Lee Dawit, Lee Sanghyub, Young Aaron J
School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA.
Department of Bioengineering, Stanford University, Stanford, CA 94305, USA.
Sci Adv. 2024 Dec 20;10(51):eadq0288. doi: 10.1126/sciadv.adq0288. Epub 2024 Dec 18.
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.
外骨骼为改善人类移动性提供了很有前景的解决方案，但一个关键挑战是确保控制器能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，该系统可在行走模式之间动态切换辅助类型，根据地面坡度调节辅助水平，并基于当前步态阶段实时及时地提供辅助。在跑步机验证期间，基于AI的辅助使代谢成本降低了6.5%，而传统辅助为3.5%。我们扩展了在实际行走中对控制器的测试，与传统辅助相比，基于AI的辅助在实际行走中显示出有效的调节和更高的用户偏好。利用基于AI的方法和全面的数据集，该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，并且在独立于用户的基础上运行，能够在各种条件下立即部署。这项研究突出了AI驱动的外骨骼在促进实际行走中的人类运动方面的潜力。
