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.
Many of the aforementioned studies demonstrate that indicators of agreement during communication are highly dependent on social signals. As such, it is possible to perform an exhaustive analysis to detect the role played by each participant in terms of influence, dominance, or submission. For instance, in @cite_41 , both the interest of observers and the dominant participants are predicted solely on the basis of behavioral motion information when looking at face-to-face (also called or dyadic) interactions. Furthermore, there are many interdisciplinary, state-of-the-art studies examining related fields from the point of view of social computing, some of which are summarized in @cite_33 @cite_2 .
@cite_41 @cite_33 @cite_2
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Although developers of communication-support tools have certainly tried to create products that support group thinking, they usually do so without adequately accounting for social context, so that all too often these systems are jarring and even downright rude. In fact, most people would agree that today's communication technology seems to be at war with human society. Technology must account for this by recognizing that communication is always socially situated and that discussions are not just words but part of a larger social dialogue. This web of social interaction forms a sort of collective intelligence; it is the unspoken shared understanding that enforces the dominance hierarchy and passes judgment about it. We have found nonlinguistic social signals to be particularly powerful for analyzing and predicting human behavior, sometimes exceeding even expert human capabilities. Psychologists have firmly established that social signals are a powerful determinant of human behavior and speculate that they might have evolved as a way to establish hierarchy and group cohesion. How can you know when someone is bluffing? Paying attention? Genuinely interested? The answer, writes Sandy Pentland in Honest Signals, is that subtle patterns in how we interact with other people reveal our attitudes toward them. These unconscious social signals are not just a back channel or a complement to our conscious language; they form a separate communication network. Biologically based "honest signaling," evolved from ancient primate signaling mechanisms, offers an unmatched window into our intentions, goals, and values. If we understand this ancient channel of communication, Pentland claims, we can accurately predict the outcomes of situations ranging from job interviews to first dates. Pentland, an MIT professor, has used a specially designed digital sensor worn like an ID badgea "sociometer"to monitor and analyze the back-and-forth patterns of signaling among groups of people. He and his researchers found that this second channel of communication, revolving not around words but around social relations, profoundly influences major decisions in our liveseven though we are largely unaware of it. Pentland presents the scientific background necessary for understanding this form of communication, applies it to examples of group behavior in real organizations, and shows how by "reading" our social networks we can become more successful at pitching an idea, getting a job, or closing a deal. Using this "network intelligence" theory of social signaling, Pentland describes how we can harness the intelligence of our social network to become better managers, workers, and communicators.