Abstract: 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. PubDate: Mon, 04 Jun 2018 00:00:00 +000
Abstract: Leaderboards and other game elements are present in many online environments, not just in videogames. When such environments have relatively few users, the implementation of those leaderboards is not usually a problem; however, that is no longer the case when they have dozens of thousands or more. For those situations we propose a method that is easy and cheap to implement. It is based on two particular data structures, a Self-Balanced Ordering Statistic Tree and a hash table, to perform proper leaderboard calculations in a fast and cheap way. More specifically, our proposal has time complexity, whereas other approaches also based on in-memory data structures like linked lists have , and others based on Hard Disk Drive operations like a relational database have . Such improvement with regard to the other approaches is corroborated with experimental results for several scenarios, also presented in this paper. PubDate: Thu, 03 May 2018 07:14:46 +000
Abstract: We propose a new pathfinding technique called xTrek that combines conventional pathfinding and influence fields; that is, we are introducing a new influence-sensitive pathfinder or influence-aware pathfinder. The leading idea of influence-aware pathfinding is to avoid unwanted regions and/or converge to desired regions of the search space during the path search. As shown throughout the paper, this region avoidance/convergence is more striking using our technique than in other field-aware pathfinders as, for example, risk-adverse pathfinders and constraint-aware navigation pathfinders. Furthermore, our technique constrains the search space even more than such state-of-the-art influence-aware pathfinders, aiming to reduce the memory space consumption, to speed up pathfinding computations, and at the same time to have better control on the paths to be discovered. PubDate: Thu, 12 Apr 2018 00:00:00 +000
Abstract: Facial analysis is a promising approach to detect emotions of players unobtrusively; however approaches are commonly evaluated in contexts not related to games or facial cues are derived from models not designed for analysis of emotions during interactions with games. We present a method for automated analysis of facial cues from videos as a potential tool for detecting stress and boredom of players behaving naturally while playing games. Computer vision is used to automatically and unobtrusively extract 7 facial features aimed at detecting the activity of a set of facial muscles. Features are mainly based on the Euclidean distance of facial landmarks and do not rely on predefined facial expressions, training of a model, or the use of facial standards. An empirical evaluation was conducted on video recordings of an experiment involving games as emotion elicitation sources. Results show statistically significant differences in the values of facial features during boring and stressful periods of gameplay for 5 of the 7 features. We believe our approach is more user-tailored, convenient, and better suited for contexts involving games. PubDate: Thu, 08 Mar 2018 00:00:00 +000
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