Exercit@rt mobile : monitorização da reabilitação respiratória na DPOC

A prevalência estimada da Doença Pulmonar Obstrutiva Crónica (DPOC) em Portugal é de 14,2% para indivíduos com idade superior a 45 anos (cerca de 800.000 indivíduos), sendo mais prevalente no sexo masculino (Observatório Nacional das Doenças Respiratórias, 2014). Com o aparecimento de soluções baseadas em novas modalidades de eHealth e mHealth surgiram novas formas de acompanhamento e monitorização das doenças crónicas, nomeadamente da DPOC. É neste contexto que foi desenvolvida a aplicação mobile Exercit@rt, em parceria com a Escola Superior de Saúde da Universidade de Aveiro e em continuidade com outros estudos do MCMM anteriormente desenvolvidos. A aplicação permite monitorizar, em tempo real, através da utilização de um oxímetro Bluetooth, os níveis de batimento cardíaco e saturação de oxigénio dos pacientes com DPOC. Com esta aplicação os pacientes podem realizar diversos exercícios de fisioterapia respiratória assim como atividades físicas de vida diária que podem ser monitorizadas, georreferenciadas e avaliadas. Para além do desenvolvimento da aplicação mobile, a presente investigação integrou ainda uma etapa de validação que contou com a participação de dez pacientes com doenças do foro respiratório – cinco utilizadores que utilizam/têm smartphone (UTS) e cinco utilizadores não utilizam/não têm smartphone (NUNTS). A cada um destes foram propostas tarefas a realizar na aplicação mobile, estando previsto que a aplicação estivesse apta para qualquer participante. A totalidade dos participantes reconheceu a utilidade da aplicação no controlo da sua doença.

Sistema integrado de localização de contentores de correio

Trabalho Final de Mestrado para obtenção do grau de Mestre em Engenharia Informática e de Computadores

Integración de Arbotix, Raspberry Pi y motores Dynamixel Ax-12+ para un robot humanoide que busca y patea pelotas

En este artículo se presenta a DeBuPa (Detección Búsqueda Pateo) un humanoide de tamaño pequeño (38 cm de alto) construido con las piezas del kit Bioloid. Del kit se ha excluido la tarjeta CM-510 para sustituirla por la tarjeta controladora Arbotix, que será la que controle los 16 motores Dynamixel Ax-12+ (para mover al robot) y 2 servomotores analógicos (para mover la cámara). Además se ha agregado un mini computador Raspberry Pi, con su cámara, para que el robot pueda detectar y seguir la pelota de forma autónoma. Todos estos componentes deben ser coordinados para que se logre cumplir la tarea de detectar, seguir y patear la pelota. Por ello se hace necesaria la comunicación entre la Arbotix y la Raspberry Pi. La herramienta empleada para ello es el framework ROS (Robot Operating System). En la Raspberry Pi se usa el lenguaje C++ y se ejecuta un solo programa encargado de captar la imagen de la cámara, filtrar y procesar para encontrar la pelota, tomar la decisión de la acción a ejecutar y hacer la petición a la Arbotix para que dé la orden a los motores de ejecutar el movimiento. Para captar la imagen de la cámara se ha utilizado la librería RasPiCam CV. Para filtrar y procesar la imagen se ha usado las librerías de OpenCV. La Arbotix, además de controlar los motores, se encarga de monitorizar que el robot se encuentre balanceado, para ello usa el sensor Gyro de Robotis. Si detecta un desbalance de un cierto tamaño puede saber si se ha caído y levantarse.

Texture-Detail Preservation Measurement in Camera Phones: An Updated Approach

Recent advances in mobile phone cameras have poised them to take over compact hand-held cameras as the consumer’s preferred camera option. Along with advances in the number of pixels, motion blur removal, face-tracking, and noise reduction algorithms have significant roles in the internal processing of the devices. An undesired effect of severe noise reduction is the loss of texture (i.e. low-contrast fine details) of the original scene. Current established methods for resolution measurement fail to accurately portray the texture loss incurred in a camera system. The development of an accurate objective method to identify the texture preservation or texture reproduction capability of a camera device is important in this regard. The ‘Dead Leaves’ target has been used extensively as a method to measure the modulation transfer function (MTF) of cameras that employ highly non-linear noise-reduction methods. This stochastic model consists of a series of overlapping circles with radii r distributed as r−3, and having uniformly distributed gray level, which gives an accurate model of occlusion in a natural setting and hence mimics a natural scene. This target can be used to model the texture transfer through a camera system when a natural scene is captured. In the first part of our study we identify various factors that affect the MTF measured using the ‘Dead Leaves’ chart. These include variations in illumination, distance, exposure time and ISO sensitivity among others. We discuss the main differences of this method with the existing resolution measurement techniques and identify the advantages. In the second part of this study, we propose an improvement to the current texture MTF measurement algorithm. High frequency residual noise in the processed image contains the same frequency content as fine texture detail, and is sometimes reported as such, thereby leading to inaccurate results. A wavelet thresholding based denoising technique is utilized for modeling the noise present in the final captured image. This updated noise model is then used for calculating an accurate texture MTF. We present comparative results for both algorithms under various image capture conditions.

