistor. Comparison with other commonly used methods is also discussed. Revue Phys. Appl. 18 (1983) 565-567 SEPTEMBRE 1983, : Classification Physics Abstracts 73.40L The series resistance of a solar cell is a parameter of special interest because of its influence in the maximum available power and fill factor. It is also a parameter that indicates in some way the quality of the device and can be used as production test There exists a number of methods to evaluate the series resistance of the cells based on I(V) characteristics, most of them using more than one I(V) curve (Refs. 1, 2). The new method is based on the dark I(V) characteristics model that can be written with the help of figure 1, where R. is the series resistance, Rsh is the shunt resistance and Dl and D2 are the two diodes accounting for Shockley diffusion term (Dl), and Space Charge Region recombination term (D2). through Dl and the second, i = 2, is the current that flows through D2. The last term takes into account the shunt current which can be an important effect for low bias levels. Our method is based in the experimental arrangement shown in figure 2 where only the addition of an external calibrated resistor Rext is needed Obviously, the model given in equation 1 is still valid and only RS must-be replaced by (Rs + Rext). Series resistance effects appears for the higher voltage values where the term given by i = 2 and the last one can be neglected in front of the diffusion term modified by the series resistance effect.

In order to prevent and reduce water pollution, promote a sustainable use, protect the environment and enhance the status of aquatic ecosystems, this article deals with the application of advanced mathematical techniques designed to aid in the management of records of different water quality monitoring networks. These studies include the development of a software tool for decision support, based on the application of fuzzy logic techniques, which can indicate water quality episodes from the behaviour of variables measured at continuous automatic water control networks. Using a few physicalchemical variables recorded continuously, the expert system is able to obtain water quality phenomena indicators, which can be associated, with a high probability of cause-effect relationship, with human pressure on the water environment, such as urban discharges or diffuse agricultural pollution. In this sense, at the proposed expert system, automatic water quality control networks complement manual sampling of official administrative networks and laboratory analysis, providing information related to specific events (discharges) or continuous processes (eutrophication, fish risk) which can hardly be detected by discrete sampling.

This paper describes a new self-adaptive hardware architecture with fault tolerance capabilities and a development system that allows the creation of applications. This bioinspired architecture is based on an array of cells with capacity for parallel processing, which implements in a distributed way self-adaptive capabilities, like self-routing, self-placement and runtime self-configuration. This cell array together with a component-level routing constitutes a SANE (Self-Adaptive Networked Entity). An integrated development environment and a physical prototype based on two FPGA boards has been built in order to assess the features of the proposed architecture.

FATE is a project funded by the European Union under the program CIP/ICT-PSP with the main objective of organizing a big pilot on the automatic falls detection in aging people living at home. Automatic detection of falls is done in indoors and outdoors conditions, and in both cases the detection generates an alarm sent to a call center. The detection system is designed around a sensor sub-system based on accelerometers and gyroscopes able to detect falls with a high reliability. The complete system is based on a communications layer based in ZigBee and Bluetooth protocols. The gateway for sending the alarm to the call center is a mobile phone. Pilots are organized in three different countries (Spain, Italy and Ireland) where different models of health service and implemented call centers are available. Pilots duration will be one year, involving 175 users and one of the main final objectives is to gain experience with the integration of an automatic fall detection service in an already care/health existing service.