###### Abstract  
Radar technology has evolved considerably in the last few decades. There are many areas where radar systems are applied, including air traffic control in airports, ocean surveillance, and research systems, to cite a few. Other types of sensors have recently appeared, which allow tracking sub-millimeter motion with high speed and accuracy rates. These millimeter-wave radars are giving rise to myriad new applications, from the recognition of the material close objects are made, to the recognition of hand gestures. They have also been recently used to identify how a person interacts with digital devices through the physical environment (Tangible User Interfaces, TUIs). In this case, the radar is used to detect the orientation, movement, or distance from the objects to the user's hands or the digital device. This paper presents a thoughtful comparative analysis of different feature extraction techniques and classification strategies applied on a series of datasets that cover problems such as the identification of materials, element counting, or determining the orientation and distance of objects to the sensor. The results outperform previous works using these datasets, especially when the accuracy was lowest, showing the benefits feature extraction techniques have on classification performance. radar signal; feature extraction; classification; stacking; tangible user interfaces +
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sensors whose location is fixed, but new approaches using unmanned aerial vehicles (UAVs) are starting to appear [10]. Another area where radar technology is being applied is related to object and people classification for systems aimed at helping create a safer driving environment, or even in autonomous driving [11; 12]. Some of the newest radar sensing applications do not try to obtain information from objects over long, mid, or short distances (in the range of meters), but just within a few centimeters. A sensor developed by Google, called Soli, was announced in 2015 and obtained great media interest. It is a millimeter-wave radar, and it has found several research applications. In [13], this sensor is used to identify up to 26 types of materials and 10 body parts from several participants. In [14], it is used to classify and distinguish five common types of materials, namely aluminum, ceramic, plastic, wood, and water, regardless of their different sizes and thicknesses. A hand gesture recognition system, which successfully distinguishes 10 gestures, is proposed in [15]. This type of radar sensor has even been used for face verification [16] or to differentiate between blood samples of disparate glucose concentrations in the range of 0.5 to 3.5 mg/mL [17]. However, the number of research studies focused on the application of this technology for daily object material classification and other interactions seems to still be scarce [18]. A Tangible User Interface (TUI) allows the user to interact with digital information through real actions. This type of user interfaces is known to be more usable and easier to understand, especially for elderly people [19], since, although not everyone knows how to operate a keyboard or mouse, everyone is familiar with grasping or moving common objects. The idea behind TUIs is that there is a direct link between the digital system and the way the physical objects are manipulated. Miniature milimeter-wave radar sensing has been proposed to enhance the interactions by identifying materials or estimating the orientation or distance at which the physical elements are located [18]. Millimeter-wave radar technology could be considered a cost effective option for material identification, counting objects/items, or estimating their position, even when they might be partially covered or occluded. This property is a great advantage when compared to other sensors such as cameras.