## 2 Datasets, Feature Extraction, and Classification Methods
### Radar Datasets  
A previous study [18] showed that Soli could be used to identify materials, estimate number of objects, their orientation, or the the distance to the sensor. [PERSON] et al. [18] created a series of supervised classification datasets, formed by on the one hand the signal acquired by this sensor, and the class of the material on the other hand. The type of material, distance, and other features were also included. The following is a brief schematic description about the different types of categories these datasets are formed by:  
* Material identification (of the material an object is made of), from a limited list of materials. * Object identification, from a series of objects in the same category (i.e., a credit card of a specific bank from the rest of the credit cards). * Counting the number of elements that might be piled up on the surface of the sensor. * Distance estimation, from an object to the sensor. * Order identification, of the items in a battery of objects. * Flipping identification, where the orientation of an object may be inferred. * Movement, where the angular position of the object or changes in the movement of one of them in relation to other(s) are obtained. The complete group of datasets can be found at: [[https://github.com/tcboy88/solinteractiondata](https://github.com/tcboy88/solinteractiondata)]([https://github.com/tcboy88/solinteractiondata](https://github.com/tcboy88/solinteractiondata)) (accessed on 18 January 2021). As stated above, the radar chip has eight channels that acquire a series of raw signals. Each of these channels has 64 data points. This information is converted into a 512 (=\(64\times 8\)) feature vector and collectively saved in a CSV file. Therefore, the original dataset dimensionality value is always constant and equal to 512. As recommended by [PERSON] et al. (2021), the easiest way to use the data was considering the Weka Graphical User Interface (GUI), converting them to Attribute-Relation File Format (i.e.,, arff) files. This final group of datasets is formed by 34 files. Figure 1a,b shows the eight 64D signals obtained by the radar sensor for two instances, each from a different dataset. We can see the complex and irregular shape of the signals detected by the sensor. Table 1 shows the total number of samples and classes for each one of the files. These datasets show different acquisition conditions over a series of distinct objects, including playing cards, poker chips, and Lego blocks. For particular details and a deeper description about the acquisition conditions for the creation of these sets, the reader is referred to Section 6 in (2021). \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Dataset** & **Problem** & **\# of Samples** & **\# of Classes** \\ \hline
1 & Count + Order Lego & C & 106 & 11 \\
2 & Count 20 chips NO case x30 sorted & C & 629 & 21 \\
3 & Count 20 chips WITH case x30 sorted & C & 630 & 21 \\
4 & Count 20 papers x10 & C & 210 & 21 \\
5 & Distance 3 mugs 10 distances & M + D & 93 & 31 \\
6 & Distance 3 mugs grouped by material & M & 93 & 4 \\
7 & Distance 7 slotting & D & 60 & 8 \\
8 & Flip 10 creditacids NO case & F + I & 220 & 21 \\
9 & Flip 10 creditacids WITH case & F + I & 220 & 21 \\
10 & Flip 52 cards & F & 105 & 3 \\
11 & Identify 10 creditacids NO case & I & 110 & 11 \\
12 & Identify 10 creditacids WITH case & I & 110 & 11 \\
13 & Identify 12 printed designs & I & 130 & 13 \\
14 & Identify 12 touch on numpad & I & 780 & 13 \\
15 & Identify 5 colors \(\times\) 20 chips & I & 240 & 6 \\
16 & Identify 6 tagged plastic cards & I & 70 & 7 \\
17 & Identify 6 users by palm & I & 210 & 7 \\
18 & Identify 6 users by touch behavior sorted & I & 780 & 7 \\ \hline \hline \end{tabular}
\end{table}