Multi-Metric Optimization for Human Walking

Abstract

Walking is an essential part of almost all activities of daily living. Depending on the situation, different gait patterns can be observed, e.g., moving around the house, performing different sports, or even in case of injury. Even though the gait has been analyzed thoroughly for many decades, there are still some unexplored aspects that require more insight, especially those related to the influence of various parameters on the optimality and diversity of gait patterns. Many gait trajectory optimization strategies have been proposed in literature, however, most of them focus merely on optimizing for one metric (e.g., energy efficiency or joint torque). In this study, a multi-metric gait optimization framework is proposed, simultaneously accounting for joint torque, fatigue, and manipulability. To that end, 45 gaits, varying in stride length, step height, and walking speed, were recorded in a motion capture experiment, together forming a solution space of dynamically stable and physiologically feasible gaits, for the proposed optimization framework. Specific user needs (gait requirements) can be accounted for by adjusting the optimization weights, after which brute-force optimization is applied to either analyze the gait within the collected subspace or select the optimal gait with respect to desired parameters. Results are presented for a baseline case (with all optimization weights set to one), which can be used as a tool for gait analysis, in particular giving insights into specific aspects of the gait, e.g., joint loading, long-term performance, and capacity to sustain ground reaction forces (GRFs).