Symbolic Data is concerned with analysing data with intrinsic variability, which is to be taken into account. In Data Mining, Multivariate Data Analysis and classical Statistics, the elements under analysis are generally individual entities for which a single value is recorded for each variable – e.g., individuals, described by age, salary, education level, etc. But when the elements of interest are classes or groups of some kind – the citizens living in given towns; car models, rather than specific vehicles – then there is variability inherent to the data.
Symbolic data goes beyond the usual data representation model, considering variables whose observed values for each element are no longer necessarily single real values or categories, but may assume the form of sets, intervals, or, more generally, distributions. In this tutorial, we shall introduce Symbolic Data Analysis with motivating examples. We then proceed to the definition of the new variable types, and introduce alternative symbolic data representations. Multivariate analysis of interval or histogram-value data will then be addressed, focusing on clustering and regression approaches.

The autonomic nervous system (ANS) regulates fundamental physiological states, upregulating and downregulating various functions within our body. While maintaining the equilibrium of the body’s systems according to both internal and external stimuli, many physiological signals reflect the activity of the ANS.
Biomedical sensors, which are usually minimally invasive equipment and often wireless, can continuously stream to common devices (e.g., smartphones), offering an excellent opportunity to monitor the physiological correlates of several psychophysiological states of human subjects. Relevant information and meaningful characteristics from physiological signals can be obtained through the application of data mining methods.
This course will expose the analysis of physiological reactions related to different induced conditions: from the design of soft sensors for data acquisition to several methodologies to extract relevant information from the gathered signals. It will show the design and development of soft sensors for wearable applications, the collection of physiological data under different induced conditions, will present raw gathered data in different experiments, and the importance of signal pre-processing, it will disclose relevant features extracted from those signals and approaches for recognizing patterns hidden in the data.
Therefore, by presenting the soft sensors for data acquisition in wearable applications, signal pre-processing techniques, and the extraction of relevant information and meaningful characteristics from those signals along with the approaches to analyze them, we have foreseen that this course will capture the interest of the intended audience.

Moreover, this course accounts for hands-on on the topics described above: attendees will have the opportunity to explore signal collection and processing for data analysis, enabling the audience to understand the several topics covered. Also, along with the distinct topics addressed, the audience will be engaged through questions and insights to promote discussion on the approaches that could be used in each part. Different approaches will also be shown in order to compare the outputs obtained.
The format of the course serves the following objectives:
– Familiarization with wearables and data collection;
– Importance of signal pre-processing;
– Relevance of data preparation, feature extraction, and feature selection;
– Approaches to analyzing data, including data-preprocessing techniques to deal with the imbalance of data, disclosing knowledge hidden in the collected data;

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