  Group meetings are frequent business events aimed to develop and conduct
project work, such as Big Room design and construction project meetings. To be
effective in these meetings, participants need to have an engaged mental state.
The mental state of participants however, is hidden from other participants,
and thereby difficult to evaluate. Mental state is understood as an inner
process of thinking and feeling, that is formed of a conglomerate of mental
representations and propositional attitudes. There is a need to create
transparency of these hidden states to understand, evaluate and influence them.
Facilitators need to evaluate the meeting situation and adjust for higher
engagement and productivity. This paper presents a framework that defines a
spectrum of engagement states and an array of classifiers aimed to detect the
engagement state of participants in real time. The Engagement Framework
integrates multi-modal information from 2D and 3D imaging and sound. Engagement
is detected and evaluated at participants and aggregated at group level. We use
empirical data collected at the lab of Konica Minolta, Inc. to test initial
applications of this framework. The paper presents examples of the tested
engagement classifiers, which are based on research in psychology,
communication, and human computer interaction. Their accuracy is illustrated in
dyadic interaction for engagement detection. In closing we discuss the
potential extension to complex group collaboration settings and future feedback
implementations.
