Stadelmayer, ThomasThomasStadelmayerHassab, YoucefYoucefHassabServadei, LorenzoLorenzoServadeiSantra, AvikAvikSantraWeigel, RobertRobertWeigelLurz, FabianFabianLurz2024-01-242024-01-242024-05-01IEEE Internet of Things Journal 11 (9): (2024)https://hdl.handle.net/11420/45264This 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%.en2327-46622024915285152981Institute of Electrical and Electronics Engineers Inc.ChirpFMCW radarGesture recognitionhand gesture recognitionInternet of Thingslight-weight processingmachine learningperson-independentRadarRadar antennasreal-timeReceiving antennasTime series analysisMLE@TUHHElectrical Engineering, Electronic EngineeringJournal Article10.1109/JIOT.2023.3347308Journal Article