张学锋, 陈雪瑞.乒乓球挥拍动作识别方法研究[J].重庆工商大学学报(自然科学版),2021,38(1):62-69
ZHANG Xue-feng,CHEN Xue-rui.Research on the Recognition Method of Table Tennis Swing[J].Journal of Chongqing Technology and Business University(Natural Science Edition),2021,38(1):62-69
乒乓球挥拍动作识别方法研究
Research on the Recognition Method of Table Tennis Swing
  
DOI:
中文关键词:  传感器  模式识别  特征选择  集成学习
英文关键词:sensor  pattern recognition  feature selection  ensemble learning
基金项目:
作者单位
张学锋, 陈雪瑞 安徽工业大学 计算机科学与技术学院,安徽 马鞍山 243002 
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中文摘要:
      乒乓球运动中有很多不同类型的挥拍动作,准确识别这些运动模式对于挥拍动作的分析有着重要的意义,针对此需求提出并设计了一种基于MPU9250加速度传感器的挥拍动作识别方法。首先将传感器采集的原始数据做加窗和滤波等预处理,对预处理后的样本进行时域分析提取出每种挥拍动作的均值、方差、周期等22个统计学特征;再采用嵌入式特征选择进行特征筛选得到10个特异性特征,并使用支持向量机、随机森林、决策树以及基于以上3种算法的集成学习进行分类器训练;最后,充分比较了4种分类器对正手攻球、反手推球、正手搓球、反手搓球4种运动模式的识别精度。实验结果表明:集成学习分类器效果最佳,平均识别准确率为94.25%。
英文摘要:
      There are many kinds of swing actions in table tennis,and accurately identifying these actions is of great significance to swing analysis.For this end, this paper designs a swing action recognition scheme based on the MPU9250 sensor.Firstly, the raw data collected by the sensor is processed using the sliding window and filtering.After that, twenty two statistical features, such as mean, variance, period and so on, are extracted by performing time domain analysis on the preprocessed data.Secondly, an embedded feature selection strategy is exploited for feature filtering, and subsequently generates ten distinguished features.Then support vector machine, random forest, decision tree and an ensemble learning based on above three algorithms are used for classifier training.Lastly, the four classifiers are fully tested for the recognition performance of four motion modes such as forehand attack, backhand push, forehand rub and backhand rub.The results showed that the ensemble learning performed best with an average recognition accuracy of 94.25%.
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