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

Stadelmayer T, Hassab Y, Servadei L, Santra A, Weigel R, Lurz F (2024)

Publication Language: English

Publication Type: Journal article

Publication year: 2024

Journal

IEEE Internet of Things Institute of Electrical and Electronics Engineers (IEEE)

Book Volume: 11

Pages Range: 15285-15298

Journal Issue: 9

DOI: 10.1109/JIOT.2023.3347308

Abstract

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%.

Authors with CRIS profile

Robert Weigel Lehrstuhl für Technische Elektronik

Additional Organisation(s)

FAU Profilzentrum Licht.Materie.Quantentechnologien (FAU LMQ)

Involved external institutions

Infineon Technologies AG DE Germany (DE) Otto-von-Guericke-Universität Magdeburg DE Germany (DE) Technische Universität Hamburg-Harburg (TUHH) DE Germany (DE)

How to cite

APA:

Stadelmayer, T., Hassab, Y., Servadei, L., Santra, A., Weigel, R., & Lurz, F. (2024). Lightweight and Person-Independent Radar-Based Hand Gesture Recognition for Classification and Regression of Continuous Gestures. IEEE Internet of Things, 11(9), 15285-15298. https://doi.org/10.1109/JIOT.2023.3347308

MLA:

Stadelmayer, Thomas, et al. "Lightweight and Person-Independent Radar-Based Hand Gesture Recognition for Classification and Regression of Continuous Gestures." IEEE Internet of Things 11.9 (2024): 15285-15298.

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