This option transforms the signal data into a vector with a specified window size, and also enables users to generate additional variables of their choice.

Window size is the portion of data that will be used for processing the training dataset (the static window approach is applied on the platform). It should be universal for all events in the training dataset, even if the duration of events is different. Therefore, for correct training of the model, the data in the training and the validation datasets (if applicable) must be brought to the same window using upsampling or downsampling, before loading into the platform.

Using the window size, the training dataset will be grouped by a selected number of samples. All variables extracted from the raw data will be calculated from these groups. For correct feature calculation, the recommended minimum window size is 5 samples/lines.

Since the platform is intended for solving TinyML tasks, it is recommended that the maximum window size does not exceed 1000 samples/lines. However, it all depends on your data and there are no technical restrictions on the window size in the platform.

You have 3 options for the windowing specification:

The platform default is 128, but you can specify the count of rows to use for windowing. For example, for the following dataset:

Specify window size in ms and frequency in Hz to define window size for the training dataset.
If you use sensors with different frequencies, then you must first bring all signals to a single frequency using downsampling or upsampling.
According to the specified data, the platform will determine the size of the window in samples/lines and it will be used in the future to calculate features and other operations.

Using Fast Fourier Transform this option automatically selects the window size based on the best model quality (this option is available only for the classification task type). For finer tuning of SRAM usage, it is recommended that you manually enter the window size and frequency of data.

The sliding shift is used for the window overlap and indicates how many rows/samples are needed to shift to form the next window. It is available for both training and inference. If the sliding shift values are equal to the window size, then the window segmentation is performed without an overlap.

SRAM depends on the window you choose and the list of features that will be created from the raw data.

If you have selected auto-detection of the window, then the estimated SRAM usage will not be calculated as the platform will determine the size of the window after the start of training

When the window size is specified, the platform automatically extracts the following features for each column in the training dataset. The same feature extraction will be automatically executed during inference on the device. You can manage calculated features for each original variable in the training dataset by marking or unmarking the appropriate checkbox.

To delete a variable/axis from the Feature Extraction block, use the Remove button, which appears when hovering over a variable. After doing so, no feature will be created for it, and therefore it will not participate in building the model. By default, Feature Extraction uses all variables/axes.

If you have deleted a variable, but want to return it, then use the “Add variables” button.

You can correct the list of features, as well as configure their parameters by clicking on the Edit button next to the list of features.

If you have deleted all features in a variable/axis, then it is automatically excluded from the list of variables/axes. If you have deleted all variables, then Windowing and Feature Extraction are turned off automatically.

The list of supported features is provided below:

You can specify the Smoothing Factor (4 by default) which is the amount of attenuation for frequencies over the cutoff frequency. It is indicated for both features and the requirements for it are the same.

The technically acceptable values of the Smoothing factor for both features are within the window size, but when choosing extreme values, the feature can be calculated with an error. Therefore, the recommended value for the smoothing factor is 10% of the window size.

For example, if the window size is 128:

In this case, choose the value that is best suited for your data and task.

These functions use fast Fourier transform to calculate the frequency of peaks and their power.

Feature complexity can be classified as light, medium, or heavy, depending on the level of difficulty in its calculation. As the feature complexity increases, so does the memory requirement for its computation. Therefore, it is recommended to use only light features if you have resource-constrained devices.

If you want to include raw data in training, you need to enable the function. This setting is applied immediately to all variables/axes specified in the Feature Extraction block, i.e., you can either apply raw data for all variables/axes in the train dataset, or exclude it.

The option is available only if the user has selected features in the Feature Extraction block.

The following algorithms are available for selecting features (you can choose only 1 of the methods):