Exoskeletons offer promising solutions for improving human mobility, but a key challenge is ensuring the controller adapts to changing walking conditions. We present an artificial intelligence (AI)–driven universal exoskeleton system that dynamically switches assistance types between walking modes, modulates assistance levels corresponding to the ground slope, and delivers assistance timely based on the current gait phase in real-time. During treadmill validation, AI-based assistance reduced metabolic cost by 6.5% compared to 3.5% for conventional assistance. We expanded testing the controller in real-world walking, where AI-based assistance showed effective modulation and higher user preference compared to conventional assistance. Leveraging the AI-based approach and a comprehensive dataset, the controller achieved superior performance in environment- and user-state estimations. This approach does not require a separate mode classifier and operates on a user-independent basis, enabling immediate deployment across diverse conditions. This study highlights the potential of AI-driven exoskeletons in facilitating human locomotion in real-world ambulation.

中文翻译：

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用于社区行走的 AI 驱动的通用下肢外骨骼系统


外骨骼为改善人类活动能力提供了有前途的解决方案，但一个关键挑战是确保控制器适应不断变化的行走条件。我们提出了一种人工智能 （AI） 驱动的通用外骨骼系统，该系统可在步行模式之间动态切换辅助类型，调节与地面坡度相对应的辅助水平，并根据当前步态阶段实时及时提供帮助。在跑步机验证期间，与传统辅助相比，基于 AI 的辅助将代谢成本降低了 6.5%。我们扩展了在真实世界步行中对控制器的测试，与传统辅助相比，基于 AI 的辅助显示出有效的调制和更高的用户偏好。利用基于 AI 的方法和全面的数据集，控制器在环境和用户状态估计方面取得了卓越的性能。这种方法不需要单独的模式分类器，并且独立于用户运行，从而可以在各种条件下立即部署。这项研究强调了 AI 驱动的外骨骼在促进人类在现实世界中移动的潜力。