Palpation Alters Auditory Pain Expressions With Gender-Specific Variations in Robopatients

Abstract

Diagnostic errors remain a major cause of preventable deaths, particularly in resource-limited regions. Medical training simulators, including robopatients, play a vital role in reducing these errors by mimicking real patients for procedural training such as palpation. However, generating multimodal feedback, especially auditory pain expressions, remains challenging due to the complex relationship between palpation behavior and pain sound. The high-dimensional nature of pain sounds makes exploration challenging with conventional methods. This study introduces a novel experimental paradigm for pain expressivity in robopatients where they dynamically generate auditory pain expressions in response to palpation force, by co-optimizing realtime human feedback using machine learning. Using Proximal Policy Optimization (PPO), a reinforcement learning (RL) technique optimized for continuous adaptation, our robot iteratively refines pain sounds based on real-time human feedback. This robot initializes randomized pain responses to palpation forces, and the RL agent learns to adjust these sounds to align with human preferences. The results demonstrated that the system adapts to an individual’s palpation forces and sound preferences and captures a broad spectrum of pain intensities, from mild discomfort to acute distress. The study further showed that pain sound perception exhibits saturation at lower forces with gender-specific thresholds. Thus, this study presents an early attempt to use human-in-the-loop reinforcement learning to co-optimize haptic input and auditory pain expression. These findings highlight the system’s potential to enhance abdominal palpation training by offering a controllable and immersive simulation platforms.