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.

전자신문 – AI로 감정까지 읽는다…KAIST, 행동과 생체신호로 감정 인과관계 읽는 알고리즘 개발

 

 

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]