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The objective description of lifestyle patterns from egocentric images captured by wearable cameras is considered the next step on health-tracking applications which goes alongside heartbeats, steps walked, or burned calories measured mainly by electronic wristbands. In this context, the classification of eating, socializing, and sedentary lifestyle patterns has been addressed in previous research, considering the following classes for each pattern, respectively: 1) no food, food but not eating, and eating; 2) not socializing, and socializing; and 3) not sedentary, and sedentary. This previous approach provides a solution that considers all the possible combinations of classes among these three patterns, thus solving a multi-class classification problem with 12 classes. Given the nature of the problem, we propose to address the classification of these three lifestyle patterns under a multi-task perspective, employing a general framework based on Inception-V3 convolutional neural network. The feature maps extracted from the employed network are used to perform a final lifestyle pattern categorization. It is worth to note that our proposed framework offers visual explanations of the results to accomplish the standards of the European Union in terms of explainability, thus increasing both transparency and confidence on the obtained results. Furthermore, in this work a web-based lifelogging tool for periodic visual summarization of lifestyle patterns is introduced. Our method has been tested on a dataset composed of more than 45,000 egocentric images, and the experimental results confirm the reliability of the selected method while outperforming state-of-the-art in terms of accuracy and F1-score.

Treballs Finals de Grau d'Enginyeria Informàtica, Facultat de Matemàtiques, Universitat de Barcelona, Any: 2016, Director: Eloi Puertas i Prats, Academic tutors at universities find themselves without enough tools or material to help their students due to the lack of knowledge of each student’s academic profile. For this reason, we have performed a comprehensive study of student data in order to obtain supplementary material for the tutor. Clusterization algorithms have been used to separate the students into groups to identify general characteristics of the students belonging to a particular cluster. Dropout of each cluster and relationships between groups have been studied as well. A k-NN classifier has been used to create a prediction model which is able to predict in which cluster a student will belong the next academic year. Finally, visualization technics have been used to present the results of the analysis.