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

Abstract
Pectus excavatum is the most common deformity of the thorax and usually comprises Computed Tomography (CT) examination for pre-operative diagnosis. Aiming at the elimination of the high amounts of CT radiation exposure, this work presents a new methodology for the replacement of CT by a laser scanner (radiation-free) in the treatment of pectus excavatum using personally modeled prosthesis. The complete elimination of CT involves the determination of ribs external outline, at the maximum sternum depression point for prosthesis placement, based on chest wall skin surface information, acquired by a laser scanner. The developed solution resorts to artificial neural networks trained with data vectors from 165 patients. Scaled Conjugate Gradient, Levenberg-Marquardt, Resilient Back propagation and One Step Secant gradient learning algorithms were used. The training procedure was performed using the soft tissue thicknesses, determined using image processing techniques that automatically segment the skin and rib cage. The developed solution was then used to determine the ribs outline in data from 20 patient scanners. Tests revealed that ribs position can be estimated with an average error of about 6.82 +/- 5.7 mm for the left and right side of the patient. Such an error range is well below current prosthesis manual modeling (11.7 +/- 4.01 mm) even without CT imagiology, indicating a considerable step forward towards CT replacement by a 3D scanner for prosthesis personalization.

Abstract
Nowadays, different techniques are available for manufacturing full-arch implantsupported prosthesis, many of them based on an impression procedure. Nevertheless, the long-term success of the prosthesis is highly influenced by the accuracy during such process, being affected by factors such as the impression material, implant position, angulation and depth. This paper investigates the feasibility of a 3D electromagnetic motion tracking system as an acquisition method for modeling such prosthesis. To this extent, we propose an implant acquisition method at the patient mouth, using a specific prototyped tool coupled with a tracker sensor, and a set of calibration procedures (for distortion correction and tool calibration), that ultimately obtains combined measurements of the implant's position and angulation, and eliminating the use of any impression material. However, in the particular case of the evaluated tracking system, the order of magnitude of the obtained errors invalidates its use for this specific application. © 2014 Taylor & Francis Group.