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

• URL: http://arxiv.org/abs/2502.19056v1
• Date: Wed, 26 Feb 2025 11:14:48 GMT
• Title: Fatigue-PINN: Physics-Informed Fatigue-Driven Motion Modulation and Synthesis
• Authors: Iliana Loi, Konstantinos Moustakas,
• Abstract summary: Fatigue modeling is essential for motion synthesis tasks to model human motions under fatigued conditions.<n>We present Fatigue-PINN, a deep learning framework based on Physics-Informed Neural Networks.<n>We simulate the fatigue-induced fluctuations in the maximum exerted joint torques by leveraging a PINN adaptation of the Three-Compartment Controller model.
• Score: 2.0308338182844263
• License: http://creativecommons.org/licenses/by/4.0/
• 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 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 realistic 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.

Related papers

• Reinforcement learning-based motion imitation for physiologically plausible musculoskeletal motor control [47.423243831156285]
  We present a model-free motion imitation framework (KINESIS) to advance the understanding of muscle-based motor control. We demonstrate that KINESIS achieves strong imitation performance on 1.9 hours of motion capture data. KINESIS generates muscle activity patterns that correlate well with human EMG activity.
  arXiv  Detail & Related papers  (2025-03-18T18:37:49Z)
• A Plug-and-Play Physical Motion Restoration Approach for In-the-Wild High-Difficulty Motions [56.709280823844374]
  We introduce a mask-based motion correction module (MCM) that leverages motion context and video mask to repair flawed motions.<n>We also propose a physics-based motion transfer module (PTM), which employs a pretrain and adapt approach for motion imitation.<n>Our approach is designed as a plug-and-play module to physically refine the video motion capture results, including high-difficulty in-the-wild motions.
  arXiv  Detail & Related papers  (2024-12-23T08:26:00Z)
• Learning Speed-Adaptive Walking Agent Using Imitation Learning with Physics-Informed Simulation [0.0]
  We create a skeletal humanoid agent capable of adapting to varying walking speeds while maintaining biomechanically realistic motions.<n>The framework combines a synthetic data generator, which produces biomechanically plausible gait kinematics from open-source biomechanics data, and a training system that uses adversarial imitation learning to train the agent's walking policy.
  arXiv  Detail & Related papers  (2024-12-05T07:55:58Z)
• MS-MANO: Enabling Hand Pose Tracking with Biomechanical Constraints [50.61346764110482]
  We integrate a musculoskeletal system with a learnable parametric hand model, MANO, to create MS-MANO. This model emulates the dynamics of muscles and tendons to drive the skeletal system, imposing physiologically realistic constraints on the resulting torque trajectories. We also propose a simulation-in-the-loop pose refinement framework, BioPR, that refines the initial estimated pose through a multi-layer perceptron network.
  arXiv  Detail & Related papers  (2024-04-16T02:18:18Z)
• Predicting Fatigue Crack Growth via Path Slicing and Re-Weighting [9.454972552320804]
  We report a statistical learning framework to predict the growth of fatigue cracks and the life-to-failure of the components under loading conditions. Digital libraries of fatigue crack patterns and the remaining life are constructed by high-fidelity physical simulations. Path-slicing and re-weighting techniques are introduced to handle the statistical noises and rare events.
  arXiv  Detail & Related papers  (2023-09-13T04:13:11Z)
• Estimating Continuous Muscle Fatigue For Multi-Muscle Coordinated Exercise: A Pilot Study on Walking [6.7733453364081635]
  assessing fatigue of multi-muscle coordination-temporal daily exercises requires neuromuscular features that represent the fatigue-induced characteristics of adaptations of multiple muscles.<n>We propose to depict fatigue by features of muscle compensation and spinal module activation changes and estimate continuous fatigue by a physiological rationale model.
  arXiv  Detail & Related papers  (2023-03-30T02:54:59Z)
• Skeleton2Humanoid: Animating Simulated Characters for Physically-plausible Motion In-betweening [59.88594294676711]
  Modern deep learning based motion synthesis approaches barely consider the physical plausibility of synthesized motions. We propose a system Skeleton2Humanoid'' which performs physics-oriented motion correction at test time. Experiments on the challenging LaFAN1 dataset show our system can outperform prior methods significantly in terms of both physical plausibility and accuracy.
  arXiv  Detail & Related papers  (2022-10-09T16:15:34Z)
• MyoSuite -- A contact-rich simulation suite for musculoskeletal motor control [7.856809409051587]
  MyoSuite is a suite of physiologically accurate biomechanical models of elbow, wrist, and hand, with physical contact capabilities. We provide diverse motor-control challenges: from simple postural control to skilled hand-object interactions.
  arXiv  Detail & Related papers  (2022-05-26T20:11:23Z)
• Imposing Temporal Consistency on Deep Monocular Body Shape and Pose Estimation [67.23327074124855]
  This paper presents an elegant solution for the integration of temporal constraints in the fitting process. We derive parameters of a sequence of body models, representing shape and motion of a person, including jaw poses, facial expressions, and finger poses. Our approach enables the derivation of realistic 3D body models from image sequences, including facial expression and articulated hands.
  arXiv  Detail & Related papers  (2022-02-07T11:11:55Z)
• Online Body Schema Adaptation through Cost-Sensitive Active Learning [63.84207660737483]
  The work was implemented in a simulation environment, using the 7DoF arm of the iCub robot simulator. A cost-sensitive active learning approach is used to select optimal joint configurations. The results show cost-sensitive active learning has similar accuracy to the standard active learning approach, while reducing in about half the executed movement.
  arXiv  Detail & Related papers  (2021-01-26T16:01:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.