The Metabolic Cost Analysis of Human Walking with a Robotic Lower-limb Exoskeleton

Lower limb exoskeletons are widely studied to provide assistance for both disabled and healthy person. The assisting efficacy would be more significant if the assisting strategy is more in line with the movement characteristics of different wearers. However, how to optimize the timing and magnitude of the assisting torque so as to provide different wearers with more personalized, comfortable and balanced assistance is still not well tackled. To address the issue, an assisting strategy with adjustable assisting timing and magnitude was designed in this paper. First, we designed a framework for smart human-exoskeleton collaboration in which machine learning methods and human-in-the-loop methodology are combined to optimize the assisting timing and magnitude. Second, an assisting strategy based on hip joints trajectory, which is embedded in the framework was designed to separately plan the assisting torque for hip flexion and extension. Third, the efficacy of the designed method was both verified with quantitative and qualitative results. Quantitative results showed that the designed method reduced the net metabolic cost of walking during load carriage by 2.68% on average. And questionnaire survey results showed that the designed assisting strategy provided subjects with more significant, comfortable and balanced assistance.