Wearable Sensors and Computational Intelligence in Alpine Skiing Analysis
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 rehabi...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10971401/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849310473019719680 |
|---|---|
| author | Ales Prochazka Hana Charvatova |
| author_facet | Ales Prochazka Hana Charvatova |
| author_sort | Ales Prochazka |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-f368986fdc5b4b7bb6f0b759e5d87f2f |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-f368986fdc5b4b7bb6f0b759e5d87f2f2025-08-20T03:53:42ZengIEEEIEEE Access2169-35362025-01-0113704147042110.1109/ACCESS.2025.356268610971401Wearable Sensors and Computational Intelligence in Alpine Skiing AnalysisAles Prochazka0https://orcid.org/0000-0002-0270-1738Hana Charvatova1https://orcid.org/0000-0001-7363-976XDepartment of Mathematics, Informatics, and Cybernetics, University of Chemistry and Technology in Prague, Prague, Czech RepublicFaculty of Applied Informatics, Tomas Bata University in Zlín, Zlín, Czech RepublicThe 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.https://ieeexplore.ieee.org/document/10971401/Computational intelligencewearable sensorsaccelerometersgyroscopesphysical activity monitoringalpine skiing |
| spellingShingle | Ales Prochazka Hana Charvatova Wearable Sensors and Computational Intelligence in Alpine Skiing Analysis IEEE Access Computational intelligence wearable sensors accelerometers gyroscopes physical activity monitoring alpine skiing |
| title | Wearable Sensors and Computational Intelligence in Alpine Skiing Analysis |
| title_full | Wearable Sensors and Computational Intelligence in Alpine Skiing Analysis |
| title_fullStr | Wearable Sensors and Computational Intelligence in Alpine Skiing Analysis |
| title_full_unstemmed | Wearable Sensors and Computational Intelligence in Alpine Skiing Analysis |
| title_short | Wearable Sensors and Computational Intelligence in Alpine Skiing Analysis |
| title_sort | wearable sensors and computational intelligence in alpine skiing analysis |
| topic | Computational intelligence wearable sensors accelerometers gyroscopes physical activity monitoring alpine skiing |
| url | https://ieeexplore.ieee.org/document/10971401/ |
| work_keys_str_mv | AT alesprochazka wearablesensorsandcomputationalintelligenceinalpineskiinganalysis AT hanacharvatova wearablesensorsandcomputationalintelligenceinalpineskiinganalysis |