A hybrid framework to predict ski jumping forces by combining data-driven pose estimation and model-based force calculation

Abstract

ABSTRACT The aim of this paper is to propose a hybrid framework that combines a data-driven pose estimation with model-based force calculation in order to predict the ski jumping force from a recorded motion video. A skeletal model consisting 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 is developed. 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. Joint coordinates can be obtained by the proposed deep neural network directly from images of jumping motion without using any markers. The validity and usefulness of the proposed method are confirmed in lab experiments. Further, our method is practically applicable to the study in a real competition environment because it is not required to attach any sensor or marker to athletes. Highlights A method to predict the ski jumping force from a recorded motion video is proposed. It combines a data-driven pose estimation with a model-based force calculation. The proposed method does not require any markers and sensors to be attached to athletes. In a laboratory environment, the relative error in the maximum jumping force is less than 7%. The method can be easily applied to a field study in a real competition environment.