A Mobile Context Sharing System Using Activity and Emotion Recognition with Bayesian Networks

Keunhyun Oh, Han-Saem Park, Sung-Bae Cho

Abstract: With the recent popularization of smart phones, context sharing systems in mobile environment attract attention of people. Mobile context sharing systems can share more information than web-based social network services because they have various sensors. For sharing high-level contexts such as activity and emotion, a user had to manually annotate them in previous works. This paper proposes a mobile context sharing system that can recognize high-level contexts automatically by using Bayesian networks based on collected mobile logs. We have implemented the Context Viewer application which consists of a phonebook and a map browser to prove the feasibility of the proposed system. Also, we have confirmed that the proposed system is useful, by evaluating Bayesian networks and performing the SUS test.