Application of OpenPose deep learning algorithm for gait parameter identification

The systematic study of human gait dynamics has allowed medical professionals to offer personalized treatment to individuals suffering from varying degrees of gait degeneration. Currently, marker based motion capture is regarded as the gold standard of motion analysis [1][2][3]. Nevertheless, it is a very time consuming and fatiguing process, as a multitude of markers need to be carefully positioned on an individuals body. The use of Inertial Measurement Units (IMUs) has facilitated a faster process of recording limb accelerations and velocities during locomotion, allowing the reconstruction and personalized study of the gait dynamics. However, IMUs cannot be used to reconstruct the limbs’ positions, unless the individual’s initial body pose is known. Currently, the initial body pose is gained via a motion capturing systems, overturning the time benefit of the IMUs.