The Flatten block performs statistical analysis on the signal. It is useful for slow-moving averages like temperature data, in combination with other blocks.
GitHub repository containing all DSP block code: edgeimpulse/processing-blocks.
Scaling
Scale axes: Multiplies axes by this number
Method
Average: Calculates the average value for the window
Minimum: Calculates the minimum value in the window
Maximum: Calculates the maximum value in the window
Root-mean square: Calculates the RMS value of the window
Standard deviation: Calculates the standard deviation of the window
Skewness: Calculates the skewness of the window
Kurtosis: Calculates the kurtosis of the window
Moving Average Number of Windows: Calculates the moving average by maintaining a rolling average of the last N windows. Note, there is no zero padding, the block will accumulate averages up to N windows. (Ex. for the first window in a sample, the moving average will equal the average). The moving average resets for each sample during training, and during inference, when run_classifier_init() is called. Note if you enable this, you probably don't want overlapping windows for training.
The Flatten block first rescales axes of the signal if value is different than 1. Then statistical analysis is performed on each window, computing between 1 and 8 features for each axis, depending on the number of selected methods.