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Applications helping us to maintain the focus on work are called “Zenware” (from concentration and Zen). While form factors, use cases and functionality vary, all these applications have a common goal: creating uninterrupted, focused attention on the task at hand. The rise of such tools exemplifies the users’ desire to control their attention within the context of omnipresent distraction. In expert interviews we investigate approaches in the context of attention-management at the workplace of knowledge workers. To gain a broad understanding, we use judgement sampling in interviews with experts from several disciplines. We especially explore how focus and flow can be stimulated. Our contribution has four components: a brief overview on the state of the art (1), a presentation of the results (2), strategies for coping with digital distractions and design guidelines for future Zenware (3) and an outlook on the overall potential in digital work environments (4).
Gamifying rehabilitation is an efficient way to improve motivation and exercise frequency. However, between flow theory, self-determination theory or Bartle's player types there is much room for speculation regarding the mechanics required for successful gamification, which in turn leads to increased motivation. For our study, we selected a gamified solution for motion training (an exergame) where the playful design elements are extremely simple. The contribution is three-fold: we show best practices from the state of the art, present a study analyzing the effects of simple gamification mechanics on a quantitative and on a qualitative level and discuss strategies for playful design in therapeutic movement games.
Designing Authentic Emotions for Non-Human Characters. A Study Evaluating Virtual Affective Behavior
(2017)
While human emotions have been researched for decades, designing authentic emotional behavior for non-human characters has received less attention. However, virtual behavior not only affects game design, but also allows creating authentic avatars or robotic companions. After a discussion of methods to model and recognize emotions, we present three characters with a decreasing level of human features and describe how established design techniques can be adapted for such characters. In a study, 220 participants assess these characters' emotional behavior, focusing on the emotion "anger". We want to determine how reliable users can recognize emotional behavior, if characters increasingly do not look and behave like humans. A secondary aim is determining if gender has an impact on the competence in emotion recognition. The findings indicate that there is an area of insecure attribution of virtual affective behavior not distant but close to human behavior. We also found that at least for anger, men and women assess emotional behavior equally well.
This work demonstrates the potentials of procedural content generation (PCG) for games, focusing on the generation of specific graphic props (reefs) in an explorer game. We briefly portray the state-of-the-art of PCG and compare various methods to create random patterns at runtime. Taking a step towards the game industry, we describe an actual game production and provide a detailed pseudocode implementation showing how Perlin or Simplex noise can be used efficiently. In a comparative study, we investigate two alternative implementations of a decisive game prop: once created traditionally by artists and once generated by procedural algorithms. 41 test subjects played both implementations. The analysis shows that PCG can create a user experience that is significantly more realistic and at the same time perceived as more aesthetically pleasing. In addition, the ever-changing nature of the procedurally generated environments is preferred with high significance, especially by players aged 45 and above.
This chapter portrays the historical and mathematical background of dynamic and procedural content generation (PCG). We portray and compare various PCG methods and analyze which mathematical approach is suited for typical applications in game design. In the next step, a structural overview of games applying PCG as well as types of PCG is presented. As abundant PCG content can be overwhelming, we discuss context-aware adaptation as a way to adapt the challenge to individual players’ requirements. Finally, we take a brief look at the future of PCG.
We present the design outline of a context-aware interactive system for smart learning in the STEM curriculum (science, technology, engineering, and mathematics). It is based on a gameful design approach and enables "playful coached learning" (PCL): a learning process enriched by gamification but also close to the learner's activities and emotional setting. After a brief introduction on related work, we describe the technological setup, the integration of projected visual feedback and the use of object and motion recognition to interpret the learner's actions. We explain how this combination enables rapid feedback and why this is particularly important for correct habit formation in practical skills training. In a second step, we discuss gamification methods and analyze which are best suited for the PCL system. Finally, emotion recognition, a major element of the final PCL design not yet implemented, is briefly outlined.
In this article, we present a taxonomy in Robot-Assisted Training; a growing body of research in Human–Robot Interaction which focuses on how robotic agents and devices can be used to enhance user’s performance during a cognitive or physical training task. Robot-Assisted Training systems have been successfully deployed to enhance the effects of a training session in various contexts, i.e., rehabilitation systems, educational environments, vocational settings, etc. The proposed taxonomy suggests a set of categories and parameters that can be used to characterize such systems, considering the current research trends and needs for the design, development and evaluation of Robot-Assisted Training systems. To this end, we review recent works and applications in Robot-Assisted Training systems, as well as related taxonomies in Human–Robot Interaction. The goal is to identify and discuss open challenges, highlighting the different aspects of a Robot-Assisted Training system, considering both robot perception and behavior control.
Dieser Artikel gibt einen Überblick der Möglichkeiten kontextbewusster Systeme und erläutert, wie diese die Autonomie zugleich erweitern und begrenzen können. Anwendungsbeispiele wie autonomes Fahren, Rehabilitation, industrielle Arbeit und Robotik zeigen die technischen Möglichkeiten auf. Neben der Erkennung von räumlichen Details werden auch die Potenziale der Erkennung von Emotionen beschrieben. Dabei wird zugunsten der Allgemeinverständlichkeit auf eine tiefe technische Detaillierung verzichtet, zugleich aber auf die jeweils relevante Forschungsliteratur verweisen.
With our society moving towards Industry 4.0, an increasing number of tasks and procedures in manual workplaces are augmented with a digital component. While the research area of Internet-of-Things focuses on combining physical objects with their digital counterpart, the question arises how the interface to human workers should be designed in such Industry 4.0 environments. The project motionEAP focuses on using Augmented Reality for creating an interface between workers and digital products in interactive workplace scenarios. In this paper, we summarize the work that has been done in the motionEAP project over the run-time of 4 years. Further, we provide guidelines for creating interactive workplaces using Augmented Reality, based on the experience we gained.