Complexity-based analysis of the correlation between stride interval variability and muscle reaction at different walking speeds

In this research, for the first time, we evaluated the correlation between the variations of leg muscle reaction and gait at different walking speeds. Since leg muscle reaction in the form of Electromyogram (EMG) signals and stride interval time series (as gait variability) have complex structures, we utilized fractal theory and sample entropy to decode their alterations at different walking speeds. Twenty-two subjects walked at three different speeds (slow, comfortable, and fast) in six trials, and we analyzed the fractal dimension and sample entropy of EMG signals and stride interval time series. Based on the results, increasing the walking speed causes lower complexity in EMG signals and stride interval time series. Besides, strong correlations were found among the changes in the complexity of EMG signals and stride interval time series at different walking speeds. This method can be applied to analyze the correlation between other complex physiological signals of humans (e.g., EEG and ECG) during walking and running.

Automated summary

This research evaluated the correlation between leg muscle reaction and gait at different walking speeds, finding that increasing walking speed decreases complexity in EMG signals and stride interval time series, with strong correlations between these changes at different speeds. The method used can be applied to analyzing the correlation between other complex physiological signals during walking and running.