Fatigue-PINN: Physics-Informed Fatigue-Driven Motion Modulation and Synthesis

Abstract

Fatigue modeling is essential for motion synthesis tasks to model human motions under fatigued conditions and biomechanical engineering applications, such as investigating the variations in movement patterns and posture due to fatigue, defining injury risk mitigation and prevention strategies, formulating fatigue minimization schemes, and creating improved ergonomic designs. Nevertheless, employing datadriven methods for synthesizing the impact of fatigue on motion, receives little to no attention in the literature. In this work, we present Fatigue-PINN, a deep learning framework based on Physics-Informed Neural Networks, for modeling fatigued human movements, while providing joint-specific fatigue configurations for adaptation and mitigation of motion artifacts on a joint level, resulting in more smooth, hence physicallyplausible animations. To account for muscle fatigue, we simulate the fatigue-induced fluctuations in the maximum exerted joint torques by leveraging a PINN adaptation of the Three-Compartment Controller model to exploit physics-domain knowledge for improving accuracy. This model also introduces parametric motion alignment with respect to joint-specific fatigue, hence avoiding sharp frame transitions. Our results indicate that Fatigue-PINN accurately simulates the effects of externally perceived fatigue on open-type human movements being consistent with findings from real-world experimental fatigue studies. Since fatigue is incorporated in torque space, Fatigue-PINN provides an end-to-end encoder-decoder-like architecture, to ensure transforming joint angles to joint torques and vice-versa, thus, being compatible with motion synthesis frameworks operating on joint angles.

21 pages, 10 pages. This work has been submitted to the IEEE for possible publication

References in corpus (18)

• Adam: A Method for Stochastic Optimization
• SciPy 1.0--Fundamental Algorithms for Scientific Computing in Python
• Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
• Physics Informed Deep Learning (Part I): Data-driven Solutions of Nonlinear Partial Differential Equations
• Deep Bidirectional and Unidirectional LSTM Recurrent Neural Network for Network-wide Traffic Speed Prediction
• Listen, Denoise, Action! Audio-Driven Motion Synthesis with Diffusion Models
• Rhythm is a Dancer: Music-Driven Motion Synthesis with Global Structure
• Physics-informed Deep Learning for Muscle Force Prediction with Unlabeled sEMG Signals
• Hybrid quantum physics-informed neural networks for simulating computational fluid dynamics in complex shapes
• Single Motion Diffusion
• A Two-part Transformer Network for Controllable Motion Synthesis
• Universal Humanoid Motion Representations for Physics-Based Control
• Analysis of Design Principles and Requirements for Procedural Rigging of Bipeds and Quadrupeds Characters with Custom Manipulators for Animation
• Discovering Fatigued Movements for Virtual Character Animation
• FORCE: Physics-aware Human-object Interaction
• sEMG-Driven Physics-Informed Gated Recurrent Networks for Modeling Upper Limb Multi-Joint Movement Dynamics
• A Unified Framework for Multimodal, Multi-Part Human Motion Synthesis
• Predicting Multi-Joint Kinematics of the Upper Limb from EMG Signals Across Varied Loads with a Physics-Informed Neural Network