Variable gravity research facility

Spin and despin requirements; sequence of activities required to assemble the Variable Gravity Research Facility (VGRF); power systems technology; life support; thermal control systems; emergencies; communication systems; space station applications; experimental activities; computer modeling and simulation of tether vibration; cost analysis; configuration of the crew compartments; and tether lengths and rotation speeds are discussed

Validación lingüística y psicométrica (adaptación cultural) de la escala Plutss para disfunción del tracto urinario inferior en niños colombianos

80% de los niños con ITU recurrente tiene algún síntoma de disfunción del tracto urinario inferior. Estos síntomas se clasifican según la ICCS (International Childrens Continence Society) de acuerdo a la fase del funcionamiento de la vejiga en la que presenten alteración, están los síntomas de llenado, los de eliminación y los asociados. Caracterizar estos síntomas, en forma objetiva para que no fueran simples relatos descriptivos de quejas de pacientes y pudieran ser utilizados para hacer diagnóstico y monitorear tratamiento obligó al uso de escalas que puntuaran cada uno de ellos. Estas escalas tienen su origen en el concepto del I-PSS (Puntaje Internacional de los Síntomas Prostáticos) que es una herramienta de gran utilidad para la clasificación de la hipertrofia prostática Hoy en día hay tres herramientas validadas para evaluar las alteraciones del tracto urinario inferior en niños; sin embargo ninguna de ellas ha sido a sido traducida al español ni adaptada culturalmente a la población hispanoamericana. El objetivo de este estudio es realizar la adaptación cultural (validación lingüística y psicométrica) de la escala PLUTSS,(4) que ya esta validada y es ampliamente utilizada; para aplicarla en un grupo de niños Colombianos estableciendo así el comportamiento de estos síntomas en nuestra población y para que pueda ser utilizada como herramienta de diagnóstico y seguimiento en los niños con alteración del tracto urinario inferior80% of children with recurrent urinary infection have any symptoms of lower urinary tract dysfunction. To characterize these symptoms, objectively forced the use of scales to rate each of them. Today there are three validated tools to assess the lower urinary tract disorders in children, but none has been been translated into Spanish and culturally adapted to the Hispanic American population. The aim of this study is to adapt the scale PLUTSS cultural, which is proven and widely used, to apply in a group of Colombian children, thus establishing the behavior of these symptoms. METHODOLOGY: The scale PLUTSS (Pediatric Symptom Score Lower Urinary Tract) was translated into Spanish adapted to Colombian dialect according to the admissions standards of translation, synthesis, back translation and recommendation of experts, was applied to a group of 34 patients with clinical diagnosis of urinary tract disorder lower and 95 healthy controls. Validation was conducted appearance, construct validation, we assessed the internal consistency of the instrument, and compared with results obtained in the original scale. RESULTS: The median of the two groups (healthy and diseased) was significantly different, with a sensitivity and specificity of 90% cut off point 1.5. Internal consistency of the 13-item scale was high alpha Crobanch, (0.88). Established the criterion validity of the scale with the clinical diagnosis found a significant correlation of strong character (CONCLUSIONS: The scale linguistic and psychometrically validated PLUTSS under international standards validation of scales is the only scale adapted Spanish. showed a high correlation with the clinical diagnosis and high power to discriminate urinary symptoms

Meeting Data Collection Specifications

This document specifies a process for collecting a new corpus of meetings in the IDIAP Smart Meeting Room. This document is a working draft that is expected to be updated and augmented throughout the data collection process. This follows from an earlier data collection effort that resulted in a corpus of 60 scripted meetings (30 train, 30 test), each of 5 minutes duration (now available at \textsf{mmm.idiap.ch}). The current data collection effort aims to address some of the limitations of the previous corpus, as well as to cater for a richer variety of research tasks

Towards Computer Understanding of Human Interactions

People meet in order to interact - disseminating information, making decisions, and creating new ideas. Automatic analysis of meetings is therefore important from two points of view: extracting the information they contain, and understanding human interaction processes. Based on this view, this article presents an approach in which relevant information content of a meeting is identified from a variety of audio and visual sensor inputs and statistical models of interacting people. We present a framework for computer observation and understanding of interacting people, and discuss particular tasks within this framework, issues in the meeting context, and particular algorithms that we have adopted. We also comment on current developments and the future challenges in automatic meeting analysis

