Lightweight and Person-Independent Radar-Based Hand Gesture Recognition for Classification and Regression of Continuous Gestures

This article proposes a novel preprocessing technique for radar-based short-range gesture sensing using a frequency modulated continuous wave (FMCW) radar. The preprocessing is lightweight and works without Fourier transformation. The signal after preprocessing represents the backscattering central dynamics of the hand as a complex-valued time signal of a point target. It is shown that the proposed processing provides competitive classification results compared to conventional frequency domain-based solutions, while being less computationally intensive and having better generalization performance. The preprocessed time domain signal preserves a high-temporal resolution of the hand movement. Due to this fact, it is possible to integrate a periodic control gesture into the system. In doing so, the system not only detects that a gesture is performed continuously and periodically, but also estimates its speed. This is an essential property for controlling scalable parameters, such as brightness or volume, at different speeds. The real-time capability was proven on a Raspberry Pi 3B with an ARM Cortex-A53 CPU. The proposed processing causes a CPU utilization of only 6%. The neural network (NN) inference is done within 75 ms with a classification accuracy of 96.7%.