# Designing Fatigue-Aware VR Interfaces via Biomechanical Models

arXiv: https://arxiv.org/abs/2603.26031 · HTML: https://mlsanity.com/paper/2603.26031/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 57 references that are in the map.
- First posted on arXiv 27 Mar 2026.

## Builds on (most cited first, 12 of 12)

- [MyoSuite -- A contact-rich simulation suite for musculoskeletal motor control](https://mlsanity.com/paper/2205.13600/) (2022)
- [Adapting User Interfaces with Model-based Reinforcement Learning](https://mlsanity.com/paper/2103.06807/) (2021)
- [Reinforcement Learning Control of a Biomechanical Model of the Upper Extremity](https://mlsanity.com/paper/2011.07105/) (2020)
- [Optimizing User Interface Layouts via Gradient Descent](https://mlsanity.com/paper/2002.10702/) (2020)
- [SIM2VR: Towards Automated Biomechanical Testing in VR](https://mlsanity.com/paper/2404.17695/) (2024)
- [SAM: A Modular Framework for Self-Adapting Web Menus](https://mlsanity.com/paper/1901.08289/) (2019)
- [Reinforcement Learning-Based Framework for the Intelligent Adaptation of User Interfaces](https://mlsanity.com/paper/2405.09255/) (2024)
- [Demystifying Reward Design in Reinforcement Learning for Upper Extremity Interaction: Practical Guidelines for Biomechanical Simulations in HCI](https://mlsanity.com/paper/2508.15727/) (2025)
- [Increasing Interaction Fidelity: Training Routines for Biomechanical Models in HCI](https://mlsanity.com/paper/2508.16581/) (2025)
- [Preference-Guided Multi-Objective UI Adaptation](https://mlsanity.com/paper/2509.18960/) (2025)
- [NICER: A New and Improved Consumed Endurance and Recovery Metric to Quantify Muscle Fatigue of Mid-Air Interactions](https://mlsanity.com/paper/2406.08875/) (2024)
- [AlphaPIG: The Nicest Way to Prolong Interactive Gestures in Extended Reality](https://mlsanity.com/paper/2503.01011/) (2025)

## Abstract (arXiv)

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.