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

2026-03-27

AI Summary

This study investigates the psychophysiological relationship between fear and muscle activity in lead climbing versus top-rope climbing, driven by differences in fall risk. Integrating electromyography (EMG), electrocardiography (ECG), and arm movement data, the work proposes a novel hybrid framework that combines linear mixed-effects models with personalized deep learning. For the first time, random effects are incorporated into deep learning to capture the nonlinear, individual-specific associations between fear and muscular responses. Evaluated on data from 19 climbers across distinct climbing phases, the model significantly outperforms baseline approaches in terms of MSE, MAE, and RMSE. Results reveal a significant positive correlation between muscle fatigue and fear specifically in lead climbing, demonstrating the efficacy and innovation of multimodal, personalized modeling in understanding climbers’ psychophysiological dynamics.

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.

Problem

• fear
• muscle activity
• climbing
• psychophysiological relationship
• lead climbing

Innovation

• personalized deep learning
• random effects
• psychophysiological modeling
• fear-muscle activity relationship
• climbing biomechanics