Ontological Modeling for Contextual Data Describing Signals Obtained From Electrodermal Activity for Emotion Recognition and Analysis

Most of the research in the field of emotion recognition is based on datasets that contain data obtained during affective computing experiments. However, each dataset is described by different metadata, stored in various structures and formats. This research can be counted among those whose aim is to provide a structural and semantic pattern for affective computing datasets, which is an important step to solve the problem of data reuse and integration in this domain. In our previous work, the ROAD ontology was introduced. This ontology was designed as a skeleton for expressing contextual data describing time series obtained in various ways from various signals and was focused on common contextual data, independent of specific signals. The aim of the presented research is to provide a carefully curated vocabulary for describing signals obtained from electrodermal activity, a very important subdomain of emotion analysis. We decided to present it as an extension to the ROAD ontology in order to offer means of sharing metadata for datasets in a unified and precise way. To meet this aim, the research methodology was defined, mostly focusing on requirements specification and integration with other existing ontologies. Application of this methodology resulted firstly in sharing the requirements to allow a broader discussion and secondly development of the EDA extension of the ROAD ontology, validated against the MAHNOB-HCI dataset. Both these results are very important with respect to the vast context of the work, i.e. providing an extendable framework for describing affective computing experiments. Introducing the methodology also opens the way for providing new extensions systematically just by executing the steps defined in the methodology.