To understand how implicit and explicit biofeedback work in games, we developed a first-person shooter (FPS) game to experiment with different biofeedback techniques. While this area has seen plenty of discussion, there is little rigorous experimentation addressing how biofeedback can enhance human-computer interaction. In our two-part study, (N=36) subjects first played eight different game stages with two implicit biofeedback conditions, with two simulation-based comparison and repetition rounds, then repeated the two biofeedback stages when given explicit information on the biofeedback. The biofeedback conditions were respiration and skin-conductance (EDA) adaptations. Adaptation targets were four balanced player avatar attributes. We collected data with psychophysiological measures (electromyography, respiration, and EDA), a game experience questionnaire, and game-play measures.According to our experiment, implicit biofeedback does not produce significant effects in player experience in an FPS game. In the explicit biofeedback conditions, players were more immersed and positively affected, and they were able to manipulate the game play with the biosignal interface. We recommend exploring the possibilities of using explicit biofeedback interaction in commercial games.

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Recently, multimodal and affective technologies have been adopted to support expressive and engaging interaction, bringing up a plethora of new research questions. Among the challenges, two essential topics are 1) how to devise truly multimodal systems that can be used seamlessly for customized performance and content generation, and 2) how to utilize the tracking of emotional cues and respond to them in order to create affective interaction loops. We present PuppetWall, a multi-user, multimodal system intended for digitally augmented puppeteering. This application allows natural interaction to control puppets and manipulate playgrounds comprising background, props, and puppets. PuppetWall utilizes hand movement tracking, a multi-touch display and emotion speech recognition input for interfacing. Here we document the technical features of the system and an initial evaluation. The evaluation involved two professional actors and also aimed at exploring naturally emerging expressive speech categories. We conclude by summarizing challenges in tracking emotional cues from acoustic features and their relevance for the design of affective interactive systems.