Real-time collection of athletes’ abnormal training data can improve the training effect of athletes. This paper studies the real-time collection method of athletes’ abnormal training data based on machine learning. The main motivation of this paper is to collect the athletes’ abnormal training data in time, which can help to evaluate and improve the training effect. Four sensor nodes are arranged in the upper and lower limbs of athletes to collect the angular velocity, acceleration, and magnetic field strength data of athletes in training state. The data are sent to the data transmission base station through wireless sensors, and the data transmission base station transmits the data to the data processing terminal. The data processing terminal calculates the difference between the sample values of each sensor to obtain the data dispersion of each sensor. The features of each dimension data in a time domain and frequency domain are obtained by using the dispersion degree to construct 32-dimensional feature vectors, and the extracted feature vectors are input into the hidden Markov model. The forward algorithm is used to obtain the probability of the final observation sequence, so as to realize the final collection of athletes’ abnormal training data. The experimental results show that the accuracy and recall rate of the abnormal data collected by this method is higher than 98%, which requires less time.

中文翻译：

---

基于机器学习的运动员异常训练数据实时采集方法

实时收集运动员异常训练数据，提高运动员训练效果。本文研究了基于机器学习的运动员异常训练数据实时采集方法。本文的主要动机是及时收集运动员的异常训练数据，有助于评估和提高训练效果。运动员上下肢设置四个传感器节点，采集运动员在训练状态下的角速度、加速度、磁场强度数据。数据通过无线传感器发送至数据传输基站，数据传输基站将数据传输至数据处理终端。数据处理终端计算各传感器样本值的差值，得到各传感器的数据离散度。利用离散度构造32维特征向量，得到时域和频域各维度数据的特征，提取的特征向量输入隐马尔可夫模型。采用前向算法获取最终观测序列的概率，从而实现运动员异常训练数据的最终采集。实验结果表明，该方法采集的异常数据的准确率和召回率均高于98%，所需时间更少。利用离散度构造32维特征向量，得到时域和频域各维度数据的特征，提取的特征向量输入隐马尔可夫模型。采用前向算法获取最终观测序列的概率，从而实现运动员异常训练数据的最终采集。实验结果表明，该方法采集的异常数据的准确率和召回率均高于98%，所需时间更少。利用离散度构造32维特征向量，得到时域和频域各维度数据的特征，提取的特征向量输入隐马尔可夫模型。采用前向算法获取最终观测序列的概率，从而实现运动员异常训练数据的最终采集。实验结果表明，该方法采集的异常数据的准确率和召回率均高于98%，所需时间更少。