Gamification in Skills Management

Employees are the human capital which, to a great extent, contributes to the success and development of high-performance and sustainable organizations. In a work environment, there is a need to provide a tool for tracking and following-up on each employees' professional progress, while staying aligned with the organization’s strategic and operational goals and objectives. The research work within this Thesis aims to contribute to improve employees' selfawareness and auto-regulation; two predominant research areas are also studied and analyzed: Visual Analytics and Gamification. The Visual Analytics enables the specification of personalized dashboard interfaces with alerts and indicators to keep employees aware of their skills and to continuously monitor how to improve their expertise, promoting simultaneously behavioral change and adoption of good-practices. The study of Gamification techniques with Talent Management features enabled the design of new processes to engage, motivate, and retain highly productive employees, and to foster a competitive working environment, where employees are encouraged to be involved in new and rewarding activities, where knowledge and experience are recognized as a relevant asset. The Design Science Research was selected as the research methodology; the creation of new knowledge is therefore based on an iterative cycle addressing concepts such as design, analysis, reflection, and abstraction. By collaborating in an international project (Active@Work), funded by the Active and Assisted Living Programme, the results followed a design thinking approach regarding the specification of the structure and behavior of the Skills Development Module, namely the identification of requirements and the design of an innovative info-structure of metadata to support the user experience. A set of mockups were designed based on the user role and main concerns. Such approach enabled the conceptualization of a solution to proactively assist the management and assessment of skills in a personalized and dynamic way. The outcomes of this Thesis aims to demonstrate the existing articulation between emerging research areas such as Visual Analytics and Gamification, expecting to represent conceptual gains in these two research fields.

Registro de pacientes con distrofinopatías en Colombia

INTRODUCCIÓN. La distrofia muscular de Duchenne es una enfermedad neuromuscular con una herencia recesiva ligada al X que afecta a 1 de cada 3500 niños nacidos vivos. Se produce por mutaciones en el gen DMD que codifica para la distrofina. Se caracteriza por manifestaciones clínicas variables típicas de una distrofia muscular proximal progresiva. OBJETIVO. Realizar el primer registro en Colombia de los pacientes identificados con distrofinopatías, teniendo en cuenta características clínicas y paraclínicas, así como las mutaciones causales de esta patología. METODOLOGÍA Es un estudio descriptivo, transversal, de la revisión de historias clínicas de los pacientes con diagnóstico de DMD atendidos en la consulta de Genética de la Universidad del Rosario durante los años 2006 a 2015. RESULTADOS Se identificaron 99 pacientes, de los cuales 56 (56,56%) corresponden al fenotipo Duchenne y 12 (12,12%) al Becker. No fue posible clasificar a 31 pacientes (31,3%) por falta de datos clínicos. La edad de inicio de los síntomas fue en promedio de 4,41 años. Las mutaciones más frecuentes fueron las deleciones (69%), seguidas por las mutaciones puntuales(14%), las duplicaciones (11%) y por otras mutaciones (4%). CONCLUSIONES Este registro de distrofinopatías es el primero reportado en Colombia y el punto de partida para conocer la incidencia de la enfermedad, caracterización clínica y molecular de los pacientes, garantizando así el acceso oportuno a los nuevos tratamientos de medicina de precisión que permitan mejorar la calidad de vida de los pacientes y sus familias.

Tracking people in 3D using position, size and shape

This paper presents a prototype tracking system for tracking people in enclosed indoor environments where there is a high rate of occlusions. The system uses a stereo camera for acquisition, and is capable of disambiguating occlusions using a combination of depth map analysis, a two step ellipse fitting people detection process, the use of motion models and Kalman filters and a novel fit metric, based on computationally simple object statistics. Testing shows that our fit metric outperforms commonly used position based metrics and histogram based metrics, resulting in more accurate tracking of people.

Destination branding case study : tracking brand equity for an emerging destination between 2003 and 2007

Place branding has become a major focus of operations for destination marketing organizations (DMOs) striving for differentiation in cluttered markets. The topic of destination branding has only received attention in the tourism literature since the late 1990s, and there has been relatively little research reported in relations to analyzing destination brand effectiveness over time. This article reports an attempt to oprationalize the concept of consumer-based brand equity (CBBE) for an emerging destination over two points in time. The purpose of the project was to track the effectiveness of the brand in 2007 against benchmarks that were established in a 2003 student at the commencement of a new destination brand campaign. The key finding was there was no change in perceived performance for the destination across the brand's performance indicators and CBBE dimensions. Because of the common challenges faced by DMOs worldwide, it is suggested the CBBE hierarchy provides destination marketers with a practical tool for evaluation brand performance over time.

