The human foot is a complex structure comprising 26 bones, whose coordinated movements facilitate proper deformation of the foot, ensuring stable and efficient locomotion. Despite their critical role, the kinematics of foot bones during movement remain largely unexplored, primarily due to the absence of non-invasive methods for measuring foot bone kinematics. This study addresses this gap by proposing a neural network model for estimating foot bone movements using surface markers. To establish a mapping between the positions and orientations of the foot bones and 41 skin markers attached on the human foot, computed tomography scans of the foot with the markers were obtained with eleven healthy adults and thirteen cadaver specimens in different foot postures. The neural network architecture comprises four layers, with input and output layers containing the 41 marker positions and the positions and orientations of the nine foot bones, respectively. The mean errors between estimated and true foot bone position and orientation were 0.5 mm and 0.6 degrees, respectively, indicating that the neural network can provide 3D kinematics of the foot bones with sufficient accuracy in a non-invasive manner, thereby contributing to a better understanding of foot function and the pathogenetic mechanisms underlying foot disorders.
Keywords: Biomechanics; Computed tomography; Foot; Machine learning; Motion capture.
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