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Table 1 A complete list of the posturographic parameters for feature extraction

From: Fall risk classification with posturographic parameters in community-dwelling older adults: a machine learning and explainable artificial intelligence approach

Parameters

Directions

Time-domain distance measures

 

 Mean distance

M-L, A-P, radius

 Maximal distance

M-L, A-P, radius

 Root mean square distance

M-L, A-P, radius

 Range

M-L, A-P, radius

 Mean velocity

M-L, A-P, radius

Time-domain area measures

 

 95% confidence ellipse area

radius

Time-domain hybrid measure

 

 Sway area per second

radius

 Mean frequency

M-L, A-P, radius

 Fractal dimension

radius

Frequency domain measure

 

 Total power spectrum density

M-L, A-P

 50% power frequency

M-L, A-P

 95% power frequency

M-L, A-P

 Centroid frequency

M-L, A-P

 Frequency dispersion

M-L, A-P

  1. We used the computation for center of pressure trajectory analysis [10, 12]
  2. M-L: medial–lateral; A-P: anterior–posterior