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The graphical abstract includes selected skiing turns with load changes on separate skis, a video of ski turns, selected accelerometric data recorded during ski runs, the...
Abstract:
The integration of wearable sensors with artificial intelligence forms the base for analyzing physical activities through digital signal processing, numerical methods, an...Show MoreMetadata
Abstract:
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.
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The graphical abstract includes selected skiing turns with load changes on separate skis, a video of ski turns, selected accelerometric data recorded during ski runs, the...
Published in: IEEE Access ( Volume: 13)