The Climber's Grip -- Personalized Deep Learning Models for Fear and Muscle Activity in Climbing

TL;DR

This study combines statistical and deep learning models to analyze how perceived fear relates to muscle activity and fatigue in climbers, highlighting personalized modeling benefits.

Contribution

It introduces a novel integrated approach using deep learning and statistical models for personalized psychophysiological analysis in climbing.

Findings

Muscle fatigue correlates with increased fear during lead climbing.

Random effects improve model performance metrics.

Deep learning captures non-linear fear-physiology relationships.

Abstract

Climbing is a multifaceted sport that combines physical demands and emotional and cognitive challenges. Ascent styles differ in fall distance with lead climbing involving larger falls than top rope climbing, which may result in different perceived risk and fear. In this study, we investigated the psychophysiological relationship between perceived fear and muscle activity in climbers using a combination of statistical modeling and deep learning techniques. We conducted an experiment with 19 climbers, collecting electromyography (EMG), electrocardiography (ECG) and arm motion data during lead and top rope climbing. Perceived fear ratings were collected for the different phases of the climb. Using a linear mixed-effects model, we analyzed the relationships between perceived fear and physiological measures. To capture the non-linear dynamics of this relationship, we extended our analysis to deep learning models and integrated random effects for a personalized modeling approach. Our results showed that random effects improved model performance of the mean squared error (MSE), mean absolute error (MAE) and root mean squared error (RMSE). The results showed that muscle fatigue correlates significantly with increased fear during \textit{lead climbing}. This study highlights the potential of combining statistical and deep learning approaches for modeling the interplay between psychological and physiological states during climbing.