Emulator-based Optimization of a Semi-Active Hip Exoskeleton Concept: Sweeping Impedance Across Walking Speeds

we are presenting the optimization of a semi-active hip exoskeleton concept using impedance control at varying walking speeds. We collected 2-minute estimations of metabolic cost across 30 combinations of impedance parameters (stiffness and reference angle) to predict the most metabolically beneficial parameter set. We also collected EMG from 8 lower limb muscles (tibialis anterior (TA), medial gastrocnemius (MG), soleus (SOL), vastus medialis (VM), rectus femoris (RF), biceps femoris (BF), gluteus maximus (GMa), and gluteus medius (GMe)) and used linear combinations of this data to predict metabolic cost. Read the paper for more details on how the data was collected and how this data has been used so far. Below is the Matlab dataset (.mat file) and a .txt “read me” file explaining how to navigate the structure.

本研究开展了针对不同步行速度下采用阻抗控制的半主动髋关节外骨骼（semi-active hip exoskeleton）方案的优化工作。我们共采集了30组阻抗参数（刚度与参考角度）组合下的2分钟代谢成本估算数据，以筛选出代谢收益最优的参数组合。同时，我们采集了8块下肢肌肉的肌电图（Electromyography, EMG）数据，包括胫骨前肌（tibialis anterior, TA）、腓肠肌内侧头（medial gastrocnemius, MG）、比目鱼肌（soleus, SOL）、股内侧肌（vastus medialis, VM）、股直肌（rectus femoris, RF）、股二头肌（biceps femoris, BF）、臀大肌（gluteus maximus, GMa）与臀中肌（gluteus medius, GMe），并通过该数据的线性组合构建代谢成本预测模型。欲了解数据采集与该数据现有应用方式的更多细节，请参阅本研究论文。本次提供的数据集包含Matlab格式数据文件（.mat）与一份"read me"说明文件，后者将指导您如何解析该数据集的结构。