Affective games:a multimodal classification system

Affective gaming is a relatively new field of research that exploits human emotions to influence gameplay for an enhanced player experience. Changes in player’s psychology reflect on their behaviour and physiology, hence recognition of such variation is a core element in affective games. Complementary sources of affect offer more reliable recognition, especially in contexts where one modality is partial or unavailable. As a multimodal recognition system, affect-aware games are subject to the practical difficulties met by traditional trained classifiers. In addition, inherited game-related challenges in terms of data collection and performance arise while attempting to sustain an acceptable level of immersion. Most existing scenarios employ sensors that offer limited freedom of movement resulting in less realistic experiences. Recent advances now offer technology that allows players to communicate more freely and naturally with the game, and furthermore, control it without the use of input devices. However, the affective game industry is still in its infancy and definitely needs to catch up with the current life-like level of adaptation provided by graphics and animation

Emotions in context: examining pervasive affective sensing systems, applications, and analyses

Pervasive sensing has opened up new opportunities for measuring our feelings and understanding our behavior by monitoring our affective states while mobile. This review paper surveys pervasive affect sensing by examining and considering three major elements of affective pervasive systems, namely; “sensing”, “analysis”, and “application”. Sensing investigates the different sensing modalities that are used in existing real-time affective applications, Analysis explores different approaches to emotion recognition and visualization based on different types of collected data, and Application investigates different leading areas of affective applications. For each of the three aspects, the paper includes an extensive survey of the literature and finally outlines some of challenges and future research opportunities of affective sensing in the context of pervasive computing

Machine Understanding of Human Behavior

A widely accepted prediction is that computing will move to the background, weaving itself into the fabric of our everyday living spaces and projecting the human user into the foreground. If this prediction is to come true, then next generation computing, which we will call human computing, should be about anticipatory user interfaces that should be human-centered, built for humans based on human models. They should transcend the traditional keyboard and mouse to include natural, human-like interactive functions including understanding and emulating certain human behaviors such as affective and social signaling. This article discusses a number of components of human behavior, how they might be integrated into computers, and how far we are from realizing the front end of human computing, that is, how far are we from enabling computers to understand human behavior

Audiovisual integration of emotional signals from others' social interactions

Audiovisual perception of emotions has been typically examined using displays of a solitary character (e.g., the face-voice and/or body-sound of one actor). However, in real life humans often face more complex multisensory social situations, involving more than one person. Here we ask if the audiovisual facilitation in emotion recognition previously found in simpler social situations extends to more complex and ecological situations. Stimuli consisting of the biological motion and voice of two interacting agents were used in two experiments. In Experiment 1, participants were presented with visual, auditory, auditory filtered/noisy, and audiovisual congruent and incongruent clips. We asked participants to judge whether the two agents were interacting happily or angrily. In Experiment 2, another group of participants repeated the same task, as in Experiment 1, while trying to ignore either the visual or the auditory information. The findings from both experiments indicate that when the reliability of the auditory cue was decreased participants weighted more the visual cue in their emotional judgments. This in turn translated in increased emotion recognition accuracy for the multisensory condition. Our findings thus point to a common mechanism of multisensory integration of emotional signals irrespective of social stimulus complexity