Viscoelasticity Estimation of Sports Prosthesis by Energy-minimizing Inverse Kinematics and Its Validation by Forward Dynamics
<|reference_start|>Viscoelasticity Estimation of Sports Prosthesis by Energy-minimizing Inverse Kinematics and Its Validation by Forward Dynamics: In this study, we present a method for estimating the viscoelasticity of a leaf-spring sports prosthesis using advanced energy minimizing inverse kinematics based on the Piece-wise Constant Strain (PCS) model to reconstruct the three-dimensional dynamic behavior. Dynamic motion analysis of the athlete and prosthesis is important to clarify the effect of prosthesis characteristics on foot function. However, three-dimensional deformation calculations of the prosthesis and viscoelasticity have rarely been investigated. In this letter, we apply the PCS model to a prosthesis deformation, which can calculate flexible deformation with low computational cost and handle kinematics and dynamics. In addition, we propose an inverse kinematics calculation method that is consistent with the material properties of the prosthesis by considering the minimization of elastic energy. Furthermore, we propose a method to estimate the viscoelasticity by solving a quadratic programming based on the measured motion capture data. The calculated strains are more reasonable than the results obtained by conventional inverse kinematics calculation. From the result of the viscoelasticity estimation, we simulate the prosthetic motion by forward dynamics calculation and confirm that this result corresponds to the measured motion. These results indicate that our approach adequately models the dynamic phenomena, including the viscoelasticity of the prosthesis.<|reference_end|>
@article{shimane2024viscoelasticity,
    title={Viscoelasticity Estimation of Sports Prosthesis by Energy-minimizing
  Inverse Kinematics and Its Validation by Forward Dynamics},
    author={Yuta Shimane, Taiki Ishigaki, Sunghee Kim and Ko Yamamoto},
    journal={Advanced Robotics, 2024, 1-13},
    year={2024},
    doi={10.1080/01691864.2024.2407118},
    archivePrefix={arXiv},
    eprint={2410.05776},
    primaryClass={cs.RO}
}