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.

翻译：疲劳建模对于疲劳状态下人体运动的运动合成任务及生物力学工程应用至关重要，例如研究因疲劳导致的运动模式和姿态变化、制定损伤风险缓解与预防策略、提出疲劳最小化方案以及创建改进的人体工学设计。然而，现有文献中鲜少关注利用数据驱动方法合成疲劳对运动的影响。本文提出Fatigue-PINN——一种基于物理信息神经网络的深度学习框架，用于对疲劳人体运动进行建模，同时提供关节特异性的疲劳配置，以实现关节层面的运动伪影自适应调节与缓解，从而生成更平滑、物理上更合理的动画。为模拟肌肉疲劳，我们通过三隔室控制器模型的PINN适配方式，利用物理域知识提升精度，模拟最大关节力矩中由疲劳引发的波动。该模型还引入针对关节特异性疲劳的参数化运动对齐，从而避免帧间突变。结果表明，Fatigue-PINN能够准确模拟外部感知疲劳对开放式人体运动的影响，结果与真实世界实验性疲劳研究的发现一致。由于疲劳在力矩空间中被整合，Fatigue-PINN提供了一种端到端的编码器-解码器式架构，可确保关节角度与关节力矩之间的双向转换，因此与基于关节角度运作的运动合成框架兼容。