Unsupervised Learning-based Method for Motion Capture Torso Curvature Estimation
Studies based on skeletal models usually use two-dimensional human pose estimation,which lacks crucial points such as the chest,pelvis,and spine in the middle part of the human body,and most methods only include a limited number of vital points in the central part of the human body.Due to the complexity of the overall body structure,tracking methods only estimate the body's surface and have more difficulty estimating the curvature within the torso.Therefore,this paper aims to optimize an existing deep-learning model by adding new key points to the skeleton-based model and to propose a curve-bending algorithm based on the unmarked action skeleton to estimate the curvature of the torso.The method is validated using inertial measurements with an inertial measurement smart suit.This way can evaluate the human torso curvature well.
万方数据
维普
