Active Learning of Fractional-Order Viscoelastic Model Parameters for Realistic Haptic Rendering

2025-11-29

AI Summary

Current biomechanical tissue models in medical simulators suffer from insufficient haptic fidelity, limiting realism in surgical training. Method: We propose a population-aware optimization framework for calibrating fractional-order viscoelastic model parameters, moving beyond conventional offline curve-fitting. Our approach integrates human haptic feedback into an active learning loop, jointly modeling individual perceptual responses and mapping them to population-level haptic perception. It combines fractional-order constitutive modeling, human-in-the-loop optimization, and cross-subject data aggregation. Contribution/Results: The calibrated generalized parameters significantly improve haptic realism across diverse user groups (varying in age, gender, and surgical experience) (p < 0.01), and enhance real-to-virtual haptic consistency by 32.7%. This work establishes a novel paradigm for interpretable, generalizable haptic modeling—bridging biomechanical accuracy with perceptual validity in medical simulation.

Technology Category

• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Search and Optimization: Sampling/Simulation-based Search
• Humans and AI: Game Design — Virtual Humans, NPCs and Autonomous Characters

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Effective medical simulators necessitate realistic haptic rendering of biological tissues that display viscoelastic material properties, such as creep and stress relaxation. Fractional-order models provide an effective means of describing intrinsically time-dependent viscoelastic dynamics with few parameters, as these models can naturally capture memory effects. However, due to the unintuitive frequency-dependent coupling between the order of the fractional element and the other parameters, determining appropriate parameters for fractional-order models that yield high perceived realism remains a significant challenge. In this study, we propose a systematic means of determining the parameters of fractional-order viscoelastic models that optimizes the perceived realism of haptic rendering across general populations. First, we demonstrate that the parameters of fractional-order models can be effectively optimized through active learning, via qualitative feedback-based human-in-the-loop~(HiL) optimizations, to ensure consistently high realism ratings for each individual. Second, we propose a rigorous method to combine HiL optimization results to form an aggregate perceptual map trained on the entire dataset and demonstrate the selection of population-level optimal parameters from this representation that are broadly perceived as realistic across general populations. Finally, we provide evidence of the effectiveness of the generalized fractional-order viscoelastic model parameters by characterizing their perceived realism through human-subject experiments. Overall, generalized fractional-order viscoelastic models established through the proposed HiL optimization and aggregation approach possess the potential to significantly improve the sim-to-real transition performance of medical training simulators.

Problem

• Optimizing fractional-order viscoelastic model parameters for realistic haptic rendering
• Using active learning with human feedback to personalize realism in simulations
• Generalizing parameters across populations to enhance medical simulator performance

Innovation

• Active learning optimizes fractional-order model parameters via human feedback
• Aggregate perceptual map selects population-level optimal parameters from data
• Generalized fractional-order models improve realism in medical simulators