Ergonomic efficiency is essential to the mass and prolonged adoption of VR/AR experiences. While VR/AR head-mounted displays unlock users' natural wide-range head movements during viewing, their neck muscle comfort is inevitably compromised by the added hardware weight. Unfortunately, little quantitative knowledge for understanding and addressing such an issue is available so far. Leveraging electromyography devices, we measure, model, and predict VR users' neck muscle contraction levels (MCL) while they move their heads to interact with the virtual environment. Specifically, by learning from collected physiological data, we develop a bio-physically inspired computational model to predict neck MCL under diverse head kinematic states. Beyond quantifying the cumulative MCL of completed head movements, our model can also predict potential MCL requirements with target head poses only. A series of objective evaluations and user studies demonstrate its prediction accuracy and generality, as well as its ability in reducing users' neck discomfort by optimizing the layout of visual targets. We hope this research will motivate new ergonomic-centered designs for VR/AR and interactive graphics applications. Source code is released at: https://github.com/NYU-ICL/xr-ergonomics-neck-comfort.

翻译：人体工效学效率对于VR/AR体验的大规模长期采用至关重要。尽管VR/AR头戴式显示器允许用户在观看时进行自然的广域头部运动，但由此增加的硬件重量不可避免地损害了颈部肌肉的舒适度。然而，目前对于理解和解决这一问题的定量知识十分匮乏。利用肌电图设备，我们在用户移动头部与虚拟环境交互时，对其颈部肌肉收缩水平（MCL）进行测量、建模与预测。具体而言，通过学习收集的生理数据，我们开发了一种生物物理启发的计算模型，用于预测不同头部运动状态下的颈部MCL。该模型不仅能量化已完成头部运动的累积MCL，还能仅依据目标头部姿态预测潜在的MCL需求。一系列客观评估和用户研究证明了其预测准确性与普适性，以及通过优化视觉目标布局来减轻用户颈部不适的能力。我们希望这项研究能推动VR/AR及交互图形学应用中新型人本工效学中心设计的发展。源代码已发布于：https://github.com/NYU-ICL/xr-ergonomics-neck-comfort。