Department of Physical Education, Nanjing Forestry University, Nanjing, Jiangsu, 210037, China.
Department of Physical Education, Beijing Foreign Studies University, Beijing, 100089, China.
BMC Med Imaging. 2024 May 24;24(1):122. doi: 10.1186/s12880-024-01304-6.
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.
针对传统运动损伤监测实时性和准确性低的问题，本文以 SVM 模型为例，对实时损伤监测系统进行研究。采用视频检测捕捉人体运动，然后进行人体关节检测。通过多项式拟合分析提取关节运动模式，计算训练数据的平均值作为参考点。然后对原始数据进行归一化处理，调整位置和方向，通过奇异值分解实现降维，提高处理效率和模型训练速度。最后使用支持向量机分类器对处理后的数据进行分类识别。实验部分监测运动损伤并调查系统监测的准确性。与随机森林和朴素贝叶斯等主流模型相比，所使用的 SVM 在准确率、灵敏度和特异性方面表现良好，分别达到 94.2%、92.5%和 96.0%。
