Smart Touchless Control with Credit-Card Sized Radar Sensor and Microcomputer

A smart, low-cost, stand-alone, and reliable touchless human-computer interaction (HCI) using a tiny 60 GHz radar sensor and a small Raspberry Pi microcomputer with advanced radar signal processing and machine learning (ML), is proposed and implemented. Three feature extraction techniques and variou...

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Bibliographic Details
Published in2022 IEEE 11th Global Conference on Consumer Electronics (GCCE) pp. 321 - 322
Main Authors Lee, Philip Hann Yung, Lu, Yilong
Format Conference Proceeding
LanguageEnglish
Published IEEE 18.10.2022
Subjects
hand gesture recognition
Home appliances
Human computer interaction
Machine learning algorithms
Microcomputers
Radar
radar sensing
Radar signal processing
Signal processing algorithms
Touchless control
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Summary:A smart, low-cost, stand-alone, and reliable touchless human-computer interaction (HCI) using a tiny 60 GHz radar sensor and a small Raspberry Pi microcomputer with advanced radar signal processing and machine learning (ML), is proposed and implemented. Three feature extraction techniques and various ML algorithms are explored and compared. Smart gesture classification is realized by an ensemble ML model with 3 non-deep and deep learning (DL) algorithms and the majority voting scheme to improve classification accuracy. Both radar signal processing and ML algorithms are implemented in Python language in a Raspberry Pi microcomputer with an optimization by TensorFlow Lite, which reduces DL CPU time by 120 times. The proposed HCI is also integrated with a menu driven use case, which could be easily adapted to many other gesture control applications for home and industrial appliances.
DOI:10.1109/GCCE56475.2022.10014250