摘要： 为运用肌电信号分析髋脱位儿童和正常儿童的差异，提出一种基于统计的聚类方法，识别步态中下肢肌电信号的周期起始时刻。使用非参数贝叶斯模型将肌电信号序列聚类为状态序列，并通过k均值聚类算法将该状态序列标记为肌肉活跃和不活跃两种状态，将肌肉活跃状态的起始时刻作为肌电信号周期的起始位置，并且利用窗函数方法提高预测准确性。实验结果表明，该方法对于预测正常儿童周期起始位置的识别误差较小，平均值为2.15%，并且在5%的置信度水平下与SampEN、SNEO和IP等检测算法相比具有较高的预测准确率。

关键词: 肌电信号, 周期识别, 统计聚类方法, 非参数贝叶斯模型, k-means算法, 滑动窗

Abstract: To promote the application of Electromyography(EMG) signals in the analysis of differences between normal children and children with hip dislocation,this paper proposes a method based on statistical clustering for detecting the starting point of the period of EMG signals from lower limb mulscles of walking children.The method employs the nonparametric Bayesian model to cluster EMG signal sequences as pattern sequences,which are subsequently marked with tags of active state and inactive state by using the k-means algorithm.The starting point of the active state of muscle activities is taken as the starting point of a period of EMG signals,and the window function method is used to improve the prediction accuracy.Experimental results show that the average recognition error of this method is as small as 2.15%,and is significantly different from that of the other detection algorithms,including SampEN,SNEO and IP when the confidence level is 5%.

Key words: Electromyography(EMG) signal, periodic identification, statistical clustering method, nonparametric Bayesian model, k-means algorithm, sliding window

中图分类号: 

• TP399