Abstract This paper aims at investigating the action prediction problem from a pure kinematic perspective. Specifically, we address the problem of recognizing future actions, indeed human intentions, underlying a same initial (and apparently unrelated) motor act. This study is inspired by neuroscientific findings asserting that motor acts at the very onset are embedding information about the intention with which are performed, even when different intentions originate from a same class of movements. To demonstrate this claim in computational and empirical terms, we designed an ad hoc experiment and built a new 3D and 2D dataset where, in both training and testing, we analyze a same class of grasping movements underlying different intentions. We investigate how much the intention discriminants generalize across subjects, discovering that each subject tends to affect the prediction by his/her own bias. Inspired by the domain adaptation problem, we propose to interpret each subject as a domain, leading to a novel subject adversarial paradigm. The proposed approach favorably copes with our new problem, boosting the considered baseline features encoding 2D and 3D information and which do not exploit the subject information.

Autism is a behavioral neurological disorder affecting a significant percentage of worldwide population. It especially starts manifesting at very low ages, but it is difficult to early diagnose it since there is not a specific exam or trial that is able to spot it safely. Its detection is in fact mainly dependent from the medical expertise used to assess the patient behavior during direct interviews. This work aims at providing an automatic objective support to the doctor for the assessment of (early) diagnosis of possible autistic subjects by only using video sequences. The underlying idea and rationale come from the psychological and neuroscience studies claiming that the executions of simple motor acts are different between pathological and healthy subjects, and this can be sufficient to discriminate between them. To this end, we devised an experiment in which we recorded, using a standard video camera, patient and healthy children performing the same simple gesture of grasping a bottle. By only processing the video clips depicting the grasping action using a recurrent deep neural network, we are able to discriminate with a good accuracy between the 2 classes of subjects. The designed deep model is also able to provide a sort of attention map in which the zones in the video of major interest are identified in space and time: this “explains” in a certain way which areas the model deems more relevant to the classification purpose, which could also be used by the doctor to make the diagnosis. In the end, this work constitutes a first step towards the development of an automatic computational system devoted to the early diagnosis of autistic subjects, providing the medical expert of a supportive objective method, potentially simple to use in clinical and also more open settings.

The coordination of a leader with group members is very important for an effective leadership given that this figure is the person who actually manages the team members to achieve a desired goal. Investigating the leadership and especially the leadership style is a prominent research topic in social and organizational psychology. However this is a new problem in social signal processing that can actually make valuable contributions by analyzing multimodal data in a more effective and efficient way. In this work we identify the leadership style of an emergent leader (i.e. the leader who naturally arises from a group not designated) as autocratic or democratic. The proposed method is applied to a dataset in-the-wild; in other words there is no role-playing which is novel for this problem. Multiple kernel learning (MKL) using multimodal nonverbal features is utilized to predict leadership styles that proved to achieve better predictions as compared to traditional learning methods. Thanks to MKL and a simple heuristic proposed the best performing features are also identified showing that better predictions can be reached only by using those features. Additionally correlation analysis between the extracted nonverbal features and the results of social psychology questionnaire is also performed. This shows that significantly high correlations exist for speaking activity based and prosodic nonverbal features.

In social psychology, the leadership investigation is performed using questionnaires which are either i) self-administered or ii) applied to group participants to evaluate other members or iii) filled by external observers. While each of these sources is informative, using them individually might not be as effective as using them jointly. This paper is the first attempt which addresses the automatic identification of leaders in small-group meetings, by learning effective models using nonverbal audio-visual features and the results of social psychology questionnaires that reflect assessments regarding leadership. Learning is based on Multi-Task Learning which is performed without using ground-truth data (GT), but using the results of questionnaires (having substantial agreement with GT), administered to external observers and the participants of the meetings, as tasks. The results show that joint learning results in better performance as compared to single task learning and other baselines.

In this paper, we address the new problem of the prediction of human intentions. There is neuro-psychological evidence that actions performed by humans are anticipated by peculiar motor acts which are discriminant of the type of action going to be performed afterwards. In other words, an actual intention can be forecast by looking at the kinematics of the immediately preceding movement. To prove it in a computational and quantitative manner, we devise a new experimental setup where, without using contextual information, we predict human intentions all originating from the same motor act. We posit the problem as a classification task and we introduce a new multi-modal dataset consisting of a set of motion capture marker 3D data and 2D video sequences, where, by only analysing very similar movements in both training and test phases, we are able to predict the underlying intention, i.e., the future, never observed action. We also present an extensive experimental evaluation as a baseline, customizing state-of-the-art techniques for either 3D and 2D data analysis. Realizing that video processing methods lead to inferior performance but show complementary information with respect to 3D data sequences, we developed a 2D+3D fusion analysis where we achieve better classification accuracies, attesting the superiority of the multimodal approach for the context-free prediction of human intentions.

In this paper, we propose an effective method for emergent leader detection in meeting environments which is based on nonverbal visual features. Identifying emergent leader is an important issue for organizations. It is also a well-investigated topic in social psychology while a relatively new problem in social signal processing (SSP). The effectiveness of nonverbal features have been shown by many previous SSP studies. In general, the nonverbal video-based features were not more effective compared to audio-based features although, their fusion generally improved the overall performance. However, in absence of audio sensors, the accurate detection of social interactions is still crucial. Motivating from that, we propose novel, automatically extracted, nonverbal features to identify the emergent leadership. The extracted nonverbal features were based on automatically estimated visual focus of attention which is based on head pose. The evaluation of the proposed method and the defined features were realized using a new dataset which is firstly introduced in this paper including its design, collection and annotation. The effectiveness of the features and the method were also compared with many state of the art features and methods.