###### Abstract  
The paper proposes a simple machine learning solution for hand-gesture classification, based on processed MM-wave radar signal. It investigates the classification up to 12 different intuitive and ergonomic gestures, which are intended to serve as a contactless user interface. The system is based on AWR1642 boost Frequency-Modulated Continuous-Wave (FMCW) radar, which allows capturing standardized data to support the scalability of the proposed solution. More than 4000 samples were collected from 4 different people, with all signatures extracted from the radar hardware available in open-access database accompanying the publication. Collected data were processed and used to train Long short-term memory (LSTM) and artificial recurrent neural network (RNN) architecture. The work studies the impact of different input parameters, the number of hidden layers, and the number of neurons in those layers. The proposed LSTM network allows for classification of different gestures, with the total accuracy ranging from 94.4% to 100% depending on use-case scenario, with a relatively small architecture of only 2 hidden layers with 32 neurons in each. The solution is also tested with additional data recorded from subjects not involved in the original training set, resulting in an accuracy drop of no more than 2.24%. This demonstrates that the proposed solution is robust and scalable, allowing quick and reliable creation of larger databases of gestures to expand the use of machine learning with radar technologies. radar; MM-wave radar; FMCW radar; hand-gesture recognition; machine learning; neural network; recurrent neural network +
Footnote †: journal: Accepted: 27 February 2022  
However, machine learning algorithms used in radar signal processing are still a nascent field. Current literature reports a classification of up to 10 motion gestures with \(>\)90% accuracy [7; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19]. In [10], it is discussed that the accuracy of such algorithms may drop by up to 40% when the classification is executed on samples from a subject not included in the training set. This emphasizes the need for large-scale open and portable data-sets, that are compatible with a number of standardized radar hardware. In [20], a classification within a set of 5 gestures demonstrated a good performance, achieving 97.6% accuracy. The work gathered 300 samples, however, with no information about the number of subjects involved. Another work [7] successfully demonstrates the classification of 6 basic hand gestures. The training set also includes some random movements, which allowed explicit classification of 'invalid' gestures. In most of the reported approaches, the raw data captured by the radar are converted into spectrogram images and fed into a Convolution Neural Network (CNN) optimized for the image classification problem. While using CNN for image classification is a well studied subject, it creates large amounts of stored and processed data, which require feature extraction in the post-processing step [21]. This publication proposes a classification algorithm of up to 12 basic gestures with classification accuracy above 94%. The motivation is to create an ergonomic and intuitive contactless interface for vending machines, ATMs, and similar devices. The work studies the impact of different parameters on the neural network proposed, as well as a trade-off between accuracy and the number of gestures involved. Overall accuracies are within 94.4% to 100%, depending on the number of gestures. Distinguishable to previous contributions, the work uses standardized off-the-shelf radar hardware, the AWR164 BOOST from Texas Instrument [22]. This allows for machine learning and data-sets that are transferable across a number of off-the-shelf radar hardware. This ensures repeatability, easy benchmarking between different solutions, and the possibility to gradually build large training data-sets from multiple sources. All recorded samples reported in the study are openly available in [23].