Sign language recognition using micro-Doppler and explainable deep learning

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

6 Citations (Scopus)


In this paper, Sign Language Recognition and classification of the micro-Doppler signatures of different British Sign Language (BSL) gestures is studied. A database of four different BSL hand gesture motions is presented in the form of micro-Doppler signals, recorded with a continuous waveform radar. For detecting the presence of the micro-Doppler signatures, joint time-frequency is applied by calculating their spectrograms. Each individual gesture is expected to contain unique spectral characteristics that are exploited in order to classify the gestures. A deep learning approach with transfer learning is studied and discussed for carrying out the classification task. Following this, a novel explainable AI algorithm is implemented to give the user visual feedback, in the form of colour highlights, for the most relevant features used to classify each signal.
Original languageEnglish
Title of host publication2021 IEEE Radar Conference (RadarConf21)
Place of PublicationPiscataway, NJ
Number of pages6
ISBN (Electronic)9781728176093
Publication statusPublished - 18 Jun 2021
Event2021 IEEE Radar Conference - Virtual/Atlanta, GA, USA, Atlanta, United States
Duration: 10 May 202114 May 2021

Publication series

NameIEEE Radar Conference
ISSN (Electronic)2375-5318


Conference2021 IEEE Radar Conference
Abbreviated titleRadarConf 2021
Country/TerritoryUnited States
Internet address


  • radar
  • explainable AI
  • BSL


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