Evaluating Affective User-Centered Design of Video Games Using Qualitative Methods

In recent years, researchers and practitioners in the human-computer interaction (HCI) community have placed a lot of focus in developing methods and processes for use in the gaming field. Affective user-centered design (AUCD) plays an important role in the game industry because it promotes emotional and mental communication, hence improving the interaction modes between users and video games. This paper looks at the development of a suitable AUCD guideline to determine if the expressed emotion, semantics, and mental concept of a tangible and intangible video gaming interface are well received by its intended users. Approaching AUCD in video games requires investigating multiple data to obtain a reliable data especially when assessing and interpreting affect and emotion. They present a challenge due to many ambiguities related to affect definition and measuring affective emotion can be very tedious due to its complexity and unpredictability. In this paper, we describe the methods and techniques used to assess affective user-centered design in video games. We also discuss our approaches within the context of existing affective gaming and user-centered design theory and data gathering procedures, including the factors affecting internal and external validity and the data analysis techniques.

Bio-Affective Computer Interface for Game Interaction

Affective bio-feedback can be an important instrument to enhance the game experience. Several studies have provided evidence of the usefulness of physiological signals for affective gaming; however, due to the limited knowledge about the distinctive autonomic signatures for every emotion, the pattern matching models employed are limited in the number of emotions they are able to classify. This paper presents a bio-affective gaming interface (BAGI) that can be used to customize a game experience according to the player’s emotional response. Its architecture offers important characteristics for gaming that are important because they make possible the reusability of previous findings and the inclusion of new models to the system. In order to prove the effectiveness of BAGI, two different types of neural networks have been trained to recognize emotions. They were incorporated into the system to customize, in real-time, the computer wallpaper according to the emotion experienced by the user. Best results were obtained with a probabilistic neural network with accuracy results of 84.46% on the training data and 78.38% on the validation for new independent data sets.