In this paper we present a non-invasive ambient intelligence framework for the semi-automatic analysis of non-verbal communication applied to the restorative justice field. In particular, we propose the use of computer vision and social signal processing technologies in real scenarios of Victim-Offender Mediations, applying feature extraction techniques to multi-modal audio-RGB-depth data. We compute a set of behavioral indicators that define communicative cues from the fields of psychology and observational methodology. We test our methodology on data captured in real world Victim-Offender Mediation sessions in Catalonia in collaboration with the regional government. We define the ground truth based on expert opinions when annotating the observed social responses. Using different state-of-the-art binary classification approaches, our system achieves recognition accuracies of 86 when predicting satisfaction, and 79 when predicting both agreement and receptivity. Applying a regression strategy, we obtain a mean deviation for the predictions between 0.5 and 0.7 in the range [1-5] for the computed social signals.
Once data from the environment have been acquired and processed to define a set of behavioral features, they serve as the basis for modelling a set of communication indicators. For instance, in @cite_25 , the authors outline a system for real-time tracking of the human body with the objective of interpreting human behavior. However, in the context of conversations we are particularly interested in behavioral traits belonging to social signals captured in the communication and in the interactions between the participants in the VOM sessions. In this sense, levels of agitation (or energy), activity, stress, and engagement are analyzed not only from their body movements, but also from their speech, facial expressions, or gaze directions, in order to predict behavioral responses.
@cite_25
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Pfinder is a real-time system for tracking people and interpreting their behavior. It runs at 10 Hz on a standard SGI Indy computer, and has performed reliably on thousands of people in many different physical locations. The system uses a multiclass statistical model of color and shape to obtain a 2D representation of head and hands in a wide range of viewing conditions. Pfinder has been successfully used in a wide range of applications including wireless interfaces, video databases, and low-bandwidth coding.