"Don't You Dare Go Hollow": How Dark Souls Helps Players Cope with Depression, a Thematic Analysis of Reddit DiscussionsEntertainment videogames have been recognized for their potential therapeutic benefits, but there is a need for more in-depth, game-specific explorations of the game features that could contribute to such benefits. This study examines how players of Dark Souls describe the game as helping them cope with depression. We conducted a thematic analysis of Reddit discussions where players narrate their mental health experiences with the game, using AI tools to assist in identifying relevant data for a purposive sample. Our findings suggest that Dark Souls could support players’ mental health, for example, by (1) cultivating resilience and perseverance through its challenging gameplay, (2) triggering existential reflections through symbolic representations of depression, and (3) enabling supportive online communities and interactions. Our findings offer rich, player-centered insights into the perceived mental health benefits of commercial videogames, highlighting their potential to transcend entertainment and inform the design of engaging digital mental health tools.2025JVJaakko Väkevä et al.Aalto UniversityGame UX & Player BehaviorSerious & Functional GamesMental Health Apps & Online Support CommunitiesCHI
SIM2VR: Towards Automated Biomechanical Testing in VRAutomated biomechanical testing has great potential for the development of VR applications, as initial insights into user behaviour can be gained in silico early in the design process. In particular, it allows prediction of user movements and ergonomic variables, such as fatigue, prior to conducting user studies. However, there is a fundamental disconnect between simulators hosting state-of-the-art biomechanical user models and simulators used to develop and run VR applications. Existing user simulators often struggle to capture the intricacies of real-world VR applications, reducing ecological validity of user predictions. In this paper, we introduce SIM2VR, a system that aligns user simulation with a given VR application by establishing a continuous closed loop between the two processes. This, for the first time, enables training simulated users directly in the same VR application that real users interact with. We demonstrate that SIM2VR can predict differences in user performance, ergonomics and strategies in a fast-paced, dynamic arcade game. In order to expand the scope of automated biomechanical testing beyond simple visuomotor tasks, advances in cognitive models and reward function design will be needed.2024FFFlorian Fischer et al.Human Pose & Activity RecognitionVR Medical Training & RehabilitationUIST
WAVE: Anticipatory Movement Visualization for VR DancingDance games are one of the most popular game genres in Virtual Reality (VR), and active dance communities have emerged on social VR platforms such as VR Chat. However, effective instruction of dancing in VR or through other computerized means remains an unsolved human-computer interaction problem. Existing approaches either only instruct movements partially, abstracting away nuances, or require learning and memorizing symbolic notation. In contrast, we investigate how realistic, full-body movements designed by a professional choreographer can be instructed on the fly, without prior learning or memorization. Towards this end, we describe the design and evaluation of WAVE, a novel anticipatory movement visualization technique where the user joins a group of dancers performing the choreography with different time offsets, similar to spectators making waves in sports events. In our user study (N=36), the participants more accurately followed a choreography using WAVE, compared to following a single model dancer.2024MLMarkus Laattala et al.Aalto UniversityFull-Body Interaction & Embodied InputSocial & Collaborative VRDance & Body Movement ComputingCHI
Comic-making to Study Game-making: Using Comics in Qualitative Longitudinal Research on Game DevelopmentThis paper reports the research method of the “Game Expats Story (GES)” project that used qualitative longitudinal research (“QLR”) incorporated with art-based research (“ABR”) in the context of game research. To facilitate greater participant engagement and a higher retention rate of longitudinal participants, we created comic artworks simultaneously while researching the case of migrant/expatriate game developers (“game expats”) in Finland 2020-2023 in two phases: (i) art creation as part of the qualitative data analysis to supplement the researcher’s inductive abstraction of the patterns, and (ii) artwork as a communication and recall tool when periodically engaging with the informants over the multi-year project span. Our findings suggest that the method of QLR-ABR helps game research as it positively influences the researcher’s abstractions of longitudinal data and participants’ continuous engagement with a high retention rate of 89%. We conclude that incorporating artistic methods provides new opportunities for ethnographic research on game development.2024SPSolip Park et al.Aalto UniversitySerious & Functional GamesMultiplayer & Social GamesRole-Playing & Narrative GamesCHI
Grand Challenges in SportsHCIThe field of Sports Human-Computer Interaction (SportsHCI) investigates interaction design to support a physically active human being. Despite growing interest and dissemination of SportsHCI literature over the past years, many publications still focus on solving specific problems in a given sport. We believe in the benefit of generating fundamental knowledge for SportsHCI more broadly to advance the field as a whole. To achieve this, we aim to identify the grand challenges in SportsHCI, which can help researchers and practitioners in developing a future research agenda. Hence, this paper presents a set of grand challenges identified in a five-day workshop with 22 experts who have previously researched, designed, and deployed SportsHCI systems. Addressing these challenges will drive transformative advancements in SportsHCI, fostering better athlete performance, athlete-coach relationships, spectator engagement, but also immersive experiences for recreational sports or exercise motivation, and ultimately, improve human well-being.2024DEDon Samitha Elvitigala et al.Monash UniversityGame UX & Player BehaviorSerious & Functional GamesMental Health Apps & Online Support CommunitiesCHI
