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Table 3 Feature categorization for supervised machine learning models

From: Role of data measurement characteristics in the accurate detection of Parkinson’s disease symptoms using wearable sensors

Feature categoryAbbreviationFeaturesNo. Tri-axial featuresNo. Magnitude features
TimeTRoot mean square, range, mean, variance, skew, kurtosis186
FrequencyFDominant frequency, Relative magnitude, Moments of power spectral density (mean, standard deviation, skew, kurtosis)186
EntropyESample entropy31
CorrelationCCross-correlation peak (XY,XZ,YZ), Cross-correlation lag (XY,XZ,YZ)60
DerivativeDMoments of the signal derivative (mean, standard deviation, skew, kurtosis)124
Total for each sensor type5717
  1. Features extracted from both accelerometer and gyroscope data signals and used as inputs for symptom models. Features are shown split into the categories used during the analysis of feature types