Analysis of a public repository for the study of automatic fall detection algorithms

The use of publicly available repositories containing movement traces of real or experimental subjects is a key aspect to define an evaluation framework that allows a systematic assessment of wearable fall detection systems. This papers presents a detailed analysis of a public dataset of traces which employed five sensing points to characterize the user’s mobility during the execution of ADLs (Activities of Daily Living) and emulated falls. The analysis is aimed at analysing two main factors: the importance of the election of the position of the sensor and the possible impact of the user’s personal features on the statistical characterization of the movements. Results reveal the importance of the nature of the ADL for the effectiveness of the discrimination of the falls.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

UMAFall: A Multisensor Dataset for the Research on Automatic Fall Detection

The progress in the field of inertial sensor technology and the widespread popularity of personal electronics such as smartwatches or smartphones have prompted the research on wearable Fall Detection Systems (FDSs). In spite of the extensive literature on FDSs, an open issue is the definition of a common framework that allows a methodical and agreed evaluation of fall detection policies. In this regard, a key aspect is the lack of a public repository of movement datasets that can be employed by the researchers as a common reference to compare and assess their proposals. This work describes UMAFall, a new dataset of movement traces acquired through the systematic emulation of a set of predefined ADLs (Activities of Daily Life) and falls. In opposition to other existing databases for FDSs, which only include the signals captured by one or two sensing points, the testbed deployed for the generation of UMAFall dataset incorporated five wearable sensing points, which were located on five different points of the body of the participants that developed the movements. As a consequence, the obtained data offer an interesting tool to investigate the importance of the sensor placement for the effectiveness of the detection decision in FDSs.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

A study of the influence of the sensor sampling frequency on the performance of wearable fall detectors

Last decade has witnessed a major research interest on wearable fall detection systems. Sampling rate in these detectors strongly affects the power consumption and required complexity of the employed wearables. This study investigates the effect of the sampling frequency on the efficacy of the detection process. For this purpose, we train a convolutional neural network to directly discriminate falls from conventional activities based on the raw acceleration signals captured by a transportable sensor. Then, we analyze the changes in the performance of this classifier when the sampling rate is progressively reduced. In contrast with previous studies, the detector is tested against a wide set of public repositories of benchmarking traces. The quality metrics achieved for the different frequencies and the analysis of the spectrum of the signals reveal that a sampling rate of 20 Hz can be enough to maximize the effectiveness of a fall detector.This research was funded by the Andalusian Regional Government (-Junta de Andalucía-) under grants FEDER UMA18-FEDERJA-022 and PAIDI P18-RT-1652, and by the Universidad de Málaga, Campus de Excelencia Internacional Andalucia Tech. Funding for open access charge: Universidad de Malaga / CBUA

Megaproyectos urbanos y productivos. Impactos socio-territoriales

El desarrollo de megaproyectos productivos trae consigo oportunidades para el crecimiento económico, la generación de empleos y el desarrollo regional. No obstante, en la actualidad, los grandes temas como la expansión urbana, el desarrollo industrial, las cementeras, la minería, el uso intensivo del agua y demás recursos naturales, preocupan a las comunidades por los impactos generados y porque en lo general, no consideran la racionalidad y responsabilidad ambiental y social hacia el entorno. En este contexto son diversos los estudios científicos que, en el marco de la política de económica imperante, intentan posicionarse como alternativas a proyectos económicos que confrontan los intereses particulares y comunitarios y que afectan la salud humana y ambiental. Megaproyectos urbanos y productivos. Impactos socio-territoriales, reúne veinticinco textos académicos sobre las afectaciones que éstos emprendimientos tienen para la sociedad y el entorno. Los temas expuestos recogen experiencias en el desarrollo urbano, industrial, turístico, portuario y aeroportuario, entre otros. Así mismo se retoman temas como la ética, la dialéctica, la política y la economía y su relación en el emprendimiento de megaproyectos. La búsqueda de esquemas productivos racionales y responsables con el entorno, que reivindiquen el derecho de las comunidades a un medio ambiente sano, a la preservación del territorio y sus recursos y de las formas de vida tradicionales, son los referentes para la realización del presente libro. Como elemento central se concibe el territorio como contenedor de identidad y vida, siendo preocupación y tema de estudio de la comunidad académica, las organizaciones de la sociedad civil y las redes de activistas organizados.UAEM, CONACyT, se

Analysis of Public Datasets for Wearable Fall Detection Systems

Due to the boom of wireless handheld devices such as smartwatches and smartphones, wearable Fall Detection Systems (FDSs) have become a major focus of attention among the research community during the last years. The effectiveness of a wearable FDS must be contrasted against a wide variety of measurements obtained from inertial sensors during the occurrence of falls and Activities of Daily Living (ADLs). In this regard, the access to public databases constitutes the basis for an open and systematic assessment of fall detection techniques. This paper reviews and appraises twelve existing available data repositories containing measurements of ADLs and emulated falls envisaged for the evaluation of fall detection algorithms in wearable FDSs. The analysis of the found datasets is performed in a comprehensive way, taking into account the multiple factors involved in the definition of the testbeds deployed for the generation of the mobility samples. The study of the traces brings to light the lack of a common experimental benchmarking procedure and, consequently, the large heterogeneity of the datasets from a number of perspectives (length and number of samples, typology of the emulated falls and ADLs, characteristics of the test subjects, features and positions of the sensors, etc.). Concerning this, the statistical analysis of the samples reveals the impact of the sensor range on the reliability of the traces. In addition, the study evidences the importance of the selection of the ADLs and the need of categorizing the ADLs depending on the intensity of the movements in order to evaluate the capability of a certain detection algorithm to discriminate falls from ADLs

