Abstract
"Generic Shooter 3000" is a First-Person shooter with semi-realistic interaction, where actions such as firing a gun or diving through underwater sections are performed with your own body- through the use of biofeedback technology. This prototype is the idealised version of a research game developed for a master's thesis project on "biofeedback interaction in video games". © 2014 ACM.

Abstract
The usage of multi-touch interfaces on a tabletop device, has been very explored for elder users in several domains. This interaction technique is an alternative to reducing the obstacles that older adults face in the use of computer systems, e.g., handling of peripherals. Many design guidelines are proposed in the literature for a wide range of products and systems for elders, e.g. websites, TV user interfaces. However, there is a lack of set of design guidelines and design recommendations of multi-touch interfaces that matches elder's needs. This paper presents a set of design guidelines and design recommendations distilled and extracted from most relevant works on design of multi-touch interfaces for elders available in the literature. The results are a set of design guidelines, useful for designers, application developers, usability specialists and researchers. Copyright © IARIA, 2014.

Abstract
Emotionally adaptive games are one of the holy grails of modern affective game research. However, current state of the art affective games rely on static game adaptation mechanics that assume a fixed emotional reaction from players every time. Not only this, most commercial titles have no affective adaptation loop whatsoever and their design is based on game design optimizations via typical beta-testing procedures, which falls short of ideal both in the level design and long-term gameplay experience fronts. In this paper, we demonstrate a generalizable approach for building predictive models of players' emotional reactions across different games and game genres. We describe a physiological approach for modelling players' emotional reactions, which relies on features extracted from players' emotional responses to game events, which were collected and extrapolated through their physiological data during actual gameplay sessions. Based on the optimal feature sets found by three feature selection algorithms (best first, sequential feature selection and genetic search), the collected features are used to create computational models of players' emotional reactions on the arousal and valence dimensions of emotion, using several machine learning algorithms. We expect this approach will allow both a more objective and quicker prototyping for digital games, as well as foster a future generation of affective games capable of modelling players' affective profiles over time, thus adapting to their changing preferences and needs.

Abstract
Current approaches to game design improvements rely on gameplay testing, an iterative process following the test, try and fix pattern, relying on target audience feedback via standard questionnaires. Besides being a very time consuming phase, it is also highly subjective. In this paper, we demonstrate a generalizable approach for building predictive models of players' affective reactions across games and genres. Our aim is two-fold: 1) That game developers can use these models to more easily and accurately tune game parameters, allowing improved gaming experiences, and 2) That these models can be used as the basis for parameterisable and adaptive affective gaming. This paper describes our preliminary results regarding a novel, physiological-based method for emotional player profiling, which consists on the following three phases: (i) monitoring players' emotional states and game events, (ii) identifying player's emotional reactions to game events and (iii) individual and cluster-based modelling of players emotional reactions.

Abstract
Current approaches to game design improvements rely on time-consuming gameplay testing processes, which rely on highly subjective feedback from a target audience. In this paper, we propose a generalizable approach for building predictive models of players' emotional reactions across different games and game genres, as well as other forms of digital stimuli. Our input agnostic approach relies on the following steps: (a) collecting players' physiologically-inferred emotional states during actual gameplay sessions, (b) extrapolating the causal relations between changes in players' emotional states and recorded game events, and (c) building hierarchical cluster models of players' emotional reactions that can later be used to infer individual player models via fuzzy cluster membership vectors. We expect this work to benefit game designers by accelerating the affective playtesting process through the offline simulation of players' reactions to game design adaptations, as well as to contribute towards individually-tailored affective gaming.

Abstract