Toward an adaptive architecture for integrating mobile affective computing to intelligent learning environments

Abstract

With the advent of mobile technology, Wireless Sensor Networks as well as Smart wearable devices, relevant Mobile Affective based learning systems could be developed. This enables not only to recognize users’ spontaneous affect state but also to react appropriately to that state. However, additional complex requirements are arising such as handling affect data heterogeneity as well as continuous and unpredictable dynamic changes of the sensing capabilities. The present work aims therefore to overcome these requirements by providing flexible and adaptive software architecture based on semantic models.