Desarrollo y versatilidad del algoritmo de discretización Ameva.

Esta tesis presentada como un compendio de artículos, analiza el problema de reconocimiento de actividades y detección de caídas en dispositivos móviles donde el consumo de batería y la precisión del sistema son las principales áreas de investigación. Estos problemas se abordan mediante el establecimiento de un nuevo algoritmo de selección, discretización y clasificación basado en el núcleo del algoritmo Ameva. Gracias al proceso de discretización, se obtiene un sistema eficiente en términos de energía y precisión. El nuevo algoritmo de reconocimiento de actividad ha sido diseñado para ejecutarse en dispositivos móviles y smartphones, donde el consumo de energía es la característica más importante a tener en cuenta. Además, el algoritmo es eficiente en términos de precisión dando un resultado en tiempo real. Estas características se probaron tanto en una amplia gama de dispositivos móviles utilizando diferentes datasets del estado del arte en reconocimiento de actividades así como en escenarios reales como la competición EvAAL donde personas no relacionadas con el equipo de investigación llevaron un smartphone con el sistema desarrollado. En general, ha sido posible lograr un equilibrio entre la precisión y el consumo de energía. El algoritmo desarrollado se presentó en el track de reconocimiento de actividades de la competición EvAAL (Evaluation of Ambient Assisted Living Systems through Competitive Benchmarking), que tiene como objetivo principal la medición del rendimiento de hardware y software. El sistema fue capaz de detectar las actividades a través del conjunto establecido de puntos de referencia y métricas de evaluación. Se desarrolló para varias clases de actividades y obtiene una gran precisión cuando hay aproximadamente el dataset está balanceado en cuanto al número de ejemplos para cada clase durante la fase de entrenamiento. La solución logró el primer premio en la edición de 2012 y el tercer premio en la edición de 2013.This thesis, presented as a set of research papers, studies the problem of activity recognition and fall detection in mobile systems where the battery draining and the accuracy are the main areas of researching. These problems are tackled through the establishment of a new selection, discretization and classification algorithm based on the core of the algorithm Ameva. Thanks to the discretization process, it allows to get an efficient system in terms of energy and accuracy. The new activity recognition algorithm has been designed to be run in mobile systems, smartphones, where the energy consumption is the most important feature to take into account. Also, the algorithm had to be efficient in terms of accuracy giving an output in real time. These features were tested both in a wide range of mobile devices by applying usage data from recognized databases and in some real scenarios like the EvAAL competition where non-related people carried a smartphone with the developed system. In general, it had therefore been possible to achieve a trade-off between accuracy and energy consumption. The developed algorithm was presented in the Activity Recognition track of the competition EvAAL (Evaluation of Ambient Assisted Living Systems through Competitive Benchmarking), which has as main objective the measurement of hardware and software performance. The system was capable of detecting some activities through the established set of benchmarks and evaluation metrics. It has been developed for multi-class datasets and obtains a good accuracy when there is approximately the same number of examples for each class during the training phase. The solution achieved the first award in 2012 competition and the third award in 2013 edition

Mobile activity recognition and fall detection system for elderly people using Ameva algorithm

Currently, the lifestyle of elderly people is regularly monitored in order to establish guidelines for rehabilitation processes or ensure the welfare of this segment of the population. In this sense, activity recognition is essential to detect an objective set of behaviors throughout the day. This paper describes an accurate, comfortable and efficient system, which monitors the physical activity carried out by the user. An extension to an awarded activity recognition system that participated in the EvAAL 2012 and EvAAL 2013 competitions is presented. This approach uses data retrieved from accelerometer sensors to generate discrete variables and it is tested in a non-controlled environment. In order to achieve the goal, the core of the algorithm Ameva is used to develop an innovative selection, discretization and classification technique for activity recognition. Moreover, with the purpose of reducing the cost and increasing user acceptance and usability, the entire system uses only a smartphone to recover all the information requiredMinisterio de Economía y Competitividad HERMES TIN2013-46801-C4-1-rJunta de Andalucía Simon P11-TIC-8052Junta de Andalucía M-Learning P11-TIC-712

Low Energy Physical Activity Recognition System on Smartphones

An innovative approach to physical activity recognition based on the use of discrete variables obtained from accelerometer sensors is presented. The system first performs a discretization process for each variable, which allows efficient recognition of activities performed by users using as little energy as possible. To this end, an innovative discretization and classification technique is presented based on the 2 distribution. Furthermore, the entire recognition process is executed on the smartphone, which determines not only the activity performed, but also the frequency at which it is carried out. These techniques and the new classification system presented reduce energy consumption caused by the activity monitoring system. The energy saved increases smartphone usage time to more than 27 h without recharging while maintaining accuracy.Ministerio de Economía y Competitividad TIN2013-46801-C4-1-rJunta de Andalucía TIC-805

