基于多特征分析的运动员训练辅助决策模型设计

Design of Aided Decision Model for Athletes Training Based on Multi-Feature Analysis

[目的]收集并统计运动员训练数据,设计基于多特征分析的运动员训练辅助决策模型。[方法]使用摄像机采集运动员的训练图像,利用尺度不变特征变换方法提取运动图像特征,在此基础上使用支持向量机获得运动员训练状态初步评估结果;运用传感器采集运动员肌电信号数据,使用小波变化提取肌电信号特征,通过时间窗识别运动员疲劳状态,得到初步运动疲劳状态评估结果;决策级融合2种初步评估结果,设计运动员训练辅助决策模型,实现运动员训练疲劳状态的有效评估。[结果]经过实验分析,使用多特征辅助决策模型后,各运动项目的平均训练分数均达到90分以上。[结论]该模型能够准确提取运动图像特征与运动员肌电特征,并对运动员运动疲劳状态作出准确评价,为制定运动员训练计划打下坚实基础。

[Objective]Collecting athlete training data and formulating an aided decision model for athletes training based on multi-feature analysis.[Method] The training images of athletes were collected by camera, and the moving image features were extracted by scale invariant feature transformation;on this basis, the preliminary evaluation results of athletes′ training state were obtained by support vector machine;the sensor is used to collect athletes′ EMG signal data, the wavelet change is used to extract the characteristics of EMG signal, and the athletes′ fatigue state is identified through the time window to obtain the preliminary evaluation results of sports fatigue state.The decision-making level integrates the two preliminary evaluation results for the design of the athlete training auxiliary decision-making model to realize effective evaluation of athletic training fatigue state.[Result] After experimental analysis, the average training scores of all sports items exceed 90 points after using the model in this paper.[Conclusion] The model can accurately extract the characteristics of sports image and athletes′ EMG,and accurately evaluate athletes′ sports fatigue state, which lays a solid foundation for formulating athletes′ training plan.