Sign Language Recognition using micro-Doppler and Explainable Deep Learning

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.