An adaptive methodology to discretize and select features

A lot of significant data describing the behavior or/and actions of systems can be collected in several domains. These data define some aspects, called features, that can be clustered in several classes. A qualitative or quantitative value for each feature is stored from measurements or observations. In this paper, the problem of finding independent features for getting the best accuracy on classification problems is considered. Obtaining these features is the main objective of this work, where an automatic method to select features is proposed. The method extends the functionality of Ameva coefficient to use it in other tasks of machine learning where it has not been defined.Ministerio de Ciencia e Innovación ARTEMISA TIN2009-14378-C02-01Junta de Andalucia Simon TIC-805

Discrete techniques applied to low-energy mobile human activity recognition. A new approach

Human activity recognition systems are currently implemented by hundreds of applications and, in recent years, several technology manufacturers have introduced new wearable devices for this purpose. Battery consumption constitutes a critical point in these systems since most are provided with a rechargeable battery. In this paper, by using discrete techniques based on the Ameva algorithm, an innovative approach for human activity recognition systems on mobile devices is presented. Furthermore, unlike other systems in current use, this proposal enables recognition of high granularity activities by using accelerometer sensors. Hence, the accuracy of activity recognition systems can be increased without sacrificing efficiency. A comparative is carried out between the proposed approach and an approach based on the well-known neural networks.Junta de Andalucia Simon TIC-805

Activity Recognition System Using AMEVA Method

This article aims to develop a minimally intrusive system of care and monitoring. Furthermore, the goal is to get a cheap, comfortable and, especially, efficient system which controls the physical activity car ried out by the user. For this purpose an innovative approach to physical activity recognition is presented, based on the use of discrete variables which employ data from accelerometer sensors. To this end, an innova tive discretization and classification technique to make the recognition process in an efficient way and at low energy cost, is presented in this work based on the χ2 distribution. Entire process is executed on the smartphone, by means of taking the system energy consumption into ac count, thereby increasing the battery lifetime and minimizing the device recharging frequency.Ministerio de Economía y Competitividad TIN2009-14378-C02-01 (ARTEMISA)Junta de Andalucía TIC-8052 (Simon

Porous organic polymers containing active metal centers for Suzuki–Miyaura heterocoupling reactions

Producción CientíficaA new generation of confined palladium(II) catalysts covalently attached inside of porous organic polymers (POPs) has been attained. The synthetic approach employed was straightforward, and there was no prerequisite for making any modification of the precursor polymer. First, POP-based catalytic supports were obtained by reacting one symmetric trifunctional aromatic monomer (1,3,5-triphenylbenzene) with two ketones having electron-withdrawing groups (4,5-diazafluoren-9-one, DAFO, and isatin) in superacidic media. The homopolymers and copolymers were made using stoichiometric ratios between the functional groups, and they were obtained with quantitative yields after the optimization of reaction conditions. Moreover, the number of chelating groups (bipyridine moieties) available to bind Pd(II) ions to the catalyst supports was modified using different DAFO/isatin ratios. The resulting amorphous polymers and copolymers showed high thermal stability, above 500 °C, and moderate–high specific surface areas (from 760 to 935 m2 g–1), with high microporosity contribution (from 64 to 77%). Next, POP-supported Pd(II) catalysts were obtained by simple immersion of the catalyst supports in a palladium(II) acetate solution, observing that the metal content was similar to that theoretically expected according to the amount of bipyridine groups present. The catalytic activity of these heterogeneous catalysts was explored for the synthesis of biphenyl and terphenyl compounds, via the Suzuki–Miyaura cross-coupling reaction using a green solvent (ethanol/water), low palladium loads, and aerobic conditions. The findings showed excellent catalytic activity with quantitative product yields. Additionally, the recyclability of the catalysts, by simply washing it with ethanol, was excellent, with a sp2–sp2 coupling yield higher than 95% after five cycles of use. Finally, the feasibility of these catalysts to be employed in tangible organic reactions was assessed. Thus, the synthesis of a bulky compound, 4,4′-dimethoxy-5′-tert-butyl-m-terphenylene, which is a precursor of a thermal rearrangement monomer, was scaled-up to 2 g, with high conversion and 96% yield of the pure product.Agencia Estatal de Investigación (projects PID2019-109403RB-C22, MAT2016-76413-C2-R2, CTQ2017-89217- P and MAT2016-76413-C2-R1)Junta de Castilla y León (project VA038G18

Biochemical markers of bone turnover and clinical outcome in patients with renal cell and bladder carcinoma with bone metastases following treatment with zoledronic acid: The TUGAMO study

