Date of Award

Fall 2025

Project Type

Thesis

Program or Major

Electrical and Computer Engineering

Degree Name

Master of Science

First Advisor

Muhammad S Mahmud

Second Advisor

Wayne Smith

Third Advisor

John LaCourse

Abstract

Rock climbers lack tools for skill assessment. Climbers ascend routes, each with varying difficulty (grade); as such, climber skill level is typically described as the highest grade recently climbed. But the subjectivity of rock climbing route difficulty grading presents complications with standardized skill assessment. Measures of climbing core abilities (power, strength, endurance, speed, control, and stability) through non-specific sport exercises and contact forces metrics present more objectivity in climber skill assessment. Presented in this study is a custom force sensor network instrumented in a bouldering route for recording contact force metrics. Twenty-four climbers, split into IRCRA Intermediate and Advanced skill groups, completed nine exercises that assessed their power, strength, and endurance core climbing abilities. Afterward, they climbed the instrumented bouldering route. Using these core climbing abilities and contact force metrics, rich features were extracted to (1) predict climbing core abilities, and (2) classify climber skill level. Two novel sets of feature data derived from Dynamic Time Warping (DTW) and autocorrelation showed significant differences between climber skill groups. In contrast to conventional contact force metrics, DTW and autocorrelation-derived metrics may be used to assess climber skill level and performance consistency regardless of successful or failed climbing attempts. Using interaction force feature data, basic machine learning models classified skill group with 80% accuracy. These contributions serve to guide future research in objective route grade assessment and provide skill assessment tools for coaches, athletes, and recreational climbers.

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