Examinando por Autor "Munoz, Roberto"

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    A Learning Analytics Framework to Analyze Corporal Postures in Students Presentations
    (MDPI, 2021) Vieira, Felipe; Cechinel, Cristian; Ramos, Vinicius; Riquelme, Fabián; Noel, Rene; Villarroel, Rodolfo; Cornide-Reyes, Hector; Munoz, Roberto
    Communicating in social and public environments are considered professional skills that can strongly influence career development. Therefore, it is important to proper train and evaluate students in this kind of abilities so that they can better interact in their professional relationships, during the resolution of problems, negotiations and conflict management. This is a complex problem as it involves corporal analysis and the assessment of aspects that until recently were almost impossible to quantitatively measure. Nowadays, a number of new technologies and sensors have being developed for the capture of different kinds of contextual and personal information, but these technologies were not yet fully integrated inside learning settings. In this context, this paper presents a framework to facilitate the analysis and detection of patterns of students in oral presentations. Four steps are proposed for the given framework: Data collection, Statistical Analysis, Clustering, and Sequential Pattern Mining. Data Collection step is responsible for the collection of students interactions during presentations and the arrangement of data for further analysis. Statistical Analysis provides a general understanding of the data collected by showing the differences and similarities of the presentations along the semester. The Clustering stage segments students into groups according to well-defined attributes helping to observe different corporal patterns of the students. Finally, Sequential Pattern Mining step complements the previous stages allowing the identification of sequential patterns of postures in the different groups. The framework was tested in a case study with data collected from 222 freshman students of Computer Engineering (CE) course at three different times during two different years. The analysis made it possible to segment the presenters into three distinct groups according to their corporal postures. The statistical analysis helped to assess how the postures of the students evolved throughout each year. The sequential pattern mining provided a complementary perspective for data evaluation and helped to observe the most frequent postural sequences of the students. Results show the framework could be used as a guidance to provide students automated feedback throughout their presentations and can serve as background information for future comparisons of students presentations from different undergraduate courses.
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    Selection of Bone fragility-Related Features Obtained with Bi-Directional Axial Transmission, Through a Machine Leaming Strategy
    (IEEE, 2021) Miranda, Diego; Olivares, Rodrigo; Munoz, Roberto; Minonzio, Jean-Gabriel
    Osteoporosis is a widespread public health problem worldwide, characterized by low bone mass, which compromises strength and increases the risk of fracture. Currently, the gold standard for assessing fracture risk is measurement of areal bone mineral density with dual-energy X-ray absorptiometry (DXA). Several ultrasound techniques, such as Bi-Directional Axial Transmission (BDAT) have been presented as alternatives. For the first studies, classification between fractured and non fractured patients was based on classical ultrasonic parameters, such as velocities or cortical thickness and porosity, obtained from an inverse problem. Recently, novel parameters obtained from structural analysis guided wave spectrum images (GWSI) have been introduced. The aim of this study is to merge both points of view and explore which parameters are the most important to obtain a robust classification using a machine learning approach. This study uses the same set of patients used in previous studies with 195 patients associated with 8 ultrasonic parameters and 3 clinical factors (age BMI and cortisone intake). In addition, each patient corresponds to 10 GWSI, from which 32 parameters of structural analysis are extracted per image, leading to a total of 43 features per image. The dataset was divided into 70% of patients (n = 136) as training and 30% as testing (n = 59). The distribution of patients was adjusted for age and target class. The accuracy was calculated for an increased number of features, which ranking was obtained using Recursive Feature Elimination (RFE). The highest accuracy of 71% is obtained with the optimized parameters and a combination between 22 and 25 features. These result, comparable to femoral DXA (AUC = 0.71, adjusted linear regression), opens perspective towards robust detection of patients at risk of fracture with ultrasound.
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    Simple hemogram to support the decision-making of COVID-19 diagnosis using clusters analysis with self-organizing maps neural network
    (Springer, 2021) De Souza, Alexandra A.; De Almeida, Danilo Candido; Barcelos, Thiago S.; Campos Bortoletto, Rodrigo; Munoz, Roberto; Waldman, Helio; Goes, Miguel Angelo; Silva, Leandro A.
    The pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) which is related to new coronavirus disease (COVID-19) has mobilized several scientifics to explore clinical data using soft-computing approaches. In the context of machine learning, previous studies have explored supervised algorithms to predict and support diagnosis based on several clinical parameters from patients diagnosed with and without COVID-19. However, in most of them the decision is based on a “black-box” method, making it impossible to discover the variable relevance in decision making. Hence, in this study, we introduce a non-supervised clustering analysis with neural network self-organizing maps (SOM) as a strategy of decision-making. We propose to identify potential variables in routine blood tests that can support clinician decision-making during COVID-19 diagnosis at hospital admission, facilitating rapid medical intervention. Based on SOM features (visual relationships between clusters and identification of patterns and behaviors), and using linear discriminant analysis , it was possible to detect a group of units of the map with a discrimination power around 83% to SARS-CoV-2-positive patients. In addition, we identified some variables in admission blood tests (Leukocytes, Basophils, Eosinophils, and Red cell Distribution Width) that, in combination had strong influence in the clustering performance, which could assist a possible clinical decision. Thus, although with limitations, we believe that SOM can be used as a soft-computing approach to support clinician decision-making in the context of COVID-19.