**4. Movements Classification**

In this section we briefly describe the dimensionality reduction techniques and the classification algorithms used in the following section to discriminate the kinds of activities under consideration. As regards features extraction, we resort to two different methods to reduce data dimensionality, the Principal Component Analysis (PCA) and the t-distributed Stochastic Neighbor Embedding (t-SNE) .

Both the maps obtained through the radar signal processing, that is, the Range-Doppler map and Time-Doppler map, are considered as images. Vectors resulting from the application of dimensionality reduction techniques to these images, that is, the principal components extracted from Principal Component Analysis (PCA) and the main dimensions given by the t-distributed Stochastic Neighbor Embedding (t-SNE) , will serve as features vectors. We have a set of *N* images *In* of dimension [*l* × *<sup>m</sup>*], with *n* = 1, ··· , *N*. Images are initially vectorized row-wise and grouped in order to form a training set **X** = [*x*(1), ··· , *<sup>x</sup>*(*N*)]*<sup>T</sup>*, where *T* denotes the transpose operator; rows of **X** correspond to observations and columns correspond to variables.