Breathing Life Into Biomechanical User ModelsForward biomechanical simulation in HCI holds great promise as a tool for evaluation, design, and engineering of user interfaces. Although reinforcement learning (RL) has been used to simulate biomechanics in interaction, prior work has relied on unrealistic assumptions about the control problem involved, which limits the plausibility of emerging policies. These assumptions include direct torque actuation as opposed to muscle-based control; direct, privileged access to the external environment, instead of imperfect sensory observations; and lack of interaction with physical input devices. In this paper, we present a new approach for learning muscle-actuated control policies based on perceptual feedback in interaction tasks with physical input devices. This allows modelling of more realistic interaction tasks with cognitively plausible visuomotor control. We show that our simulated user model successfully learns a variety of tasks representing different interaction methods, and that the model exhibits characteristic movement regularities observed in studies of pointing. We provide an open-source implementation which can be extended with further biomechanical models, perception models, and interactive environments.2022AIAleksi Ikkala et al.Human Pose & Activity RecognitionComputational Methods in HCIUIST
Personalized Game Difficulty Prediction Using Factorization MachinesThe accurate and personalized estimation of task difficulty provides many opportunities for optimizing user experience. However, user diversity makes such difficulty estimation hard, in that empirical measurements from some user sample do not necessarily generalize to others. In this paper, we contribute a new approach for personalized difficulty estimation of game levels, borrowing methods from content recommendation. Using factorization machines (FM) on a large dataset from a commercial puzzle game, we are able to predict difficulty as the number of attempts a player requires to pass future game levels, based on observed attempt counts from earlier levels and levels played by others. In addition to performance and scalability, FMs offer the benefit that the learned latent variable model can be used to study the characteristics of both players and game levels that contribute to difficulty. We compare the approach to a simple non-personalized baseline and a personalized prediction using Random Forests. Our results suggest that FMs are a promising tool enabling game designers to both optimize player experience and learn more about their players and the game.2022JKJeppe Theiss Kristensen et al.Recommender System UXGame UX & Player BehaviorSerious & Functional GamesUIST
Vibing Together: Dance Experiences in Social Virtual RealityDancing is a universal human activity, and also a domain of enduring significance in Human-Computer Interaction (HCI) research. However, there has been limited investigation into how computing supports the experiences of recreational dancers. Concurrently, a diverse and sizeable dance community has been emerging in VRChat. Little is known about these dancers’ experiences, motivations, and practices. Yet shedding light into these could inform both VR technology development and the design of systems that better support embodied and complex social interactions. To bridge this gap, we interviewed participants active in the VRChat dance scene. Through thematic analysis, we identified six central facets of their experiences related to freedom, community, dance as an individual experience, dance as a shared experience, dance as a performance, and self-expression and -exploration. Based on these findings, we discuss emerging tensions and highlight beneficial impacts of dancing in VR as well as problems that still await resolving.2022RPRoosa Piitulainen et al.Aalto UniversitySocial & Collaborative VRDance & Body Movement ComputingCHI
Predicting Mid-Air Interaction Movements and Fatigue Using Deep Reinforcement LearningA common problem of mid-air interaction is excessive arm fatigue, known as the "Gorilla arm" effect. To predict and prevent such problems at a low cost, we investigate user testing of mid-air interaction without real users, utilizing biomechanically simulated AI agents trained using deep Reinforcement Learning (RL). We implement this in a pointing task and four experimental conditions, demonstrating that the simulated fatigue data matches human fatigue data. We also compare two effort models: 1) instantaneous joint torques commonly used in computer animation and robotics, and 2) the recent Three Compartment Controller (3CC-) model from biomechanical literature. 3CC- yields movements that are both more efficient and relaxed, whereas with instantaneous joint torques, the RL agent can easily generate movements that are quickly tiring or only reach the targets slowly and inaccurately. Our work demonstrates that deep RL combined with the 3CC- provides a viable tool for predicting both interaction movements and user experiencein silico, without users.2020NCNoshaba Cheema et al.Max-Planck Institute for Informatics & German Research Center for Artificial Intelligence (DFKI)Full-Body Interaction & Embodied InputHuman Pose & Activity RecognitionCHI
Social Play in an Exergame: How the Need to Belong Predicts AdherenceThe general trend in exercise interventions, including those based on exergames, is to see high initial enthusiasm but significantly declining adherence. Social play is considered a core tenet of the design of exercise interventions help foster motivation to play. To determine whether social play aids in adherence to exergames, we analyzed data from a study involving five waves of six-week exergame trials between a single-player and multiplayer group. In this paper, we examine the multiplayer group to determine who might benefit from social play and why. We found that people who primarily engage in group play have superior adherence to people who primarily play alone. People who play alone in a multiplayer exergame have worse adherence than playing a single-player version, which can undo any potential benefit of social play. The primary construct distinguishing group versus alone players is their sense of program belonging. Program belonging is, thus, crucial to multiplayer exergame design.2019MKMaximus D. Kaos et al.Queen's University & Aalto UniversityHuman Pose & Activity RecognitionSerious & Functional GamesGamification DesignCHI
Review of Intrinsic Motivation in Simulation-based Game TestingThis paper presents a review of intrinsic motivation in player modeling, with a focus on simulation-based game testing. Modern AI agents can learn to win many games; from a game testing perspective, a remaining research problem is how to model the aspects of human player behavior not explained by purely rational and goal-driven decision making. A major piece of this puzzle is constituted by intrinsic motivations, i.e., psychological needs that drive behavior without extrinsic reinforcement such as game score. We first review the common intrinsic motivations discussed in player psychology research and artificial intelligence, and then proceed to systematically review how the various motivations have been implemented in simulated player agents. Our work reveals that although motivations such as competence and curiosity have been studied in AI, work on utilizing them in simulation-based game testing is sparse, and other motivations such as social relatedness, immersion, and domination appear particularly underexplored.2018SRShaghayegh Roohi et al.Aalto UniversityGame UX & Player BehaviorSerious & Functional GamesGamification DesignCHI