Towards the Creation of Scalable Tools for automatic Quality of Experience Evaluation and a Multi-Purpose Dataset for Affective Computing

Traditional tools used to evaluate the Quality of Experience (QoE) of users after browsing an ad, using a product, or performing any kind of task typically involves surveys, user testing, and analytics. However, these methods provide limited insights and have limitations due to the need of users’ active cooperation and sincerity, the long testing time, the high cost, and the limited scalability. On this work we present the tools we are developing to automatically evaluate QoE in different use cases such as dashboards that show on real time reactions to different events in the form of emotions and affections predicted by different models based on physiological data. To develop these tools, we require datasets on affective computing. We highlight some limitations of the available ones, the difficulties during the creation of such data, and our current work in the confection of a new one with automatic annotation of ground truth.