When remote sports injuries are traditionally monitored, the feedback is not timely, and there are some problems such as low running speed and poor accuracy of sports injury monitoring. Therefore, this paper designs a remote sports injury monitoring method based on a wireless sensor network. Firstly, the terminal node of the sports injury monitoring process is designed, and three-terminal devices are tied to the experimental object's body to collect motion information, to realize the collection of human motion information based on ZigBee wireless sensor network; Secondly, the USB module circuit interface is designed to realize the series connection of each line, and the local processing ability of network nodes is used to make a centralized decision. Then, the skeleton coordinate system is constructed, and the rotation of the human skeleton is measured by an inertial sensor. Through a variety of posture fitting, the error of remote sports injury monitoring is reduced from the two directions of joint error and muscle error. Finally, the training sample set is learned through the BP algorithm, the fitness function of the genetic algorithm is obtained, the external structural parameters of the adaptive neural network model are adjusted, the discrimination deviation and fitness function are calculated, the adaptive neural network model with the best generalization ability is output, and the local processing ability of remote sports injury monitoring method is improved combined with wireless sensor network technology, The design of remote sports injury monitoring method based on wireless sensor network is realized. The experimental results show that the accuracy of the method is 99%, the average time delay is 1 s, and the accuracy of the method is 92% even with noise. Therefore, the method can effectively improve the running speed of the remote sports injury monitoring method and improve the accuracy of sports injury monitoring.

传统远程运动损伤监测时，反馈不及时，存在跑步速度慢、运动损伤监测准确性差等问题。因此，本文设计了一种基于无线传感器网络的远程运动损伤监测方法。首先，设计了运动损伤监测流程的终端节点，将三终端设备绑定到实验对象的身体上采集运动信息，实现基于ZigBee无线传感器网络的人体运动信息采集；其次，设计USB模块电路接口，实现每条线路的串联，利用网络节点的本地处理能力集中决策。然后，构建骨架坐标系，人体骨骼的旋转由惯性传感器测量。通过多种姿势拟合，从关节误差和肌肉误差两个方向减少远程运动损伤监测的误差。最后通过BP算法学习训练样本集，得到遗传算法的适应度函数，调整自适应神经网络模型的外部结构参数，计算判别偏差和适应度函数，得到自适应神经网络模型输出泛化能力最好的，结合无线传感器网络技术提高远程运动损伤监测方法的本地处理能力，实现了基于无线传感器网络的远程运动损伤监测方法的设计。实验结果表明，该方法的准确率为99%，平均时延为1 s，即使有噪声，该方法的准确率为92%。因此，该方法可以有效提高远程运动损伤监测方法的运行速度，提高运动损伤监测的准确性。