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Table 4 Summary of feature variables extracted from acceleration data

From: Assessing the utility and limitations of accelerometers and machine learning approaches in classifying behaviour during lactation in a phocid seal

Feature code Description References
stX, stY, stZ Static acceleration (body posture) in each separate axis [17, 54,55,56]
dyX, dyY, dyZ Dynamic acceleration in each separate axis [23, 54, 55, 57]
PBDAx, PBDAy, PBDAz Partial dynamic body acceleration (absolute acceleration in each axis) [40, 58, 59]
ODBA Overall dynamic body acceleration [60, 61]
VeDBA, VeDBAs Vectorial dynamic body acceleration, smoothed  
ratioX, ratioY, ratioZ Ratio of VeDBA to PDBA [25]
jerkX, jerkY, jerkZ Jerk, derivative of acceleration, in each separate axis [18]
jerkN Norm of jerk in all axes  
Pitch, Roll Pitch and roll in radians [54]
PSD1x, PSD1y, PSD1z Primary dominant power spectrum density in each axis [25, 62]
PSD2x, PSD2y, PSD2z Secondary dominant power spectrum density in each axis  
Freq1x, Freq1y, Freq1z Frequency corresponding to the primary dominant power spectrum density in each axis  
Freq2x, Freq2y, Freq2z Frequency corresponding to the secondary dominant power spectrum density in each axis  
  1. Summary of feature variables used to classify behaviour through machine learning methods. All feature variables, with the exception of those relating to power spectrum density and frequency (e.g. PSD1x, Freq1x), were summarized according to their mean value over 1-s windows of time (50 samples at 50 Hz, 25 at 25 Hz). Power spectrum and frequency elements (PSD1x–Freq2z) were derived over a 3-s moving window (1-s overlap on either side) to minimize spectral leakage