Maintaining player engagement is pivotal for video game success, yet achieving the optimal difficulty level that adapts to diverse player skills remains a significant challenge. Initial difficulty settings in games often fail to accommodate the evolving abilities of players, necessitating adaptive difficulty mechanisms to keep the gaming experience engaging. This study introduces a custom first-person-shooter (FPS) game to explore Dynamic Difficulty Adjustment (DDA) techniques, leveraging both performance metrics and emotional responses gathered from physiological sensors. Through a within-subjects experiment involving casual and experienced gamers, we scrutinized the effects of various DDA methods on player performance and self-reported game perceptions. Contrary to expectations, our research did not identify a singular, most effective DDA strategy. Instead, findings suggest a complex landscape where no one approach—be it performance-based, emotion-based, or a hybrid—demonstrably surpasses static difficulty settings in enhancing player engagement or game experience. Noteworthy is the data’s alignment with Flow Theory, suggesting potential for the Emotion DDA technique to foster engagement by matching challenges to player skill levels. However, the overall modest impact of DDA on performance metrics and emotional responses highlights the intricate challenge of designing adaptive difficulty that resonates with both the mechanical and emotional facets of gameplay. Our investigation contributes to the broader dialogue on adaptive game design, emphasizing the need for further research to refine DDA approaches. By advancing our understanding and methodologies, especially in emotion recognition, we aim to develop more sophisticated DDA strategies. These strategies aspire to dynamically align game challenges with individual player states, making games more accessible, engaging, and enjoyable for a wider audience.

Emerging Virtual Reality (VR) displays with embedded eye trackers are currently becoming a commodity hardware (e.g., HTC Vive Pro Eye). Eye-tracking data can be utilized for several purposes, including gaze monitoring, privacy protection, and user authentication/identification. Identifying users is an integral part of many applications due to security and privacy concerns. In this paper, we explore methods and eye-tracking features that can be used to identify users. Prior VR researchers explored machine learning on motion-based data (such as body motion, head tracking, eye tracking, and hand tracking data) to identify users. Such systems usually require an explicit VR task and many features to train the machine learning model for user identification. We propose a system to identify users utilizing minimal eye-gaze-based features without designing any identification-specific tasks. We collected gaze data from an educational VR application and tested our system with two machine learning (ML) models, random forest (RF) and k-nearest-neighbors (kNN), and two deep learning (DL) models: convolutional neural networks (CNN) and long short-term memory (LSTM). Our results show that ML and DL models could identify users with over 98% accuracy with only six simple eye-gaze features. We discuss our results, their implications on security and privacy, and the limitations of our work.

Achieving precise real-time localization and ensuring robustness are critical challenges in multi-user mobile AR applications. Leveraging collaborative information to augment tracking accuracy on lightweight devices and fortify overall system robustness emerges as a crucial necessity. In this paper, we propose a robust centralized collaborative rnulti-agent VI-SLAM system for mobile AR interaction and server-side efficient consistent mapping. The system deploys a lightweight VIO frontend on mobile devices for real-time tracking, and a backend running on a remote server to update multiple submaps. When overlapping areas between submaps across agents are detected, the system performs submap fusion to establish a globally consistent map. Additionally, we propose a map registration and fusion strategy based on covisibility areas for online registration and fusion in multi-agent scenarios. To improve the tracking accuracy of the frontend on agent, we introduce a strategy for updating the global map to the local map at a moderate frequency between the camera-rate pose estimation of the frontend VIO and the low-frequency global map optimization, using a tightly coupled strategy to achieve consistency of the multi-agent frontend poses estimation in the global map. The effectiveness of the proposed method is further confirmed by executing backend mapping on the server and deploying VIO frontends on multiple mobile devices for AR demostration. Additionally, we discuss the scalability of the proposed system by analyzing network traffic, synchronization frequency, and other factors at both the agent and server ends.

This study investigated undergraduate nursing students' perceptions of integrating virtual reality (VR) technology into a community nursing course, specifically home and environmental patient safety assessments. This study used a mixed-methods approach with an explanatory design. Participants were recruited from a community health course. The students engaged in a VR simulation involving home visits using the vizHome platform. Quantitative and qualitative data were gathered using the Technology Acceptance Model (TAM) and the System Usability Scale (SUS). Semi-structured interviews were conducted. Quantitative data were collected through Qualtrics and secure Zoom connections for the interviews. The data were analyzed with SPSS and MAXQDA. The results indicated that participants perceived VR as valuable and easy to use for learning home assessment skills. The System Usability Scale (SUS) score revealed room for improvement. Technical limitations were identified as challenges that must be addressed to enhance the user experience. Participants acknowledged VR's potential to supplement traditional learning methods, providing safe and realistic exposure to diverse home environments. While VR was seen as beneficial, it was not considered a replacement for actual home visits in community nursing education. This study provided clinical teaching evidence on the usability and student perceptions of VR in community courses.

Virtual reality (VR) makes learning more interesting for students and could help them remember what they have learned better than traditional methods. However, a student could get distracted in a VR environment because of stress, wandering thoughts, unwanted noise, outside sounds, etc. Distractions could be classified as either external (due to the environment) or internal (due to internal thoughts). To identify external distractions, previous researchers have used eye-gaze data. Eye-gaze data cannot, however, detect internal distractions because a user may be looking at the educational material in VR while also thinking about something else. We explored the usage of electroencephalogram (EEG) data to detect internal distractions. We designed an educational VR environment and trained three machine learning models: Random Forest (RF), Support Vector Machine (SVM), and k-nearest-neighbors (kNN), to detect internal distractions of students. For data labeling, we considered two window lengths (20 and 30 seconds) starting at 5 seconds after the distraction task started. We did cross-subject and cross-session tests, and our results show that kNN provides a better accuracy (64%) compared to RF and SVM. We also found that the shorter window length of 20 seconds provided a slightly better accuracy then the 30 second window. Our results are not far from such random guessing. Therefore, our contribution lies more in the fostering of ideas for future work that must employ more advanced and sophisticated techniques.