Abstract

Recognizing emotions from physiological signals has proven to be important in various scenarios. To assist in developing emotion recognizers, software frameworks and toolboxes have emerged, offering ready-to-use components. However,these have limitations regarding the type of physiological signals supported, the recognition steps covered, or the acquisition of multiple physiological signals. This paper presents metaFERA, an architectural meta-framework for creating software frameworks for end-to-end emotion recognition from physiological signals. The modularity and flexibility of the meta-framework and the resulting frameworks allow the fast prototyping of emotion recognition systems and experiments to test and validate new algorithms. To that end, metaFERA offers: (i) a set of pre-configured blocks to which we can add behavior to create framework components; (ii) an easy way to add behavior to the pre-configured blocks; (iii) a channel-based communication mechanism that transparently and efficiently supports the exchange of information between components; (iv) a simple and easy way to use and link components from a resulting framework to create applications. Additionally, we provide a set of Web services, already configured, to make the resulting recognition systems available as a service. To validate metaFERA, we created a framework for Electrodermal Activity, an emotion recognizer to identify high/low arousal using the aforementioned framework, and a layer to offer the recognizer as a service.

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