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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., Comment: 21 pages

Although psychological research indicates that bodily expressions convey important affective information, to date research in emotion recognition focused mainly on facial expression or voice analysis. In this paper we propose an approach to realtime automatic emotion recognition from body movements. A set of postural, kinematic, and geometrical features are extracted from sequences 3D skeletons and fed to a multi-class SVM classifier. The proposed method has been assessed on data acquired through two different systems: a professionalgrade optical motion capture system, and Microsoft Kinect. The system has been assessed on a "six emotions" recognition problem, and using a leave-one-subject-out cross validation strategy, reached an overall recognition rate of 61.3% which is very close to the recognition rate of 61.9% obtained by human observers. To provide further testing of the system, two games were developed, where one or two users have to interact to understand and express emotions with their body.