IEEE Trans Neural Syst Rehabil Eng. 2019 Aug;27(8):1597-1605. doi: 10.1109/TNSRE.2019.2924536. Epub 2019 Jun 24.
Knowledge of human-exoskeleton interaction forces is crucial to assess user comfort and effectiveness of the interaction. The subject-exoskeleton collaborative movement and its interaction forces can be predicted in silico using computational modeling techniques. We developed an optimal control framework that consisted of three phases. First, the foot-ground (Phase A) and the subject-exoskeleton (Phase B) contact models were calibrated using three experimental sit-to-stand trials. Then, the collaborative movement and the subject-exoskeleton interaction forces, of six different sit-to-stand trials were predicted (Phase C). The results show that the contact models were able to reproduce experimental kinematics of calibration trials (mean root mean square differences - RMSD - coordinates ≤ 1.1° and velocities ≤ 6.8°/s), ground reaction forces (mean RMSD≤ 22.9 N), as well as the interaction forces at the pelvis, thigh, and shank (mean RMSD ≤ 5.4 N). Phase C could predict the collaborative movements of prediction trials (mean RMSD coordinates ≤ 3.5° and velocities ≤ 15.0°/s), and their subject-exoskeleton interaction forces (mean RMSD ≤ 13.1° N). In conclusion, this optimal control framework could be used while designing exoskeletons to have in silico knowledge of new optimal movements and their interaction forces.
人体-外骨骼相互作用力的知识对于评估用户舒适度和交互有效性至关重要。使用计算建模技术，可以在计算机中对受试者-外骨骼协同运动及其相互作用力进行预测。我们开发了一个最优控制框架，该框架由三个阶段组成。首先，使用三个实验的坐站试验对脚-地（阶段 A）和受试者-外骨骼（阶段 B）接触模型进行了校准。然后，预测了六个不同坐站试验的协同运动和受试者-外骨骼相互作用力（阶段 C）。结果表明，接触模型能够再现校准试验的实验运动学（校准试验坐标的均方根差 RMSD≤1.1°，速度≤6.8°/s；地面反作用力 RMSD≤22.9N；骨盆、大腿和小腿处的相互作用力 RMSD≤5.4N）。阶段 C 可以预测预测试验的协同运动（坐标 RMSD≤3.5°，速度≤15.0°/s）及其受试者-外骨骼相互作用力（RMSD≤13.1°N）。总之，该最优控制框架可用于设计外骨骼，以便在计算机中获得新的最优运动及其相互作用力的知识。
