Un enfoque de bajo costo para monitorear la salud estructural de los puentes peatonales

Changes in dynamic properties of structures, such as damping ratios and natural frequencies can be detected by periodic monitoring (e.g. one time by year). These changes are often indications of structural damage thereby, the maintenance or demolition of the structure can be doing in due time. In the case of pedestrian bridges, people’s movements may produce a resonance state, which leads to excessive deflection that accelerates the deterioration of these structures. Typically, these dynamic properties are detected by using high-cost vibration measurement equipment to achieve high levels of precision (i.e. a very low noise levels). This article studies the measurement of dynamic properties in pedestrian bridges using a tri-axial accelerometer integrated into a mobile phone as a low-cost and alternative practice. Accelerations were recorded on a steel pedestrian bridge (flexible) and on a post-tensioned concrete pedestrian bridge (rigid) located in Barranquilla City (Colombia). Vibrations were induced by a person (e.g., by jumping). Previous studies based on traditional measuring techniques show that two dominant frequencies in both types of bridges can be identified. However, in this study a reliable damping ratio could only be established for the steel bridge that it is associated with the flexibility and the low amplitude of the induced vibrations by a single pedestrian use

Increasing Performance of Multiclass Ensemble Gradient Boost uses Newton-Raphson Parameter in Physical Activity Classifying

The sophistication of smartphones with various sensors they have can be used to recognize human physical activity by placing the smartphone on the human body. Classification of human activities, the best performance is obtained when using machine learning methods, while statistical methods such as logistic regression give poor results. However, the weakness of the logistic regression method in classifying human activities is corrected by using the ensemble technique. This paper proposes to apply the Multiclass Ensemble Gradient Boost technique to improve the performance of the Logistic Regression classification in classifying human activities such as walking, running, climbing stairs, and descending stairs. The results show that the Multiclass Ensemble Gradient Boost Classifier by Estimating the Newton-Raphson Parameter succeeded in improving the performance of logistic regression in terms of accuracy by 29.11%

Solar current output as a function of sun elevation: students as toolmakers

Solar current is an increasingly important aspect of modern life and will be even more so crucial in the students’ future. Encouraging students to be the ‘toolmakers’ allows students to take ownership of scientific investigations, as well as forcing them to refine their research questions and hypothesis the design and refinement of their tools. The modern day has seen an unprecedented opportunity for toolmaking, in the form of adapting and programming apps that use the micro-electro-mechanical sensors that are an intrinsic part of smartphone technology. A sample in-class experiment and an experimental investigation are presented; these represent an increase in toolmaking and student ownership with a corresponding decrease in teacher guidance. Toolmaking progresses from the construction of a physical sunspotter, using a hand lens and cut-off tube, using apps, to future considerations such as programming, adapting pre-existing code samples to be able to manipulate the smartphone sensors

IMU-based smartphone-to-vehicle positioning

This is the author accepted manuscript. The final version is available from the publisher via the DOI in this recordIn this paper, we address the problem of using inertial measurements to position a smartphone with respect to a vehicle-fixed accelerometer. Using rigid body kinematics, this is cast as a nonlinear filtering problem. Unlike previous publications, we consider the complete three-dimensional kinematics, and do not approximate the angular acceleration to be zero. The accuracy of an estimator based on the unscented Kalman filter is compared with the Cramer-Rao bound. As is illustrated, the estimates can be expected to be better in the horizontal plane than in the vertical direction of the vehicle frame. Moreover, implementation issues are discussed and the system model is motivated by observability arguments. The efficiency of the method is demonstrated in a field study which shows that the horizontal RMSE is in the order of 0.5 [m]. Last, the proposed estimator is benchmarked against the state-of-the-art in left/right classification. The framework can be expected to find use in both insurance telematics and distracted driving solutions

Vers la mise en place d'une plateforme IoT d'aide au diagnostic des maladies neuromusculaires via les smartphones comme objets connectés

