Doppler Radar Combined Learning Models: An Efficient Tool for Gesture Recognition
TL;DR
The results suggest that as the number of gestures to be classified increases, different methods may become preferable and that employing more complex architectures does not always result in better performance.
Abstract
Continuous technological advancement has highlighted the need for more sympathetic interaction between human beings and computerized machines. This contribution analyzes the use of Doppler radar as a communication sensor between humans and machines. In fact, there is a massive use of frequency-modulated radars with multiple antennas in spite of Doppler radars, which are characterized by ease of use and relatively low cost. In addition, Doppler radar is the best solution for a micro-Doppler analysis to recognize hand gestures with less computational resources. This feature is very beneficial for developing algorithms that categorize gestures. Contextually, machine learning (ML) models reinforce this interaction by making machines responsive to the user’s requests. Thus, one purpose of this article is to detect hand gestures, emphasizing the correct application of the short-time Fourier transform (STFT). Moreover, this work implements two different data analysis approaches: the end-to-end (E2E) and the rocket methods. Five algorithms were developed to provide a complete and thorough overview. This contribution shows how dataset complexity impacts the performance of the algorithms. The results suggest that as the number of gestures to be classified increases, different methods may become preferable and that employing more complex architectures does not always result in better performance.