SIM2VR: Towards Automated Biomechanical Testing in VR

Abstract

Automated biomechanical testing has great potential for the development of VR applications, as initial insights into user behaviour can be gained in silico early in the design process. In particular, it allows prediction of user movements and ergonomic variables, such as fatigue, prior to conducting user studies. However, there is a fundamental disconnect between simulators hosting state-of-the-art biomechanical user models and simulators used to develop and run VR applications. Existing user simulators often struggle to capture the intricacies of real-world VR applications, reducing ecological validity of user predictions. In this paper, we introduce SIM2VR, a system that aligns user simulation with a given VR application by establishing a continuous closed loop between the two processes. This, for the first time, enables training simulated users directly in the same VR application that real users interact with. We demonstrate that SIM2VR can predict differences in user performance, ergonomics and strategies in a fast-paced, dynamic arcade game. In order to expand the scope of automated biomechanical testing beyond simple visuomotor tasks, advances in cognitive models and reward function design will be needed.

23 pages, 10 figures, 1 tables; Supplementary Material: 5 pages, 4 figures, 1 table

References in corpus (2)

• Inferring Cognitive Models from Data using Approximate Bayesian Computation
• Optimal Feedback Control for Modeling Human-Computer Interaction

Cited by in corpus (6)

• Increasing Interaction Fidelity: Training Routines for Biomechanical Models in HCI
• Harnessing Large Language Model for Virtual Reality Exploration Testing: A Case Study
• What Makes a Model Breathe? Understanding Reinforcement Learning Reward Function Design in Biomechanical User Simulation
• Demystifying Reward Design in Reinforcement Learning for Upper Extremity Interaction: Practical Guidelines for Biomechanical Simulations in HCI
• Mind & Motion: Opportunities and Applications of Integrating Biomechanics and Cognitive Models in HCI
• MyoInteract: A Framework for Fast Prototyping of Biomechanical HCI Tasks using Reinforcement Learning