Wearable sensors and computational intelligence in alpine skiing analysis

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F70883521%3A28140%2F25%3A63596818" target="_blank" >RIV/70883521:28140/25:63596818 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/68407700:21730/25:00388067 RIV/60461373:22340/25:43932958

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/10971401" target="_blank" >https://ieeexplore.ieee.org/document/10971401</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ACCESS.2025.3562686" target="_blank" >10.1109/ACCESS.2025.3562686</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Wearable sensors and computational intelligence in alpine skiing analysis

Popis výsledku v původním jazyce

The integration of wearable sensors with artificial intelligence forms the base for analyzing physical activities through digital signal processing, numerical methods, and machine learning. Computational intelligence and communication technologies enable personalized monitoring, training, and rehabilitation, with applications in sports, neurology, and biomedicine. This paper focuses on motion analysis in alpine skiing using real accelerometric, gyroscopic, positioning, and video data to evaluate ski movement patterns. The proposed methodology employs functional transforms to estimate motion patterns and utilizes artificial intelligence for signal segmentation and feature classification related to lower limb movement. Machine learning results indicate differences in energy distribution before and after ski turns and demonstrate the feasibility of classifying associated motion patterns with accuracies of 98.1% and 90.7%, respectively, using a two-layer neural network. The interdisciplinary application of computational intelligence in this domain enhances motion analysis, injury prevention, and performance optimization. This study highlights the unifying role of digital signal processing, which uses similar mathematical tools across various applications.

Název v anglickém jazyce

Wearable sensors and computational intelligence in alpine skiing analysis

Popis výsledku anglicky

The integration of wearable sensors with artificial intelligence forms the base for analyzing physical activities through digital signal processing, numerical methods, and machine learning. Computational intelligence and communication technologies enable personalized monitoring, training, and rehabilitation, with applications in sports, neurology, and biomedicine. This paper focuses on motion analysis in alpine skiing using real accelerometric, gyroscopic, positioning, and video data to evaluate ski movement patterns. The proposed methodology employs functional transforms to estimate motion patterns and utilizes artificial intelligence for signal segmentation and feature classification related to lower limb movement. Machine learning results indicate differences in energy distribution before and after ski turns and demonstrate the feasibility of classifying associated motion patterns with accuracies of 98.1% and 90.7%, respectively, using a two-layer neural network. The interdisciplinary application of computational intelligence in this domain enhances motion analysis, injury prevention, and performance optimization. This study highlights the unifying role of digital signal processing, which uses similar mathematical tools across various applications.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/EH22_008%2F0004590" target="_blank" >EH22_008/0004590: Robotika a pokročilá průmyslová výroba</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

IEEE Access

ISSN

2169-3536

e-ISSN

2169-3536

Svazek periodika

13

Číslo periodika v rámci svazku

Neuveden

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

8

Strana od-do

70414-70421

Kód UT WoS článku

001479471700028

EID výsledku v databázi Scopus

2-s2.0-105003468740