  Synchronization is a fundamental component of computational models of human
behavior, at both intra-personal and inter-personal level. Event
synchronization analysis was originally conceived with the aim of providing a
simple and robust method to measure synchronization between two time series. In
this paper we propose a novel method extending the state-of-the-art of the
event synchronization techniques: the Multi-Event-Class Synchronization (MECS)
algorithm. MECS measures the synchronization between relevant events belonging
to different event classes that are detected in multiple time series. Its
motivation emerged from the need to model non-verbal multimodal signals in
Human-Computer Interaction. Using MECS, synchronization can be computed between
events belonging to the same class (intra-class synchronization) or between
events belonging to different classes (inter-class synchronization). In the
paper we also show how our technique can deal with macro-events (i.e., sets of
events satisfying constraints) and macro-classes (i.e., sets of classes). In
the last part of the paper, we apply the proposed method to two types of data
i) artificial and 2) real-world case study concerning analysis of human
multimodal behavior.
