Task-agnostic exoskeleton control via biological joint moment estimation

• 成果类型: Article
• 署名作者: Molinaro, Dean D.; Scherpereel, Keaton L.; Schonhaut, Ethan B.; Evangelopoulos, Georgios; Shepherd, Max K.; Young, Aaron J.
• 署名单位: University System of Georgia; Georgia Institute of Technology; University System of Georgia; Georgia Institute of Technology; Northeastern University; Northeastern University; Alphabet Inc.; Google Incorporated
• 刊物名称: Nature
• ISSN/ISSBN: 0028-5759
• DOI: 10.1038/s41586-024-08157-7
• 发表日期: 2024-11-14
• 关键词: ankle exoskeleton; human walking; energetics; assistance; validation; mechanics; cost; gait

摘要

Lower-limb exoskeletons have the potential to transform the way we move1-14, but current state-of-the-art controllers cannot accommodate the rich set of possible human behaviours that range from cyclic and predictable to transitory and unstructured. We introduce a task-agnostic controller that assists the user on the basis of instantaneous estimates of lower-limb biological joint moments from a deep neural network. By estimating both hip and knee moments in-the-loop, our approach provided multi-joint, coordinated assistance through our autonomous, clothing-integrated exoskeleton. When deployed during 28 activities, spanning cyclic locomotion to unstructured tasks (for example, passive meandering and high-speed lateral cutting), the network accurately estimated hip and knee moments with an average R2 of 0.83 relative to ground truth. Further, our approach significantly outperformed a best-case task classifier-based method constructed from splines and impedance parameters. When tested on ten activities (including level walking, running, lifting a 25 lb (roughly 11 kg) weight and lunging), our controller significantly reduced user energetics (metabolic cost or lower-limb biological joint work depending on the task) relative to the zero torque condition, ranging from 5.3 to 19.7%, without any manual controller modifications among activities. Thus, this task-agnostic controller can enable exoskeletons to aid users across a broad spectrum of human activities, a necessity for real-world viability. A task-agnostic controller assists the user on the basis of instantaneous estimates of lower-limb biological joint moments from a deep neural network so exoskeletons can aid users across a broad spectrum of human activities.