Balance assessment during physical rehabilitation often relies on rubric-oriented battery tests to score a patient's physical capabilities, leading to subjectivity. While some objective balance assessments exist, they are often limited to tracking the center of pressure (COP), which does not fully capture the whole-body postural stability. This study explores the use of the center of mass (COM) state space and presents a promising avenue for monitoring the balance capabilities in humans. We employ a musculoskeletal model integrated with a balance controller, trained through reinforcement learning (RL), to investigate balancing capabilities. The RL framework consists of two interconnected neural networks governing balance recovery and muscle coordination respectively, trained using Proximal Policy Optimization (PPO) with reference state initialization, early termination, and multiple training strategies. By exploring recovery from random initial COM states (position and velocity) space for a trained controller, we obtain the final BR enclosing successful balance recovery trajectories. Comparing the BRs with analytical postural stability limits from a linear inverted pendulum model, we observe a similar trend in successful COM states but more limited ranges in the recoverable areas. We further investigate the effect of muscle weakness and neural excitation delay on the BRs, revealing reduced balancing capability in different regions. Overall, our approach of learning muscular balance controllers presents a promising new method for establishing balance recovery limits and objectively assessing balance capability in bipedal systems, particularly in humans.

翻译：物理康复中的平衡评估常依赖评估表导向的成套测试来评定患者体能，导致主观性偏差。现有客观平衡评估手段多局限于追踪压力中心（COP），难以全面捕捉全身姿势稳定性。本研究探索了质心（COM）状态空间的应用，为监测人体平衡能力提供了新思路。我们采用集成平衡控制器的肌肉骨骼模型，通过强化学习（RL）训练，研究平衡调控能力。该RL框架包含两个相互连接的神经网络，分别负责平衡恢复与肌肉协调，采用带参考状态初始化、提前终止及多重训练策略的邻近策略优化（PPO）算法进行训练。通过探索训练后控制器在随机初始COM状态（位置与速度）空间中的恢复过程，我们获得了包含成功平衡恢复轨迹的最终平衡恢复域（BR）。将BR与线性倒立摆模型的解析姿势稳定边界对比，发现成功COM状态呈现相似趋势，但可恢复区域范围更小。我们进一步研究了肌力减弱和神经兴奋延迟对BR的影响，揭示了不同区域平衡能力的下降。总体而言，本研究提出的肌肉平衡控制器学习方法，为建立双足系统（尤其是人类）的平衡恢复极限和客观评估平衡能力提供了创新途径。