[2603.26575] The Climber's Grip -- Personalized Deep Learning Models for Fear and Muscle Activity in Climbing

Computer Science > Machine Learning arXiv:2603.26575 (cs) [Submitted on 27 Mar 2026] Title:The Climber's Grip -- Personalized Deep Learning Models for Fear and Muscle Activity in Climbing Authors:Matthias Boeker, Dana Swarbrick, Ulysse T.A. Côté-Allard, Marc T.P. Adam, Hugo L. Hammer, Pål Halvorsen View a PDF of the paper titled The Climber's Grip -- Personalized Deep Learning Models for Fear and Muscle Activity in Climbing, by Matthias Boeker and 5 other authors View PDF HTML (experimental) 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. Ou...