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

TL;DR

Fatigue-PINN is a physics-informed deep learning framework that models fatigued human movements, enabling realistic motion synthesis with joint-specific fatigue adaptation and smooth, physically plausible animations.

Contribution

The paper introduces Fatigue-PINN, a novel physics-informed neural network approach for accurately simulating fatigue effects on human motion at a joint level.

Findings

Fatigue-PINN accurately replicates fatigue effects consistent with experimental studies.

The model provides joint-specific fatigue configurations for motion adaptation.

It offers an end-to-end architecture compatible with existing motion synthesis frameworks.

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.