Light-weight and person-independent radar-based hand gesture recognition for classification and regression of continuous gestures

This paper proposes a novel pre-processing technique for radar-based short-range gesture sensing using a frequency modulated continuous wave (FMCW) radar. The pre-processing is light-weight and works without Fourier transformation. The signal after pre-processing 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 pre-processed 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 nn inference is done within 75 ms with a classification accuracy of 96.7%.

Subjects

Chirp

FMCW radar

Gesture recognition

hand gesture recognition

Internet of Things

light-weight processing

machine learning

person-independent

Radar

Radar antennas

real-time

Receiving antennas

Time series analysis

MLE@TUHH

DDC Class

621: Applied Physics

Options

Light-weight and person-independent radar-based hand gesture recognition for classification and regression of continuous gestures