基于时频点密度的肌电信号起止点自适应检测

An adaptive detection method for start and end of sEMG signal based on time-frequency point density

针对传统方法使用经验阈值检测表面肌电信号活动段起止点的不足,提出了一种基于时频点密度的表面肌电信号起止点自适应检测方法,该方法创新地使用时频点密度作为表面肌电信号的特征参数。首先利用巴特沃斯带通滤波和小波阈值去噪对Ninapro DB8数据集中的表面肌电信号进行预处理;利用短时傅里叶变换进行时频分析;接着将表面肌电信号分割成多个连续的单位时频窗口,统计窗口内频率点的数量,提取时频点密度特征参数;最后对特征提取结果进行区间[-1,1]的自适应归一化,并利用基于滑动窗口的双值判断法检测表面肌电信号的起始与结束。实验结果表明:该方法能实现在0.5 s内准实时检测sEMG信号活动段的开始和结束,准确率近乎100%,较之于其他常见的算法具有更好的准确性;通过归一化后的正值和非正值可以消除个体差异性影响,自适应性强;此外,该方法在手势识别系统中具有较强的实用性。

Aiming at the deficiency of using empirical threshold to detect the starting and ending points of active segment of surface electromyography(sEMG)signal in traditional methods,an adaptive detection method of sEMG starting and ending points based on time-frequency point density is proposed.Time-frequency point density is innovatively proposed as the characteristic parameter of surface EMG signal in this method.Firstly,butterworth bandpass filtering and wavelet threshold denoising are used to preprocess the sEMG signals in Ninapro DB8 dataset.Short-time Fourier transform is used for time-frequency analysis of signals.Secondly,the sEMG signal is divided into several continuous unit time-frequency windows,the number of frequency points in the windows is counted,and the time-frequency point density(TFPD)characteristic parameters are extracted.Finally,the TFPD results are adaptively normalized in the interval[-1,1],and the start and end of EMG signals are detected by using the binary judgment method based on sliding window.The experimental results show that this method can detect the start and end of sEMG signal activity segment in quasi-real time within 0.5 s,and the accuracy is nearly 100%.Compared with other common algorithms,the proposed method has better accuracy.The influence of individual differences can be eliminated through normalized positive and non-positive values,and the adaptability is strong.In addition,the proposed method has strong practicability in gesture recognition system.