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Suspense is key to the enjoyment of games and interactive storytelling. Although suspense has been studied extensively in traditional media such as fiction and film, there has been relatively little research on managing suspense in games. As interactive media, games differ from traditional media in many ways. Although many conventional techniques for managing suspense still apply to games, certain suspense manipulation techniques are specific to games. In this paper, we discuss a framework for managing suspense in games and present a comprehensive study of various game design techniques for managing suspense. In particular, we focus on the use of game mechanics and game artifacts to manipulate suspense, which has not received enough attention in previous works. Our goal is to provide a comprehensive guide for game designers to explore different ways to manage suspense in their games or use it to analyze the suspense management techniques in existing games. 

In this paper, we describe the design of an interactive cartographic storytelling platform for the 1906 Atlanta Race Massacre, a horrific incident that had a profound impact on the civil and human rights movement in the United States. This four-day event happened at various locations in downtown Atlanta and involved many people. Although multiple books and articles have been written about the 1906 Atlanta Race Massacre, they described the past events using conventional storytelling methods. We want to tell this story from a cartographic perspective because the locations are essential to this story. We also want to connect the past with the present because most people walking on the same streets today do not know the history and significance of the locations. Furthermore, most people are unaware that some major institutions are intricately connected to the people involved in the 1906 events. Telling the story this way requires us to handle a complex spatio-temporal structure and an extensive social network, which is unusual in traditional cartographic storytelling. In this paper, we discuss our design decisions and rationals. We believe our discussion will benefit other interactive story designers who deal with similar complex stories. 

Kinematic motion analysis is widely used in health-care, sports medicine, robotics, biomechanics, sports science, etc. Motion capture systems are essential for motion analysis. There are three types of motion capture systems: marker-based capture, vision-based capture, and volumetric capture. Marker-based motion capture systems can achieve fairly accurate results but attaching markers to a body is inconvenient and time-consuming. Vision-based, marker-less motion capture systems are more desirable because of their non-intrusiveness and flexibility. Volumetric capture is a newer and more advanced marker-less motion capture system that can reconstruct realistic, full-body, animated 3D character models. But volumetric capture has rarely been used for motion analysis because volumetric motion data presents new challenges. We propose a new method for conducting kinematic motion analysis using volumetric capture data. This method consists of a three-stage pipeline. First, the motion is captured by a volumetric capture system. Then the volumetric capture data is processed using the Iterative Closest Points (ICP) algorithm to generate virtual markers that track the motion. Third, the motion tracking data is imported into the biomechanical analysis tool OpenSim for kinematic motion analysis. Our motion analysis method enables users to apply numerical motion analysis to the skeleton model in OpenSim while also studying the full-body, animated 3D model from different angles. It has the potential to provide more detailed and in-depth motion analysis for areas such as healthcare, sports science, and biomechanics. 

As video games and esports continue to grow in popularity, gaming injuries are also on the rise. In recent years, medical professionals have placed greater emphasis on preventing and treating gaming injuries and proposed specific gaming health guidelines. However, the game industry and game research community have not done enough to address the hazards of gaming injuries or raise awareness about such hazards to players, parents, and game designers. In this paper, we propose a framework of injury-aware game design that addresses the two main causes of gaming injuries: prolonged gaming and repetitive microtrauma. We have identified a set of injury-aware game design techniques to help raise awareness of gaming-related hazards, promote healthy gaming behavior, and optimize gameplay to prevent injuries. We believe an effective way to deliver gaming-related health information to game players is through games themselves. To demonstrate this framework, we have developed an injury-aware game and conducted a user study with players and game designers. The results from the proof-of-concept game and user study show that both players and designers have a positive reception to the idea of implementing more inclusive measures into games, with nearly all participants of the user study being interested in the idea of hand exercise recommendations. 

Suspense is an important emotion for the enjoyment of games. Various methods have been proposed to manage suspense in games. Most existing works focus on managing suspense via storytelling or artifacts, such as sound effects. However, little work has been done in studying how to use gameplay to manage suspense. In this paper, we present a study in which we developed a horror-adventure game with a built-in suspense manager based on adaptive gameplay. We conducted a small user study to evaluate the effect of dynamic suspense management on game players. Our results showed that gameplay could potentially be used to manage the level of suspense experienced by players, independent of the story and artifacts in the game. The work discussed in this paper will provide game designers with new tools for suspense management in non-narrative-based games. 

The emergence of photorealistic and cinematic non-player character (NPC) animation presents new challenges for video game developers. Game player expectations of cinematic acting styles bring a more sophisticated aesthetic in the representation of social interaction. New methods can streamline workflow by integrating actor-driven character design into the development of game character AI and animation. A workflow that tracks actor performance to final neural network (NN) design depends on a rigorous method of producing single-actor video corpora from which to train emotion AI NN models. While numerous video corpora have been developed to study emotion elicitation of the face from which to test theoretical models and train neural networks to recognize emotion, developing single-actor corpora to train NNs of NPCs in video games is uncommon. A class of facial emotion recognition (FER) products have enabled production of single-actor video corpora that use emotion analysis data. This paper introduces a single-actor game character corpora workflow for game character developers. The proposed method uses a single actor video corpus and dataset with the intent to train and implement a NN in an off-the-shelf video game engine for facial animation of an NPC. The efficacy of using a NN-driven animation controller has already been demonstrated (Schiffer, 2021, Kozasa et. al 2006). This paper focuses on using a single-actor video corpus for the purpose of training a NN-driven animation controller. 

Heuristic evaluation has been an important part of data visualization. Many heuristic rules and guidelines for evaluating data visualization have been proposed and reviewed. However, applying heuristic evaluation in practice is not trivial. First, the heuristic rules are discussed in different publications across different disciplines. There is no central repository of heuristic rules for data visualization. There are no consistent guidelines on how to apply them. Second, it is difficult to find multiple experts who are knowledgeable about the heuristic rules, their pitfalls, and counterpoints. To address this issue, we present a computer-assisted heuristic evaluation method for data visualization. Based on this method, we developed a Python-based tool for evaluating plots created by the visualization tool Plotly. Recent advances in declarative data visualization libraries have made it feasible to create such a tool. By providing advice, critiques, and recommendations, this tool serves as a knowledgeable virtual assistant to help data visualization developers evaluate their visualizations as they code. 

In recent years, various mechanisms have been proposed to optimize players’ emotional experience. In this paper, we focus on suspense, one of the key emotions in gameplay. Most previous research on suspense management in games focused on narratives. Instead, we propose a new computational model of Suspense for Non-Narrative Gameplay (SNNG). SNNG is built around a Player Suspense Model (PSM) with three key factors: hope, fear, and uncertainty. These three factors are modeled as three sensors that can be triggered by particular game objects (e.g., NPCs) and game mechanics (e.g., health). A player’s feeling of suspense can be adjusted by altering the level of hope, fear, and uncertainty. Therefore, an SNNG-enhanced game engine could manage a player’s level of suspense by adding or removing game objects, diverting NPCs, adjusting game mechanics, and giving or withholding information. We tested our model by integrating SNNG into a Pacman game. Our preliminary experiment with nine subjects was encouraging.