TY - GEN
T1 - An optimal control model for assessing human agility trajectories
AU - Joseph, Christine
AU - Zaferiou, Antonia
AU - Ojeda, Lauro
AU - Perkins, Noel
AU - Stirling, Leia
N1 - Publisher Copyright: © 2018 IEEE.
PY - 2018/6/25
Y1 - 2018/6/25
N2 - Agility is typically defined as the ability to rapidly change velocity or direction. However, measurement of agility is often experimentally limited to completion time in a planned agility course, which does not reveal the underlying biomechanics contributing to performance. Additional contributing factors to interpreting agility include understanding the trajectory of the path and the technique used to achieve that path. In selecting a motor strategy, previous research has shown that human motion planning can be a function of kinematic, dynamic, and time criteria. It is unclear how these criteria may affect the trajectory in a planned agility course. In this paper, an agility task is formulated as an optimal control problem and the relationship between estimated path trajectories and the selected objective function is investigated. Here we specifically consider the criterion of minimizing the magnitude of the squared jerk and minimizing final time, with constraints on speed, acceleration, and maximum ground reaction force that can be produced while running without slipping. Since this frictional constraint takes gravity into account, the trajectories are examined for Earth, as well as reduced gravity environments such as the Moon and Mars. The computed optimal trajectories for the agility task are compared to previously collected experimental data. By comparing the experimental and optimal trajectories, insight is gained on participant strategy. Extending to reduced gravity conditions provides quantitative insights on limitations for astronaut locomotion.
AB - Agility is typically defined as the ability to rapidly change velocity or direction. However, measurement of agility is often experimentally limited to completion time in a planned agility course, which does not reveal the underlying biomechanics contributing to performance. Additional contributing factors to interpreting agility include understanding the trajectory of the path and the technique used to achieve that path. In selecting a motor strategy, previous research has shown that human motion planning can be a function of kinematic, dynamic, and time criteria. It is unclear how these criteria may affect the trajectory in a planned agility course. In this paper, an agility task is formulated as an optimal control problem and the relationship between estimated path trajectories and the selected objective function is investigated. Here we specifically consider the criterion of minimizing the magnitude of the squared jerk and minimizing final time, with constraints on speed, acceleration, and maximum ground reaction force that can be produced while running without slipping. Since this frictional constraint takes gravity into account, the trajectories are examined for Earth, as well as reduced gravity environments such as the Moon and Mars. The computed optimal trajectories for the agility task are compared to previously collected experimental data. By comparing the experimental and optimal trajectories, insight is gained on participant strategy. Extending to reduced gravity conditions provides quantitative insights on limitations for astronaut locomotion.
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U2 - 10.1109/AERO.2018.8396508
DO - 10.1109/AERO.2018.8396508
M3 - Conference contribution
T3 - IEEE Aerospace Conference Proceedings
SP - 1
EP - 10
BT - 2018 IEEE Aerospace Conference, AERO 2018
PB - IEEE Computer Society
T2 - 2018 IEEE Aerospace Conference, AERO 2018
Y2 - 3 March 2018 through 10 March 2018
ER -