Abstract

This paper describes a hybrid framework that combines a data-driven pose estimation with a model-based force calculation in order to predict the ski jumping force from a recorded motion video. The model consists of five joints (ear, hip, knee, ankle, and toe) and four rigid segments (head/arm/trunk or HAT, thigh, shank, and foot) connecting each joint. The joint forces are calculated from the dynamic equilibrium equations, which requires the time history of joint coordinates. They are estimated from a recorded motion video using a deep neural network for pose estimation trained with human motion data. The validity and usefulness of the proposed method are confirmed in lab experiments. Further, our method is practically applicable to the study in real competition environment because it is not required to attached any sensor or marker to athletes.