3D Head Motion Detection Using Millimeter-Wave Doppler Radar

Muneeba Raja*, Zahra Vali, Sameera Palipana, David G. Michelson, Stephan Sigg

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

15 Citations (Scopus)
391 Downloads (Pure)


In advanced driver assistance systems to conditional automation systems, monitoring of driver state is vital for predicting the driver's capacity to supervise or maneuver the vehicle in cases of unexpected road events and to facilitate better in-car services. The paper presents a technique that exploits millimeter-wave Doppler radar for 3D head tracking. Identifying the bistatic and monostatic geometry for antennas to detect rotational vs. translational movements, the authors propose the biscattering angle for computing a distinctive feature set to isolate dynamic movements via class memberships. Through data reduction and joint time-frequency analysis, movement boundaries are marked for creation of a simplified, uncorrelated, and highly separable feature set. The authors report movement-prediction accuracy of 92%. This non-invasive and simplified head tracking has the potential to enhance monitoring of driver state in autonomous vehicles and aid intelligent car assistants in guaranteeing seamless and safe journeys.

Original languageEnglish
Article number8998250
Pages (from-to)32321-32331
Number of pages11
JournalIEEE Access
Publication statusPublished - 1 Jan 2020
MoE publication typeA1 Journal article-refereed


  • 3D motion detection
  • Bistatic radar
  • Doppler effect
  • head movements
  • STFT
  • wireless sensing


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