Comments (1)
@vanessabin it is mostly to help with comparison between devices and signal types.
If you calculate cross-correlation using xcorr(x, y, scale="coeff")
then division by ac_lag0
wouldn't do anything as the data is already normalized. However, the other scale
methods are not normalized and can't be directly compared.
Imagine one device that measures acceleration and another device that measures linear acceleration. The ac_d1
values for each device, for example, would be measuring different things and on different metrics (as one signal includes gravity, while the other doesn't). These values can't be directly compared against each other without first dividing each by their own "baseline" values to put them on a common metric
If you're looking to replicate the exact regularity values as calculated by Moe-Nilssen, then you could simply multiply the regularity values by the zero-lag autocorrelation peak value (ac_lag0
) to reverse the process
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