Contactless Hand Gesture Sensor Based on Array of CW Radar for Human to Machine Interface

Contactless Human to Machine Interface (HMI) is an indispensable technology for handling the machine or equipment in a pandemic situation where the virus can spread through direct contact. The contactless hand gesture sensor is an essential part needed for the HMI system as previously mentioned. Sev...

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Bibliographic Details
Published in:IEEE sensors journal 2021-07, Vol.21 (13), p.15196-15208
Main Authors: Pramudita, Aloysius Adya, Lukas, Edwar
Format: Article
Language:English
Subjects:
Array
Configurations
contactless sensor
Continuous radiation
Cross correlation
Data processing
Doppler effect
Doppler radar
Feature extraction
hand gesture
human to machine
interface
Radar
Radar arrays
Radar detection
Sensor arrays
Sensors
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Summary:Contactless Human to Machine Interface (HMI) is an indispensable technology for handling the machine or equipment in a pandemic situation where the virus can spread through direct contact. The contactless hand gesture sensor is an essential part needed for the HMI system as previously mentioned. Several hand gestures with a similar Doppler response will become problems in applying Doppler radar as a hand gesture sensor that requires a more complex recognition method. This paper proposes spatial diversity implementation for obtaining more accurate hand gesture features to cope with this problem. Array radar was selected as a sensor configuration to create the spatial diversity feature. In this paper, an array configuration of four Continuous Wave (CW) radars is proposed as a contactless sensor for hand gestures. Peak detection based on cross-correlation was employed to determine the time position of the hand gesture Doppler response detected by each CW radar. The time position pattern then becomes a feature of each hand gesture used. The CW radar array is realized with an operating frequency of 10 GHz by using the HB 100 as a CW radar component. The experimental results show that the proposed method can distinguish the hand gesture feature with an accuracy of 96.6 % at a sensing distance of 50 cm. It can differentiate the hand gesture pairs that have the opposite direction movement with a similar Doppler effect, and also requires simple data processing for recognizing different hand gestures.
ISSN:1530-437X
1558-1748
DOI:10.1109/JSEN.2021.3073263