Nowadays, smartphones and laptops equipped with cameras have become an integral part of our daily lives. The pervasive use of cameras enables the collection of an enormous amount of data, which can be easily extracted through video images processing. This opens up the possibility of using technologies that until now had been restricted to laboratories, such as eye-tracking and emotion analysis systems, to analyze users’ behavior in the wild, during the interaction with websites. In this context, this paper introduces a toolkit that takes advantage of deep learning algorithms to monitor user’s behavior and emotions, through the acquisition of facial expression and eye gaze from the video captured by the webcam of the device used to navigate the web, in compliance with the EU General data protection regulation (GDPR). Collected data are potentially useful to support user experience assessment of web-based applications in the wild and to improve the effectiveness of e-commerce recommendation systems.

A toolkit for the automatic analysis of human behavior in HCI applications in the wild

Generosi A.;Ceccacci S.;Faggiano S.;Giraldi L.;Mengoni M.
2020-01-01

Abstract

Nowadays, smartphones and laptops equipped with cameras have become an integral part of our daily lives. The pervasive use of cameras enables the collection of an enormous amount of data, which can be easily extracted through video images processing. This opens up the possibility of using technologies that until now had been restricted to laboratories, such as eye-tracking and emotion analysis systems, to analyze users’ behavior in the wild, during the interaction with websites. In this context, this paper introduces a toolkit that takes advantage of deep learning algorithms to monitor user’s behavior and emotions, through the acquisition of facial expression and eye gaze from the video captured by the webcam of the device used to navigate the web, in compliance with the EU General data protection regulation (GDPR). Collected data are potentially useful to support user experience assessment of web-based applications in the wild and to improve the effectiveness of e-commerce recommendation systems.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11566/286870
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