Personalizing exoskeleton control remains a critical challenge for clinical users with gait disabilities. Online adaptation (OA) offers an effective solution by adapting in real time to subject variability, device fit, and diverse locomotor tasks. However, OA involves a continual stream of user state data, which can lead to catastrophic forgetting of previously learned locomotor contexts. Here, we develop a manifold-aware experience replay-based online personalization framework designed to maintain user-specific representations across diverse tasks during OA of exoskeleton control. By replaying previously experienced tasks from a replay buffer, we preserve the personalized exoskeleton assistance across all learned tasks. Furthermore, we capture a gait manifold that distinguishes between different locomotor tasks, removing the need for explicit task labeling when selecting target replay bins. We evaluated our framework on emulated hemiplegic gait, which largely deviates from able-bodied patterns, across multiple forgetting scenarios with speed and incline transitions. Our manifold-aware replay framework achieved 40% and 60% improvements in torque and gait phase tracking accuracy, respectively, compared to a baseline framework without replay, which exhibited catastrophic forgetting during task transitions. This demonstrates that our proposed framework personalizes exoskeleton control in real time across diverse locomotor contexts in daily ambulation of clinical populations.

翻译：针对具有步态障碍的临床用户，外骨骼控制的个性化仍是关键挑战。在线自适应通过实时适应个体差异、设备适配及多样化运动任务提供有效解决方案。然而，在线自适应涉及持续的用户状态数据流，可能导致先前习得运动情境的灾难性遗忘。为此，我们开发了一种基于流形感知经验回放的在线个性化框架，旨在外骨骼控制的在线自适应过程中，跨不同任务维持用户特异性表征。通过从回放缓冲区重放先前经历的任务，我们能够保留所有已习得任务的个性化外骨骼辅助。此外，我们捕获了区分不同运动任务的步态流形，从而在选取目标回放任务时无需显式任务标签。我们基于模拟的偏瘫步态（显著偏离健全步态模式）评估该框架，涵盖速度与坡道转换的多重遗忘场景。与未使用回放的基准框架相比（该框架在任务转换时出现灾难性遗忘），我们的流形感知回放框架在力矩与步态相位跟踪精度上分别提升40%与60%。这表明所提出的框架能在临床人群日常步行中，跨多样化运动情境实现外骨骼控制的实时个性化。