Citation

Ren L, Wang Y, Li K. BMC Med. Imaging 2024; 24(1): e122.

Copyright

(Copyright © 2024, Holtzbrinck Springer Nature Publishing Group - BMC)

DOI

10.1186/s12880-024-01304-6

PMID

38789963

Abstract

In response to the low real-time performance and accuracy of traditional sports injury monitoring, this article conducts research on a real-time injury monitoring system using the SVM model as an example. Video detection is performed to capture human movements, followed by human joint detection. Polynomial fitting analysis is used to extract joint motion patterns, and the average of training data is calculated as a reference point. The raw data is then normalized to adjust position and direction, and dimensionality reduction is achieved through singular value decomposition to enhance processing efficiency and model training speed. A support vector machine classifier is used to classify and identify the processed data. The experimental section monitors sports injuries and investigates the accuracy of the system's monitoring. Compared to mainstream models such as Random Forest and Naive Bayes, the SVM utilized demonstrates good performance in accuracy, sensitivity, and specificity, reaching 94.2%, 92.5%, and 96.0% respectively.

Language: en

Keywords

*Athletic Injuries/diagnostic imaging; *Deep Learning; *Support Vector Machine; Algorithms; Deep learning algorithms; Humans; Machine learning; Medical applications; Sensitivity and Specificity; Sports injury monitoring; Video Recording