Toward Optimized VR/AR Ergonomics: Modeling and Predicting User Neck Muscle Contraction

TL;DR

This paper presents a bio-physically inspired model to predict neck muscle contraction in VR/AR users, aiming to improve ergonomic design and reduce discomfort through physiological data analysis and optimization.

Contribution

It introduces a novel computational model based on electromyography data to predict neck muscle contraction levels during VR/AR interactions, enhancing ergonomic understanding.

Findings

Model accurately predicts neck muscle contraction levels.

User studies show the model's predictions are reliable and generalizable.

Optimizing visual target layout reduces neck discomfort.

Abstract

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.