Abstract
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 language | English |
|---|---|
| Title of host publication | 2021 IEEE Radar Conference (RadarConf21) |
| Place of Publication | Piscataway, NJ |
| Publisher | IEEE |
| Number of pages | 6 |
| ISBN (Electronic) | 9781728176093 |
| DOIs | |
| Publication status | Published - 18 Jun 2021 |
| Event | 2021 IEEE Radar Conference - Virtual/Atlanta, GA, USA, Atlanta, United States Duration: 10 May 2021 → 14 May 2021 https://ewh.ieee.org/conf/radar/2021/ |
Publication series
| Name | IEEE Radar Conference |
|---|---|
| Publisher | IEEE |
| ISSN (Electronic) | 2375-5318 |
Conference
| Conference | 2021 IEEE Radar Conference |
|---|---|
| Abbreviated title | RadarConf 2021 |
| Country/Territory | United States |
| City | Atlanta |
| Period | 10/05/21 → 14/05/21 |
| Internet address |
Keywords
- radar
- explainable AI
- BSL