Motion Tactics Model Combining Multiple Tactics to Represent Individual Differences in Human Running Motion

Human running is a simple, periodic motion; however, the underlying mechanisms that generate this motion are complex, resulting in individual variations even when the same locomotion goal is pursued. Current generalized locomotion studies have not adequately explained these variations. To address this gap, we focus on the concept of motion strategy, which involves selecting different approaches to accomplish a task. Our objective is to develop a simulation that integrates these motion strategies. Achieving this requires both a deep understanding of the mechanisms behind human motion generation and the development of a system that can reproduce these mechanisms within a simulation. In this study, we propose a motion tactics model and develop a simulation system to replicate motions guided by these strategies. The simulation results were compared with experimental data to assess the effectiveness of the proposed model in replicating individual differences in motion. The model generated multiple motion states and showed it could select unique outcomes by combining different tactics, confirming its validity and feasibility. This approach demonstrated the potential for representing the characteristics in individual motion.