Sampling and Digital Filtering Effects When Recognizing Postural Control with Statistical Tools and the Decision Tree Classifier

The postural control can be investigated from time series data of the center-of-pressure (COP) displacements. Detrended fluctuation analysis and scaled windowed variance are commonly employed to measure fractality in COP signal, while sample entropy and multiscale sample entropy are often used to address its regularity and complexity, respectively. Based on COP data from 19 post stroke adults and 19 healthy matched subjects, we first support previous findings that the sampling and/or the digital filtering of those data may influence the interpretations on postural control provided by such types of metrics. Then, we show evidences that the digital filtering lead to less accurate information on the entropy in postural sway with either traditional statistical tools or the decision tree (DT) classifier. Thus, when computing entropy-related features, it is not advisable to filter the data. However, if fractal features are considered instead, the use of digital filters and downsampling techniques can provide more discriminative information. When combining fractal and entropy-related features, both original and processed COP data should be considered for either DT or other popular classifiers. Lastly, with the aid of a DT, we could classify the individuals with an accuracy of 77.6% for fractal features only (best case), 68.4% for entropy-related features only, and 76.3% after combining fractal and entropy-related features.