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

arXiv: https://arxiv.org/abs/2502.19056 · HTML: https://mlsanity.com/paper/2502.19056/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 79 references that are in the map.
- First posted on arXiv 26 Feb 2025.

## Builds on (most cited first, 25 of 25)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Physics Informed Deep Learning (Part I): Data-driven Solutions of Nonlinear Partial Differential Equations](https://mlsanity.com/paper/1711.10561/) (2017)
- [MoFusion: A Framework for Denoising-Diffusion-based Motion Synthesis](https://mlsanity.com/paper/2212.04495/) (2022)
- [Listen, Denoise, Action! Audio-Driven Motion Synthesis with Diffusion Models](https://mlsanity.com/paper/2211.09707/) (2022)
- [Universal Humanoid Motion Representations for Physics-Based Control](https://mlsanity.com/paper/2310.04582/) (2023)
- [Single Motion Diffusion](https://mlsanity.com/paper/2302.05905/) (2023)
- [Deep Bidirectional and Unidirectional LSTM Recurrent Neural Network for Network-wide Traffic Speed Prediction](https://mlsanity.com/paper/1801.02143/) (2018)
- [Rhythm is a Dancer: Music-Driven Motion Synthesis with Global Structure](https://mlsanity.com/paper/2111.12159/) (2021)
- [Combining Recurrent Neural Networks and Adversarial Training for Human Motion Synthesis and Control](https://mlsanity.com/paper/1806.08666/) (2018)
- [GUESS:GradUally Enriching SyntheSis for Text-Driven Human Motion Generation](https://mlsanity.com/paper/2401.02142/) (2024)
- [Physics-informed deep-learning applications to experimental fluid mechanics](https://mlsanity.com/paper/2203.15402/) (2022)
- [GAN-based Reactive Motion Synthesis with Class-aware Discriminators for Human-human Interaction](https://mlsanity.com/paper/2110.00380/) (2021)
- [Physics-informed Deep Learning for Musculoskeletal Modelling: Predicting Muscle Forces and Joint Kinematics from Surface EMG](https://mlsanity.com/paper/2207.01435/) (2022)
- [Hybrid quantum physics-informed neural networks for simulating computational fluid dynamics in complex shapes](https://mlsanity.com/paper/2304.11247/) (2023)
- [UnderPressure: Deep Learning for Foot Contact Detection, Ground Reaction Force Estimation and Footskate Cleanup](https://mlsanity.com/paper/2208.04598/) (2022)
- [A Unified Framework for Multimodal, Multi-Part Human Motion Synthesis](https://mlsanity.com/paper/2311.16471/) (2023)
- [Boosting Personalised Musculoskeletal Modelling with Physics-informed Knowledge Transfer](https://mlsanity.com/paper/2211.12315/) (2022)
- [A Two-part Transformer Network for Controllable Motion Synthesis](https://mlsanity.com/paper/2304.12571/) (2023)
- [A Multi-Resolution Physics-Informed Recurrent Neural Network: Formulation and Application to Musculoskeletal Systems](https://mlsanity.com/paper/2305.16593/) (2023)
- [Physics-informed Deep Learning for Muscle Force Prediction with Unlabeled sEMG Signals](https://mlsanity.com/paper/2412.04213/) (2024)
- [Discovering Fatigued Movements for Virtual Character Animation](https://mlsanity.com/paper/2310.08583/) (2023)
- [Analysis of Design Principles and Requirements for Procedural Rigging of Bipeds and Quadrupeds Characters with Custom Manipulators for Animation](https://mlsanity.com/paper/1502.06419/) (2015)
- [Predicting Multi-Joint Kinematics of the Upper Limb from EMG Signals Across Varied Loads with a Physics-Informed Neural Network](https://mlsanity.com/paper/2312.09418/) (2023)
- [sEMG-Driven Physics-Informed Gated Recurrent Networks for Modeling Upper Limb Multi-Joint Movement Dynamics](https://mlsanity.com/paper/2408.16599/) (2024)

## Built on by (most cited first, 1 of 1)

- [FatigueFusion: Latent Space Fusion for Fatigue-Driven Motion Synthesis](https://mlsanity.com/paper/2604.10199/) (2026)

## Abstract (arXiv)

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.