Investigating Internal Biomechanics for Insights into Climbing Difficulty

Abstract

This thesis presents an analysis of rock climbing biomechanics, with a focus on understanding how force distribution across the climber’s limbs impacts route difficulty. By applying a static equilibrium model to pose estimations found using machine learning techniques, we examine climber movements filmed in an indoor climbing gym. The two main objectives were to understand optimal body positioning to reduce the amount of force climbers must put out in their arms and to determine whether there was a correlation in the amount of arm force and the rated difficulty of the climbing route. The force distribution across a climber’s limbs for different center of mass positions was found, which can inform more optimal positioning, and there seems to be a trend that as the listed difficulty of a climb increases, so does the required arm force, but the increase is not statistically significant.