Wearable and Non-Invasive Sensors for Rock Climbing Applications: Science-Based Training and Performance Optimization
Overview
Affiliations
Rock climbing has evolved from a method for alpine mountaineering into a popular recreational activity and competitive sport. Advances in safety equipment and the rapid growth of indoor climbing facilities has enabled climbers to focus on the physical and technical movements needed to elevate performance. Through improved training methods, climbers can now achieve ascents of extreme difficulty. A critical aspect to further improve performance is the ability to continuously measure body movement and physiologic responses while ascending the climbing wall. However, traditional measurement devices (e.g., dynamometer) limit data collection during climbing. Advances in wearable and non-invasive sensor technologies have enabled new applications for climbing. This paper presents an overview and critical analysis of the scientific literature on sensors used during climbing. We focus on the several highlighted sensors with the ability to provide continuous measurements during climbing. These selected sensors consist of five main types (body movement, respiration, heart activity, eye gazing, skeletal muscle characterization) that demonstrate their capabilities and potential climbing applications. This review will facilitate the selection of these types of sensors in support of climbing training and strategies.
Towards Automatic Object Detection and Activity Recognition in Indoor Climbing.
Vrzakova H, Koskinen J, Andberg S, Lee A, Amon M Sensors (Basel). 2024; 24(19).
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Moaveninejad S, Janes A, Porcaro C Sensors (Basel). 2024; 24(14).
PMID: 39065974 PMC: 11280810. DOI: 10.3390/s24144576.
Climbing Technique Evaluation by Means of Skeleton Video Stream Analysis.
Beltran Beltran R, Richter J, Kostermeyer G, Heinkel U Sensors (Basel). 2023; 23(19).
PMID: 37837046 PMC: 10574944. DOI: 10.3390/s23198216.
Gaze Estimation Based on Convolutional Structure and Sliding Window-Based Attention Mechanism.
Li Y, Chen J, Ma J, Wang X, Zhang W Sensors (Basel). 2023; 23(13).
PMID: 37448073 PMC: 10346721. DOI: 10.3390/s23136226.