Fatigue-PINN: Physics-Informed Fatigue-Driven Motion Modulation and Synthesis
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, formu...
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IEEE
2025-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/11048929/ |
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| author | Iliana Loi Konstantinos Moustakas |
| author_facet | Iliana Loi Konstantinos Moustakas |
| author_sort | Iliana Loi |
| collection | DOAJ |
| description | 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 data-driven 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 physically-plausible 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. |
| format | Article |
| id | doaj-art-b2892d2109f9448d8a3ca47cdf3ad7f5 |
| institution | Matheson Library |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-b2892d2109f9448d8a3ca47cdf3ad7f52025-06-30T23:00:54ZengIEEEIEEE Access2169-35362025-01-011310937810939810.1109/ACCESS.2025.358273111048929Fatigue-PINN: Physics-Informed Fatigue-Driven Motion Modulation and SynthesisIliana Loi0https://orcid.org/0000-0001-9112-0638Konstantinos Moustakas1https://orcid.org/0000-0001-7617-227XDepartment of Electrical and Computer Engineering, University of Patras, Patras, GreeceDepartment of Electrical and Computer Engineering, University of Patras, Patras, GreeceFatigue 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 data-driven 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 physically-plausible 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.https://ieeexplore.ieee.org/document/11048929/Animationbiomechanicsdeep learningPINNs3CC |
| spellingShingle | Iliana Loi Konstantinos Moustakas Fatigue-PINN: Physics-Informed Fatigue-Driven Motion Modulation and Synthesis IEEE Access Animation biomechanics deep learning PINNs 3CC |
| title | Fatigue-PINN: Physics-Informed Fatigue-Driven Motion Modulation and Synthesis |
| title_full | Fatigue-PINN: Physics-Informed Fatigue-Driven Motion Modulation and Synthesis |
| title_fullStr | Fatigue-PINN: Physics-Informed Fatigue-Driven Motion Modulation and Synthesis |
| title_full_unstemmed | Fatigue-PINN: Physics-Informed Fatigue-Driven Motion Modulation and Synthesis |
| title_short | Fatigue-PINN: Physics-Informed Fatigue-Driven Motion Modulation and Synthesis |
| title_sort | fatigue pinn physics informed fatigue driven motion modulation and synthesis |
| topic | Animation biomechanics deep learning PINNs 3CC |
| url | https://ieeexplore.ieee.org/document/11048929/ |
| work_keys_str_mv | AT ilianaloi fatiguepinnphysicsinformedfatiguedrivenmotionmodulationandsynthesis AT konstantinosmoustakas fatiguepinnphysicsinformedfatiguedrivenmotionmodulationandsynthesis |