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.

通过测量、建模和预测 VR 用户的颈部肌肉收缩水平，研究开发了一种生物物理启发的计算模型，可以量化完成头部运动的累积肌肉收缩水平并预测目标头部姿势下的潜在肌肉收缩要求，并通过优化视觉目标的布局来减少用户的颈部不适，旨在推动用于 VR/AR 和交互图形应用的人机工程设计。

优化虚拟/增强现实人体工程学：用户颈肌收缩的建模与预测