Background: Levels of bone turnover markers (BTM) might be correlated with outcome in terms of skeletal-related events (SRE), disease progression, and death in patients with bladder cancer (BC) and renal cell carcinoma (RCC) with bone metastases (BM). We try to evaluate this possible correlation in patients who receive treatment with zoledronic acid (ZOL). Methods: This observational, prospective, and multicenter study analysed BTM and clinical outcome in these patients. Serum levels of bone alkaline phosphatase (BALP), procollagen type I amino-terminal propeptide (PINP), and beta-isomer of carboxyterminal telopeptide of type I collagen (b-CTX) were analysed. Results: Patients with RCC who died or progressed had higher baseline b-CTX levels and those who experienced SRE during follow-up showed high baseline BALP levels. In BC, a poor rate of survival was related with high baseline b-CTX and BALP levels, and new SRE with increased PINP levels. Cox univariate analysis showed that b-CTX levels were associated with higher mortality and disease progression in RCC and higher mortality in BC. Bone alkaline phosphatase was associated with increased risk of premature SRE appearance in RCC and death in BC. Conclusion: Beta-isomer of carboxy-terminal telopeptide of type I collagen and BALP can be considered a complementary tool for prediction of clinical outcomes in patients with BC and RCC with BM treated with ZOLNovartis Oncology Spain for supporting this stud

Usefulness of bone turnover markers as predictors of mortality risk, disease progression and skeletal-related events appearance in patients with prostate cancer with bone metastases following treatment with zoledronic acid: TUGAMO study

Owing to the limited validity of clinical data on the treatment of prostate cancer (PCa) and bone metastases, biochemical markers are a promising tool for predicting survival, disease progression and skeletal-related events (SREs) in these patients. The aim of this study was to evaluate the predictive capacity of biochemical markers of bone turnover for mortality risk, disease progression and SREs in patients with PCa and bone metastases undergoing treatment with zoledronic acid (ZA). Methods: This was an observational, prospective and multicenter study in which ninety-eight patients were included. Patients were treated with ZA (4mg every 4 weeks for 18 months). Data were collected at baseline and 3, 6, 9, 12, 15 and 18 months after the beginning of treatment. Serum levels of bone alkaline phosphtase (BALP), aminoterminal propeptide of procollagen type I (P1NP) and beta-isomer of carboxiterminal telopeptide of collagen I (b-CTX) were analysed at all points in the study. Data on disease progression, SREs development and survival were recorded. Results: Cox regression models with clinical data and bone markers showed that the levels of the three markers studied were predictive of survival time, with b-CTX being especially powerful, in which a lack of normalisation in visit 1 (3 months after the beginning of treatment) showed a 6.3-times more risk for death than in normalised patients. Levels of these markers were also predictive for SREs, although in this case BALP and P1NP proved to be better predictors. We did not find any relationship between bone markers and disease progression. Conclusion: In patients with PCa and bone metastases treated with ZA, b-CTX and P1NP can be considered suitable predictors for mortality risk, while BALP and P1NP are appropriate for SREs. The levels of these biomarkers 3 months after the beginning of treatment are especially importantThis study was supported by Novartis Oncology Spai

Izaña Atmospheric Research Center. Activity Report 2019-2020

Editors: Emilio Cuevas, Celia Milford and Oksana Tarasova.[EN]The Izaña Atmospheric Research Center (IARC), which is part of the State Meteorological Agency of Spain (AEMET), is a site of excellence in atmospheric science. It manages four observatories in Tenerife including the high altitude Izaña Atmospheric Observatory. The Izaña Atmospheric Observatory was inaugurated in 1916 and since that date has carried out uninterrupted meteorological and climatological observations, contributing towards a unique 100-year record in 2016. This reports are a summary of the many activities at the Izaña Atmospheric Research Center to the broader community. The combination of operational activities, research and development in state-of-the-art measurement techniques, calibration and validation and international cooperation encompass the vision of WMO to provide world leadership in expertise and international cooperation in weather, climate, hydrology and related environmental issues.[ES]El Centro de Investigación Atmosférica de Izaña (CIAI), que forma parte de la Agencia Estatal de Meteorología de España (AEMET), representa un centro de excelencia en ciencias atmosféricas. Gestiona cuatro observatorios en Tenerife, incluido el Observatorio de Izaña de gran altitud, inaugurado en 1916 y que desde entonces ha realizado observaciones meteorológicas y climatológicas ininterrumpidas y se ha convertido en una estación centenaria de la OMM. Estos informes resumen las múltiples actividades llevadas a cabo por el Centro de Investigación Atmosférica de Izaña. El liderazgo del Centro en materia de investigación y desarrollo con respecto a las técnicas de medición, calibración y validación de última generación, así como la cooperación internacional, le han otorgado una reputación sobresaliente en lo que se refiere al tiempo, el clima, la hidrología y otros temas ambientales afines