Hi Vincent,
Thanks for your prompt reply and explain the advantage of GGIR in ENMO. I also noticed that the ENMO 'values presented is the average ENMO over all the available data normalised per 24 hour cycles, with invalid data imputed by the average at similar timepoints on different days of the week', from which I understand GGIR automatically imputed invalid data if there is a time gap from sensor values, and smooth ENMO results than other function.
Two questions further:
1. Are ENMO results affected by different window size and cut points for LPA and MVPA?
We tested ENMO and angles results with the same participant, by three scenarios of window size and cut points for elder people(case 0 as the benchmark). Please check the attached files of summary results from R.
case_0: using (1,600,900), mvpathreshold =c(60), threshold.lig = c(18), threshold.mod = c(60),
case_1: using (1,900,900) instead, keep other params the same
case_2: keeping window size as (1,600,900), tuning mvpathreshold =c(14), threshold.lig = c(7), threshold.mod = c(14)
I thought the ENMO calculation should be independent at the beginning of GGIR shell sections, reflecting the fact how participant moved, and not changed according to window size (the 2nd and 3rd size) and cut points which would be focused on sleep detection. But slight mismatch popped up.
2. Are there any updates for angle calculation?
Could you please let us know more about the above two questions?
Many thanks.
Regards,
Alex