International audienceActuellement, nous assistons à une croissance considérable de la population mondiale estimée à environ 6 milliards d'habitants. L'une des conséquences de cette dernière est la forte difficulté de l'accès aux soins médicaux de qualité à temps voulu. A ce niveau, nous identifions les maladies neuromusculaires constituant un vrai problème d'handicap chez les deux sujets les plus sensibles de la population, à savoir les personnes âgées et les enfants. Un diagnostic retardé de ces maladies risque de conduire à l'aggravation des symptômes allant jusqu'à la perte définitive de la fonction locomotive et voir même à la mort. La détection des premiers symptômes exige une consultation spécialisée couteuse financièrement et difficile à accéder pour les malades éloignés des grandes structures de santé ou en cas de situation de fragilité. Une des pistes de recherche dans ce cadre serait d'utiliser les Smartphones comme objets connectés du fait qu'ils sont peu couteux, accessibles par la population cible et intégrant de nombreux capteurs et de technologies du mobile et de la communication. Dans cette lignée, notre étude vise à mettre en place une plateforme IoT (Internet of Things en anglais) d'aide au diagnostic des maladies neuromusculaires. La dite-plateforme facilitera et améliorera l'activité professionnelle du médecin en lui accordant la possibilité de procéder à des suivis et la prise en charge des patients à distance. Le processus de consultation médicale sera ainsi beaucoup plus facile et moins couteux. Notre communication portera dans un premier temps sur la mise en situation des exigences fonctionnelles de notre système connecté. Puis nous ferons un tour d'horizon sur l'état de l'art et ses manques par rapport aux spécifications du projet. Enfin, dans un second temps nous décrirons la méthodologie de notre étude et les résultats qui ont suivi chaque étape. La conclusion sera un bilan du travail réalisé jusqu'à présent et fera une ouverture sur des travaux en perspective

Implementation and evaluation of a mobile web application for auditory stimulation of chronic Tinnitus patients

Tinnitus is a prevalent disease that mainly states a big mystery to all kinds of scientific faculties and causes enormous costs due to further research. An initial assumption of the disease was the coherence of Tinnitus with a worse spatial hearing ability of the patient. With the assistance of mobile devices, it is the aim of this thesis to realize a mobile web application that allows it to draw conclusions that might support this theory in an easy available and ambulant way. The application is created as a game that has its focus on spatial hearing. The thesis depicts the used Application Programming Interfaces and names possible improvements

Fusion of Smartphone Motion Sensors for Physical Activity Recognition

For physical activity recognition, smartphone sensors, such as an accelerometer and a gyroscope, are being utilized in many research studies. So far, particularly, the accelerometer has been extensively studied. In a few recent studies, a combination of a gyroscope, a magnetometer (in a supporting role) and an accelerometer (in a lead role) has been used with the aim to improve the recognition performance. How and when are various motion sensors, which are available on a smartphone, best used for better recognition performance, either individually or in combination? This is yet to be explored. In order to investigate this question, in this paper, we explore how these various motion sensors behave in different situations in the activity recognition process. For this purpose, we designed a data collection experiment where ten participants performed seven different activities carrying smart phones at different positions. Based on the analysis of this data set, we show that these sensors, except the magnetometer, are each capable of taking the lead roles individually, depending on the type of activity being recognized, the body position, the used data features and the classification method employed (personalized or generalized). We also show that their combination only improves the overall recognition performance when their individual performances are not very high, so that there is room for performance improvement. We have made our data set and our data collection application publicly available, thereby making our experiments reproducible

Інструментальні засоби розпізнавання поведінки за кермом

Робота містить теоретичні та практичні аспекти розпізнавання поведінки водія, зокрема за допомогою мобільного застосунку з використанням сенсорів мобільного телефону. Метою роботи є аналіз інструментальних засобів розпізнавання поведінки водія, зокрема при аномальному водінні (петляння, швидкий розворот, різке гальмування тощо), а також створення відповідного мобільного додатку для розпізнавання поведінки при агресивному керуванні. Для досягнення поставленої мети розроблено мобільний застосунок, який виявляє та ідентифікує агресивну поведінку у реальному часі, та протестовано додаток в реальних умовах.The paper contains theoretical and practical aspects of recognition driver behavior, in particular using mobile application using mobile phone sensors. The purpose of this work is to analyze instrumental ways of recognition driver behavior, in particular abnormal driver behavior (weaving, fast U-turn, sudden breaking etc.), and to create a corresponding mobile application for recognition abnormal driver behavior. To achieve this aim, a mobile application that detects and identifies aggressive behavior in real time has been developed, and the application has been tested in real conditions.Работа содержит теоретические и практические аспекты распознавания поведения водителя, в частности с помощью мобильного приложения с использованием сенсоров мобильного телефона. Целью работы является анализ инструментальных средств распознавания поведения водителя, в частности при аномальном вождении (петляние, быстрый разворот, резкое торможение и т.д.), а также создание соответствующего мобильного приложения для распознавания поведения при агрессивном вождении. Для достижения поставленной цели разработано мобильное приложение, которое обнаруживает и идентифицирует агрессивное поведение в реальном времени, и протестировано приложение в реальных условиях