Improved simultaneous computation of motion detection and optical flow for object tracking

Object tracking systems require accurate segmentation of the objects from the background for effective tracking. Motion segmentation or optical flow can be used to segment incoming images. Whilst optical flow allows multiple moving targets to be separated based on their individual velocities, optical flow techniques are prone to errors caused by changing lighting and occlusions, both common in a surveillance environment. Motion segmentation techniques are more robust to fluctuating lighting and occlusions, but don't provide information on the direction of the motion. In this paper we propose a combined motion segmentation/optical flow algorithm for use in object tracking. The proposed algorithm uses the motion segmentation results to inform the optical flow calculations and ensure that optical flow is only calculated in regions of motion, and improve the performance of the optical flow around the edge of moving objects. Optical flow is calculated at pixel resolution and tracking of flow vectors is employed to improve performance and detect discontinuities, which can indicate the location of overlaps between objects. The algorithm is evaluated by attempting to extract a moving target within the flow images, given expected horizontal and vertical movement (i.e. the algorithms intended use for object tracking). Results show that the proposed algorithm outperforms other widely used optical flow techniques for this surveillance application.

Automatic tracking, super-resolution and recognition of human faces from surveillance video

Identifying an individual from surveillance video is a difficult, time consuming and labour intensive process. The proposed system aims to streamline this process by filtering out unwanted scenes and enhancing an individual's face through super-resolution. An automatic face recognition system is then used to identify the subject or present the human operator with likely matches from a database. A person tracker is used to speed up the subject detection and super-resolution process by tracking moving subjects and cropping a region of interest around the subject's face to reduce the number and size of the image frames to be super-resolved respectively. In this paper, experiments have been conducted to demonstrate how the optical flow super-resolution method used improves surveillance imagery for visual inspection as well as automatic face recognition on an Eigenface and Elastic Bunch Graph Matching system. The optical flow based method has also been benchmarked against the ``hallucination'' algorithm, interpolation methods and the original low-resolution images. Results show that both super-resolution algorithms improved recognition rates significantly. Although the hallucination method resulted in slightly higher recognition rates, the optical flow method produced less artifacts and more visually correct images suitable for human consumption.

Vision-based detection and tracking of aerial targets for UAV collision avoidance

Machine vision represents a particularly attractive solution for sensing and detecting potential collision-course targets due to the relatively low cost, size, weight, and power requirements of the sensors involved (as opposed to radar). This paper describes the development and evaluation of a vision-based collision detection algorithm suitable for fixed-wing aerial robotics. The system was evaluated using highly realistic vision data of the moments leading up to a collision. Based on the collected data, our detection approaches were able to detect targets at distances ranging from 400m to about 900m. These distances (with some assumptions about closing speeds and aircraft trajectories) translate to an advanced warning of between 8-10 seconds ahead of impact, which approaches the 12.5 second response time recommended for human pilots. We make use of the enormous potential of graphic processing units to achieve processing rates of 30Hz (for images of size 1024-by- 768). Currently, integration in the final platform is under way.

A modified particle filter for simultaneous robot localization and landmark tracking in an indoor environment

This paper presents the implementation of a modified particle filter for vision-based simultaneous localization and mapping of an autonomous robot in a structured indoor environment. Through this method, artificial landmarks such as multi-coloured cylinders can be tracked with a camera mounted on the robot, and the position of the robot can be estimated at the same time. Experimental results in simulation and in real environments show that this approach has advantages over the extended Kalman filter with ambiguous data association and various levels of odometric noise.

Experiments with underwater robot localization and tracking

This paper describes a novel experiment in which two very different methods of underwater robot localization are compared. The first method is based on a geometric approach in which a mobile node moves within a field of static nodes, and all nodes are capable of estimating the range to their neighbours acoustically. The second method uses visual odometry, from stereo cameras, by integrating scaled optical flow. The fundamental algorithmic principles of each localization technique is described. We also present experimental results comparing acoustic localization with GPS for surface operation, and a comparison of acoustic and visual methods for underwater operation.

Description and evaluation of a Newton-based electronic appetite rating system for temporal tracking of appetite in human subjects

This study assessed the reliability and validity of a palm-top-based electronic appetite rating system (EARS) in relation to the traditional paper and pen method. Twenty healthy subjects [10 male (M) and 10 female (F)] — mean age M=31 years (S.D.=8), F=27 years (S.D.=5); mean BMI M=24 (S.D.=2), F=21 (S.D.=5) — participated in a 4-day protocol. Measurements were made on days 1 and 4. Subjects were given paper and an EARS to log hourly subjective motivation to eat during waking hours. Food intake and meal times were fixed. Subjects were given a maintenance diet (comprising 40% fat, 47% carbohydrate and 13% protein by energy) calculated at 1.6×Resting Metabolic Rate (RMR), as three isoenergetic meals. Bland and Altman's test for bias between two measurement techniques found significant differences between EARS and paper and pen for two of eight responses (hunger and fullness). Regression analysis confirmed that there were no day, sex or order effects between ratings obtained using either technique. For 15 subjects, there was no significant difference between results, with a linear relationship between the two methods that explained most of the variance (r2 ranged from 62.6 to 98.6). The slope for all subjects was less than 1, which was partly explained by a tendency for bias at the extreme end of results on the EARS technique. These data suggest that the EARS is a useful and reliable technique for real-time data collection in appetite research but that it should not be used interchangeably with paper and pen techniques.