发表机构
State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences; The School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院自动化研究所多模态人工智能系统国家重点实验室; 中国科学院大学人工智能学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对动作捕捉成本高和直接缩放扭矩非最优的问题，提出基于动力学优化的下肢外骨骼助力扭矩估计方法，实现实时预测，显著降低代谢率、心率和肌肉激活。
AI 中文摘要
真实的人体关节扭矩估计依赖于动作捕捉系统，该系统存在户外可用性受限和部署成本高昂的问题。此外，直接缩放真实关节扭矩以获得电机扭矩指令并非最优策略。为解决上述局限，受人体运动生成过程的启发，本文提出了一种基于动力学模型的助力扭矩估计新方法。从优化角度来看，所提方法直接生成电机助力扭矩，降低了数据采集成本。随后，训练了一个数据驱动的助力扭矩预测网络，以实现复杂户外环境下的精确实时预测。实验结果表明，优化（估计）的助力扭矩与步态轨迹具有更好的相位一致性，并与任务特征更吻合。相对于零扭矩条件，预测扭矩可分别使代谢率降低11.8%-17.7%，心率降低8.9%-14.3%，峰值肌肉激活水平降低28.2%-54.0%。这为低成本自适应外骨骼助力提供了新视角。
英文摘要
Ground-truth human joint torque estimation relies on motion capture systems, which suffer from limited outdoor usability and significant deployment expenses. Furthermore, direct scaling of ground-truth joint torques to obtain motor torque commands is not necessarily the optimal strategy. To address the aforementioned limitations, inspired by the human motion generation process, this paper proposes a novel assistance torque estimation method based on the dynamic model. From an optimization perspective, the proposed method directly generates motor-assist torque and lowers the cost of data acquisition. Then, a data-driven assistance torque prediction network is trained to enable accurate real-time prediction under complex outdoor environments. Experimental results demonstrate that optimized (estimated) assistance torque exhibits better phase consistency with gait trajectories and better alignment with task characteristics. Relative to the Zero torque condition, the predicted torque can decrease metabolic rate by 11.8%-17.7%, heart rate by 8.9%-14.3%, and peak muscle activation levels by 28.2%-54.0%, respectively. This provides a new perspective for low-cost adaptive exoskeleton assistance.
Comments8 pages, 11 figures, https://youtu.be/CGxDD0jKpak
