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No Interface, No Problem: Gesture Recognition on Physical Objects Using Radar Sensing.

Nuwan T Attygalle¹, Luis A Leiva², Matjaž Kljun¹

¹Faculty of Mathematics, Natural Sciences and Information Technologies (FAMNIT), University of Primorska, Glagoljaška 8, 6000 Koper, Slovenia.

Sensors (Basel, Switzerland)

|September 10, 2021

Summary

This summary is machine-generated.

This study introduces a novel method for detecting micro-gestures on everyday objects using radar technology. This approach significantly improves gesture recognition accuracy for controlling digital content.

Keywords:

deep learning gesture recognition human factors radar sensing

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Area of Science:

Human-Computer Interaction
Machine Learning
Sensor Technology

Background:

Physical objects lack inherent interaction capabilities for digital content control.
Existing gesture detection methods on unmodified objects have limited spatial resolution and fidelity.
The 'missing interface' problem highlights the potential of using everyday objects for digital interaction.

Purpose of the Study:

To develop and evaluate micro-gesture detection on physical objects using radar sensing.
To address limitations in spatial resolution and detection fidelity of current on-object gesture recognition.
To enable intuitive control of digital content through gestures on unmodified physical objects.

Main Methods:

Utilized Google Soli's radar sensor for micro-gesture detection.
Developed two novel deep learning architectures: a three-dimensional convolutional neural network (Conv3D) and a spectrogram-based ConvNet.
Processed range Doppler images generated by the radar sensor.

Main Results:

Achieved approximately 94% accuracy for a five-gesture set, surpassing prior state-of-the-art by up to 39%.
Demonstrated robust on-object gesture detection capabilities.
Identified that the decibel (dB) Doppler range significantly impacts system performance, with accuracy varying up to 20%.

Conclusions:

The proposed deep learning architectures enable effective micro-gesture detection on physical objects.
Radar-based gesture recognition offers a promising solution to the 'missing interface' problem.
Guidelines for optimal radar sensor calibration are provided to maximize system performance.