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The conveyance and recognition of affect and emotion partially determine how people interact with others and how they carry out and perform in their day-to-day activities. Hence, it is becoming necessary to endow technology with the ability to recognize users' affective states to increase the technologies' effectiveness. This paper makes three contributions to this research area. First, we demonstrate recognition models that automatically recognize affective states and affective dimensions from non-acted body postures instead of acted postures. The scenario selected for the training and testing of the automatic recognition models is a body-movement-based video game. Second, when attributing affective labels and dimension levels to the postures represented as faceless avatars, the level of agreement for observers was above chance level. Finally, with the use of the labels and affective dimension levels assigned by the observers as ground truth and the observers' level of agreement as base rate, automatic recognition models grounded on low-level posture descriptions were built and tested for their ability to generalize to new observers and postures using random repeated subsampling validation. The automatic recognition models achieve recognition percentages comparable to the human base rates as hypothesized.

While the use of virtual characters in medical education is becoming more and more commonplace, an understanding of the role they can play in empathetic communication skills training is still lacking. This paper presents a study aimed at building this understanding by determining if students can respond to a virtual patient's statement of concern with an empathetic response. A user study was conducted at the [blinded] College of Medicine in which early stage medical students interacted with virtual patients in one session and real humans trained to portray real patients (i.e., standardized patients) in a separate session about a week apart. During the interactions, the virtual and ‘real' patients presented the students with empathetic opportunities which were later rated by outside observers. The results of pairwise comparisons indicate that empathetic responses made to virtual patients were rated as significantly more empathetic than responses made to standardized patients. Even though virtual patients may be perceived as artificial, the educational benefit of employing them for training medical students' empathetic communications skills is that virtual patients offer a low pressure interaction which allows students to reflect on their responses.

Conveyance and recognition of human emotion and affective expression is influenced by many factors, including culture. Within the user modeling field, it has become increasingly necessary to understand the role affect can play in personalizing interactive interfaces using embodied animated agents. However, little research within the computer science field aims at understanding cultural differences within this vein. Therefore, we conducted a study to evaluate if differences exist in the way various cultures perceive emotion from body posture. We used static posture images of affectively expressive avatars to conduct recognition experiments with subjects from three cultures. After analyzing the subjects' judgments using multivariate analysis, we grounded the identified differences into a set of low-level posture features. We then used Mixture Discriminant Analysis (MDA) and an unsupervised expectation maximization (EM) model to build separate cultural models for affective posture recognition. Our results could prove useful to aide designers in creating more effective affective avatars.

The recognition of affective human communication may be used to provide developers with a rich source of information for creating systems that are capable of interacting well with humans. Posture has been acknowledged as an important modality of affective communication in many fields. Behavioral studies have shown that posture can communicate discrete emotion categories as well as affective dimensions. In the affective computing field, while models for the automatic recognition of discrete emotion categories from posture have been proposed, to our knowledge, there are no models for the automatic recognition of affective dimensions from static posture. As a continuation of our previous study, the two main goals of this study are: i) to build automatic recognition models to discriminate between levels of affective dimensions based on low-level postural features; and ii) to investigate both the discriminative power and the limitations of the postural features proposed. The models were built on the basis of human observers' ratings of posture according to affective dimensions directly (instead of emotion category) in conjunction with our posture features.