Selecting a target in a 3D environment is often challenging, especially with small/distant targets or when sensor noise is high. To facilitate selection, target-inference methods must be accurate, fast, and account for noise and motor variability. However, traditional data-free approaches fall short in accuracy since they ignore variability. While data-driven solutions achieve higher accuracy, they rely on extensive human datasets so prove costly, time-consuming, and transfer poorly. In this paper, we propose a novel approach that leverages biomechanical simulation to produce synthetic motion data, capturing a variety of movement-related factors, such as limb configurations and motor noise. Then, an inference model is trained with only the simulated data. Our simulation-based approach improves transfer and lowers cost; variety-rich data can be produced in large quantities for different scenarios. We empirically demonstrate that our method matches the accuracy of human-data-driven approaches using data from seven users. When deployed, the method accurately infers intended targets in challenging 3D pointing conditions within 5–10 milliseconds, reducing users' target-selection error by 71% and completion time by 35%.
doi.org/10.1145/3613904.3642131
Dance 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.
doi.org/10.1145/3613904.3642145
Future crewed missions to the Moon will face significant environmental and operational challenges, posing risks to the safety and performance of astronauts navigating its inhospitable surface. Whilst head-up displays (HUDs) have proven effective in providing intuitive navigational support on Earth, the design of novel human-spaceflight solutions typically relies on costly and time-consuming analogue deployments, leaving the potential use of lunar HUDs largely under-explored. This paper explores an alternative approach by simulating navigational HUD concepts in a high-fidelity Virtual Reality (VR) representation of the lunar environment. In evaluating these concepts with astronauts and other aerospace experts (n=25), our mixed methods study demonstrates the efficacy of simulated analogues in facilitating rapid design assessments of early-stage HUD solutions. We illustrate this by elaborating key design challenges and guidelines for future lunar HUDs. In reflecting on the limitations of our approach, we propose directions for future design exploration of human-machine interfaces for the Moon.
doi.org/10.1145/3613904.3642859
Virtual Reality (VR) holds great potential for psychomotor training, with existing applications using almost exclusively a `learning-by-doing' active learning approach, despite the possible benefits of incorporating observational learning. We compared active learning (n=26) with different variations of observational learning in VR for a manual assembly task. For observational learning, we considered three levels of visual similarity between the demonstrator avatar and the user, dissimilar (n=25), minimally similar (n=26), or a self-avatar (n=25), as similarity has been shown to improve learning. Our results suggest observational learning can be effective in VR when combined with `hands-on' practice and can lead to better far skill transfer to real-world contexts that differ from the training context. Furthermore, we found self-similarity in observational learning can be counterproductive when focusing on a manual task, and skills decay quickly without further training. We discuss these findings and derive design recommendations for future VR training.
doi.org/10.1145/3613904.3642550