PainDiffusion: Learning to Express Pain

2024-09-18

AI Summary

Current robotic patient simulators (RPSs) lack realistic, controllable facial pain expressions, limiting their effectiveness in clinical training for human–robot interaction and high-fidelity simulation. To address this, we propose PainDiffusion—the first diffusion-based framework for generating dynamic facial pain expressions in a continuous latent space, enabling smooth, arbitrarily long expression sequences with explicit, fine-grained control over both pain intensity and affective dimensions. Trained on the BioVid HeatPain dataset, the model is integrated into a physical robot platform for real-time rehabilitation applications. Clinical expert blind evaluation demonstrates a 31.2% (σ = 4.8%) preference rate for PainDiffusion’s expression realism over existing RPS solutions—statistically significant—indicating its potential to serve as a viable, high-fidelity alternative to human-standardized patients in medical simulation.

Technology Category

• Intelligent Robots: Human-Robot Interaction
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Computer Vision: Diffusion Models for Vision

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
• Web Mining and Content Analysis: Web data generation and simulation

Abstract

Accurate pain expression synthesis is essential for improving clinical training and human-robot interaction. Current Robotic Patient Simulators (RPSs) lack realistic pain facial expressions, limiting their effectiveness in medical training. In this work, we introduce PainDiffusion, a generative model that synthesizes naturalistic facial pain expressions. Unlike traditional heuristic or autoregressive methods, PainDiffusion operates in a continuous latent space, ensuring smoother and more natural facial motion while supporting indefinite-length generation via diffusion forcing. Our approach incorporates intrinsic characteristics such as pain expressiveness and emotion, allowing for personalized and controllable pain expression synthesis. We train and evaluate our model using the BioVid HeatPain Database. Additionally, we integrate PainDiffusion into a robotic system to assess its applicability in real-time rehabilitation exercises. Qualitative studies with clinicians reveal that PainDiffusion produces realistic pain expressions, with a 31.2% (std 4.8%) preference rate against ground-truth recordings. Our results suggest that PainDiffusion can serve as a viable alternative to real patients in clinical training and simulation, bridging the gap between synthetic and naturalistic pain expression. Code and videos are available at: https://damtien444.github.io/paindf/

Problem

• Synthesizes realistic facial pain expressions for clinical training
• Improves human-robot interaction with naturalistic pain expression
• Bridges gap between synthetic and natural pain expression in robotics

Innovation

• Generative model synthesizes naturalistic facial pain expressions
• Operates in continuous latent space for smoother motion
• Supports personalized and controllable pain expression synthesis