Research

Current research projects

Human-computer interaction Lab.

Past research projects

- Affective engagement with a socially intelligent embodied agent (2012, @ USC ICT)

  • Previous research illustrates that people can be influenced by the emotional displays of computer-generated agents. What is less clear is if these influences arise from cognitive or affective process (i.e., do people use agent displays as information or do they provoke user emotions). To unpack these processes we examine the decisions and physiological reactions of participants (heart rate and electrodermal activity) when engaged in a decision task (prisoner’s dilemma game) with emotionally-expressive agents.

- ARGraphy: Information visualization based on Augmented Reality Technology ( 2010- 2012, KOCCA, KIST)

  • Development of mobile AR tour guide system, personalized intelligent information visualization in a mobile platform

- BioPebble : Stone-Type Physiological Sensing Device ( 2007, MIC, UCN Project)

  • In this work, we propose a stone-type physiological sensing device for general users, rather than professional experts. We found that our device was comfortable, stable and had aesthetic appeal for users during monitoring. To develop an affective shape, and to increase comfort, we applied a user-centered design process. We also used context-based physiological signal analysis to obtain stable analysis results according to individual users.

- Context based decision making method (2006)

  • With the advent of light-weight, high-performance sensing and processing technology, a pervasive physiological sensing device has been actively studied. However, a pervasive sensing device is easily a ected by the external factors and environmental changes such as noise, temperature or weather. In addition, it is hard to deal with the internal factors of the user and personal di erences based on physiological characteristics. To address these issues, we propose a Context-based decision making method of a pervasive sensing device which concerns the user type, gender and sensing environments such as outdoor temperature, measurement time for detecting the normal physiological condition of the user. From the research conducted, we found that the personalized analysis for multiple users regular data shows reliable results and reduces errors.