Audio-visual probabilistic tracking of multiple speakers in meetings

Tracking speakers in multiparty conversations constitutes a fundamental task for automatic meeting analysis. In this paper, we present a probabilistic approach to jointly track the location and speaking activity of multiple speakers in a multisensor meeting room, equipped with a small microphone array and multiple uncalibrated cameras. Our framework is based on a mixed-state dynamic graphical model defined on a multiperson state-space, which includes the explicit definition of a proximity-based interaction model. The model integrates audio-visual (AV) data through a novel observation model. Audio observations are derived from a source localization algorithm. Visual observations are based on models of the shape and spatial structure of human heads. Approximate inference in our model, needed given its complexity, is performed with a Markov Chain Monte Carlo particle filter (MCMC-PF), which results in high sampling efficiency. We present results -based on an objective evaluation procedure- that show that our framework (1) is capable of locating and tracking the position and speaking activity of multiple meeting participants engaged in real conversations with good accuracy; (2) can deal with cases of visual clutter and partial occlusion; and (3) significantly outperforms a traditional sampling-based approach

A Mixed-State I-Particle Filter for Multi-Camera Speaker Tracking

Tracking speakers in multi-party conversations represents an important step towards automatic analysis of meetings. In this paper, we present a probabilistic method for audio-visual (AV) speaker tracking in a multi-sensor meeting room. The algorithm fuses information coming from three uncalibrated cameras and a microphone array via a mixed-state importance particle filter, allowing for the integration of AV streams to exploit the complementary features of each modality. Our method relies on several principles. First, a mixed state space formulation is used to define a generative model for camera switching. Second, AV localization information is used to define an importance sampling function, which guides the search process of a particle filter towards regions of the configuration space likely to contain the true configuration (a speaker). Finally, the measurement process integrates shape, color, and audio observations. We show that the principled combination of imperfect modalities results in an algorithm that automatically initializes and tracks speakers engaged in real conversations, reliably switching across cameras and between participants

Modeling Individual and Group Actions in Meetings With Layered HMMs

We address the problem of recognizing sequences of human interaction patterns in meetings, with the goal of structuring them in semantic terms. The investigated patterns are inherently group-based (defined by the individual activities of meeting participants, and their interplay), and multimodal (as captured by cameras and microphones). By defining a proper set of individual actions, group actions can be modeled as a two-layer process, one that models basic individual activities from low-level audio-visual features, and another one that models the interactions. We propose a two-layer Hidden Markov Model (HMM) framework that implements such concept in a principled manner, and that has advantages over previous works. First, by decomposing the problem hierarchically, learning is performed on low-dimensional observation spaces, which results in simpler models. Second, our framework is easier to interpret, as both individual and group actions have a clear meaning, and thus easier to improve. Third, different HMM models can be used in each layer, to better reflect the nature of each subproblem. Our framework is general and extensible, and we illustrate it with a set of eight group actions, using a public five-hour meeting corpus. Experiments and comparison with a single-layer HMM baseline system show its validity

On automatic annotation of meeting databases

In this paper, we discuss meetings as an application domain for multimedia content analysis. Meeting databases are a rich data source suitable for a variety of audio, visual and multi-modal tasks, including speech recognition, people and action recognition, and information retrieval. We specifically focus on the task of semantic annotation of audio-visual (AV) events, where annotation consists of assigning labels (event names) to the data. In order to develop an automatic annotation system in a principled manner, it is essential to have a well-defined task, a standard corpus and an objective performance measure. In this work we address each of these issues to automatically annotate events based on participant interactions

Multimodal Group Action Clustering in Meetings

We address the problem of clustering multimodal group actions in meetings using a two-layer HMM framework. Meetings are structured as sequences of group actions. Our approach aims at creating one cluster for each group action, where the number of group actions and the action boundaries are unknown a priori. In our framework, the first layer models typical actions of individuals in meetings using supervised HMM learning and low-level audio-visual features. A number of options that explicitly model certain aspects of the data (e.g., asynchrony) were considered. The second layer models the group actions using unsupervised HMM learning. The two layers are linked by a set of probability-based features produced by the individual action layer as input to the group action layer. The methodology was assessed on a set of multimodal turn-taking group actions, using a public five-hour meeting corpus. The results show that the use of multiple modalities and the layered framework are advantageous, compared to various baseline methods

Multimodal Group Action Clustering in Meetings

We address the problem of clustering multimodal group actions in meetings using a two-layer HMM framework. Meetings are structured as sequences of group actions. Our approach aims at creating one cluster for each group action, where the number of group actions and the action boundaries are unknown a priori. In our framework, the first layer models typical actions of individuals in meetings using supervised HMM learning and low-level audio-visual features. A number of options that explicitly model certain aspects of the data (e.g., asynchrony) were considered. The second layer models the group actions using unsupervised HMM learning. The two layers are linked by a set of probability-based features produced by the individual action layer as input to the group action layer. The methodology was assessed on a set of multimodal turn-taking group actions, using a public five-hour meeting corpus. The results show that the use of multiple modalities and the layered framework are advantageous, compared to various baseline methods