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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\u2019s 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.<\/jats:p>","DOI":"10.1186\/s12880-024-01304-6","type":"journal-article","created":{"date-parts":[[2024,5,24]],"date-time":"2024-05-24T14:02:15Z","timestamp":1716559335000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Real-time sports injury monitoring system based on the deep learning algorithm"],"prefix":"10.1186","volume":"24","author":[{"given":"Luyao","family":"Ren","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanyan","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kaiyong","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,5,24]]},"reference":[{"issue":"10","key":"1304_CR1","doi-asserted-by":"publisher","first-page":"1757","DOI":"10.1007\/s40279-020-01326-4","volume":"50","author":"ST Fonseca","year":"2020","unstructured":"Fonseca ST. 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