Deep Learning Methods to Analyze the Forces and Torques in Joints Motion

Abstract

This paper proposes a composite model that combines convolutional neural network models and mechanical analysis to determine the forces acting on an object. First, we establish a model using Newtonian mechanics to analyze the forces experienced by the human body during movement, particularly the forces on joints. The model calculates the mapping relationship between the object’s movement and the forces on the joints. Then, by analyzing a large number of fencing competition videos using a deep learning model, we extract video features to study the torques and forces on human joints. Our analysis of numerous images reveals that, in certain movement patterns, the peak pressure on the knee joint can be two to three times higher than in a normal state, while the driving knee can withstand peak torques of 400–600 Nm. This straightforward model can effectively capture the forces and torques on the human body during movement using a deep neural network. Furthermore, this model can also be applied to problems involving non-rigid body motion.

Keywords

• convolutional neural network
• mean squared error
• multi-point motion model
• forces and moments