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

Publikationstyp
Journal Article
Date Issued
2024-05-01
Sprache
English
Author(s)
Stadelmayer, Thomas  
Hassab, Youcef 
Hochfrequenztechnik E-3  
Servadei, Lorenzo  
Santra, Avik  
Weigel, Robert  
Lurz, Fabian  
Hochfrequenztechnik E-3  
TORE-URI
https://hdl.handle.net/11420/45264
Journal
IEEE internet of things journal  
Volume
11
Issue
9
Start Page
15285
End Page
152981
Citation
IEEE Internet of Things Journal 11 (9): (2024)
Publisher DOI
10.1109/JIOT.2023.3347308
Scopus ID
2-s2.0-85181571818
Publisher
Institute of Electrical and Electronics Engineers Inc.
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
TUHH
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