**1. Introduction**

Recognition of a person's type of movement has implications for many aspects of daily life, from security applications to monitoring for assisted living. Discriminating whether a person is running or walking normally in airports or shopping centers, for example, may help video surveillance to detect possible dangerous situations [1–3]. Tools designed for this purpose involve the use of contactless devices, and radar technology is particularly suitable for the mentioned scenario.

Besides its ability to detect the presence of targets of all kinds, sometimes even at considerable distances, radar technology has attracted a large attention thanks to its versatility and usefulness in several fields, from medical applications [4] to traffic surveillance monitoring [5].

In this paper we consider the use of an automotive radar to classify different types of monitored actions. With respect to the work described in Reference [6], the examined activities present less evident differences, since our goal is to distinguish people's way of walking on the basis of their speed. Moreover, the radar here considered works with a higher frequency range and therefore a smaller wavelength, thus allowing a better interaction with objects and improved performance in the micro-Doppler extraction. In addition, the millimeter wave technology exploited allows us to discriminate with a good accuracy also the position of the hands during the walk, whether they are in free movement or hold in pockets. Speed and hands movement classification is performed by using Principal Component Analysis (PCA) and t-distributed Stochastic Neighbor Embedding (t-SNE) methods for feature extraction and supervised learning for the classification task. Different algorithms have been tested, obtaining the best performance in terms of accuracy by using the Nearest Neighbor (NN) and the Support Vector Machine (SVM).
