Past research on recognizing human affect has made use of a variety of physiological sensors in many ways. Nonetheless, how affective dynamics are influenced in the context of human daily life has not yet been explored. In this work, we present a wearable affective life-log system (ALIS), that is robust as well as easy to use in daily life to detect emotional changes and determine their cause-and-effect relationship on users’ lives. The proposed system records how a user feels in certain situations during long-term activities with physiological sensors. Based on the long-term monitoring, the system analyzes how the contexts of the user’s life affect his/her emotion changes.

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Related publications

1. BH Kim, S Jo, S Choi,  ALIS: Learning Affective Causality behind Daily Activities from a Wearable Life-Log System, IEEE Transactions on Cybernetics, early access [LINK] [PDF]