Sports injuries of high-level athletes restrict the improvement of sports performance. Under this premise, an efficient and accurate sports injury assessment method is needed to detect potential sports injuries and conduct injury prevention training. Therefore, this paper proposes a novel sports injury prediction algorithm based on visual analysis technology. The proposed algorithm first takes the time-frequency of sensed data as the convolutional neural network (CNN) input. The one-dimensional time series collected by the sensor is converted into two-dimensional images using the Gram angle domain algorithm. The one-dimensional sensed data provides a new perspective and provides a basis for better use of convolutional neural networks and computer vision technology. Second, combining the residual network’s structure and advantages and hole convolution and multihole convolution kernel residual module is proposed. It improves the model’s ability to extract features at different scales while effectively controlling the parameter scale. Based on these modules, a single-sensor-based athlete action recognition algorithm is proposed. Several comparative experiments have been conducted on a public data set containing only acceleration sensors to verify the proposed algorithm’s effectiveness.

中文翻译：

---

基于视觉分析技术的运动损伤康复干预算法

高水平运动员的运动损伤限制了运动成绩的提高。在此前提下，需要一种有效，准确的运动损伤评估方法，以发现潜在的运动损伤并进行损伤预防训练。因此，本文提出了一种基于视觉分析技术的运动损伤预测算法。该算法首先将感测数据的时频作为卷积神经网络（CNN）的输入。使用Gram角域算法将传感器收集的一维时间序列转换为二维图像。一维感测数据提供了新的视角，并为更好地使用卷积神经网络和计算机视觉技术提供了基础。第二，结合残差网络的结构和优点，提出了孔卷积和多孔卷积核残差模块。它提高了模型提取不同比例特征的能力，同时有效地控制了参数比例。基于这些模块，提出了一种基于单传感器的运动员动作识别算法。在仅包含加速度传感器的公共数据集上进行了一些比较实验，以验证所提出算法的有效性。