When processing .gt3x files with ActiGraph Idle Sleep Mode, time gaps are present in the data which are handled with last observation carrief forwared after normalizing the last epoch to 1 g (i.e., assuming no movement). This creates synthetic flatline epochs (0 magnitude, 0 variability). Because ML models were never trained on zero-variance inputs, some of them misclassify these idle epochs as active classes (e.g., lightGames) rather than sedentary.
Proposal: Rule-based Override to flag those affected epochs and force them to sedentary in classify() if they are not flagged as non-wear or sleep.
When processing .gt3x files with ActiGraph Idle Sleep Mode, time gaps are present in the data which are handled with last observation carrief forwared after normalizing the last epoch to 1 g (i.e., assuming no movement). This creates synthetic flatline epochs (0 magnitude, 0 variability). Because ML models were never trained on zero-variance inputs, some of them misclassify these idle epochs as active classes (e.g., lightGames) rather than sedentary.
Proposal: Rule-based Override to flag those affected epochs and force them to sedentary in
classify()if they are not flagged as non-wear or sleep.