Open Access

Matthias G. Ehrnsperger, Thomas Brenner, Henri L. Hoese, Uwe Siart, Thomas F. Eibert

• Classical signal processing methodologies have been infiltrated by machine learning (ML) approaches for a long time, where the ML approaches are in particular applied when it comes to gesture recognition. In this paper, we investigate naïve gesture recognition methodologies and compare classical and novel machine learning (nML) algorithms. The considered gestures are simple human gestures such as swiping a hand or kicking with a foot. For the sake of comparability, the algorithms are assessed with respect to their true positive rate (TPR), false-positive rate (FPR), their real-time capability together with the required computational power, and their implementability on low-cost hardware. Two different data sets are utilized separately for the training process of the ML algorithms, where both have been recorded by making use of low-cost radar hardware. The results show that all ML approaches are superior to naïve gesture recognition methodologies, e.g., threshold detection. ML algorithms allow almost assured gesture detection. However, our primary contribution is a design approach for scalable neural networks (NNs) that allow such gesture recognition algorithms to be executable on low-cost microcontroller units (MCUs).