No interface, no problem : Gesture recognition on physical objects using radar sensing

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

2 Scopus Citations
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Author(s)

  • Nuwan T. Attygalle
  • Luis A. Leiva
  • Matjaž Kljun
  • Alexander Plopski
  • Hirokazu Kato
  • Klen Čopič Pucihar

Related Research Unit(s)

Detail(s)

Original languageEnglish
Article number5771
Journal / PublicationSensors
Volume21
Issue number17
Online published27 Aug 2021
Publication statusPublished - Sep 2021

Link(s)

Abstract

Physical objects are usually not designed with interaction capabilities to control digital content. Nevertheless, they provide an untapped source for interactions since every object could be used to control our digital lives. We call this the missing interface problem: Instead of embedding computational capacity into objects, we can simply detect users’ gestures on them. However, gesture detection on such unmodified objects has to date been limited in the spatial resolution and detection fidelity. To address this gap, we conducted research on micro-gesture detection on physical objects based on Google Soli’s radar sensor. We introduced two novel deep learning architectures to process range Doppler images, namely a three-dimensional convolutional neural network (Conv3D) and a spectrogram-based ConvNet. The results show that our architectures enable robust on-object gesture detection, achieving an accuracy of approximately 94% for a five-gesture set, surpassing previous state-of-the-art performance results by up to 39%. We also showed that the decibel (dB) Doppler range setting has a significant effect on system performance, as accuracy can vary up to 20% across the dB range. As a result, we provide guidelines on how to best calibrate the radar sensor.

Research Area(s)

  • Deep learning, Gesture recognition, Human factors, Radar sensing

Citation Format(s)

No interface, no problem : Gesture recognition on physical objects using radar sensing. / Attygalle, Nuwan T.; Leiva, Luis A.; Kljun, Matjaž; Sandor, Christian; Plopski, Alexander; Kato, Hirokazu; Čopič Pucihar, Klen.

In: Sensors, Vol. 21, No. 17, 5771, 09.2021.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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