Toward Optimized VR/AR Ergonomics: Modeling and Predicting User Neck
  Muscle Contraction
Yunxiang Zhang Kenneth Chen Qi Sun
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.