Analysis of a Smartphone-Based Architecture with Multiple Mobility Sensors for Fall Detection with Supervised Learning

This paper describes a wearable Fall Detection System (FDS) based on a body-area network consisting of four nodes provided with inertial sensors and Bluetooth wireless interfaces. The signals captured by the nodes are sent to a smartphone which simultaneously acts as another sensing point. In contrast to many FDSs proposed by the literature (which only consider a single sensor), the multisensory nature of the prototype is utilized to investigate the impact of the number and the positions of the sensors on the effectiveness of the production of the fall detection decision. In particular, the study assesses the capability of four popular machine learning algorithms to discriminate the dynamics of the Activities of Daily Living (ADLs) and falls generated by a set of experimental subjects, when the combined use of the sensors located on different parts of the body is considered. Prior to this, the election of the statistics that optimize the characterization of the acceleration signals and the efficacy of the FDS is also investigated. As another important methodological novelty in this field, the statistical significance of all the results (an aspect which is usually neglected by other works) is validated by an analysis of variance (ANOVA)

A cross-dataset evaluation of wearable fall detection systems

The popularity of wearables and their seamless integration into our daily lives have transformed these devices into an appealing resource to deploy automatic fall detection systems. During last years, a massive literature on new methods and algorithms for these wearable detectors has been produced. However, in most cases these algorithms are tested against one single (or at best two) datasets containing signals captured from falls and conventional movements. This work evaluates the behavior of a fall detection system based on a convolutional neural network when different public repositories of movements are alternatively used for training and testing the model. After a systematic cross-dataset evaluation involving four well-known datasets, we show the difficulty of extrapolating the results achieved by a certain classifier for a particular database when another dataset is considered. Results seem to indicate that classification methods tend to overlearn the particular conditions (typology of movements, characteristics of the employed sensor, experimental subjects) under which the training samples were generated.Universidad de Málaga, Campus de Excelencia Internacional Andalucia Tech. Fondos FEDER (proyecto UMA18-FEDERJA-022), Junta de Andalucía (proyecto PAIDI P18-RT-1652

The Campus as a Smart City: University of Málaga Environmental, Learning, and Research Approaches

For the past few years, the concept of the Internet of Things (IoT) has been a recurrent view of the technological environment where nearly every object is expected to be connected to the network. This infrastructure will progressively allow one to monitor and efficiently manage the environment. Until recent years, the IoT applications have been constrained by the limited computational capacity and especially by efficient communications, but the emergence of new communication technologies allows us to overcome most of these issues. This situation paves the way for the fulfillment of the Smart-City concept, where the cities become a fully efficient, monitored, and managed environment able to sustain the increasing needs of its citizens and achieve environmental goals and challenges. However, many Smart-City approaches still require testing and study for their full development and adoption. To facilitate this, the university of Málaga made the commitment to investigate and innovate the concept of Smart-Campus. The goal is to transform university campuses into “small” smart cities able to support efficient management of their area as well as innovative educational and research activities, which would be key factors to the proper development of the smart-cities of the future. This paper presents the University of Málaga long-term commitment to the development of its Smart-Campus in the fields of its infrastructure, management, research support, and learning activities. In this way, the adopted IoT and telecommunication architecture is presented, detailing the schemes and initiatives defined for its use in learning activities. This approach is then assessed, establishing the principles for its general application

Search for Higgs and ZZ Boson Decays to J/ψγJ/\psi\gamma and Υ(nS)γ\Upsilon(nS)\gamma with the ATLAS Detector

A search for the decays of the Higgs and $Z$ bosons to $J/\psi\gamma$ and $\Upsilon(nS)\gamma$ ($n=1,2,3$) is performed with $pp$ collision data samples corresponding to integrated luminosities of up to $20.3\mathrm{fb}^{-1}$ collected at $\sqrt{s}=8\mathrm{TeV}$ with the ATLAS detector at the CERN Large Hadron Collider. No significant excess of events is observed above expected backgrounds and 95% CL upper limits are placed on the branching fractions. In the $J/\psi\gamma$ final state the limits are $1.5\times10^{-3}$ and $2.6\times10^{-6}$ for the Higgs and $Z$ bosons, respectively, while in the $\Upsilon(1S,2S,3S)\,\gamma$ final states the limits are $(1.3,1.9,1.3)\times10^{-3}$ and $(3.4,6.5,5.4)\times10^{-6}$, respectively