*Related Work*

Automotive radars with Frequency Modulated Continuous Wave (FMCW) transmission find many applications beyond their original purpose. Recent studies revealed how radars can be exploited to improve road safety and autonomous driving by recognizing the presence of pedestrians starting from micro-Doppler tracks, focusing in particular on the recognition of different parts of the human body [7–9], sometimes applying classification algorithms able to distinguish whether the detected target is a pedestrian or not with a very high accuracy [10]. Through the micro-Doppler components it is also possible to characterize a person's movement or to identify a fall [11]. Moreover, micro-Doppler features have been often exploited in several aspects of human recognition, such as arm motion analysis [12], identification of target human motions [13], or to distinguish people walking in a noisy background [14]. Low power Frequency Modulated Continuous Wave (FMCW) radar and micro-Doppler tracks have been recently used with various scopes, such as discriminating armed from unarmed people [15], identifying people on the basis of their gait characteristics [16–18] or their movements [19], and for gestures recognition [20]. Moreover, radar technology has been successfully applied to the medical field [21,22], for example to remotely monitor the cardiac and respiratory frequency [23]. Principal Component Analysis (PCA) has been often exploited in radar applications, as an instrument to reduce the dimensionality of the available feature space and to automatize the feature extraction and selection procedure [24,25], together with deep learning algorithms for fall detection [26] and human activity recognition [27]. Recent works considered the application of deep learning techniques for gait classification, using smart sensors [28] and radar-based techniques [29] to discriminate aided from unaided motion.

From the year 2022 the 24 GHz bandwidth will no longer be available because of new regulations and ETSI and FCC standards, making it necessary to move towards other frequencies [30]. As regards radar applications, the only available bandwidth will be the ISM bandwidth that have only 250 MHz available, with a performance loss in distance. This explains the presence on the radar market of sensor for industrial and automotive applications working at frequencies over 76 GHz.

As an alternative to radar systems, different contactless technologies have been proposed for gait analysis and walking classification, based on the processing of video (RGB) or video+depth (RGBD) signals. Generally, the main purpose of these research activities is for medical purposes or related to safety issues. In Reference [31], a system able to perform an automatic detection and classification of gait impairments, based on the analysis of a single 2D video, is presented. The main drawback related to the use of video signal is related to privacy issues. To solve this problem, in Reference [32], a representation of gait appearance, with the aim of person identification and classification, is described, based on simple features such as moments extracted from orthogonal view video silhouettes of human walking motion. The availability of a low cost, marker-free, and portable device as Microsoft Kinect Camera allows to develop methods that can respond to the changes in the gait features during the swing stage, tracking the skeletal positional data of body joints to assess and evaluate the gait performance [33]. While being a low cost sensor, Microsoft Kinect is able to track human motion without using wearable sensors and with acceptable reliability. In Reference [34], the standard error of measurement and minimal detectable change sing Kinect is evaluated, confirming the validity of this sensor with standardized clinical tests in individuals with stroke.

The rest of the paper is organized as follows. Section 2 describes the radar used, along with the composition of the transmitted signals and the devices configuration. In Section 3 we outline the signal processing applied to extract spectrograms and maps from the data obtained. Section 4 the dimensionality reduction techniques and classification algorithms applied are introduced. Experimental results are shown in Section 5. Finally Section 6 concludes the paper.
