2.3.1. Averaged-Frequency-Band Features

As base features, the so-called averaged-frequency-band features are used, the calculation method of which is visualized in Figure 6. To calculate the afb features for each 1 s measurement interval, the data of the interval is first transformed into the frequency domain by means of an FFT. The resulting amplitude spectrum is divided into frequency bands of equal width. Finally, the average values of the amplitudes within the formed frequency bands are used as features. Thus, an afb feature describes the average value of the amplitudes within a frequency band.

**Figure 6.** Determination of the averaged-frequency-band features, adapted with permission from ref. [20].

Based on preliminary investigations and in order to keep the total number of features and thus the model complexity at a moderate level, the number of frequency bands in this case is set to 8.

#### 2.3.2. Rolling Mean Features

In order to utilize information from the temporal past, rolling means can be used. In the case presented here, these rolling means were calculated from the afb features presented previously. To be able to represent the short-term dynamics as well as the long-term behavior, several averages are formed over different time spans. Progressively increasing time spans seem to make sense for this use, which was confirmed in preliminary studies. The progressive staggering of rolling means is shown in Figure 7 for three rolling mean durations using the time course of afb1(8).

**Figure 7.** Progression of an averaged-frequency-band and three associated rolling means.

#### 2.3.3. Cumulative Features

Another way to account for temporal information is to use accumulated quantities. Already in [25], the cumulative sum of values was proposed to generate features with monotonic behavior. These accumulated features provide long-term trends, which helps the ML algorithm in its decision making. In the case presented here, the afb features are used for accumulation. Each afb feature is summed up cumulatively from the beginning of the experiment.
