Designing Fatigue-Aware VR Interfaces via Biomechanical Models

2026-03-26

AI Summary

Prolonged mid-air interactions in virtual reality (VR) often induce arm fatigue, degrading user experience, yet conventional ergonomic designs rely heavily on extensive human-subject experiments. This work proposes the first VR interface layout optimization method that directly incorporates biomechanical muscle fatigue as an optimization signal. We develop a hierarchical reinforcement learning framework integrating a three-compartment control and recovery (3CC-r) fatigue model with biomechanical simulation, enabling an agent to perform button-pressing tasks while estimating muscular effort; fatigue feedback then drives UI layout optimization. Experimental results demonstrate that the model-predicted fatigue trends align closely with real-user data. Moreover, layouts optimized by our approach significantly reduce subjective fatigue in subsequent human studies and successfully generalize to long-sequence interaction scenarios with non-uniform task frequencies.

Abstract

Prolonged mid-air interaction in virtual reality (VR) causes arm fatigue and discomfort, negatively affecting user experience. Incorporating ergonomic considerations into VR user interface (UI) design typically requires extensive human-in-the-loop evaluation. Although biomechanical models have been used to simulate human behavior in HCI tasks, their application as surrogate users for ergonomic VR UI design remains underexplored. We propose a hierarchical reinforcement learning framework that leverages biomechanical user models to evaluate and optimize VR interfaces for mid-air interaction. A motion agent is trained to perform button-press tasks in VR under sequential conditions, using realistic movement strategies and estimating muscle-level effort via a validated three-compartment control with recovery (3CC-r) fatigue model. The simulated fatigue output serves as feedback for a UI agent that optimizes UI element layout via reinforcement learning (RL) to minimize fatigue. We compare the RL-optimized layout against a manually-designed centered baseline and a Bayesian optimized baseline. Results show that fatigue trends from the biomechanical model align with human user data. Moreover, the RL-optimized layout using simulated fatigue feedback produced significantly lower perceived fatigue in a follow-up human study. We further demonstrate the framework's extensibility via a simulated case study on longer sequential tasks with non-uniform interaction frequencies. To our knowledge, this is the first work using simulated biomechanical muscle fatigue as a direct optimization signal for VR UI layout design. Our findings highlight the potential of biomechanical user models as effective surrogate tools for ergonomic VR interface design, enabling efficient early-stage iteration with less reliance on extensive human participation.

Problem

• virtual reality
• arm fatigue
• ergonomic design
• mid-air interaction
• user interface

Innovation

• biomechanical modeling
• fatigue-aware design
• hierarchical reinforcement learning
• virtual reality interfaces
• ergonomic optimization